Activity grouping (except CNAE 56, 64-66 and 95.1)	Main variables	Size of the enterprise	Total
Total Enterprises	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	8,47
Total Enterprises	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	6,84
Total Enterprises	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	13,83
Total Enterprises	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	28,53
Total Enterprises	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	6,31
Total Enterprises	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	4,89
Total Enterprises	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	10,95
Total Enterprises	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	23,83
Total Enterprises	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	39,45
Total Enterprises	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	31,06
Total Enterprises	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	50,27
Total Enterprises	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	64,17
Total Enterprises	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	52,99
Total Enterprises	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	54,53
Total Enterprises	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	49,67
Total Enterprises	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	51,57
Total Enterprises	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	44,29
Total Enterprises	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	43,33
Total Enterprises	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	44,86
Total Enterprises	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	48,67
Total Enterprises	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	34,27
Total Enterprises	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	30,85
Total Enterprises	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	37,28
Total Enterprises	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	47,67
Total Enterprises	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	58,66
Total Enterprises	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	53,43
Total Enterprises	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	66,2
Total Enterprises	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	72,18
Total Enterprises	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	19,47
Total Enterprises	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	19,47
Total Enterprises	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	16,1
Total Enterprises	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	27,51
Total Enterprises	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	38,84
Total Enterprises	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	42,41
Total Enterprises	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	33,15
Total Enterprises	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	30,92
Total Enterprises	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,62
Total Enterprises	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,89
Total Enterprises	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	5,84
Total Enterprises	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	13,42
Total Enterprises	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	5,91
Total Enterprises	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	4,88
Total Enterprises	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	10,23
Total Enterprises	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	18,47
Total Enterprises	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	41,34
Total Enterprises	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	45,33
Total Enterprises	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	33,67
Total Enterprises	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	27,63
Total Enterprises	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	60,11
Total Enterprises	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	62,42
Total Enterprises	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	55,19
Total Enterprises	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	53,81
Total Enterprises	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	46,4
Total Enterprises	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	46,85
Total Enterprises	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	46,1
Total Enterprises	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	42,97
Total Enterprises	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	48,76
Total Enterprises	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	50,94
Total Enterprises	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	47,25
Total Enterprises	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	31,95
Total Enterprises	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	23,64
Total Enterprises	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	27,12
Total Enterprises	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	16,42
Total Enterprises	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	13,51
Total Enterprises	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	57,61
Total Enterprises	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	57,72
Total Enterprises	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	57,8
Total Enterprises	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	55,92
Total Enterprises	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	34,8
Total Enterprises	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	36,23
Total Enterprises	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	31,9
Total Enterprises	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	30,37
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	17,23
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	18,45
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	15,19
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	11,94
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	32,04
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	34,76
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	25,02
Total Enterprises	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	28,97
Total Enterprises	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	8,47
Total Enterprises	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	6,52
Total Enterprises	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	12,28
Total Enterprises	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	12,63
Total Enterprises	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	10,26
Total Enterprises	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	8,78
Total Enterprises	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	11,3
Total Enterprises	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	18,03
Total Enterprises	F.8 % enterprises with specific training in Big Data (4)	Total	12,12
Total Enterprises	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	10,49
Total Enterprises	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	12,43
Total Enterprises	F.8 % enterprises with specific training in Big Data (4)	250 or more	22,81
Total Enterprises	F.9 % enterprises with Big Data training provided by external providers (5)	Total	87,45
Total Enterprises	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	85,79
Total Enterprises	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	91,01
Total Enterprises	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	87,95
1. Total Industry (CNAE 10-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	6,43
1. Total Industry (CNAE 10-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	4,53
1. Total Industry (CNAE 10-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	11,32
1. Total Industry (CNAE 10-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	28,23
1. Total Industry (CNAE 10-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	4,51
1. Total Industry (CNAE 10-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	2,79
1. Total Industry (CNAE 10-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	8,83
1. Total Industry (CNAE 10-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	24,72
1. Total Industry (CNAE 10-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	55,08
1. Total Industry (CNAE 10-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	32,66
1. Total Industry (CNAE 10-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	70,65
1. Total Industry (CNAE 10-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	88,85
1. Total Industry (CNAE 10-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	49,16
1. Total Industry (CNAE 10-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	56,85
1. Total Industry (CNAE 10-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	40,86
1. Total Industry (CNAE 10-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	42,75
1. Total Industry (CNAE 10-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	36,77
1. Total Industry (CNAE 10-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	40,63
1. Total Industry (CNAE 10-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	32,99
1. Total Industry (CNAE 10-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	32,9
1. Total Industry (CNAE 10-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	28,16
1. Total Industry (CNAE 10-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	22,58
1. Total Industry (CNAE 10-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	27,79
1. Total Industry (CNAE 10-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	44,04
1. Total Industry (CNAE 10-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	48,54
1. Total Industry (CNAE 10-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	38,37
1. Total Industry (CNAE 10-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	52,47
1. Total Industry (CNAE 10-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	69,36
1. Total Industry (CNAE 10-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	21,76
1. Total Industry (CNAE 10-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	30,2
1. Total Industry (CNAE 10-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	8,92
1. Total Industry (CNAE 10-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	21,3
1. Total Industry (CNAE 10-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	40,04
1. Total Industry (CNAE 10-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	37,84
1. Total Industry (CNAE 10-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	47,81
1. Total Industry (CNAE 10-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	32,43
1. Total Industry (CNAE 10-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,21
1. Total Industry (CNAE 10-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,46
1. Total Industry (CNAE 10-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	4,92
1. Total Industry (CNAE 10-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	13,05
1. Total Industry (CNAE 10-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	4,47
1. Total Industry (CNAE 10-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,69
1. Total Industry (CNAE 10-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	11,17
1. Total Industry (CNAE 10-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	20,85
1. Total Industry (CNAE 10-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	38,45
1. Total Industry (CNAE 10-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	48,72
1. Total Industry (CNAE 10-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	28,26
1. Total Industry (CNAE 10-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	29,14
1. Total Industry (CNAE 10-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	50,09
1. Total Industry (CNAE 10-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	44,04
1. Total Industry (CNAE 10-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	56,72
1. Total Industry (CNAE 10-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	53,55
1. Total Industry (CNAE 10-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	50,02
1. Total Industry (CNAE 10-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	47,02
1. Total Industry (CNAE 10-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	51,95
1. Total Industry (CNAE 10-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	56,12
1. Total Industry (CNAE 10-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	54,41
1. Total Industry (CNAE 10-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	64,86
1. Total Industry (CNAE 10-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	45,82
1. Total Industry (CNAE 10-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	39,16
1. Total Industry (CNAE 10-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	24,25
1. Total Industry (CNAE 10-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	33,19
1. Total Industry (CNAE 10-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	16,86
1. Total Industry (CNAE 10-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	11,28
1. Total Industry (CNAE 10-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	66,32
1. Total Industry (CNAE 10-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	73,51
1. Total Industry (CNAE 10-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	60,41
1. Total Industry (CNAE 10-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	55,81
1. Total Industry (CNAE 10-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	36,02
1. Total Industry (CNAE 10-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	39,69
1. Total Industry (CNAE 10-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	30,98
1. Total Industry (CNAE 10-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	37,25
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	21,09
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	31,77
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	10,53
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	11,22
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	34,32
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	44,08
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	23,14
1. Total Industry (CNAE 10-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	30,26
1. Total Industry (CNAE 10-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	4
1. Total Industry (CNAE 10-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	1,12
1. Total Industry (CNAE 10-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	6,65
1. Total Industry (CNAE 10-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	9,96
1. Total Industry (CNAE 10-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	6,92
1. Total Industry (CNAE 10-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	3,4
1. Total Industry (CNAE 10-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	8,39
1. Total Industry (CNAE 10-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	17,7
1. Total Industry (CNAE 10-39)	F.8 % enterprises with specific training in Big Data (4)	Total	7,84
1. Total Industry (CNAE 10-39)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	1,85
1. Total Industry (CNAE 10-39)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	13
1. Total Industry (CNAE 10-39)	F.8 % enterprises with specific training in Big Data (4)	250 or more	20,84
1. Total Industry (CNAE 10-39)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	93,11
1. Total Industry (CNAE 10-39)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
1. Total Industry (CNAE 10-39)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	98,53
1. Total Industry (CNAE 10-39)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	84,08
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	4,79
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	3,44
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	10,27
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	18,79
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	3,02
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	1,84
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	7,49
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	16,78
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	59,03
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	49,95
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	60,01
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	88,73
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	45,37
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	60,68
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	21,74
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	47,76
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	49,01
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	59,32
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	34,2
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	48
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	24,61
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	17,84
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	27,55
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	41,44
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	46,77
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	41,05
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	39,1
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	85,25
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	10,31
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	8,9
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	7,55
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	21,87
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	50,05
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	50,05
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	63,68
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	17,5
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	2,52
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	1,76
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	5,95
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	8,56
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	2,96
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	1,83
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	7,86
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	17,33
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	49,46
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	61,67
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	37,87
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	31,38
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	42,47
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	29,39
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	58,3
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	52,69
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	49,31
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	49,35
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	49,57
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	48,44
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	46,09
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	55,11
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	38,24
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	30,83
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	24,09
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	38,12
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	6,35
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	15,2
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	66,58
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	73,77
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	53,61
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	72,37
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	34,6
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	49,35
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	10,05
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	41
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	25,8
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	42,73
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	2,33
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	20,55
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	47,46
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	66,34
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	26,38
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	27,93
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	2,67
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	0
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	4,91
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	11,86
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	5,05
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	0
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	8,97
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	23,33
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.8 % enterprises with specific training in Big Data (4)	Total	4,82
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	0
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	7,21
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.8 % enterprises with specific training in Big Data (4)	250 or more	26,2
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	76,34
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	.
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
1.1. Food, beverages, tobacco, textile, clothing, leather and footwear, wood and cork, paper, graphic arts and reproduction of recorded media (CNAE 10-18)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	57,31
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	9,17
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	6,98
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	11,82
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	30,98
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	6,71
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	4,92
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	8,88
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	24,49
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	39,75
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	12,35
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	64,99
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	83,6
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	63,02
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	83,14
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	48,98
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	23,65
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	29,29
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	30,11
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	26,08
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	31,93
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	26,84
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	11,62
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	42,14
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	49,13
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	35
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	19,29
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	48,47
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	61,76
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	32,21
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	50,6
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	4,39
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	20,21
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	41,04
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	37,22
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	49,33
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	39,51
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,78
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,45
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	5,9
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	14,74
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	8,08
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	4,92
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	15,91
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	29,21
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	40,76
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	59,82
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	20,06
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	38,1
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	57,07
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	58,66
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	52,49
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	65,59
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	57,31
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	55,79
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	56,31
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	65,64
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	57,55
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	66,7
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	53,31
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	38,81
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	19,15
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	19,35
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	21,21
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	12,11
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	55,28
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	56,55
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	55,72
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	49,54
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	24,42
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	8,6
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	37,85
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	38,12
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	16
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	8,6
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	27,48
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	6,41
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	13,29
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	11,48
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	8,69
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	33,71
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	3,37
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	0
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	7,94
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	7,3
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	7,4
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	0
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	12,07
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	25,09
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.8 % enterprises with specific training in Big Data (4)	Total	14,04
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	8,7
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	23,84
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.8 % enterprises with specific training in Big Data (4)	250 or more	15,95
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	98,39
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
1.2 Coke and petroleum refining pharmaceutical products rubber and plastics non-metallic mineral products (CNAE 19-23)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	91,28
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	6,04
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	5,37
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	7,92
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	23,52
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	4,6
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	3,97
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	6,82
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	18,11
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	29,51
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	13
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	69,71
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	88,47
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	52,13
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	52,07
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	56,4
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	41,11
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	44,23
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	45,77
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	52,76
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	5,32
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	23,14
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	22,65
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	17,74
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	42,73
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	30,55
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	22,33
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	49,69
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	62,26
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	25,9
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	33,08
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	6,51
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	5,32
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	43,94
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	44,58
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	43,8
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	37,74
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,4
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	3,51
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	1,68
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	10,61
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	3,46
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,01
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	11
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	21,73
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	11,04
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	0
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	23,99
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	12,92
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	26,71
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	8,23
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	42,11
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	57,43
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	23,89
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	8,23
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	37,28
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	48,38
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	71
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	100
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	44,88
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	31,47
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	9,9
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	0
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	22,67
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	6,46
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	75,69
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	91,77
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	60,7
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	55,98
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	68,9
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	100
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	38,4
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	37,42
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	50,07
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	91,77
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	10,97
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	0
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	16,54
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	8,23
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	23,35
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	30,82
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	3,61
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	2,73
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	5,43
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	8,65
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	3,19
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	2,73
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	6,5
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	0
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.8 % enterprises with specific training in Big Data (4)	Total	2,46
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	0
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	7,3
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.8 % enterprises with specific training in Big Data (4)	250 or more	16,85
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	100
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	.
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
1.3 Metallurgy, manufacture of metallic products (CNAE 24-25)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	5,94
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	3,29
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	11,51
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	31,91
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	4,11
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	1,61
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	9,36
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	28,75
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	68,98
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	41,57
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	75,91
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	88,7
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	32,42
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	21,48
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	38,31
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	35,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	27,79
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	29,94
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	31,47
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	19,71
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	34,63
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	60,6
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	18,7
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	30,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	71,98
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	89,13
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	64,06
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	65,19
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	24,47
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	47,4
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	14,02
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	15,17
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	26,1
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	0
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	37,05
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	38,13
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	2,94
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,18
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	4,34
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	11,4
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	5,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	3,48
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	12,56
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	17,05
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	45,33
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	58,25
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	33,08
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	26,83
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	65,02
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	67,63
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	67,78
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	37,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	57,94
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	59,06
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	55,98
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	60,41
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	56,4
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	64,08
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	49,55
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	43,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	39,15
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	61,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	16,72
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	12,96
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	70,72
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	76,86
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	67,36
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	50,68
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	32,15
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	29,4
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	35,46
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	32,91
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	10,66
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	17,48
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	1,53
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	13
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	51,02
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	67,71
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	33,88
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	33,01
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	6,97
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	3,19
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	11,38
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	7,12
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	11,45
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	14,64
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	9,16
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	8,86
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.8 % enterprises with specific training in Big Data (4)	Total	9,97
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	0
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	17,35
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.8 % enterprises with specific training in Big Data (4)	250 or more	17,72
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	92,3
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	.
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	95,87
1.4. Computer, electronic and optical products electric equipment, machinery and mechanical equipment motor vehicles transport material furniture manufacturing industry, repair of machinery and equipment (CNAE 26-33)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	86,31
1.5. Energy and water (CNAE 35-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	13,73
1.5. Energy and water (CNAE 35-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	8,87
1.5. Energy and water (CNAE 35-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	20,91
1.5. Energy and water (CNAE 35-39)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	43,39
1.5. Energy and water (CNAE 35-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	10,29
1.5. Energy and water (CNAE 35-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	5,38
1.5. Energy and water (CNAE 35-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	16,84
1.5. Energy and water (CNAE 35-39)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	42,35
1.5. Energy and water (CNAE 35-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	87,47
1.5. Energy and water (CNAE 35-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	78,97
1.5. Energy and water (CNAE 35-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	90,53
1.5. Energy and water (CNAE 35-39)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	95,22
1.5. Energy and water (CNAE 35-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	55,89
1.5. Energy and water (CNAE 35-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	40,26
1.5. Energy and water (CNAE 35-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	60,68
1.5. Energy and water (CNAE 35-39)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	71,19
1.5. Energy and water (CNAE 35-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	29,25
1.5. Energy and water (CNAE 35-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	15,86
1.5. Energy and water (CNAE 35-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	27,51
1.5. Energy and water (CNAE 35-39)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	49,58
1.5. Energy and water (CNAE 35-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	34,2
1.5. Energy and water (CNAE 35-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	12,5
1.5. Energy and water (CNAE 35-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	34,2
1.5. Energy and water (CNAE 35-39)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	63,67
1.5. Energy and water (CNAE 35-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	62,86
1.5. Energy and water (CNAE 35-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	56,1
1.5. Energy and water (CNAE 35-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	64,17
1.5. Energy and water (CNAE 35-39)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	70,41
1.5. Energy and water (CNAE 35-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	16,33
1.5. Energy and water (CNAE 35-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	6,49
1.5. Energy and water (CNAE 35-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	9,82
1.5. Energy and water (CNAE 35-39)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	37,76
1.5. Energy and water (CNAE 35-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	35,43
1.5. Energy and water (CNAE 35-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	37,41
1.5. Energy and water (CNAE 35-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	38,37
1.5. Energy and water (CNAE 35-39)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	29,1
1.5. Energy and water (CNAE 35-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	7,05
1.5. Energy and water (CNAE 35-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	5,36
1.5. Energy and water (CNAE 35-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	6,19
1.5. Energy and water (CNAE 35-39)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	27,79
1.5. Energy and water (CNAE 35-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	4,81
1.5. Energy and water (CNAE 35-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,66
1.5. Energy and water (CNAE 35-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	8,72
1.5. Energy and water (CNAE 35-39)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	24,61
1.5. Energy and water (CNAE 35-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	8,9
1.5. Energy and water (CNAE 35-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	0
1.5. Energy and water (CNAE 35-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	12,85
1.5. Energy and water (CNAE 35-39)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	19,35
1.5. Energy and water (CNAE 35-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	34,9
1.5. Energy and water (CNAE 35-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	13,6
1.5. Energy and water (CNAE 35-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	50,54
1.5. Energy and water (CNAE 35-39)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	50,39
1.5. Energy and water (CNAE 35-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	43,4
1.5. Energy and water (CNAE 35-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	27,68
1.5. Energy and water (CNAE 35-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	58,24
1.5. Energy and water (CNAE 35-39)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	49,73
1.5. Energy and water (CNAE 35-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	27,02
1.5. Energy and water (CNAE 35-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	13,6
1.5. Energy and water (CNAE 35-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	20,56
1.5. Energy and water (CNAE 35-39)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	61,97
1.5. Energy and water (CNAE 35-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	9,01
1.5. Energy and water (CNAE 35-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	0
1.5. Energy and water (CNAE 35-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	25,7
1.5. Energy and water (CNAE 35-39)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	0
1.5. Energy and water (CNAE 35-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	71,88
1.5. Energy and water (CNAE 35-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	85,92
1.5. Energy and water (CNAE 35-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	76,45
1.5. Energy and water (CNAE 35-39)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	38,7
1.5. Energy and water (CNAE 35-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	35,65
1.5. Energy and water (CNAE 35-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	31
1.5. Energy and water (CNAE 35-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	42,83
1.5. Energy and water (CNAE 35-39)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	33,24
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	3,94
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	0
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	6,42
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	7,44
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	31,79
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	43,8
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	24,41
1.5. Energy and water (CNAE 35-39)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	20,84
1.5. Energy and water (CNAE 35-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	3,63
1.5. Energy and water (CNAE 35-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	0
1.5. Energy and water (CNAE 35-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	0
1.5. Energy and water (CNAE 35-39)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	17
1.5. Energy and water (CNAE 35-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	7,74
1.5. Energy and water (CNAE 35-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	4,38
1.5. Energy and water (CNAE 35-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	1,27
1.5. Energy and water (CNAE 35-39)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	24,82
1.5. Energy and water (CNAE 35-39)	F.8 % enterprises with specific training in Big Data (4)	Total	7,69
1.5. Energy and water (CNAE 35-39)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	0
1.5. Energy and water (CNAE 35-39)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	4,87
1.5. Energy and water (CNAE 35-39)	F.8 % enterprises with specific training in Big Data (4)	250 or more	28,73
1.5. Energy and water (CNAE 35-39)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	100
1.5. Energy and water (CNAE 35-39)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	.
1.5. Energy and water (CNAE 35-39)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
1.5. Energy and water (CNAE 35-39)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
2. Total Construction (CNAE 41-43)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	4,33
2. Total Construction (CNAE 41-43)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	3,94
2. Total Construction (CNAE 41-43)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	6,87
2. Total Construction (CNAE 41-43)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	19,02
2. Total Construction (CNAE 41-43)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	3,09
2. Total Construction (CNAE 41-43)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	2,68
2. Total Construction (CNAE 41-43)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	5,96
2. Total Construction (CNAE 41-43)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	16,3
2. Total Construction (CNAE 41-43)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	48,23
2. Total Construction (CNAE 41-43)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	45,93
2. Total Construction (CNAE 41-43)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	51,29
2. Total Construction (CNAE 41-43)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	79,84
2. Total Construction (CNAE 41-43)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	62,02
2. Total Construction (CNAE 41-43)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	63,21
2. Total Construction (CNAE 41-43)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	54,32
2. Total Construction (CNAE 41-43)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	73,76
2. Total Construction (CNAE 41-43)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	44,43
2. Total Construction (CNAE 41-43)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	44,85
2. Total Construction (CNAE 41-43)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	40,52
2. Total Construction (CNAE 41-43)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	54,01
2. Total Construction (CNAE 41-43)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	20,2
2. Total Construction (CNAE 41-43)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	14,62
2. Total Construction (CNAE 41-43)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	39,07
2. Total Construction (CNAE 41-43)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	44,76
2. Total Construction (CNAE 41-43)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	44,58
2. Total Construction (CNAE 41-43)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	45,18
2. Total Construction (CNAE 41-43)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	37,51
2. Total Construction (CNAE 41-43)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	64,92
2. Total Construction (CNAE 41-43)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	25,65
2. Total Construction (CNAE 41-43)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	23,06
2. Total Construction (CNAE 41-43)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	35,21
2. Total Construction (CNAE 41-43)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	33,43
2. Total Construction (CNAE 41-43)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	47,87
2. Total Construction (CNAE 41-43)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	50,21
2. Total Construction (CNAE 41-43)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	42,42
2. Total Construction (CNAE 41-43)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	26,24
2. Total Construction (CNAE 41-43)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	1,62
2. Total Construction (CNAE 41-43)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	1,41
2. Total Construction (CNAE 41-43)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	3,3
2. Total Construction (CNAE 41-43)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	5,93
2. Total Construction (CNAE 41-43)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	2,75
2. Total Construction (CNAE 41-43)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,5
2. Total Construction (CNAE 41-43)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	4,66
2. Total Construction (CNAE 41-43)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	10,97
2. Total Construction (CNAE 41-43)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	56,21
2. Total Construction (CNAE 41-43)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	62,83
2. Total Construction (CNAE 41-43)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	25,46
2. Total Construction (CNAE 41-43)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	28,12
2. Total Construction (CNAE 41-43)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	64,2
2. Total Construction (CNAE 41-43)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	64,18
2. Total Construction (CNAE 41-43)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	68,99
2. Total Construction (CNAE 41-43)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	35,56
2. Total Construction (CNAE 41-43)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	34,67
2. Total Construction (CNAE 41-43)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	35,39
2. Total Construction (CNAE 41-43)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	30,8
2. Total Construction (CNAE 41-43)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	34,8
2. Total Construction (CNAE 41-43)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	41,69
2. Total Construction (CNAE 41-43)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	37,21
2. Total Construction (CNAE 41-43)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	67,69
2. Total Construction (CNAE 41-43)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	28,88
2. Total Construction (CNAE 41-43)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	9,61
2. Total Construction (CNAE 41-43)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	8,33
2. Total Construction (CNAE 41-43)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	18,03
2. Total Construction (CNAE 41-43)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	0
2. Total Construction (CNAE 41-43)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	44,28
2. Total Construction (CNAE 41-43)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	37,57
2. Total Construction (CNAE 41-43)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	78,5
2. Total Construction (CNAE 41-43)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	54,15
2. Total Construction (CNAE 41-43)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	38,5
2. Total Construction (CNAE 41-43)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	38,28
2. Total Construction (CNAE 41-43)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	36,74
2. Total Construction (CNAE 41-43)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	56,44
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	5,27
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	4,28
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	11,45
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	0
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	37,86
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	44,6
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	5,16
2. Total Construction (CNAE 41-43)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	17,73
2. Total Construction (CNAE 41-43)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	5,47
2. Total Construction (CNAE 41-43)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	3,76
2. Total Construction (CNAE 41-43)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	16,2
2. Total Construction (CNAE 41-43)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	0
2. Total Construction (CNAE 41-43)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	0,45
2. Total Construction (CNAE 41-43)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	0
2. Total Construction (CNAE 41-43)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	2,1
2. Total Construction (CNAE 41-43)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	4,14
2. Total Construction (CNAE 41-43)	F.8 % enterprises with specific training in Big Data (4)	Total	3,23
2. Total Construction (CNAE 41-43)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	3,41
2. Total Construction (CNAE 41-43)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	0
2. Total Construction (CNAE 41-43)	F.8 % enterprises with specific training in Big Data (4)	250 or more	13,14
2. Total Construction (CNAE 41-43)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	100
2. Total Construction (CNAE 41-43)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
2. Total Construction (CNAE 41-43)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	.
2. Total Construction (CNAE 41-43)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	10,39
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	8,61
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	16,06
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	29,24
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	7,89
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	6,39
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	12,7
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	23,88
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	34,75
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	29,04
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	43,64
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	51,77
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	53,04
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	53,1
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	52,17
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	54,86
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	46,11
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	43,65
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	48,99
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	55,95
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	37,15
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	34,26
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	40,2
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	49,52
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	62,52
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	57,16
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	72,69
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	73,82
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	18,3
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	17,07
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	17,04
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	30,22
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	37,67
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	42,36
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	27,77
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	30,4
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	4,3
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	3,49
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	6,65
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	14,04
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	7,41
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	6,53
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	10,72
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	17,87
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	40,65
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	42,78
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	37,03
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	26,78
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	62,42
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	65,56
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	53,39
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	54,72
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	46,59
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	48,09
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	44,25
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	36,15
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	47,93
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	49,93
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	46,46
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	28,15
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	24,86
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	28,1
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	16,07
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	15,29
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	56,58
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	57,08
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	54,93
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	56,05
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	34,1
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	35,37
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	32,01
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	25,52
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	17,36
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	17,6
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	17,84
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	12,83
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	30,85
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	31,98
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	27,44
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	28,74
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	9,98
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	8,07
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	13,87
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	14,31
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	12,17
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	11,09
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	12,86
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	18,72
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.8 % enterprises with specific training in Big Data (4)	Total	14,18
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	13,3
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	13,05
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.8 % enterprises with specific training in Big Data (4)	250 or more	24,06
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	86,32
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	84,9
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	88,56
3. Total Services (CNAE 45-82, excluding CNAE 56: food and drink services, CNAE 75 and financial services)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	89,18
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	8,6
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	6,88
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	16,38
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	30,49
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	6,24
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	4,82
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	12,68
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	24,41
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	31,88
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	25,39
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	42,58
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	49,77
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	45,83
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	43,98
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	49,6
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	48,66
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	46,25
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	45,85
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	43,87
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	57,09
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	30,68
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	30,03
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	25,04
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	54,08
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	58,57
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	54,1
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	68,97
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	61,08
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	8,73
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	5,93
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	10,36
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	25,98
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	43,13
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	49,37
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	27,77
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	42,26
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,44
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,86
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	5,42
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	15,16
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	6,66
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	6,08
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	9,35
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	18,07
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	51,31
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	55,65
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	39,2
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	18,07
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	70,87
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	74,31
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	58,45
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	55,55
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	49,38
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	50,64
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	48,63
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	29,17
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	55,98
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	58,69
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	51,29
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	24,17
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	31,16
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	35,26
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	16,1
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	13,91
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	48,2
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	44,33
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	64,32
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	57,42
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	38,37
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	39,9
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	36,36
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	17,99
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	13,3
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	10,89
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	25,55
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	10,45
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	35,27
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	39,02
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	18,65
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	30,21
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	10,34
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	6,39
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	19,79
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	15,37
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	8,31
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	7,81
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	6,69
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	18,25
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.8 % enterprises with specific training in Big Data (4)	Total	6
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	5,42
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	4,23
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.8 % enterprises with specific training in Big Data (4)	250 or more	17,19
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	98,24
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
3.1. Sale and repair of motor vehicles, wholesale and retail (CNAE 45-47)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	91,69
3.2. Transport and storage (CNAE 49-53)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	13,99
3.2. Transport and storage (CNAE 49-53)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	13,04
3.2. Transport and storage (CNAE 49-53)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	16,34
3.2. Transport and storage (CNAE 49-53)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	31,45
3.2. Transport and storage (CNAE 49-53)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	11,39
3.2. Transport and storage (CNAE 49-53)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	10,42
3.2. Transport and storage (CNAE 49-53)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	14,4
3.2. Transport and storage (CNAE 49-53)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	25,97
3.2. Transport and storage (CNAE 49-53)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	37,58
3.2. Transport and storage (CNAE 49-53)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	33,64
3.2. Transport and storage (CNAE 49-53)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	43,74
3.2. Transport and storage (CNAE 49-53)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	69,29
3.2. Transport and storage (CNAE 49-53)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	93,24
3.2. Transport and storage (CNAE 49-53)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	94,84
3.2. Transport and storage (CNAE 49-53)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	90,83
3.2. Transport and storage (CNAE 49-53)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	80,09
3.2. Transport and storage (CNAE 49-53)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	19,76
3.2. Transport and storage (CNAE 49-53)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	15,93
3.2. Transport and storage (CNAE 49-53)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	30,72
3.2. Transport and storage (CNAE 49-53)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	35,65
3.2. Transport and storage (CNAE 49-53)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	20,79
3.2. Transport and storage (CNAE 49-53)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	15,62
3.2. Transport and storage (CNAE 49-53)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	38,14
3.2. Transport and storage (CNAE 49-53)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	34,75
3.2. Transport and storage (CNAE 49-53)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	58,63
3.2. Transport and storage (CNAE 49-53)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	55,53
3.2. Transport and storage (CNAE 49-53)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	72,79
3.2. Transport and storage (CNAE 49-53)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	55,76
3.2. Transport and storage (CNAE 49-53)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	15,28
3.2. Transport and storage (CNAE 49-53)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	14,8
3.2. Transport and storage (CNAE 49-53)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	15,94
3.2. Transport and storage (CNAE 49-53)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	19,48
3.2. Transport and storage (CNAE 49-53)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	28,31
3.2. Transport and storage (CNAE 49-53)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	29,67
3.2. Transport and storage (CNAE 49-53)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	18,54
3.2. Transport and storage (CNAE 49-53)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	40,13
3.2. Transport and storage (CNAE 49-53)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	4,46
3.2. Transport and storage (CNAE 49-53)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	4,17
3.2. Transport and storage (CNAE 49-53)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	4,83
3.2. Transport and storage (CNAE 49-53)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	11,46
3.2. Transport and storage (CNAE 49-53)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	3,92
3.2. Transport and storage (CNAE 49-53)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,76
3.2. Transport and storage (CNAE 49-53)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	7,72
3.2. Transport and storage (CNAE 49-53)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	25,76
3.2. Transport and storage (CNAE 49-53)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	23,99
3.2. Transport and storage (CNAE 49-53)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	21,67
3.2. Transport and storage (CNAE 49-53)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	28,01
3.2. Transport and storage (CNAE 49-53)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	26,05
3.2. Transport and storage (CNAE 49-53)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	71,79
3.2. Transport and storage (CNAE 49-53)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	81,13
3.2. Transport and storage (CNAE 49-53)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	59,72
3.2. Transport and storage (CNAE 49-53)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	55,35
3.2. Transport and storage (CNAE 49-53)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	47,48
3.2. Transport and storage (CNAE 49-53)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	55,2
3.2. Transport and storage (CNAE 49-53)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	41,85
3.2. Transport and storage (CNAE 49-53)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	25,32
3.2. Transport and storage (CNAE 49-53)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	57,82
3.2. Transport and storage (CNAE 49-53)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	64,99
3.2. Transport and storage (CNAE 49-53)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	54,41
3.2. Transport and storage (CNAE 49-53)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	33,65
3.2. Transport and storage (CNAE 49-53)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	7,21
3.2. Transport and storage (CNAE 49-53)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	0
3.2. Transport and storage (CNAE 49-53)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	19,89
3.2. Transport and storage (CNAE 49-53)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	13,29
3.2. Transport and storage (CNAE 49-53)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	44
3.2. Transport and storage (CNAE 49-53)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	40,08
3.2. Transport and storage (CNAE 49-53)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	51,18
3.2. Transport and storage (CNAE 49-53)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	46,77
3.2. Transport and storage (CNAE 49-53)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	39,47
3.2. Transport and storage (CNAE 49-53)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	46,06
3.2. Transport and storage (CNAE 49-53)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	29,69
3.2. Transport and storage (CNAE 49-53)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	30,34
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	25,67
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	32,92
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	20,13
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	5,31
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	38,53
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	45,6
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	27,94
3.2. Transport and storage (CNAE 49-53)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	28,9
3.2. Transport and storage (CNAE 49-53)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	16,08
3.2. Transport and storage (CNAE 49-53)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	17,43
3.2. Transport and storage (CNAE 49-53)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	10,86
3.2. Transport and storage (CNAE 49-53)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	12,86
3.2. Transport and storage (CNAE 49-53)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	8,67
3.2. Transport and storage (CNAE 49-53)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	9,89
3.2. Transport and storage (CNAE 49-53)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	1,77
3.2. Transport and storage (CNAE 49-53)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	12
3.2. Transport and storage (CNAE 49-53)	F.8 % enterprises with specific training in Big Data (4)	Total	11,26
3.2. Transport and storage (CNAE 49-53)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	12,6
3.2. Transport and storage (CNAE 49-53)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	5,08
3.2. Transport and storage (CNAE 49-53)	F.8 % enterprises with specific training in Big Data (4)	250 or more	10,87
3.2. Transport and storage (CNAE 49-53)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	70,57
3.2. Transport and storage (CNAE 49-53)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	74,54
3.2. Transport and storage (CNAE 49-53)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	10,47
3.2. Transport and storage (CNAE 49-53)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	88,84
3.3. Accommodation services (CNAE 55)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	12,6
3.3. Accommodation services (CNAE 55)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	9,81
3.3. Accommodation services (CNAE 55)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	19,7
3.3. Accommodation services (CNAE 55)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	28,7
3.3. Accommodation services (CNAE 55)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	5,96
3.3. Accommodation services (CNAE 55)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	3,47
3.3. Accommodation services (CNAE 55)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	12,52
3.3. Accommodation services (CNAE 55)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	18,93
3.3. Accommodation services (CNAE 55)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	10,05
3.3. Accommodation services (CNAE 55)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	0
3.3. Accommodation services (CNAE 55)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	17,81
3.3. Accommodation services (CNAE 55)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	17,9
3.3. Accommodation services (CNAE 55)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	32,79
3.3. Accommodation services (CNAE 55)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	36,74
3.3. Accommodation services (CNAE 55)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	30,48
3.3. Accommodation services (CNAE 55)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	26,64
3.3. Accommodation services (CNAE 55)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	79,52
3.3. Accommodation services (CNAE 55)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	82,36
3.3. Accommodation services (CNAE 55)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	73,68
3.3. Accommodation services (CNAE 55)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	92,6
3.3. Accommodation services (CNAE 55)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	51,28
3.3. Accommodation services (CNAE 55)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	58,73
3.3. Accommodation services (CNAE 55)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	49,1
3.3. Accommodation services (CNAE 55)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	30,53
3.3. Accommodation services (CNAE 55)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	43,75
3.3. Accommodation services (CNAE 55)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	0
3.3. Accommodation services (CNAE 55)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	79,51
3.3. Accommodation services (CNAE 55)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	69,57
3.3. Accommodation services (CNAE 55)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	20,79
3.3. Accommodation services (CNAE 55)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	17,64
3.3. Accommodation services (CNAE 55)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	20,73
3.3. Accommodation services (CNAE 55)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	33,74
3.3. Accommodation services (CNAE 55)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	54,8
3.3. Accommodation services (CNAE 55)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	82,36
3.3. Accommodation services (CNAE 55)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	34,2
3.3. Accommodation services (CNAE 55)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	30,43
3.3. Accommodation services (CNAE 55)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	9,97
3.3. Accommodation services (CNAE 55)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	7,65
3.3. Accommodation services (CNAE 55)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	15,91
3.3. Accommodation services (CNAE 55)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	23,24
3.3. Accommodation services (CNAE 55)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	11,78
3.3. Accommodation services (CNAE 55)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	11,79
3.3. Accommodation services (CNAE 55)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	10,32
3.3. Accommodation services (CNAE 55)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	22,08
3.3. Accommodation services (CNAE 55)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	38,6
3.3. Accommodation services (CNAE 55)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	32,29
3.3. Accommodation services (CNAE 55)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	67,44
3.3. Accommodation services (CNAE 55)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	35,59
3.3. Accommodation services (CNAE 55)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	65,46
3.3. Accommodation services (CNAE 55)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	62,83
3.3. Accommodation services (CNAE 55)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	74,57
3.3. Accommodation services (CNAE 55)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	73,95
3.3. Accommodation services (CNAE 55)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	36,6
3.3. Accommodation services (CNAE 55)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	31,43
3.3. Accommodation services (CNAE 55)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	63,25
3.3. Accommodation services (CNAE 55)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	23,99
3.3. Accommodation services (CNAE 55)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	39,55
3.3. Accommodation services (CNAE 55)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	33,23
3.3. Accommodation services (CNAE 55)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	67,43
3.3. Accommodation services (CNAE 55)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	40,14
3.3. Accommodation services (CNAE 55)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	30,09
3.3. Accommodation services (CNAE 55)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	25,37
3.3. Accommodation services (CNAE 55)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	49,85
3.3. Accommodation services (CNAE 55)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	33,92
3.3. Accommodation services (CNAE 55)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	54,46
3.3. Accommodation services (CNAE 55)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	56,17
3.3. Accommodation services (CNAE 55)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	50,16
3.3. Accommodation services (CNAE 55)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	43,43
3.3. Accommodation services (CNAE 55)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	22,88
3.3. Accommodation services (CNAE 55)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	16,52
3.3. Accommodation services (CNAE 55)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	53,01
3.3. Accommodation services (CNAE 55)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	16,45
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	24,13
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	28,69
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	9,7
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	4,83
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	26,76
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	26,41
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	22,72
3.3. Accommodation services (CNAE 55)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	45,43
3.3. Accommodation services (CNAE 55)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	8,8
3.3. Accommodation services (CNAE 55)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	7,85
3.3. Accommodation services (CNAE 55)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	7,79
3.3. Accommodation services (CNAE 55)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	20,37
3.3. Accommodation services (CNAE 55)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	9,12
3.3. Accommodation services (CNAE 55)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	0
3.3. Accommodation services (CNAE 55)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	20,99
3.3. Accommodation services (CNAE 55)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	25,76
3.3. Accommodation services (CNAE 55)	F.8 % enterprises with specific training in Big Data (4)	Total	4,97
3.3. Accommodation services (CNAE 55)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	0
3.3. Accommodation services (CNAE 55)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	13,51
3.3. Accommodation services (CNAE 55)	F.8 % enterprises with specific training in Big Data (4)	250 or more	5
3.3. Accommodation services (CNAE 55)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	100
3.3. Accommodation services (CNAE 55)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	.
3.3. Accommodation services (CNAE 55)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	100
3.3. Accommodation services (CNAE 55)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
3.4. Information and communications (CNAE 58-63)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	17,58
3.4. Information and communications (CNAE 58-63)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	14,23
3.4. Information and communications (CNAE 58-63)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	23,56
3.4. Information and communications (CNAE 58-63)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	48,03
3.4. Information and communications (CNAE 58-63)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	15,97
3.4. Information and communications (CNAE 58-63)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	13,19
3.4. Information and communications (CNAE 58-63)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	20,72
3.4. Information and communications (CNAE 58-63)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	42,16
3.4. Information and communications (CNAE 58-63)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	30,01
3.4. Information and communications (CNAE 58-63)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	18,14
3.4. Information and communications (CNAE 58-63)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	45,67
3.4. Information and communications (CNAE 58-63)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	59,23
3.4. Information and communications (CNAE 58-63)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	40,8
3.4. Information and communications (CNAE 58-63)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	32,75
3.4. Information and communications (CNAE 58-63)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	51,92
3.4. Information and communications (CNAE 58-63)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	59,48
3.4. Information and communications (CNAE 58-63)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	75,35
3.4. Information and communications (CNAE 58-63)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	80,81
3.4. Information and communications (CNAE 58-63)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	65,36
3.4. Information and communications (CNAE 58-63)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	67,78
3.4. Information and communications (CNAE 58-63)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	59,41
3.4. Information and communications (CNAE 58-63)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	62,11
3.4. Information and communications (CNAE 58-63)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	49,39
3.4. Information and communications (CNAE 58-63)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	66,28
3.4. Information and communications (CNAE 58-63)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	64,41
3.4. Information and communications (CNAE 58-63)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	56,62
3.4. Information and communications (CNAE 58-63)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	71,56
3.4. Information and communications (CNAE 58-63)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	90,1
3.4. Information and communications (CNAE 58-63)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	31,93
3.4. Information and communications (CNAE 58-63)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	32,91
3.4. Information and communications (CNAE 58-63)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	24,71
3.4. Information and communications (CNAE 58-63)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	41,95
3.4. Information and communications (CNAE 58-63)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	43,34
3.4. Information and communications (CNAE 58-63)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	51,17
3.4. Information and communications (CNAE 58-63)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	33,22
3.4. Information and communications (CNAE 58-63)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	23,69
3.4. Information and communications (CNAE 58-63)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	6,02
3.4. Information and communications (CNAE 58-63)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	4,68
3.4. Information and communications (CNAE 58-63)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	8,82
3.4. Information and communications (CNAE 58-63)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	16,55
3.4. Information and communications (CNAE 58-63)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	20,69
3.4. Information and communications (CNAE 58-63)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	20,46
3.4. Information and communications (CNAE 58-63)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	22,3
3.4. Information and communications (CNAE 58-63)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	16,77
3.4. Information and communications (CNAE 58-63)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	28,96
3.4. Information and communications (CNAE 58-63)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	31,84
3.4. Information and communications (CNAE 58-63)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	16,2
3.4. Information and communications (CNAE 58-63)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	38,56
3.4. Information and communications (CNAE 58-63)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	45,01
3.4. Information and communications (CNAE 58-63)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	48,17
3.4. Information and communications (CNAE 58-63)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	32,73
3.4. Information and communications (CNAE 58-63)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	41,14
3.4. Information and communications (CNAE 58-63)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	45,41
3.4. Information and communications (CNAE 58-63)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	47,19
3.4. Information and communications (CNAE 58-63)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	36,78
3.4. Information and communications (CNAE 58-63)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	57,67
3.4. Information and communications (CNAE 58-63)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	31,39
3.4. Information and communications (CNAE 58-63)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	32,53
3.4. Information and communications (CNAE 58-63)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	25,22
3.4. Information and communications (CNAE 58-63)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	44,4
3.4. Information and communications (CNAE 58-63)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	23,28
3.4. Information and communications (CNAE 58-63)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	27,66
3.4. Information and communications (CNAE 58-63)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	6,41
3.4. Information and communications (CNAE 58-63)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	16,87
3.4. Information and communications (CNAE 58-63)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	79,03
3.4. Information and communications (CNAE 58-63)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	84,05
3.4. Information and communications (CNAE 58-63)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	58,16
3.4. Information and communications (CNAE 58-63)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	84,74
3.4. Information and communications (CNAE 58-63)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	28,04
3.4. Information and communications (CNAE 58-63)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	30,15
3.4. Information and communications (CNAE 58-63)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	19,57
3.4. Information and communications (CNAE 58-63)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	27,82
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	17,91
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	19,92
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	9,24
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	22,65
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	33,22
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	31,9
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	38,97
3.4. Information and communications (CNAE 58-63)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	29,38
3.4. Information and communications (CNAE 58-63)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	13,43
3.4. Information and communications (CNAE 58-63)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	14,29
3.4. Information and communications (CNAE 58-63)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	8,76
3.4. Information and communications (CNAE 58-63)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	18,96
3.4. Information and communications (CNAE 58-63)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	14,46
3.4. Information and communications (CNAE 58-63)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	10,9
3.4. Information and communications (CNAE 58-63)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	18,74
3.4. Information and communications (CNAE 58-63)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	23,14
3.4. Information and communications (CNAE 58-63)	F.8 % enterprises with specific training in Big Data (4)	Total	34,23
3.4. Information and communications (CNAE 58-63)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	28,44
3.4. Information and communications (CNAE 58-63)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	43,58
3.4. Information and communications (CNAE 58-63)	F.8 % enterprises with specific training in Big Data (4)	250 or more	43,33
3.4. Information and communications (CNAE 58-63)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	75,25
3.4. Information and communications (CNAE 58-63)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	61,84
3.4. Information and communications (CNAE 58-63)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	94,14
3.4. Information and communications (CNAE 58-63)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	79,06
3.5. Real estate activities (CNAE 68)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	8,5
3.5. Real estate activities (CNAE 68)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	5,37
3.5. Real estate activities (CNAE 68)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	41,74
3.5. Real estate activities (CNAE 68)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	25,95
3.5. Real estate activities (CNAE 68)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	5,83
3.5. Real estate activities (CNAE 68)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	3,45
3.5. Real estate activities (CNAE 68)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	30,84
3.5. Real estate activities (CNAE 68)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	20,34
3.5. Real estate activities (CNAE 68)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	23,83
3.5. Real estate activities (CNAE 68)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	4,98
3.5. Real estate activities (CNAE 68)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	48,96
3.5. Real estate activities (CNAE 68)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	22,06
3.5. Real estate activities (CNAE 68)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	44,34
3.5. Real estate activities (CNAE 68)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	23,01
3.5. Real estate activities (CNAE 68)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	71,08
3.5. Real estate activities (CNAE 68)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	54,66
3.5. Real estate activities (CNAE 68)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	37,44
3.5. Real estate activities (CNAE 68)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	14,12
3.5. Real estate activities (CNAE 68)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	70,18
3.5. Real estate activities (CNAE 68)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	23,28
3.5. Real estate activities (CNAE 68)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	73,69
3.5. Real estate activities (CNAE 68)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	80,91
3.5. Real estate activities (CNAE 68)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	65,04
3.5. Real estate activities (CNAE 68)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	67,4
3.5. Real estate activities (CNAE 68)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	35,94
3.5. Real estate activities (CNAE 68)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	14,12
3.5. Real estate activities (CNAE 68)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	55,87
3.5. Real estate activities (CNAE 68)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	100
3.5. Real estate activities (CNAE 68)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	7,22
3.5. Real estate activities (CNAE 68)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	0
3.5. Real estate activities (CNAE 68)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	14,69
3.5. Real estate activities (CNAE 68)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	22,06
3.5. Real estate activities (CNAE 68)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	67,45
3.5. Real estate activities (CNAE 68)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	85,88
3.5. Real estate activities (CNAE 68)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	52,46
3.5. Real estate activities (CNAE 68)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	0
3.5. Real estate activities (CNAE 68)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,91
3.5. Real estate activities (CNAE 68)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	1,92
3.5. Real estate activities (CNAE 68)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	25,08
3.5. Real estate activities (CNAE 68)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	14,58
3.5. Real estate activities (CNAE 68)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	3,86
3.5. Real estate activities (CNAE 68)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,95
3.5. Real estate activities (CNAE 68)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	18,7
3.5. Real estate activities (CNAE 68)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	13,64
3.5. Real estate activities (CNAE 68)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	13,79
3.5. Real estate activities (CNAE 68)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	6,15
3.5. Real estate activities (CNAE 68)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	28,82
3.5. Real estate activities (CNAE 68)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	55,56
3.5. Real estate activities (CNAE 68)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	91,54
3.5. Real estate activities (CNAE 68)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	92,43
3.5. Real estate activities (CNAE 68)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	87,22
3.5. Real estate activities (CNAE 68)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	100
3.5. Real estate activities (CNAE 68)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	50,64
3.5. Real estate activities (CNAE 68)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	47,76
3.5. Real estate activities (CNAE 68)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	58,41
3.5. Real estate activities (CNAE 68)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	55,56
3.5. Real estate activities (CNAE 68)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	62,53
3.5. Real estate activities (CNAE 68)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	58,38
3.5. Real estate activities (CNAE 68)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	87,22
3.5. Real estate activities (CNAE 68)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	0
3.5. Real estate activities (CNAE 68)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	0
3.5. Real estate activities (CNAE 68)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	0
3.5. Real estate activities (CNAE 68)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	0
3.5. Real estate activities (CNAE 68)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	0
3.5. Real estate activities (CNAE 68)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	26,97
3.5. Real estate activities (CNAE 68)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	13,76
3.5. Real estate activities (CNAE 68)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	52,71
3.5. Real estate activities (CNAE 68)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	100
3.5. Real estate activities (CNAE 68)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	81,06
3.5. Real estate activities (CNAE 68)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	93,85
3.5. Real estate activities (CNAE 68)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	47,29
3.5. Real estate activities (CNAE 68)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	55,56
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	0
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	0
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	0
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	0
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	19,2
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	7,57
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	47,29
3.5. Real estate activities (CNAE 68)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	55,56
3.5. Real estate activities (CNAE 68)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	11,05
3.5. Real estate activities (CNAE 68)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	0
3.5. Real estate activities (CNAE 68)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	25,93
3.5. Real estate activities (CNAE 68)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	25,55
3.5. Real estate activities (CNAE 68)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	19,4
3.5. Real estate activities (CNAE 68)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	0
3.5. Real estate activities (CNAE 68)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	45,8
3.5. Real estate activities (CNAE 68)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	42,84
3.5. Real estate activities (CNAE 68)	F.8 % enterprises with specific training in Big Data (4)	Total	28,83
3.5. Real estate activities (CNAE 68)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	35,28
3.5. Real estate activities (CNAE 68)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	17,19
3.5. Real estate activities (CNAE 68)	F.8 % enterprises with specific training in Big Data (4)	250 or more	42,84
3.5. Real estate activities (CNAE 68)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	90,15
3.5. Real estate activities (CNAE 68)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
3.5. Real estate activities (CNAE 68)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	56,25
3.5. Real estate activities (CNAE 68)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	12,5
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	10,75
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	18,86
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	30,96
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	9,79
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	8,2
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	15,16
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	28,5
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	49,23
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	41,63
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	69,17
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	58,72
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	28,9
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	22,5
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	41,65
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	47,95
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	48,23
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	47,05
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	49,73
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	54,13
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	50,94
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	49,89
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	55,51
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	47,24
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	78,65
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	79,26
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	75,75
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	81,44
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	31,38
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	31,94
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	29,9
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	30,68
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	29,46
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	30,92
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	26,87
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	24,29
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	4,19
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	3,42
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	7,35
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	10,49
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	10,04
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	7,91
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	19,41
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	34
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	28,79
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	27,15
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	34,92
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	22,09
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	49,55
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	52,01
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	42,6
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	51,89
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	34,09
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	34,1
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	31,86
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	41,72
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	41,68
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	46,24
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	34,43
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	26,59
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	11
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	11,32
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	8,48
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	16,93
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	54,64
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	59,65
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	42,31
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	53,07
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	24,83
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	22,93
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	25,95
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	37,67
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	10,45
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	7,25
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	16,05
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	19,2
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	20,17
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	14,04
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	34,28
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	25,44
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	3,94
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	1,09
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	11,96
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	7,86
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	19,59
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	18,87
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	19,65
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	26,76
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.8 % enterprises with specific training in Big Data (4)	Total	19,77
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	18,31
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	18,27
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.8 % enterprises with specific training in Big Data (4)	250 or more	39,22
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	94,81
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	94,69
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	97,97
3.6. Professional, scientific and technical activities (excl. veterinary) (CNAE 69-74)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	90,77
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	6,27
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	4,91
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	7,12
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	20,11
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	4,88
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	3,87
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	5,47
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	15,35
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	32,15
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	34,3
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	18,69
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	40,99
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	71,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	89,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	35,11
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	56,83
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	37,29
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	31,69
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	41,96
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	49,2
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	22,28
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	6,36
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	47,64
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	42,21
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	64,08
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	51,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	82,38
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	83,32
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	13,95
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	11,13
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	6,25
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	31,5
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	31,89
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	39,67
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	22,6
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	18,59
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	3,72
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	2,97
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	3,8
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	12,62
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	3,41
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	2,66
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	4,97
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	8,51
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	65,1
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	74,96
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	56,43
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	39,18
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	77,14
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	84,96
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	72,74
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	51,15
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	78,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	94,19
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	58,85
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	46,46
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	61,13
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	66,61
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	68,39
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	20,23
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	44,46
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	62,73
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	23,15
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	7,96
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	69,31
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	77,32
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	57,45
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	58,8
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	52,81
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	67,5
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	38,32
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	17,71
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	42
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	59,1
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	18,74
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	15,12
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	28,56
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	34,34
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	20,01
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	21,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	4,11
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	0
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	8,12
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	11,78
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	18,17
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	21,74
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	15,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	11,1
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.8 % enterprises with specific training in Big Data (4)	Total	19,59
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	24,95
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	4,53
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.8 % enterprises with specific training in Big Data (4)	250 or more	20,82
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	95,57
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	100
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	14,02
3.7. Administrative and support service activities (incl. travel agencies) (CNAE 77-82)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	100
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	Total	16,62
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 10 to 49	15,01
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	From 50 to 249	16,2
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1-3 % of enterprises that analyzed large data sources (Big Data) for their enterprise with their employees or through an external enterprise	250 or more	45,92
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	Total	15,48
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 10 to 49	14,16
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	From 50 to 249	14,76
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1 % enterprises that  performed Big Data analysis neither by its own employees	250 or more	41,41
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	Total	40,22
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 10 to 49	30,64
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	From 50 to 249	58,14
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.A % of enterprises that analyzed Big Data by source type: data using sensors or smart devices (1)	250 or more	64,04
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	Total	43,82
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 10 to 49	34,83
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	From 50 to 249	65,16
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.B % of enterprises that analyzed Big Data by source type: geolocation data from wearable devices (1)	250 or more	58,2
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	Total	70,03
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 10 to 49	77,29
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	From 50 to 249	51,49
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.C % of enterprises that analyzed Big Data by source type: data generated by social media (1)	250 or more	60,73
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	Total	63,56
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 10 to 49	68,57
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	From 50 to 249	47,06
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.1.D % of enterprises that analyzed Big Data by source type: other Big Data sources (1)	250 or more	63,68
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	Total	73,49
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 10 to 49	69,13
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	From 50 to 249	79,35
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.A % of enterprises that analyzed Big Data by Machine Learning (1)	250 or more	88,38
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	Total	34,42
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 10 to 49	35,86
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	From 50 to 249	27,62
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.B % of enterprises that analyzed Big Data by Natural language processing (NLP), natural language generation (NGL) or speech recognition (1)	250 or more	38,05
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	Total	40,83
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 10 to 49	47,08
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	From 50 to 249	27,36
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.2.C % of enterprises that analyzed Big Data by other Big Data analysis methods (1)	250 or more	28,45
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	Total	5,11
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 10 to 49	5,01
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	From 50 to 249	3,79
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.3 % of enterprises where a company or organisation other than their own analysed Big Data for their company	250 or more	13,33
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	Total	17,38
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 10 to 49	17,2
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	From 50 to 249	18,12
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.4 % of enterprises that did not perform Big Data analysis but considered doing so (2)	250 or more	16,54
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	Total	22,17
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 10 to 49	23,02
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	From 50 to 249	18,63
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.A % of enterprises that did not perform Big Data analysis by the cost seems too high compared to the benefits (3 )	250 or more	28,29
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	Total	35,33
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 10 to 49	32,51
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	From 50 to 249	45,89
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.B % of enterprises that did not perform Big Data analysis by human resources, skills or staff profiles are insufficient (3)	250 or more	24,66
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	Total	34,89
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 10 to 49	32,84
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	From 50 to 249	40,19
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.C % of enterprises that did not perform Big Data analysis by insufficient sources of Big Data that would be needed to perform the analysis (3)	250 or more	46,95
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	Total	24,78
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 10 to 49	19,39
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	From 50 to 249	41,56
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.D % of enterprises that did not perform Big Data analysis by insufficient ICT infrastructure (3)	250 or more	32,99
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	Total	14,06
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 10 to 49	14,61
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	From 50 to 249	13,27
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.E % of enterprises that did not perform Big Data analysis by difficulty in complying with privacy laws (3)	250 or more	5,74
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	Total	73,37
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 10 to 49	77,29
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	From 50 to 249	60,37
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.F % of enterprises that did not perform Big Data analysis by not a priority for the company (3)	250 or more	74,09
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	Total	14,14
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 10 to 49	10,46
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	From 50 to 249	26,6
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.G % of enterprises that did not perform Big Data analysis by insufficient quality of Big Data sources (3)	250 or more	11,18
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	Total	22,51
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 10 to 49	26,54
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	From 50 to 249	10,54
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by Big Data analysis is not useful for the company (3)	250 or more	11,64
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	Total	35,36
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 10 to 49	35,02
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	From 50 to 249	37,31
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.5.H % of enterprises that did not perform Big Data analysis by other factors (3)	250 or more	28,24
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	Total	13,03
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 10 to 49	12,5
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	From 50 to 249	12,48
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.6 % of enterprises that sold or gave access to its own Big Data to another company or organisation (4)	250 or more	16,96
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.7 % of enterprises that purchased or accessed Big Data (4)	Total	14,85
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 10 to 49	12,05
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.7 % of enterprises that purchased or accessed Big Data (4)	From 50 to 249	19,73
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.7 % of enterprises that purchased or accessed Big Data (4)	250 or more	21,81
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.8 % enterprises with specific training in Big Data (4)	Total	35,14
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.8 % enterprises with specific training in Big Data (4)	From 10 to 49	29,84
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.8 % enterprises with specific training in Big Data (4)	From 50 to 249	45,93
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.8 % enterprises with specific training in Big Data (4)	250 or more	45,62
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.9 % enterprises with Big Data training provided by external providers (5)	Total	73,96
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.9 % enterprises with Big Data training provided by external providers (5)	From 10 to 49	64,35
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.9 % enterprises with Big Data training provided by external providers (5)	From 50 to 249	91,33
4. Sector ICT (261-264, 268, 465, 582, 61, 6201, 6202, 6203, 6209, 631, 951)	F.9 % enterprises with Big Data training provided by external providers (5)	250 or more	78,2