WP5 Recommendations and observatory D5.3 Observatory reportdata-reuse.eu/.../D5.3_ObservatoryReport-v1.0...09.pdf · D5.3 Observatory report Public Report – Version 1.0 – 9 February

WP5 Recommendations and

observatory

D5.3 Observatory report

Final version – 9 February 2018

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 645244. This document does reflect the authors view only. The European Commission is not responsible for any use that may be made of the information the document contains.


Work package WP5 Recommendations and observatory

Lead author Liliya Pullmann (FRAUNHOFER)

Contributing author(s) Daniel Bachlechner (FRAUNHOFER)

Due date M36 (January 2018)

Date 9 February 2018

Version 1.0

Type Report

Dissemination level Public


Public Report – Version 1.0 – 9 February 2018

III

Document history

Version Date Author(s) Notes

0.1 19 September 2017 Liliya Pullmann (FRAUNHOFER) First draft of the document structure

0.2 8 January 2018 Liliya Pullmann (FRAUNHOFER) Addition of content to all sections

1.0 9 February 2018 Daniel Bachlechner (FRAUNHOFER)

Final check



IV

EuDEco in a nutshell

EuDEco assists European science and industry in understanding and exploiting the potentials of data reuse

in the context of big and open data. The aim is to establish a self-sustaining data market and thereby

increase the competitiveness of Europe. To be able to extract the benefits of data reuse, it is crucial to

understand the underlying economic, societal, legal and technological framework conditions and

challenges to build useful applications and services. Despite the amount of activities in this domain, an

effort is missing to develop use cases and business models that are economically viable, legally certain

and take societal needs and concerns into account. EuDEco will accomplish this by leveraging the

engagement of other projects conducting pilots on data reuse as well as by the engagement of external

experts and stakeholders. EuDEco moves beyond the classic approaches by applying the approach of

complex adaptive systems to model the data economy in order to identify value networks, use cases and

business models for data reuse. In the course of the project, we further develop and refine the data

economy model in several steps by case studies on previous pilots on data reuse, by in-depth analysis

from legal, socio-economic and technological points of view, and by extensive tests of use cases and

business models with other projects. Therefore, it will analyse framework conditions relevant and

challenges related to data reuse and the emergence of a self-sustaining data market. Finally, EuDEco will

deliver a model of the data economy including viable use cases and business models as well as suggestions

and recommendations addressing the main legal, contractual, societal and technological concerns and

challenges such as contractual framework or data protection. Above that, EuDEco will develop an

observatory for policy makers enabling them to track the development of the data economy.

Disclaimer

© – 2018 – IVSZ, ROOTER, LEIDEN, ASCORA, FRAUNHOFER. All rights reserved. Licensed to the European

Union (EU) under conditions.



V

Table of contents

Executive summary ............................................................................................................................1

1 Introduction ...............................................................................................................................2

1.1 Purpose and scope ........................................................................................................................ 3

1.2 Structure of the document ............................................................................................................ 4

1.3 Relationships to other deliverables ............................................................................................... 4

2 Towards an observatory of the data economy .............................................................................5

2.1 Limitations and barriers ................................................................................................................ 5

2.2 Methodology ................................................................................................................................. 6

2.2.1 Index methodology ................................................................................................................... 6

2.2.2 Data constraints ........................................................................................................................ 7

2.2.3 Descriptive statistics ................................................................................................................. 7

2.3 Categories and indicators ............................................................................................................. 8

3 The observatory ....................................................................................................................... 15

3.1 Business sector activities ............................................................................................................. 15

3.1.1 Enterprises using and analysing data ...................................................................................... 15

3.1.2 Productivity of data-related sectors ....................................................................................... 17

3.1.3 Investment in ICT services ...................................................................................................... 18

3.1.4 Share of data companies......................................................................................................... 19

3.1.5 Trade in data-related services ................................................................................................. 20

3.1.6 Index on business sector activities .......................................................................................... 21

3.2 Business environment ................................................................................................................. 26

3.2.1 Chance for getting credit ........................................................................................................ 26

3.2.2 Government support to business R&D ................................................................................... 30

3.2.3 Policy and governance framework.......................................................................................... 31

3.2.4 Index on business environment .............................................................................................. 35

3.3 Innovation potential .................................................................................................................... 43

3.3.1 Scientific publications ............................................................................................................. 43

3.3.2 R&D investments in data-related activities ............................................................................ 44

3.3.3 Researchers in data-related fields .......................................................................................... 45

3.3.4 Talent pool potential ............................................................................................................... 46

3.3.5 Index on innovation potential ................................................................................................. 50



VI

3.4 Infrastructure .............................................................................................................................. 54

3.4.1 Broadband infrastructure ....................................................................................................... 54

3.4.2 Adoption of IPv6 ..................................................................................................................... 56

3.4.3 Index on infrastructural conditions ......................................................................................... 59

3.5 Technology diffusion ................................................................................................................... 63

3.5.1 RFID technologies.................................................................................................................... 63

3.5.2 Cloud computing ..................................................................................................................... 64

3.5.3 CRM software solutions .......................................................................................................... 65

3.5.4 Machine-to-Machine subscriptions ........................................................................................ 66

3.5.5 Index on the diffusion of data-related technologies .............................................................. 67

3.6 Security status ............................................................................................................................. 72

3.6.1 Global Cyber Security Index 2017 ........................................................................................... 72

3.6.2 Addressing security risks by enterprises ................................................................................. 73

3.6.3 Secure Internet servers ........................................................................................................... 74

3.6.4 Index on security-related aspects ........................................................................................... 77

3.7 Privacy protection ....................................................................................................................... 82

3.7.1 Enterprises addressing privacy-related risks .......................................................................... 82

3.7.2 Awareness of privacy-related risks among individuals ........................................................... 83

3.7.3 Privacy Control Index .............................................................................................................. 84

3.7.4 Privacy protection index ......................................................................................................... 86

3.8 Societal participation .................................................................................................................. 89

3.8.1 E-government participation .................................................................................................... 89

3.8.2 Participation in social or professional networks ..................................................................... 91

3.8.3 Data-driven purchasing decisions ........................................................................................... 91

3.8.4 Sharing economy ..................................................................................................................... 93

3.8.5 Index on society's participations in data-related activities .................................................... 94

3.9 Data openness ........................................................................................................................... 101

3.9.1 Open data readiness ............................................................................................................. 101

3.9.2 Open data portal maturity .................................................................................................... 102

3.9.3 Index on data openness ........................................................................................................ 104

4 Discussion of findings ............................................................................................................. 108

4.1 High potential countries ............................................................................................................ 111

4.2 Countries with average capacities ............................................................................................ 111

4.3 Below average performing countries ........................................................................................ 112



VII

5 Conclusions and outlook ......................................................................................................... 113

6 List of references .................................................................................................................... 115

List of tables

Table 1 Overview of key categories and indicators .................................................................................... 14

Table 2 Business sector activities – indices ................................................................................................. 25

Table 3 Business sector activities – descriptive statistics ........................................................................... 25

Table 4 Business environment – indices 1 .................................................................................................. 40

Table 5 Business environment – indices 2 .................................................................................................. 41

Table 6 Business environment – descriptive statistics ............................................................................... 42

Table 7 Innovation potential – indices ........................................................................................................ 53

Table 8 Innovation potential – descriptive statistics .................................................................................. 53

Table 9 Infrastructure – indices .................................................................................................................. 62

Table 10 Infrastructure – descriptive statistics ........................................................................................... 63

Table 11 Technology diffusion – indices ..................................................................................................... 70

Table 12 Technology diffusion – descriptive statistics................................................................................ 71

Table 13 Security status – indices ............................................................................................................... 81

Table 14 Security status – descriptive statistics ......................................................................................... 81

Table 15 Privacy protection – indices ......................................................................................................... 88

Table 16 Privacy protection – descriptive statistics .................................................................................... 89

Table 17 Societal participation – indices .................................................................................................... 99

Table 18 Societal participation – descriptive statistics ............................................................................. 100

Table 19 Data openness – indices ............................................................................................................. 107

Table 20 Data openness – descriptive statistics ....................................................................................... 107

List of figures

Figure 1 Enterprises analysing big data, 2016 (by size of enterprises) ....................................................... 16

Figure 2 Domains of data for data analysis performed by enterprises, 2016 ............................................ 17

Figure 3 Value added at factor cost per full-time employee in J62 and J 631 ............................................ 18

Figure 4 Investment rate in ICT services ..................................................................................................... 19

Figure 5 Estimates of the share of data companies .................................................................................... 20

Figure 6 RCA in countries' world trade in data processing and other computer services, 2015 ................ 21

Figure 7 Index on business sector activities, 2015/2016 ............................................................................ 23

Figure 8 Legal rights strength and credit information depth, 2016 ............................................................ 27

Figure 9 Domestic credit to private sector as percentage of GDP, 2015 .................................................... 28



VIII

Figure 10: Venture capital investments as a percentage of GDP, 2016 (or the latest available year) ....... 29

Figure 11 Venture capital investments as percentage of GDP, 2015 ......................................................... 30

Figure 12 Total government support for business R&D, as percentage of GDP, 2015 (or the latest

available year) ..................................................................................................................................... 31

Figure 13 Government Effectiveness Index, 2016 ...................................................................................... 33

Figure 14 Regulatory Quality Index ............................................................................................................. 34

Figure 15 Control of Corruption Index ........................................................................................................ 35

Figure 16 Index on business environment, 2016 ........................................................................................ 38

Figure 17 Scientific publications in data science related fields .................................................................. 44

Figure 18 Business enterprise R&D expenditure in J62 and J63 (Euro per inhabitant in purchasing power

standard (PPS), constant 2005 prices) ................................................................................................ 45

Figure 19 Share of researchers in J62 and J63 in total business sector researchers .................................. 46

Figure 20 Percentage of enterprises in ICT service sector that had hard-to-fill vacancies for jobs requiring

ICT specialist skills ............................................................................................................................... 48

Figure 21 Full time employees (average annual growth between 2008 and 2015) ................................... 49

Figure 22 Share of graduates in natural sciences, mathematics and statistics, ICT and engineering, 2015

............................................................................................................................................................ 50

Figure 23 Index on innovation capacity of the data economy, 2015-2017 ................................................ 52

Figure 24 Percentage of households' with the Internet fixed or mobile broadband connection .............. 55

Figure 25 Percentage of fibre connections in total broadband subscriptions, December 2016 ................ 56

Figure 26 Country adoption of IPv6 according to Google's metrics, 2016 ................................................. 57

Figure 27 Maximum contracted download speed of the fastest fixed Internet connection in 2017 ......... 58

Figure 28 Akamai's measured average speed, Q1 2016 ............................................................................. 59

Figure 29 Index on infrastructural conditions, 2016/2017 ......................................................................... 61

Figure 30 Percentage of enterprises using RFID ......................................................................................... 64

Figure 31 Share of enterprises buying cloud computer services ................................................................ 65

Figure 32 Percentage of enterprises using CRM ......................................................................................... 66

Figure 33 M2M cards, per 100 inhabitants, 2016 ....................................................................................... 67

Figure 34 Index on diffusion of data related technologies, 2016/2017 ..................................................... 69

Figure 35 Global Cybersecurity Index, 2017 ............................................................................................... 73

Figure 36 Percentage of enterprises whose ICT security policy addressed security risks, 2015 ................ 75

Figure 37 Percentage of enterprises whose ICT security policy was defined or reviewed within the last 12

months, 2015 ...................................................................................................................................... 76

Figure 38 Secure Internet servers per 1 million inhabitants ...................................................................... 77

Figure 39 Index on security related aspects ............................................................................................... 79

Figure 40 Percentage of businesses with formal policy to manage ICT privacy risks, 2015 ....................... 83

Figure 41 Individuals who manage access to their personal information on the Internet, 2016 .............. 84

Figure 42 Privacy control index ................................................................................................................... 85

Figure 43 Privacy protection index, 2015/2016 .......................................................................................... 87



IX

Figure 44 Percentage of individuals interacting with public authorities via Internet ................................ 90

Figure 45 Percentage of individuals taking part in on-line consultations or voting ................................... 91

Figure 46 Percentage of individuals participating in social and professional networks, 2017 ................... 92

Figure 47 Percentage of individuals finding information about goods and services on the Internet prior to

their purchase ..................................................................................................................................... 93

Figure 48 Participation in sharing economy, 2017 ..................................................................................... 94

Figure 49 Index on social participation in data related activities ............................................................... 97

Figure 50 Open data readiness ................................................................................................................. 102

Figure 51 Open data portal maturity ........................................................................................................ 104

Figure 52 Data Openness Index, 2017 ...................................................................................................... 106

Figure 53 Distribution of average index scores ........................................................................................ 108

Figure 54 Scores of European countries ................................................................................................... 110

List of abbreviations

CRM Customer relationship management

CSI Cyber Security Index

ICT Information and communication technology

IoT Internet of Things

ITU International Telecommunication Union

M2M Machine-to-Machine

R&D Research and development

RCA Revealed comparative advantage

RFID Radio frequency identification

WGI Worldwide Governance Indicators

WTO World Trade Organization



1

Executive summary

The observatory provides a measurement concept to describe the current status of the data economy in

Europe using indices based on selective subsets of individual indicators, which describe different

important aspects and underlying framework conditions of the data economy. It aims to capture

developments and achievements of countries in the previously specified dimensions relevant for the data

economy that directly affect the capacity to participate in and build up a competitive data economy.

Furthermore, the observatory strives to contribute to the understanding of whether and to which extent

important framework conditions necessary to reap benefits from the data economy are in place in

different European countries. Finally, it helps to identify current trends and to reveal individual strengths

as well as weaknesses of countries that need to be overcome to be able to unleash the full potential of

the data economy.

The results of the analysis reveal a lot of dispersion across countries indicating their varying capabilities

and potentials to use the business opportunities big data offers. Considerable variation was identified

between countries with regard to the data-driven business sector activities indicating that at present,

European countries reap to very differing degrees economic impact from data-based activities. Big-data-

related technologies as well as the underlying infrastructure were found to be very unevenly distributed

across European countries. Furthermore, the analysis provides evidence that in most countries targeted

efforts are needed to allocate more resources in the research and skill capacities to enable a successful,

innovation-driven development of the data economy and to be able to sustain in international

competition. Apart from that, there are significant cyber security gaps within Europe. Big discrepancies

across countries were also found in the context of data openness. Finally, the level to which individuals

participate in and benefit from data-related activities was found to differ considerably across Europe.

Overall, significant divides over the majority of indicators have been identified between individual groups

of countries. The gap is particularly large between best performing countries and those countries that are

grouped in the low tail of distribution. As big-data-related technologies are rapidly evolving, there is a

considerable risk for these countries to be left behind in terms of their participation and contribution to

a knowledge-intensive data economy. Therefore, there is an urgent need for targeted measures in poorly

performing countries to foster their innovative capability, support business sector activities and the use

of advanced technologies.

To monitor the evolution of the data economy over time and to see whether the trends are persistent,

further studies will be necessary. Apart from that, there is a substantial need for further research to

investigate backgrounds and causes of the observed trends and uneven developments more closely. This

will be necessary to more reliably identify policy options, which are well suited to respond to the key

challenges European countries are facing.



2

1 Introduction

Data economy is an emerging and rapidly evolving field. A sound measurement of the data economy that

allows an evidence based assessment of a broad range of relevant framework conditions and capabilities,

on which the data economy largely depends, is critical to realize the full potential of the data economy in

the European countries and to ensure its successful development. Moreover, consistent and empirically

based information is necessary to inform relevant policy decisions at national and European levels to help

policy makers in evaluating the efficiency of their policy measures.

So far, there is only very little solid knowledge available about the data economy and its development. To

the best of our knowledge, there is hardly any evidence-based assessment of its social and economic

impact, neither in Europe nor in other parts of the world. The observatory aims to counteract this situation

providing a valuable and empirically based foundation for future attempts to make developments in the

data economy visible.

The development of the observatory builds upon the EuDEco model of the European data economy. Based

on the model and a review of related literature, key dimensions and indicators describing them have been

identified that cover different aspects. From a legal perspective, a broad range of private and public laws

and regulations need to be taken into account. However, the qualitative character of many relevant

aspects represents a major challenge to a quantitative, indicator-based analysis. Further important goal

is to assess to which extend security measures are taken at country level to prevent security related risks.

From a technological perspective, the focus lies on the technologies available to participants in the data

economy as well as on the fundamental technical infrastructure on which data economy relies. From a

socio-economic perspective, aspects such as the business environment, research and innovation efforts

in data related fields, policy framework conditions, data openness, the availability of capital and skilled

labour as well as the size and efficiency of the data market need to be considered.

The concept to measure the development of the European data economy developed by EuDEco differs

from the usual observatory methodologies, which use traditional surveying methods to collect data

through, for instance, online surveys or interviews. By contrast, the EuDEco method uses for the

assessment of the data economy available indicators from official statistics that provide representative

data. This allows an access to reliable data for future monitoring of the data economy in different

countries, but it also provides an opportunity to extend and further develop the observatory in the future.

With respect to the methodological development of the observatory concerning the data analysis and the

calculation of indices, established methodological procedures have been followed.

A combination of the observatory with tools such as the Knowledge Base that provides access to and

allows a debate on key resources is considered promising. It will increase the community's awareness of

the foundation that has been laid by EuDEco and make developments in the data economy visible. This is

a prerequisite to join forces in the future and build on existing achievements. During the course of the



3

project, it has become evident that there is quite some interest in tools, which help understand the

development, and to some extent also the functioning, of the data economy.

A lot of work is still needed to be done in order to establish an empirical link between data related

activities and their economic and social impact. Notwithstanding some constraints, the observatory

provides useful, evidence based information for economic decisions, planning and policy measures, which

may be highly relevant for policy makers and academics but also business representatives that could

exploit the results or build their own observation activities on them.

1.1 Purpose and scope

The general purpose of D5.3 is to provide a conceptual framework for the description and measurement

of the data economy. In particular, the aim is to benchmark and analyse the development as well as the

underlying framework conditions of the data economy in Europe and to identify relevant trends as well

as the extent of existing dispersions among the European countries.

A further goal of this study is to help identify fields and regions in the European Union where more

concentrated measures are needed to create the necessary framework conditions for the data economy

and to promote its progression. Moreover, it provides an important base for a more qualitative

investigation of the backgrounds of good practices and success stories of those countries, which perform

best, that could be transferred to the less successfully performing countries.

For this, important determinants and framework conditions affecting countries' capacity to participate in

the data economy as well as key indicators measuring central aspects of the data economy and its future

potentials were identified. Countries have been assessed according to the following central dimensions

(categories) for which data are analysed and compiled:

business sector activities

business environment

innovation potential of the data economy

infrastructural conditions

diffusion of big-data-related technologies

social participation in data-related activities

security-related aspects

privacy control

data openness

For an overall assessment of countries' positioning in different categories that are relevant for the data

economy and also to enable comparisons between countries, composed indices comprising different

individual indicators that describe selected categories were calculated. However, the goal of this study is



4

also to provide a more refined analysis, which goes beyond the aggregate indicators to display the

developments within a specific category and to better understand individual dynamics behind the

aggregate numbers of indices. Therefore, an in-depth analysis of individual indicators was included in the

study to deliver a more detailed information on individual achievements and performances of countries

in specific areas.

Since the European economies and their businesses are increasingly exposed to the vigorous international

competition, international comparisons within individual categories were striven for whenever the data

situation made it possible. The study also seeks to show and analyse the evolvements over time, however,

at this point of time, it is feasible only to a limited extent as many representative indicators reflecting

important aspects of the data economy have been collected and made available only since recently.

1.2 Structure of the document

The document is structured as follows:

Section 1 provides an overview of the deliverable’s purpose and scope, its structure, and its

relationships to other deliverables;

Section 2 describes limitations and barriers, and the methodology, and provides an overview of

the categories and indicators;

Section 3 presents the observatory by going into details with respect to the categories and

indicators;

Section 4 discusses the results of measuring the European data economy based on the

observatory; and

Section 5 concludes the deliverable and provides an outlook on promising future activities.

1.3 Relationships to other deliverables

D5.3 builds primarily on D4.1, which, among other things, describes the final model of the European data

economy. Concerning the observatory, environmental factors are the most relevant aspects covered by

the model. Therefore, there is also a strong relationship between D1.2 and D5.3. The understanding of

the European data economy in general and framework conditions in particular guided the selection of the

observatory's determinants and indicators. Since there is a separate deliverable on the observatory, which

is completed at the end of the project, the observatory is not addressed in D5.2, which serves as a final

report of the project covering the other key results.



5

2 Towards an observatory of the data economy

This section introduces the measurement concept.

2.1 Limitations and barriers

There is a number of different fundamental challenges, which create obstacles for a comparative cross-

country analysis. At present, the availability of representative data and metrics, which would allow a

measurement of the data economy, as well as of common taxonomies making the indicators comparable

across countries is rather limited. Besides, the observation of some data economy relevant developments

over time are impossible, since many indicators are not available for time series. Another general

challenge is the adequate quantification of some important qualitative aspects, which play a critical role

for data related activities, such as privacy and security issues.

The whole complexity of data economy relevant developments and framework conditions makes it

difficult to adequately capture them by a number of quantitative indicators. Consequently, many

significant indicators have to be omitted due to the lack of their availability or difficulty to measure.

The current data on the data economy are not complete both in terms of time and country coverage. Due

to the data coverage constraints, analysis of data and the subsequent calculation of indices is only possible

for a short period of time (mostly one or two points of time). For this reason, the composite index can be

calculated only for one period of time using data of the latest available year.

Calculated indices are limited in coverage to the European countries (mainly EU28 countries plus Norway,

Switzerland and Iceland) because only for these countries a reasonable coverage of data is provided by

official statistics, mostly Eurostat. Apart from Eurostat, data used in this study are sourced from different

international organizations such as the OECD, the World Bank, the International Telecommunication

Union (ITU) and the World Trade Organization (WTO) as well as from Elsevier (Scopus) and representative

studies.

Since it is not possible to fully reflect the broad range of aspects that describe the identified categories,

which are relevant for the development of the data economy, the calculated indices are based on selective

subsets of available indicators, which relate to some important aspects of the data economy as well as its

underlying framework conditions. The present measurement concept aims to estimate the performance

of countries and to uncover relevant development trends on the basis of selected indicators. It attempts

to provide a realistic basis for the evaluation of the status quo of each country in one specific category

and to indicate development trends within it. To monitor the evolution over time and to see whether the

trends are persistent, follow-on studies will be necessary.



6

2.2 Methodology

This section outlines the index methodology as well as data constraints and descriptive statistics.

2.2.1 Index methodology

The identified categories of the data economy represent complex and multidimensional systems and

therefore cannot be captured by a single indicator. They comprise a number of different indicators, each

one of them reflecting only some individual aspects of a certain phenomenon.

To get a more comprehensive information on capabilities of countries in one specific field, different

characteristics in one thematic area can be combined to a composed indicators, which are also referred

to as indices. This way, a synthetic indicator to measure capabilities in one specific field with a clear

interpretation content is formed. An index can be particularly useful in identifying data related

development trends and gaps within a group of countries and assessing the progress of individual

countries over time. It allows for benchmarking the performance of selected countries in one specific field

against a set of other countries.

The first step on the way to the indices is to define the categories or dimensions, which are highly relevant

for the uptake and development of the data economy and to identify indicators, which describe these

dimensions. To be aggregated, the indicators must be normalized, to set off their different scales and

measures. To normalize the data, the following formula was used:

Ȳ𝑖, 𝑗 = 𝑌𝑖,𝑗−min{𝑌𝑖,𝑗}

max{𝑌𝑖,𝑗}−min{𝑌𝑖,𝑗} ,

where 𝑌𝑖, 𝑗 is the value for country i and specific indicator j (e. g. percentage of enterprises analysing data)

before normalising and Ȳ𝑖, 𝑗 after normalising.

This general procedure leads to standardized indicators ranging from 0 to 1. The next step is the

aggregation of individual indicators to indices using weights. For the sake of simplicity and transparency,

a linear approach is preferred. We also use equal weightings assuming that all components of one

category play a comparable role for this category. The choice of equal weights avoids the problem of

defining the weights in a somewhat arbitrary way. This approach is widely used in relevant studies1. This

leads to the following formula of index I for country i, where J is the total number of individual indicators:

𝐼𝑖 =1

𝐽∑Ȳ𝑖, 𝑗

𝐽

𝑗=1

1 See, for example, Archibugi and Coco (2004) and Acatech, BDI, Fraunhofer ISI, and ZEW (2017)



7

The competitive position of each country within the selected group of countries was identified using

ranking.

2.2.2 Data constraints

One important methodological issue is the treatment of omitted data. Since representative data is not

available for all countries under consideration, final indices have to be adjusted for the number of

observations available for each country and weighted according to this number. Due to constraints of the

availability of reliable data and the lack of the data on a year-to-year basis, the majority of indicators relate

only to up to three years between 2012 and 2017. Therefore, direct comparisons over time are often not

possible. The average index is calculated only for the current period since data for many relevant

indicators, which the final indices comprise of, are provided by official statistics not earlier than 2015.

2.2.3 Descriptive statistics

For each indicator descriptive statistics are performed to describe the characteristics of data using

measures of central tendency (e.g., mean, median) and dispersion (e.g., standard deviation, variance,

variance coefficient, quartiles, minimum, maximum). The descriptive statistics are performed to get a

better understanding of the variables and their significance for index calculation. In particular, the

distribution of the variables needs to be analysed to see how dispersed the variables are and how this

dispersion evolves over time. The exploration of the dispersion of the variables and its evolvement over

time is focused on to identify uneven developments, as well as convergence or divergence trends within

the given time spans in the group of countries concerned.



8

2.3 Categories and indicators

Table 1 provides an overview of the categories and indicators selected.

Indicator Data source Unit Time period Definition

Business sector activities

Enterprises using and analysing data

Eurostat Percentage 2016 Enterprises analysing big data from any data source. Percentage of all enterprises, without financial sector (10 persons employed or more)

Productivity of data-related sectors

Eurostat Value added at factor cost per full-time employee, in €

2010-2015 Value added at factor cost per full-time employee of sectors (according to NACE Rev. 2): J62 - Computer programming, consultancy and related activities and J631 - Data processing, hosting and related activities; web portals

Investment in ICT services

Eurostat Percentage of the value added at factors cost

2010-2015 Share of investment in value added at factors cost

Share of data companies

European Data Market Monitoring Tool, IDC 2016

Percentage 2013-2016 Estimates of the share of data companies in the total companies of J and M sectors (NACE Rev. 2)

Revealed comparative advantage (RCA) for trade in data-related services

Own calculations based on data from the WTO

Ratio 2012-2015 Ratio between the country's exports and imports in data related and other computer services divided by the ratio between the country's exports and imports in commercial services

Business environment

Government support to business R&D

OECD, Measuring Tax Support for R&D and Innovation

Percentage of GDP

2015 Total government support for business R&D



9

Government effectiveness

Worldwide Governance Indicators (WGI), World Bank

Estimate scores between -2,5 and 2,5

2014-2016 Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. It ranges from approximately -2.5 (weak) to 2.5 (strong) governance performance.

Regulatory quality Worldwide Governance Indicators (WGI), World Bank


2014-2016 Reflects perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. It ranges from approximately -2.5 (weak) to 2.5 (strong) regulatory quality.

Control of corruption Worldwide Governance Indicators (WGI), World Bank


2014-2016 Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. It ranges from approximately -2.5 (weak) to 2.5 (strong) control of corruption.

Domestic credit to private sector

World Development Indicators, World Bank

Percentage of GDP

2014-2016 Financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment.

Venture capital investments

OECD, Entrepreneurship at a Glance 2017

Percentage of GDP

2016 Financing that private investors provide to start-up companies and small enterprises in form of private equity capital

Depth of credit information index

World Bank, Doing Business project

Scores between 0 and 8

2014-2016 It measures rules affecting the scope, accessibility, and quality of credit information available through public or private credit registries. The index ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a public registry or a private bureau, to facilitate lending decisions.



10

Strength of legal rights index

World Bank, Doing Business project


2014-2016 It measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending. The index ranges from 0 to 12, with higher scores indicating that these laws are better designed to expand access to credit.

Innovation potential

Scientific publications

Scopus Number of publications per 1 Mio. inhabitants

2010-2017 Outcome of the database searches at country level using search words "data science", or "big data", or "data analytics", or "data mining

R&D investments in data-related activities

Eurostat Euro per inhabitant in purchasing power standard (PPS), constant 2005 prices

2012-2015 Business enterprise R&D expenditure (BERD) of sectors (according to NACE Rev. 2): J62 - Computer programming, consultancy and related activities and J63 - Data processing, hosting and related activities; web portals and other other information service activities

R&D personnel in data-related activities

Eurostat Percentage of total R&D personnel

2012-2014 R&D personnel in sectors (according to NACE Rev. 2): J62 - Computer programming, consultancy and related activities and J63 - Data processing, hosting and related activities; web portals and other information service activities as percentage of total business sector R&D researchers

Talent pool Eurostat, OECD Percentage of graduates

2015 Share of graduates in natural sciences, mathematics, statistics, engineering and information and communication technologies in all tertiary education graduates (ISCED2011 levels 5 to 8)

Infrastructure

Households with broadband access

Eurostat Percentage of all households

2007-2017 Household with broadband Internet connection (fixed or mobile) as percentage of all households



11

Speed of Internet connection

Eurostat Percentage of all enterprises

2014-2017 The maximum contracted download speed of the fastest fixed internet connection per speed tiers: less than 2 Mb/s; at least 2 but less than 10 Mb/s; at least 10 but less than 30 Mb/s; at least 30 but less than 100 Mb/s; at least 100 Mb/s

Fibre connections OECD, Broadband Statistics

Percentage in total broadband subscriptions

December, 2016

Percentage of fibre connections in total broadband among countries reporting fibre subscribers

Country adoption of IPv6

Google (2016), “Per-country IPv6 adoption” in: OECD, Digital Economy Outlook 2017

Percentage 2016 Country adoption of Internet Protocol version 6 according to Google's metrics

Technology diffusion

Enterprises using radio frequency identification (RFID) technologies

Eurostat Percentage of all enterprises

2014-2017 Enterprises using Radio Frequency identification (RFID) technologies as percentage of all enterprises, without financial sector (10 persons employed or more)

Cloud computing Eurostat Percentage 2014-2017 All enterprises, without financial sector (10 persons employed or more) that buy cloud computing services used over the internet

M2M OECD M2M cards per 100 inhabitants

December, 2016

Machine to Machine (M2M) embedded mobile cellular subscriptions

CRM solutions Eurostat Percentage 2014-2017 Enterprises using software solutions like Customer Relationship Management (CRM) (all enterprises without financial sector)



12

Security status

Global Cyber Security Index 2017

International Telecommunication Union (ITU)

Index 2014-2017 Composite index combining 25 indicators into one benchmark measure to monitor and compare the level of ITU Member States cybersecurity commitment with regard to the five pillars: legal, technical, organizational, capacity building, cooperation

Enterprises addressing security risks

Eurostat Percentage 2010, 2015 Enterprises whose ICT security policy addressed the risks of destruction or corruption of data, disclosure of confidential data and unavailability of ICT services due to an attack or an accident as percentage of all enterprises, without financial sector (10 persons employed or more)

Enterprises with updated security policy

Eurostat Percentage 2015 Enterprises whose ICT security policy was defined or most recently reviewed within the last 12 months as percentage of all enterprises, without financial sector (10 persons employed or more)

Secure Internet servers

World Development Indicators, World Bank

Servers per 1 million inhabitants

2010-2016 Servers using encryption technology in Internet transactions

Privacy protection

Awareness of privacy-related risks among individuals

Eurostat Percentage 2016 Individuals who managed access to their personal information on the Internet by reading privacy policy statements before providing personal information, or by restricting access to their geographical location, or by limiting access to their profile or content on social networking sites, or by not allowing the use of personal information for advertising purposes, or by checking that the website where they needed to provide personal information was secure (e.g. https sites, safety logo or certificate), or by asking websites or search engines to access the information hold about them to be updated or deleted as percentage of all individuals



13

Enterprises addressing privacy-related risks

Eurostat Percentage 2010, 2015 Enterprises whose ICT security policy addressed the risks of disclosure of confidential data due to intrusion, pharming, phishing attacks or by accident as percentage of all enterprises, without financial sector (10 persons employed or more)

Privacy Control Index

Quantifying Key Characteristics of 71 Data Protection Law (Nieuwesteeg, 2017)

Index 2015 Codes of the key six characteristics 71 Data Protection Laws (DPLs): data collection requirements; the data breach notification requirement; the presence of a data protection authority; the requirement of a data protection officer; the level of monetary sanctions; and the presence of criminal sanctions

Societal participation

Interaction with public authorities via Internet

Eurostat Percentage of individuals

2010-2017 Percentage of all individuals who interacts with public authorities via Internet

Participation in social or professional networks


2013-2017 Percentage of all individuals who participate in social (e.g., Facebook, Twitter) or professional networks (e.g., LinkedIn, Xing) creating user profile, posting messages or other contributions

Data-driven purchasing decisions

Eurostat Percentage of all individuals

2010-2017 Percentage of all individuals using Internet data on goods and services for the purchase decision

Percentage of individuals taking part in on-line consultations or voting


2011-2017 Percentage of individuals taking part in on-line consultations or voting to define civic or political issues (e.g. urban planning, signing a petition)

Participation in sharing economy

Eurostat Weighted average

2017 Weighted average of individuals used any website or app to arrange an accommodation or/and transport from another individual



14

Data openness

Open Data Readiness

European Data Portal


2015-2017 Assesses to what extent countries have an Open Data policy in place, licensing norms and the extent of national coordination regarding guidelines and setting common approaches. In addition, the transposition of the revised PSI Directive is taken into account. Besides the presence of an Open Data policy, the use made of the Open Data available and the estimated political, social and economic impact of Open Data are assessed.

Portal Maturity European Data Portal


2015-2017 Assesses the usability of the portal regarding the availability of functionalities, the overall re-usability of data such as machine readability and accessibility of data sets, for example, as well as the spread of data across domains.

Table 1 Overview of key categories and indicators



15

3 The observatory

This section describes the categories and indicators in detail.

3.1 Business sector activities

Business sector performance plays the most significant role for the economic development of a country.

Business activities of private enterprises are the most important motor for innovations boosting

productivity growth, raising competitiveness of a country's economy and increasing people's standard of

living.

Data driven activities of companies have great potential to boost innovation and economic growth. In this

section, we use the available empirical evidence to analyse the data driven activities of the business sector

across European countries.

3.1.1 Enterprises using and analysing data

Enterprises increasingly recognise the value of big data for their business activities. Many enterprises

perform big data analyses to gain decision-relevant insights from data to optimize their internal processes

and to exploit them for new business opportunities. On average, 10% of all enterprises in the EU28

countries performed data analysis in 2016 (Figure 1). Among all EU countries, Netherlands, Malta and

Belgium have the highest shares of companies engaged in big data related activities. However, there are

large discrepancies in the extent to which large, medium and small enterprises use big data. As of 2016,

big data analysis are mainly performed by large enterprises: the proportion of large companies analysing

data is on average two to three times larger than those of small enterprises. In some countries, like Cyprus,

Bulgaria, Denmark, Slovenia, Italy and Sweden, this gap is even larger. The lowest overall percentage of

enterprises performing big data analysis are registered in Germany, Poland, Hungary, Bulgaria and Cyprus.

The main domains of data, which enterprises use for their data analysis are data from enterprises' smart

devices or sensors, data from geolocation of portable devices and social media data. However, data from

other sources are also increasingly used by the European enterprises (Figure 2).



16

Figure 1 Enterprises analysing big data, 2016 (by size of enterprises)2

Note: Data relate to all enterprises, without financial sector (10 persons employed or more).

2 Figure based on data from Eurostat, Digital Economy and Society Database.

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Medium enterprises (50-249 persons employed) Large enterprises (250 persons employed or more)



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Figure 2 Domains of data for data analysis performed by enterprises, 20163


3.1.2 Productivity of data-related sectors

Productivity is one of the key components of economic growth – higher income growth can only be

achieved when there are increases in productivity. Rising productivity in industrial sectors contributes to

the competitiveness and better economic performance of these sectors. Growing productivity is thus

imperative for the economic success and prosperity of the business sector.

Knowledge intensive activities like data related activities exhibit higher productivity rates. Calculations

reveal that in Europe, productivity in data related sectors J62 (Computer programming, consultancy and

related activities) and J631 (Data processing, hosting and related activities; web portals) is on average 40%

higher than the overall economic productivity. Moreover, the exploitation of data entails additional

productivity and efficiency gains in different fields of the economy and life spheres.

In a European comparison, the highest productivity levels in data related sectors J62 and J631 are achieved

by Ireland followed by Belgium, United Kingdom, Denmark, Finland and Sweden (Figure 3). The highest

annual average growth rates in 2010-2015 were found for Finland (7.3%), Cyprus (6.8%), Ireland (6.6%),

the United Kingdom (5.4%) and Sweden (5.2%). Lithuania, Bulgaria, Romania and Estonia exhibit also quite

high productivity growth rates during 2010-2015 of 7.2%, 6%, 5.5% and 4.8% respectively, albeit starting

3 Figure based on data from Eurostat, Digital Economy and Society Database.

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Analyse own big data from enterprise's smart devices or sensors

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Analyse big data generated from social media

Analyse big data from other sources



18

from a very low base. However, there are large disparities concerning the development of productivity

within the EU. Countries such as Lithuania, Latvia, Bulgaria and Romania display productivity levels in data

related sectors that equal less than 20% of the EU-wide highest level of productivity reached by Ireland.

Figure 3 Value added at factor cost per full-time employee in J62 and J 6314

Note: J62 – Computer programming, consultancy and related activities; J631 – Data processing, hosting

and related activities; web portals. For Luxembourg, Malta and Slovenia data of the apparent labour

productivity (gross value added per person employed) are used.

3.1.3 Investment in ICT services

To use new business opportunities, which arise from big data, high investments are expected to be made

in software, ICT and data related activities. Increasing investments in software and databases contribute

decisively to the growing data intensity of the economy (OECD, 2015). Since direct data on investments in

software and databases are not available, it was considered important to include instead an indicator,

which measures the overall investment rate of each country in ICT services. This indicator reveals that

many European countries allocate a lot of financial resources in ICT services. Between 2010 and 2015, the

largest increase in investments – by about 160% – showed Ireland (Figure 4). High increases of investment

rates are also observed for Finland (60%), Italy (30%), Denmark (28%), Norway (20%) and Slovenia (16%).

4 Figure based on data from Eurostat, own calculations.

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However, many countries exhibit a drop in investments in ICT services during 2010-2015.

Figure 4 Investment rate in ICT services5

3.1.4 Share of data companies

The data economy landscape encompasses various groups of actors including data holders, data

distributors, data users and solution providers, interacting with each other in an interdepending and

complementary way. A growing number of market actors recognize the intrinsic value of data and

incorporate data into their business models. So far, no official statistics contain data that allows the

measurement of the data market in each country. IDC provided an estimate for the share of the total data

companies in the ICT and professional services industries6. According to this estimate (Figure 5), Ireland,

United Kingdom with shares of above 20% rank first in terms of the share of companies with a data focus,

whereas a large group of Central and Eastern European countries including Croatia, Bulgaria, Hungary,

Slovenia, Romania, Latvia, Estonia, Lithuania, Slovakia and the Czech Republic has an estimated relatively

low percentage of data companies of approx. 5% each.

5 Figure based on data from Eurostat. 6 Details on the methodology are described by IDC and OpenEvidence (2017).

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Figure 5 Estimates of the share of data companies7

3.1.5 Trade in data-related services

Along with the share of data companies, size of revenues generating by them as well as the sectoral

comparative advantage in trade with data related services are good indicators to assess the dynamics and

evolvement of the data market in each country. Increasing revenues and a relative trade specialisation in

data related services of the countries reflect business growth and competitiveness of the data markets in

these countries. Calculations based on the trade data from the WTO, identified a comparative advantage

in trade with data related services in a group of countries comprising Austria, Belgium, Germany, South

Korea, and Sweden (Figure 6). As the result of a global fragmentation of the value chains, some data

related activities have been outsourced to lower-cost locations, including countries in Central and Eastern

Europe, which high scores of revealed comparative advantage (RCA) for Bulgaria, Poland, Latvia, Lithuania,

Romania and Croatia indicate.

Ireland stands out particularly with a RCA score of 80, indicating a strong domination of exports in data

related services in the overall commercial services of the country. In international comparison, Israel also

shows high levels of trade specialisation in data related services.

7 Figure based on data from IDC, EU Data Landscape.

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Figure 6 RCA in countries' world trade in data processing and other computer services, 20158

Note: Data are deflated using GDP deflator. RCAs were determined by putting the world imports and

exports of countries' data related and other computer services in relation to the import and export of

commercial services according to the formula:

RCA = (𝑋𝑚𝑛/𝑀𝑚𝑛)

(𝑋𝑛/𝑀𝑛) ,

where 𝑋𝑚𝑛 and 𝑀𝑚𝑛 are exports and imports of country n in data processing and other computer

services, and 𝑋𝑛 and 𝑀𝑛 are exports and imports of country n in commercial services. If RCA > 1, the

country in question has a comparative advantage in trade of data processing and other computer services.

3.1.6 Index on business sector activities

The composite index on business sector activities across European countries based on selected categories

is calculated to reflect the level of engagement of individual European countries in data related activities

as well as their capacity to exploit business opportunities that big data are offering. The assessment of

each country performance is based on five indicators: share of enterprises analysing big data, labour

productivity of each country in data related sectors, overall investments rate in ICT services, share of

8 Figure based on WTO, Statistics: Trade in commercial services. Own calculations.

AU AT BE

BG

HR

CZ

DKEE

FR

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GRHKGHU

IL

IT

JP

KR LV LT

LU

NL

NO

PL

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USA

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22

companies with big data focus and the trade specialization of each country in data related services

measured as RCA.

Due to the data coverage constraints, indices can be calculated only for the time period for which data in

all categories are available. Data coverage concerning labour productivity, investment rate, specialization

and the estimates of the share of data companies, relates to 2013-2015, so that their subindices can be

calculated for both 2013 and 2015 to allow comparisons over time. Data to measure the share of

enterprises analysing data are only available for the year 2016. The average index comprising of all five

indicators was calculated using data of the latest available year.

A look at index scores (Table 2), which measure the performance of countries in the data related business

activities and their descriptive statistics reveals a lot of disparities among countries. A relatively high

variance has the distribution of the specialization indicator measuring the extent to which countries

specialize in exports of data related services9. There are also significant disparities within the selected

group of the European countries in terms of investment rates in ICT services, where coefficient of variation

of the index score distribution was higher in 2015 around the smaller mean in comparison to 2012 (Table

3). The smaller mean is largely the result of the considerable decline in investment rates in many countries

in 2015 compared to 2012. The wide variation of performance can also be observed across countries in

terms of labour productivity. The least dispersion display indicators measuring the share of enterprises,

which are engaged in analysing big data.

Calculated average index scores reveal that Ireland reaches the highest ranks in all categories followed by

Malta, the United Kingdom, the Netherlands and Belgium that achieve the highest scores in the majority

of selected categories (Figure 7). Finland, Sweden and Denmark belong to further best performing

countries. Their high average index scores imply that these European countries are better prepared to

compete successfully in the emerging data economy and to exploit its business opportunities. However,

there is a significant polarization between countries grouped in the upper quartile indicating considerable

differences between countries. It is primarily due to the outstanding performance of Ireland that

outperforms its European peers by far in the selected fields of data related business sector activities. At

the same time, there is a large group of closely bunched countries at the bottom end of distribution

suggesting a large divide between best and worst performing European countries. To this country group

belong Estonia, Poland, the Czech Republic, Hungary, Slovakia, Latvia and Croatia. Latvia and Croatia

occupy the last places reflecting that in terms of their performance in the selected categories they appear

to be falling behind even further.

9 The RCA score of 80 for Ireland was capped to 3 to set off its outlier effect on the indices of other countries.



23

Figure 7 Index on business sector activities, 2015/201610

10 Based on own calculations.

0.00 0.20 0.40 0.60 0.80 1.00 1.20

CroatiaLatvia

SlovakiaHungarySlovenia

Czech RepublicPolandEstonia

LithuaniaCyprus

BulgariaSpain

RomaniaFrance

GermanyGreece

ItalyAustria

LuxembourgPortugal

DenmarkSwedenFinland

BelgiumNetherlands

United KingdomMalta

Ireland



24

Country

Enterprises analysing big data, fraction of enterprises

Productivity of data related sectors (value added at factor cost per full-time employee)

Investment in ICT services (investment per value added at factors cost)

Share of data companies of total J and M sectors

RCA for trade in data related services

Average index

2016 2012 2015 2012 2015 2013 2016 2012 2015 2015/2016

Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank

Ireland

1.00 1 1.00 1 1.00 1 1.00 1 1.00 1 1.00 1

1.00 1 1.00 1

Malta 1.00 1

0.42 13

0.44 11 0.41 12

0.61 2

United Kingdom 0.75 4 0.78 3 0.69 3 0.15 18 0.06 21 0.83 2 0.90 2

0.60 3

Netherlands 1.00 2 0.63 8 0.53 9

0.55 7 0.63 3

0.17 17 0.58 4

Belgium 0.88 3 0.96 2 0.74 2 0.32 13

0.41 13 0.37 14 0.38 11 0.31 9 0.57 5

Finland 0.75 4

0.61 5 0.03 25 0.30 8 0.39 14 0.40 13

0.51 6

Sweden 0.44 16 0.65 7 0.60 6 0.10 21 0.03 22 0.49 9 0.49 9 1.00 1 0.77 2 0.47 7

Denmark 0.56 9 0.77 4 0.62 4 0.20 15 0.19 12 0.59 3 0.50 8 0.14 17 0.17 15 0.41 8

Portugal 0.63 6 0.16 18 0.09 21 0.65 5 0.84 2 0.36 15 0.37 15 0.18 16 0.03 21 0.39 9

Luxembourg 0.63 6 0.56 11 0.46 10 0.65 5 0.39 6 0.46 10 0.41 11 0.25 13 0.03 20 0.38 10

Austria

0.67 6 0.57 7 0.14 19 0.10 18 0.58 4 0.56 5 0.45 6 0.28 11 0.38 11

Italy 0.38 17 0.57 10 0.45 11 0.04 23 0.23 9 0.57 5 0.57 4 0.11 19 0.17 16 0.36 12

Germany 0.19 23 0.73 5 0.54 8 0.00 26 0.01 23 0.54 8 0.54 6 0.49 4 0.38 8 0.33 13

France 0.50 11 0.58 9 0.45 12 0.18 16 0.10 18 0.34 16 0.32 16 0.20 14 0.18 14 0.31 14

Greece 0.50 11 0.23 16 0.09 22 0.57 7

0.57 6 0.50 7 0.20 15 0.13 18 0.30 15

Romania 0.50 11 0.00 26 0.00 28 0.68 3 0.43 5 0.04 26 0.02 23 0.87 3 0.42 5 0.28 16

Spain 0.31 20 0.33 13 0.24 15 0.06 22 0.20 11 0.28 17 0.30 17

0.26 17

Bulgaria 0.25 21 0.02 25 0.01 27 0.43 11 0.45 3 0.04 24 0.03 20 0.96 2 0.56 3 0.26 18

Cyprus 0.00 25 0.39 12 0.39 14 0.77 2 0.17 14 0.42 12 0.45 10

0.25 19

Lithuania 0.56 9 0.04 24 0.04 25 0.41 12 0.15 16 0.05 22 0.01 26 0.42 8 0.41 6 0.24 20

Estonia 0.63 6 0.11 21 0.10 19 0.68 3 0.10 18 0.04 25 0.02 25 0.44 7 0.24 12 0.22 21

Poland 0.19 23 0.18 17 0.13 18 0.18 16 0.19 13 0.27 18 0.26 18 0.30 12 0.30 10 0.21 22

http://stats.oecd.org/OECDStat_Metadata/ShowMetadata.ashx?Dataset=EDU_GRAD_FIELD_TEST&Coords=%5bCOUNTRY%5d.%5bDEU%5d&ShowOnWeb=true&Lang=en



25

Czech Republic 0.38 17 0.25 15 0.16 16 0.13 20 0.00 24 0.03 27 0.00 28 0.45 5 0.51 4 0.21 23

Slovenia 0.50 11 0.14 19 0.10 20 0.47 10 0.34 7 0.06 20 0.02 22 0.14 18 0.09 19 0.21 24

Hungary 0.25 21 0.10 22 0.07 24 0.52 8 0.45 4 0.04 23 0.03 21 0.40 10 0.23 13 0.20 25

Slovakia 0.50 11 0.28 14 0.16 17 0.04 24 0.12 17 0.00 28 0.01 27

0.20 26

Latvia

0.07 23 0.02 26 0.50 9 0.16 15 0.05 21 0.02 24 0.41 9 0.40 7 0.15 27

Croatia 0.38 17 0.13 20 0.07 23 0.28 14 0.21 10 0.07 19 0.04 19 0.00 20 0.00 22 0.14 28

Table 2 Business sector activities – indices

Statistical measure

Enterprises analysing big data, fraction of enterprises

Productivity of data related sectors (value added at factor cost per full-time employee)

Investment in ICT services (investment per value added at factors cost)

Share of data companies of total J and M sectors

RCA for trade in data related services

Average index

2016 2012 2015 2012 2015 2013 2016 2012 2015 2015/2016

Quartile coefficient of dispersion 0.56 1.74 1.52 1.55 1.46 1.32 1.29 0.69 1.00 0.77

Upper quartile 0.63 0.65 0.56 0.59 0.38 0.55 0.50 0.45 0.42 0.45

Lower quartile 0.34 0.12 0.09 0.12 0.10 0.05 0.02 0.18 0.16 0.21

Median 0.50 0.30 0.31 0.30 0.19 0.38 0.37 0.39 0.26 0.31

Coefficient of variation 0.48 0.76 0.81 0.77 0.92 0.78 0.83 0.69 0.77 0.51

Standard deviation 0.24 0.30 0.27 0.27 0.24 0.26 0.27 0.27 0.24 0.18

Variance 0.06 0.09 0.07 0.07 0.06 0.07 0.08 0.07 0.06 0.03

Mean 0.51 0.40 0.33 0.35 0.26 0.34 0.33 0.39 0.31 0.36

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.14

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Table 3 Business sector activities – descriptive statistics



26

3.2 Business environment

The importance of the ICT sector as one of the main drivers of innovations, productivity and economic

growth is widely recognized by policy decision makers. Policy decisions influence to a considerable extend

business activities.

Framework conditions and the overall business environment for innovations and investments in new

technologies, including innovative data driven activities are of great importance for the business sector

development. Policy rules and measures provide important incentives to business agents to put their

innovative products and new business models into reality and to establish businesses. A favourable

business environment is a basic precondition for a successful and dynamic business sector development

of a country.

A particular role play policies targeted at the support of start-ups and early business development since

they are engines of innovation and growth of an economy. Thus, it was regarded essential to integrate in

the analysis an assessment of general policy framework and regulatory conditions, which have important

impacts on business and innovation activities. It should help to inform how national policies create

framework conditions to foster businesses.

3.2.1 Chance for getting credit

Access to finance is one of the most crucial aspect for growth and innovations. Particularly at the early

stages of development, enterprises rely on secure financing opportunities. Start-ups and small and

medium-sized enterprises (SMEs) often face difficulties accessing finance because of the inherent

riskiness and serious weaknesses of financing conditions as well as information asymmetries with regard

to both lenders and borrowers.



27

3.2.1.1 Regulatory environment

Figure 8 Legal rights strength and credit information depth, 201611

Note: Strength of legal rights index (0=weak to 12=strong); Depth of credit information index (0=low to

8=high).

To estimate the regulatory environment of the financing opportunities, two indicators of the World Bank,

Doing Business were used. One important element of the business environment of a country is the

strength of legal rights index, which measures the degree to which collateral and bankruptcy laws protect

the rights of borrowers and lenders and thus facilitate lending12. The index ranges from 0 for the weakest

legal rights to 12 for the strongest legal basis in terms of the protection of the rights of the borrowers and

lenders. The second index focuses on the information asymmetries preventing lenders from getting

necessary information for their lending decisions measuring rules, which influence the scope, accessibility,

and quality of credit information available through public or private credit registries. The index ranges

from 0 to 8, with higher values indicating less information asymmetries in the debt market and the

availability of more credit information, from either a public registry or a private bureau, thus facilitating

lending decisions13. These two indices work best together and should therefore be considered

simultaneously, since they are equally important for chances to get credits.

11 Figure based on World Bank, Doing Business project. 12 World Bank, Doing Business 13 World Bank, Doing Business

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rtu

gal

Legal rights strength Credit information depth



28

Data suggest that a number of countries including Australia, United States, Hungary, Romania, Bulgaria,

Canada, Czech Republic, Estonia, Ireland, Poland, United Kingdom, Germany and Lithuania (Figure 8)

provide favourable framework conditions for getting credits in terms of protection of the borrowers' and

lenders' rights as well as addressing the credit market information asymmetries.

3.2.1.2 Domestic credit to private sector

Figure 9 Domestic credit to private sector as percentage of GDP, 201514

The Indicator Domestic credit to private sector provided by the World Bank measures the level of financial

resources made available to the private sector by financial institutions like banks, monetary authorities

and other financial corporations, such as leasing companies, insurance corporations, pension funds,

through loans, purchases of nonequity securities etc. The higher levels of domestic credits indicate greater

opportunities of the private sector to get access to finance resources, thus offering higher potential for

investments of businesses in growth and innovations. Data demonstrate (Figure 9) that United States,

Japan, Switzerland, Denmark, Korea and China have the highest ratio of domestic credit to the private

sector, whereas a large group of the EU countries exhibits much lower levels of financial resources

provided to the private sector by financial institutions.

14 Figure based on World Bank Indicators.

0

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29

3.2.1.3 Venture capital

Venture capital as a form of equity financing plays a very important role in the financing of young

technology driven companies. Especially in the early seed stage and start-up stage of their activities, young

innovative companies, which are often characterized by a high entrepreneurial risk, need financial

resources in order to implement their innovative ideas and successfully launch their products onto the

market. Venture capital is particularly important for supporting innovative small and medium-sized

enterprises, which often have limited access to financial resources. Figure 11 shows that for many EU

countries, access to venture capital for the strategically important early development stage for financing

new businesses remains problematic. Moreover, most EU countries display in international comparison

extremely low levels of venture capital investments for early and later stage ventures constituting less

than 0.05% of GDP (Figure 10). Hence, in the most European countries the access to venture capital for

smaller young businesses with growth potential in their early stages of development, like data driven

companies, is not adequate.

Figure 10: Venture capital investments as a percentage of GDP, 2016 (or the latest available year)15

15 Figure based on OECD, Entrepreneurship at a Glance 2017.

0.00

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Seed/start-up/early stage Later stage venture



30

Figure 11 Venture capital investments as percentage of GDP, 201516

3.2.2 Government support to business R&D

Government support to businesses that encourages them to invest in new technologies fostering

innovation and growth is one of the further important aspects contributing to a favourable business

environment in countries. Since investments in research and development are often associated with a

high risk and since investors do not consider positive effects of their innovation on the economy as a

whole and ignore social benefits from their innovations, business investments in R&D remain mostly

below the optimum level for the economy. This also concerns the data driven innovations, which are

expected to boost productivity growth and contribute to the well-being of the society. Therefore,

additional mechanisms are needed to encourage higher investment in R&D.

Data on the total government support for business R&D indicate that in international comparison, many

European countries including France, Belgium, Ireland, Hungary, Austria and United Kingdom offer

relatively high levels of support for business R&D (Figure 12). This would imply a stronger stimulating

effect on the business research and innovation activities in these countries. By contrast, in Poland,

Lithuania, Slovak Republic and Latvia, the total government support for business R&D is insignificant

suggesting that the stimulation of the business R&D is not among the priorities of these countries'

governments.

16 Figure based on Eurostat.

0.0

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Total Seed, start-up and later stage



31

Figure 12 Total government support for business R&D, as percentage of GDP, 2015 (or the latest available year)17

3.2.3 Policy and governance framework

Evidence about important aspects shaping the overall business environment and the economic

development of a country in general, provide Worldwide Governance Indicators published by the World

Bank Institute, which capture different dimensions of policy and governance framework conditions. Their

relevance for the economic success of countries is widely recognized in the scientific literature18.

For this analysis, only those indicators were chosen, which have direct impact on the economic activities

of the private sector: Government Effectiveness, Regulatory Quality, and Control of Corruption. They are

composite indicators reflecting estimates of governance performance ranging from -2.5 (for weak) to 2.5

(for strong governance performance). Details on the methodology and the underlying data sources are

discussed in the Worldwide Governance methodology paper (Kaufmann, Kraay, & Mastruzzi, 2010).

3.2.3.1 Government effectiveness

Government effectiveness index reflects "perceptions of the quality of public services, the quality of the

civil service and the degree of its independence from political pressures, the quality of policy formulation

and implementation, and the credibility of the government's commitment to such policies."19 Data

17 Figure based on OECD, Measuring Tax Support for R&D and Innovation, http://www.oecd.org/sti/rd-tax-stats.htm. 18 For example, the seminal work of North (1990). 19 https://data.worldbank.org/data-catalog/worldwide-governance-indicators

0

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http://www.oecd.org/sti/rd-tax-stats.htm

https://data.worldbank.org/data-catalog/worldwide-governance-indicators



32

provide evidence (Figure 13) that within the selected set of countries, government quality is high in

Switzerland, Denmark, Norway, Finland, Netherlands, Japan, Canada, Sweden, Germany, Luxembourg,

United Kingdom and Australia. In contrast, Italy, Croatia, Hungary, China, Bulgaria, Greece and, in

particular, Romania display a relatively low level of the government effectiveness.

3.2.3.2 Regulatory quality

The regulatory quality Index reflects "perceptions of the ability of the government to formulate and

implement sound policies and regulations that permit and promote private sector development"20. Data

indicate that among all selected counties Netherlands, Switzerland, Australia, Sweden, Finland, Germany,

United Kingdom, Ireland and Canada stand out through their very good regulatory quality (Figure 14). This

would imply that compared to the others, this group of countries has the most favourable regulatory

framework conditions for the activities of the private sector. Italy, Bulgaria, Slovenia, Hungary, Romania,

Croatia, Greece, and China are the countries with the lowest overall regulatory quality, indicating less

favorable regulatory conditions for business operations of the private enterprises.

3.2.3.3 Control of corruption

The Control of Corruption Index "reflects perceptions of the extent to which public power is exercised for

private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites

and private interests"21. Corruption has very adverse effects on the economic development through

distorted conditions of competition and economic decisions, legal uncertainty and increased costs. It is

therefore a serious barrier for investments in new technologies and innovations. Data provided by the

World Bank Institute show that among observed countries Finland, Denmark, Sweden, Norway,

Luxembourg, Switzerland, Iceland, Canada and the Netherlands stand out in terms of low levels of

corruption, whereas Slovak Republic, Croatia, Hungary, Italy, and, particularly, Romania, Greece, Bulgaria,

China still face serious problems with corruption (Figure 15).

20 https://data.worldbank.org/data-catalog/worldwide-governance-indicators 21 https://data.worldbank.org/data-catalog/worldwide-governance-indicators





33

Figure 13 Government Effectiveness Index, 201622

Note: Index ranges from -2.5 (weak) to 2.5 (strong)

22 Figure based on World Bank Governance Indicators.

-0.5

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Figure 14 Regulatory Quality Index23

Note: Index ranges from -2.5 (weak) to 2.5 (strong).


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Figure 15 Control of Corruption Index24

Note: Index ranges from -2.5 (weak) to 2.5 (strong).

3.2.4 Index on business environment

An enabling business environment is a necessary prerequisite to ensure successful business sector

development and innovation in the data economy. The index that measures business environment across

countries encompass the following key indicators: legal rights strength, credit information depth, level of

domestic credit to private sector, venture capital investments, total government support for business R&D

government effectiveness, regulatory quality, and control of corruption.

With the exception of venture capital investments and total government support for business R&D, for

which reliable data are available for only one point of time, data coverage for the other indicators permits

the measurement of countries' performance between 2014 and 2016.

In terms of legal rights strength reflecting the degree to which collateral and bankruptcy laws protect the

rights of borrowers and lenders, Romania, Hungary, Latvia, Bulgaria and Denmark rank as the best

performing countries, while Portugal and Italy display the lowest rankings among all observed European

countries (Table 4, Table 5).


-0.5

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36

As for the credit information depth index, which measures information asymmetries in the debt market,

best scores achieve in 2016 United Kingdom, Lithuania, Latvia, Germany and Poland followed by a large

group of countries with second best scores.

Qualitative characteristics of policy and governance framework conditions, such as government

effectiveness, regulatory quality and control of corruption play an important role for the economic success

of private enterprises and a favourable business environment. In terms of government effectiveness,

countries' performance varies widely across Europe. Noteworthy is the excellent performance of

Denmark, Norway, Finland, Netherlands and Sweden that are among best performing countries in both

periods of observation. Compared to 2014, there is some improvement of the government effectiveness

and less dispersion within the selected group of countries in 2016, as the descriptive statistics indicate.

Many countries with lower government effectiveness scores in 2014 display larger improvements in 2016,

moving faster ahead compared to the rest of the countries. The most significant improvements achieved

Bulgaria, although starting from a very low base. As a result, even though Bulgaria could reduce the score

gap considerably, it moved up by only one place in the ranking list in 2016. Significant improvements of

government effectiveness occurred also in Italy, which gained 3 ranking positions, Portugal climbing from

the 19th up to the 14th rank, while Slovenia and Italy gained three positions each in 2016. However, the

less variation in performance across countries in 2016 is not only due to moving faster ahead of a number

of countries, but also because of slowdown or drawbacks in performance of some countries. The most

notable losses in performance experienced Cyprus, losing 6 positions and Ireland moving from the 9th in

2014 to 12th rank in 2016.

With respect to the regulatory quality indicator, measuring the overall regulatory framework conditions

for the activities of the business sector, the Netherlands ranks first, followed at some distance by Sweden,

Finland and Germany, whereas Croatia and Greece remain the worst performing countries during both

periods. Although there is an overall improvement in performance as well as decline in variation between

countries in 2016, the polarization within the group of countries is still considerable. The most meaningful

improvements are registered in Croatia (by 170%), which started from a very low base and still has a long

way to go to improve its relative position in Europe. Considerable growth display Bulgaria (by 80%) moving

three ranks up and Spain (by 74%) improving its relative positioning by four ranks. On the other hand,

there is a country group that experienced some drawbacks in their relative positioning. To these countries

belong Ireland, Poland, Latvia, United Kingdom, Hungary, Finland and Denmark.

A somewhat different picture emerges when comparing the development of the control of corruption

across countries. On the whole, there was hardly any meaningful improvement of the control of

corruption within the selected group of countries. Due to the stagnation and setbacks in the fight against

corruption in many European countries, there is an increasing variation and polarization between

countries, as the coefficient of variation and of quartile dispersion indicate. The most notable drawbacks

are revealed in Hungary (-40%), Cyprus (-24%), Croatia (-22%), Spain (-20%) and Greece (-11%). Romania



37

and the Czech Republic show the highest rate of improvement - by 22% and 11% respectively, whereas

Iceland, United Kingdom, Poland, Lithuania, Finland, Sweden, Austria, Slovenia and Latvia display some

minor improvements in terms of control of corruption. In 2016, Finland, Denmark, Sweden and Norway

remain the countries with the lowest level of corruption, whereas Italy, Romania, Greece, Bulgaria retain

their last positions.

Cyprus, Denmark, Norway, United Kingdom and Sweden belong to the countries with the highest level of

financial resources provided to the private sector by financial institutions suggesting that businesses in

these countries have better opportunities of getting access to finance resources (Table 4, Table 5). In

contrast, in Slovenia, Hungary, Romania and Lithuania, the level of credits provided to the business sector

is relatively low. In the aftermath of financial crisis, the budget consolidation efforts led to declining

lending rates in many countries resulting in a sharp drop of domestic credits to the private sectors. The

decreasing levels of domestic credits in many countries is the reason for the less dispersion among

countries in 2016 (Table 6).

Among all countries, for which data on the venture capital investments are available, Ireland ranks first,

followed more further behind by Finland, Sweden, France and Spain. However, the data demonstrates

considerable dispersion within the group of the European countries, which measures of spread indicate.

For example, the index value of the best performing country - Ireland - is 35 points higher than those of

the second best performing country - Finland.

The coverage of data on the further key indicator used to measure the business environment in the

European countries is also incomplete. In terms of total government support for business R&D, France,

Belgium, Ireland and Hungary perform best among all European countries, for which data are available,

whereas Poland, Lithuania, Slovak Republic and Latvia occupy last places. This indicator and the calculated

index thereof is also marked by a wide variation across countries.

Since a business-friendly environment for starting up and developing new businesses in the data economy

is essential, the index based on these eight individual indicators aims to give an indication of the countries'

overall business environment having particularly in mind young technology based businesses that often

face considerable barriers. The index reveals that in Ireland, Cyprus, the United Kingdom and Denmark

businesses seem to encounter the most favourable business environment (Figure 16). According to the

calculations, a large group of other European countries including Finland, Norway, France, Sweden,

Iceland, Germany, Austria, Hungary, the Netherlands and Belgium provides a good basis for starting and

developing businesses.

On the whole, there is less dispersion in the average performance among countries indicating that all

European countries make efforts to create a more benign business environment. However, compared to

well performing countries, the Czech Republic, Lithuania, Poland, Portugal, Croatia, Slovak Republic and,

in particular, Slovenia, Luxembourg, Italy and Greece seem to suffer from some weaknesses, which may



38

constitute a barrier for new dynamic knowledge based businesses like data driven businesses. However,

this index does not allow a definitive conclusion about the overall business environment in the observed

countries, since many other aspects that shape the business environment of a country could not be

captured by it.

Figure 16 Index on business environment, 201625

25 Figure based on own calculations.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

GreeceItaly

LuxembourgSlovenia

Slovak RepublicCroatia

PortugalPoland

LithuaniaCzech Republic

BulgariaSpainLatvia

EstoniaRomaniaBelgium

NetherlandsHungary

AustriaGermany

IcelandSweden

FranceNorwayFinland

DenmarkUnited Kingdom

CyprusIreland



39

Country Legal rights strength Credit information depth Domestic credit to private sector Venture capital investments Average index

2014 2016 2014 2016 2014 2016 2014/2016 2016

Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank

Ireland 0.63 6 0.63 6 0.88 5 0.88 6 0.23 16 0.17 25 1.00 1 0.72 1

Cyprus 0.63 6 0.63 6 0.50 27 0.63 23 1.00 1 1.00 1

0.68 2

United Kingdom 0.63 6 0.63 6 1.00 1 1.00 1 0.49 3 0.57 4 0.38 10 0.67 3

Denmark 0.75 5 0.75 5 0.75 16 0.75 17 0.65 2 0.73 2 0.40 8 0.65 4

Finland 0.63 6 0.63 6 0.75 16 0.75 17 0.28 12 0.38 12 0.65 2 0.59 5

Norway 0.38 17 0.38 17 0.75 16 0.75 17 0.45 7 0.61 3 0.36 12 0.59 6

France 0.25 22 0.25 22 0.75 16 0.75 17 0.29 11 0.39 11 0.47 4 0.58 7

Sweden 0.50 14 0.50 14 0.63 23 0.63 23 0.46 4 0.54 5 0.52 3 0.58 8

Iceland 0.38 17 0.38 17 0.88 5 0.88 6 0.30 10 0.34 13

0.56 9

Germany 0.50 14 0.50 14 1.00 1 1.00 1 0.22 17 0.30 16 0.39 9 0.54 10

Austria 0.38 17 0.38 17 0.88 5 0.88 6 0.26 15 0.34 14 0.19 15 0.54 11

Hungary 1.00 1 1.00 1 0.63 23 0.63 23 0.06 27 0.10 27 0.36 13 0.53 12

Netherlands 0.13 24 0.13 24 0.88 5 0.88 6 0.40 8 0.45 8 0.34 14 0.53 13

Belgium 0.25 22 0.25 22 0.63 23 0.63 23 0.12 21 0.24 19 0.36 11 0.53 14

Romania 1.00 1 1.00 1 0.88 5 0.88 6 0.00 29 0.07 28

0.51 15

Estonia 0.63 6 0.63 6 0.88 5 0.88 6 0.17 19 0.28 17 0.40 7 0.50 16

Latvia 0.88 3 0.88 3 0.75 16 1.00 1 0.09 24 0.25 18 0.41 6 0.50 17

Spain 0.38 17 0.38 17 0.88 5 0.88 6 0.45 5 0.46 7 0.47 5 0.49 18

Bulgaria 0.88 3 0.88 3 0.63 23 0.63 23 0.13 20 0.19 23

0.46 19

Czech Republic 0.63 6 0.63 6 0.88 5 0.88 6 0.09 26 0.18 24 0.03 21 0.42 20

Lithuania 0.50 14 0.50 14 1.00 1 1.00 1 0.04 28 0.00 29

0.41 21

Poland 0.63 6 0.63 6 1.00 1 1.00 1 0.10 23 0.19 22 0.07 20 0.40 22

Portugal 0.00 28 0.00 28 0.88 5 0.88 6 0.45 6 0.46 6 0.11 17 0.39 23

Croatia 0.38 17 0.38 17 0.75 16 0.75 17 0.17 18 0.23 20

0.39 24

Slovak Republic 0.63 6 0.63 6 0.75 16 0.75 17 0.09 25 0.21 21 0.16 16 0.36 25



40

Slovenia 0.13 24 0.13 24 0.50 27 0.50 28 0.11 22 0.16 26 0.10 18 0.30 26

Luxembourg 0.13 24 0.13 24 0.00 29 0.00 29 0.27 13 0.40 10 0.02 22 0.29 27

Italy 0.00 28 0.00 28 0.88 5 0.88 6 0.26 14 0.34 15 0.07 19 0.29 28

Greece 0.13 27 0.13 27 0.88 5 0.88 6 0.39 9 0.44 9 0.00 23 0.29 29

Table 4 Business environment – indices 1

Country Total government support for business R&D

Government effectiveness Regulatory quality Control of corruption Average index

2015 2014 2016 2014 2016 2014 2016 2014-2016


Ireland 0.90 3 0.80 9 0.74 12 0.92 5 0.87 6 0.74 10 0.73 10 0.72 1

Cyprus

0.58 15 0.56 21 0.49 16 0.49 17 0.53 15 0.40 16 0.68 2

United Kingdom 0.61 6 0.82 8 0.87 8 0.96 2 0.88 5 0.80 9 0.84 8 0.67 3

Denmark 0.35 12 0.91 3 1.00 1 0.87 7 0.78 10 1.00 1 0.98 2 0.65 4

Finland 0.17 18 1.00 1 0.98 3 1.00 1 0.91 3 0.97 3 1.00 1 0.59 5

Norway 0.48 7 0.92 2 1.00 2 0.85 9 0.85 9 0.99 2 0.97 4 0.59 6

France 1.00 1 0.71 12 0.77 11 0.48 17 0.50 16 0.63 13 0.62 13 0.58 7

Sweden 0.33 14 0.90 5 0.95 5 0.95 3 0.93 2 0.96 4 0.97 3 0.58 8

Iceland 0.43 9 0.75 11 0.77 10 0.57 12 0.62 13 0.83 8 0.88 6 0.56 9

Germany 0.17 18 0.87 6 0.93 6 0.88 6 0.91 4 0.84 7 0.81 9 0.54 10

Austria 0.72 5 0.79 10 0.82 9 0.75 11 0.71 11 0.69 12 0.70 12 0.54 11

Hungary 0.90 3 0.28 25 0.30 26 0.27 21 0.25 26 0.16 24 0.10 25 0.53 12

Netherlands 0.43 9 0.91 4 0.98 4 0.93 4 1.00 1 0.90 6 0.86 7 0.53 13

Belgium 0.97 2 0.70 13 0.73 13 0.53 15 0.65 12 0.73 11 0.72 11 0.53 14

Romania

0.00 29 0.00 29 0.16 26 0.24 27 0.05 27 0.07 27 0.51 15

Estonia 0.15 20 0.52 17 0.63 17 0.87 8 0.85 8 0.62 14 0.56 14 0.50 16



41

Latvia 0.00 24 0.49 21 0.57 20 0.54 14 0.51 15 0.27 21 0.27 22 0.50 17

Spain 0.30 15 0.59 14 0.63 16 0.27 22 0.47 18 0.35 19 0.28 20 0.49 18

Bulgaria

0.05 28 0.22 27 0.15 27 0.28 24 0.00 29 0.00 29 0.46 19

Czech Republic 0.35 12 0.52 16 0.60 19 0.44 19 0.46 19 0.25 22 0.27 21 0.42 20

Lithuania 0.07 22 0.50 20 0.61 18 0.56 13 0.54 14 0.32 20 0.34 19 0.41 21

Poland 0.12 21 0.42 23 0.42 23 0.47 18 0.44 20 0.35 18 0.37 18 0.40 22

Portugal 0.40 10 0.50 19 0.68 14 0.27 23 0.38 22 0.48 16 0.46 15 0.39 23

Croatia

0.36 24 0.32 25 0.04 28 0.11 28 0.19 23 0.15 24 0.39 24

Slovak Republic 0.04 23 0.45 22 0.52 22 0.36 20 0.41 21 0.16 25 0.16 23 0.36 25

Slovenia 0.48 7 0.51 18 0.63 15 0.21 24 0.27 25 0.39 17 0.39 17 0.30 26

Luxembourg

0.83 7 0.91 7 0.84 10 0.86 7 0.93 5 0.92 5 0.29 27

Italy 0.20 16 0.20 27 0.34 24 0.20 25 0.31 23 0.09 26 0.08 26 0.29 28

Greece 0.20 16 0.21 26 0.19 28 0.00 29 0.00 29 0.05 28 0.04 28 0.29 29

Table 5 Business environment – indices 2

Statistical measure Legal rights strength

Credit information depth

Domestic credit to private sector

Venture capital as share of GDP

Total government support for business R&D as share of GDP

Government effectiveness

Regulatory quality

Control of corruption

Average index

2014 2016 2014 2016 2014 2016 2014/2016 2012-2015 2014 2016 2014 2016 2014 2016 2016

Quartile coefficient of dispersion

0.75 0.75 0.21 0.21 1.23 0.78 0.87 1.16 0.68 0.67 1.12 1.00 1.16 1.38 0.36

Upper quartile 0.63 0.63 0.88 0.88 0.42 0.46 0.41 0.58 0.82 0.89 0.87 0.85 0.83 0.85 0.58

Lower quartile 0.25 0.25 0.69 0.69 0.10 0.19 0.10 0.17 0.43 0.47 0.27 0.34 0.22 0.21 0.40

Median 0.50 0.50 0.88 0.88 0.26 0.34 0.36 0.35 0.58 0.63 0.53 0.51 0.53 0.46 0.51

Coefficient of variation 0.58 0.58 0.26 0.25 0.76 0.60 0.74 0.73 0.46 0.41 0.56 0.48 0.62 0.64 0.24



42

Standard deviation 0.28 0.28 0.20 0.20 0.21 0.21 0.23 0.30 0.27 0.27 0.31 0.27 0.33 0.33 0.12

Variance 0.08 0.08 0.04 0.04 0.04 0.04 0.05 0.09 0.07 0.07 0.09 0.07 0.11 0.11 0.01

Mean 0.48 0.48 0.77 0.78 0.28 0.35 0.32 0.41 0.59 0.64 0.55 0.57 0.53 0.52 0.49

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.29

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.72

Table 6 Business environment – descriptive statistics



43

3.3 Innovation potential

Innovations play the pivotal role for a successful development of an economy and improvement of the

living standards of the society. Data driven innovations can help promote innovation processes, improve

productivity and people's well-being. However, innovation opportunities, which data are offering cannot

be fully realized when countries fail to build up a critical mass of innovation capacities necessary to adapt

and invent new technologies and techniques.

In the following, some critical factors will be analysed that are linked to innovation capacities of countries

in the data economy.

3.3.1 Scientific publications

Scientific publications and studies reflect the codified knowledge in one specific area, contributing to the

knowledge creation and knowledge diffusion in this area. Scientific publications can be used for

international comparisons of knowledge creation and scientific research output.

For the analysis, the largest international database that provides abstracts and citations of peer reviewed

scientific literature - Scope - was used to analyse scientific publications in data related fields.26 In Scopus,

the document search is performed to find search terms in the title, abstract or key words. The searches

were carried out using key words "data science", "big data", "data analytics" and "data mining". To focus

on the scientific advance of the data science in the relevant science fields, the searches were restricted to

the key domains: computer science, decision sciences, engineering, mathematics, medicine, physics and

astronomy, and multidisciplinary.

Worldwide, the number of scientific publications in data science related fields increased manifold since

2010 (Figure 31). China ranks first in terms of the overall number of scientific publications in data science

related fields, followed at some distance by the United States and India. High levels of data science

publications are also recorded for Germany, Australia, Italy, South Korea, Japan, France and Spain.

By 2017, scientific publications have increased in many countries by a multiple over the 2010 level. In a

number of countries, like China, United States, United Kingdom, Germany, Australia, Italy, Netherlands,

Greece, Singapore, Portugal, Switzerland and some others, the scientific publication output rose almost

threefold or more between 2010 and 2017. The highest rise in data science publication intensity was

achieved by India, recording six times more publications in 2017 than in 2010. A considerable growth was

also found for South Korea, where scientific publications grew by a factor of 4.2, Sweden - by a factor of

26 However, some limitations must be taken into account when analyzing data on scientific publication based on the Scopus database that exclude languages other than English. Although the majority of scientific publications is nowadays published in English, one should be nevertheless aware of some English language bias.



44

5, Austria - by a factor of 4.5 and Norway - by a factor of 5. The growth trend for the data science related

publications is likely to continue in future, given the growing significance of big data.

Figure 17 Scientific publications in data science related fields27

3.3.2 R&D investments in data-related activities

Innovations require a lot of investments in R&D. Investments in R&D are widely used to measure the

efforts devoted to research activities aimed to develop new technologies and to promote innovations.

Since the direct data on data economy related R&D investments are not available, business R&D

investments in sectors J63 (data processing, hosting and related activities; web portals and other

information service activities) and J62 (computer programming, consultancy and related activities) are

chosen as proxies to reflects trends of business sector investments in the data related research and

innovation.

Data reveal that in Europe companies with the overall highest level of investments in R&D in J63 and J62

are located in Iceland and Ireland (Figure 33). A relatively high investments display Norway, Finland,

Netherlands, Denmark, Austria, Estonia, Belgium and Czech Republic indicating their considerable R&D

efforts in these fields. The majority of the European countries showed an upward trend in R&D

27 Figure is based on Scopus.



45

investments, however in some countries including Denmark, Spain, Hungary, Romania, Croatia and Cyprus

there was a decline in the R&D intensity in 2015. A large group of countries exhibits between quite

moderate (Italy, Portugal, Spain, Hungary, Slovakia, Poland, Bulgaria, Greece, Lithuania) and very low

(Latvia, Romania, Luxembourg, Croatia, Cyprus) levels of R&D investment, which results in a significant

variation across the selected set of European countries.

Figure 18 Business enterprise R&D expenditure in J62 and J63 (Euro per inhabitant in purchasing power standard (PPS), constant 2005 prices)28

Note: J63 (NACE Rev. 2): Data processing, hosting and related activities; web portals and other information

service activities; J62 (NACE Rev. 2): Computer programming, consultancy and related activities.

3.3.3 Researchers in data-related fields

A high proportion of experts with key qualifications indicates a significant potential to generate

innovations and to implement them efficiently. It is widely recognized that a targeted combination of

specific knowledge, skills and experience of R&D personnel promotes the innovation process (see, for

example, Lundvall (1995, pp. 23–44)).

Innovative capabilities in the data economy are strongly associated with human skills. Data science related

skills are vital for the adoption and continuous development of innovative technologies and methods for

the data economy, which makes these specific skills a key enabler of the data driven economy. Therefore,

28 Figure based on Eurostat, own calculations.

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the indicator that refers to the share of R&D personnel in the fields of economic activities, which are

strongly related to the data related activities, was included in the analysis. Statistic data on the share of

researchers in J63 (data processing, hosting and related activities; web portals and other information

service activities) and J62 (computer programming, consultancy and related activities) in the total business

sector researchers reveals that Malta, Iceland, Ireland, Poland, Estonia, Czech Republic and Lithuania have

the highest proportion of researchers within these business activities (Figure 20). The most significant

increases in the share of researchers between 2012 and 2015 were observed in the Czech Republic (98%),

Latvia (165%), Italy (79%), Lithuania (53%), Finland (28%), Slovenia (24%) and Netherlands (21%).

However, the percentage of researchers fell in many European countries in 2015 in comparison to the

year 2012. The greatest relative reduction experienced Romania (by 60%), Croatia (by 53%), Hungary (by

43%), Croatia (by 53%), Portugal (by 30%), Germany (by 25%), and Estonia (by 24%).

Figure 19 Share of researchers in J62 and J63 in total business sector researchers

Note: J62 - Computer programming, consultancy and related activities, J63 - Data processing, hosting and

related activities; web portals and other information service activities.

3.3.4 Talent pool potential

There is wide evidence for the shortage of skilled personnel representing a major barrier to the successful

development of the data economy (OECD, 2015). Due to the lack of the official data on the supply and

demand of the data scientists, it is difficult to measure the skill gap in the current data economy. However,

calculations based on data provided by Eurostat show an unprecedented employment growth in the data

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related sectors in the most European countries between 2008 and 2015 (Figure 42) indicating a

continuous high demand of skilled workers in these sectors. Data from Eurostat also reveals that the

majority of enterprises in ICT service sector - that also encompasses those with the data focus - have at

the same time considerable difficulties to fill their job vacancies requiring ICT specialist skills (Figure 38).

Over the last 5 years, this trend has increased markedly in many of the EU countries indicating a rising skill

mismatch in ICT.

The increasing demand for skilled and talented data specialists and the need to foster the development

of higher level skills and advanced competences require concentrated and targeted efforts on the part of

both the governments and business sector. Some companies take measures to cope with the

consequences of the shortage of skilled professionals and a talent retention problem. For example,

ArcelorMittal Spain, the world's largest steel company, that uses big data technologies to increase and

improve services provided to companies, is developing and implementing a series of strategies to

counteract the skill shortage problems. These include incentive programs by hired profile in order to

exploit the network of the employees’ contacts and a junior programme developed in collaboration with

the Government of the Principality of Asturias, which aims to attract and retain talent in megatrends. The

company also supports its employees' access to elite universities such as Stanford, MIT or Michigan State

University to ensure that the acquired knowledge is transferred and used in the company.

To reflect the talent pool potential for the data economy, it is useful to look at indicators measuring the

share of graduates in natural sciences, mathematics, statistics, information and communication

technologies and engineering, since the majority of skilled human resources for the data related activities

originates from these science fields. The data provides an evidence that in international comparison in

particular India, followed by Malta, South Korea, Finland, Greece, Mexico, Romania, United Kingdom and

Ireland have high proportions of graduates in fields that are relevant for the development of the data

related skills (Figure 22). By contrast, country group with low levels of tertiary graduates in natural

sciences, mathematics, statistics, ICT and engineering hold less potential to develop talent pools for the

data economy.



48

Figure 20 Percentage of enterprises in ICT service sector that had hard-to-fill vacancies for jobs requiring ICT specialist skills29

Note: The ICT service sector covers according to NACE Rev. 2 Section J (58-63) — Information and

Communication. Data relate to enterprises with 10 persons employed or more, which tried to recruit

personnel for jobs requiring ICT specialist skills.


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Figure 21 Full time employees (average annual growth between 2008 and 2015)30

Note: Data processing, hosting and related activities; web portals (J631, NACE Rev. 2): for Belgium and

Switzerland, data relate to the period 2009-2015. For Poland, data are available only for 2011-2015.

Computer programming, consultancy and related activities (J62, NACE Rev. 2): For Luxembourg, data are

available for 2012-2015, for Poland - 2011-2015; for Switzerland - 2009-2015.

30 Figure based on Eurostat, own calculations.

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Computer programming, consultancy and related activities



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Figure 22 Share of graduates in natural sciences, mathematics and statistics, ICT and engineering, 201531

Note: Data relate to the total tertiary education (ISCED2011 levels 5 to 8). ISCED 5: Short-cycle tertiary education; ISCED6: Bachelor’s or equivalent level; ISCED 7: Master’s or equivalent level; ISCED 8: Doctoral or equivalent level.

3.3.5 Index on innovation potential

The composed index on the innovation potential aims to capture the performance and achievements of

countries in the following areas: publications in relevant science fields to reflect the advance of the data

science, business enterprise R&D expenditures in data related business activities, researchers' intensity in

data related business activities. The share of graduates in graduates in natural sciences, mathematics,

statistics, information and communication technologies and engineering is used to indicate the talent pool

potential for the data economy in each country.

Due to the limited data availability of the selected indicators, indices were calculated only for one period

of time in all categories except for scientific publications, where data are available on a year-to-year basis.

Scientific publications are one important source of knowledge creation and knowledge flow. In terms of

the intensity of scientific publications (number of publications per one million inhabitants) Luxembourg

manifests the highest level of publications per 1 million inhabitants, followed by the second and third best

31 Figure based on OECD Statistics, Eurostat, own calculations.

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51

performers - Finland and Ireland (Table 6). However, the distance between the best and second-best

performers of 51 index points is quite significant, as indicated by the coefficient of quartile dispersion

(Table 7).

A polarized distribution within the selected group of countries is also shown for the business enterprise

R&D expenditure in fields, which are linked to data activities. The difference between the best performing

country (Ireland) and the second best performing country (Finland) is quite significant, whereas a large

group of countries are more tightly bunched towards the low tail of the distribution.

There is also a wide variation across European countries for the level of researchers in data related

business activities. However, the index distribution for the share of graduates in natural sciences,

mathematics, statistics and information and communication technologies is less uneven varying from 23%

in Malta, Finland and Greece to 11% in Luxembourg and the Netherlands.

It is quite clear that it is hardly possible to assess the overall innovation potential of countries in the data

economy based on these four indicators. , Nevertheless, the index provides empirical evidence on the

achievements and development trends of countries in some key areas that play an important role for the

building up of the innovative capacity in the data economy. The average index consisting of four previously

specified indicators suggests that within the group of European countries in particular Sweden, Finland

and Malta have a considerable capacity in these categories. An overall good performance is also found for

Greece, Estonia, United Kingdom, Portugal, Austria and Germany. However, these measures do not

account for differences in quality of the research and education systems that may vary significantly from

country to country.

Unfortunately, a number of further important aspects of the data related innovative capacities could not

be covered due to the lack of the reliable data reflecting them or difficulties to quantify and measure

them. Moreover, many important innovations that contribute to the advance of data science are based

on open source innovations that are difficult to record for measurements and therefore cannot be

accounted for in such analysis.



52

Figure 23 Index on innovation capacity of the data economy, 2015-201732

Country Scientific publications per 1 Mio. inhabitants

Business enterprise R&D expenditure in J62 and J63

Share of researchers in J62 and J63 in total business sector researchers

Share of graduates in natural sciences, mathematics, statistics, ICT and engineering

Average index

2014 2017 2015 2015 2015 2015-2017

Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank

Ireland 0.48 2 0.47 3 1.00 1 0.51 2 0.82 1 0.70 1

Malta 0.02 26 0.14 20 0.27 11 1.00 1 1.00 3 0.60 2

Finland 0.42 3 0.49 2 0.56 2 0.30 12 0.98 12 0.58 3

Sweden 0.17 15 0.36 7

0.75 4 0.56 4

Greece 0.35 5 0.46 4 0.05 20 0.38 7 0.96 11 0.46 5

Estonia 0.15 16 0.21 15 0.36 6 0.45 4 0.76 6 0.44 6

United Kingdom 0.21 11 0.32 9 0.29 10 0.28 13 0.86 10 0.44 7


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

CyprusLatvia

BelgiumBulgariaHungary

ItalySlovakia

LithuaniaCroatia

DenmarkRomania

NetherlandsPoland

SloveniaFrance

LuxembourgCzech Republic

SpainGermany

AustriaPortugal

United KingdomEstoniaGreece

SwedenFinland

MaltaIreland



53

Portugal 0.28 7 0.40 5 0.12 15 0.34 10 0.78 14 0.41 8

Austria 0.34 6 0.38 6 0.37 5 0.18 21 0.68 2 0.40 9

Germany 0.20 12 0.20 16 0.30 9 0.08 26 1.00 9 0.39 10

Spain 0.17 14 0.24 13 0.12 16 0.25 15 0.78 21 0.35 11

Czech Republic 0.15 17 0.20 17 0.33 8 0.45 5 0.39 28 0.34 12

Luxembourg 1.00 1 1.00 1

0.00 27 0.00 13 0.33 13

France 0.12 20 0.18 18 0.22 12 0.20 19 0.70 15 0.33 14

Slovenia 0.36 4 0.26 11 0.21 13 0.17 23 0.60 23 0.31 15

Poland 0.07 22 0.11 21

0.46 3 0.31 27 0.29 16

Netherlands 0.25 10 0.34 8 0.41 3 0.36 8 0.07 5 0.29 17

Romania 0.05 24 0.09 24 0.01 23 0.14 25 0.89 24 0.28 18

Denmark 0.27 8 0.27 10 0.40 4 0.18 20 0.25 8 0.28 19

Croatia 0.14 18 0.11 22 0.00 24 0.15 24 0.81 18 0.27 20

Lithuania 0.10 21 0.06 26 0.04 21 0.44 6 0.52 22 0.26 21

Slovakia

0.09 18 0.35 9 0.36 19 0.26 22

Italy 0.14 19 0.23 14 0.13 14 0.17 22 0.50 16 0.26 23

Hungary 0.05 25 0.10 23 0.09 17 0.24 16 0.59 17 0.26 24

Bulgaria 0.00 27 0.08 25 0.06 19 0.26 14 0.54 26 0.23 25

Belgium 0.26 9 0.25 12 0.35 7 0.23 18 0.10 20 0.23 26

Latvia 0.06 23 0.17 19 0.01 22 0.31 11 0.43 25 0.23 27

Cyprus 0.18 13 0.00 27 0.00 24 0.24 17 0.22 28 0.11 28

Table 7 Innovation potential – indices

Statistical measure Scientific publications per 1 Mio. inhabitants

Business enterprise R&D expenditure in J62 and J63

Share of researchers in J62 and J63 in total business sector researchers

Share of graduates in natural sciences, mathematics, statistics, ICT and engineering

Average index

2014 2017 2015 2015 2015 2015-2017

Quartile coefficient of dispersion 1.04 1.09 1.43 0.73 0.71 0.52

Upper quartile 0.28 0.36 0.35 0.38 0.82 0.43

Lower quartile 0.10 0.11 0.06 0.18 0.36 0.26

Median 0.17 0.23 0.21 0.26 0.64 0.32

Coefficient of variation 0.88 0.74 0.95 0.61 0.49 0.37

Standard deviation 0.19 0.19 0.22 0.18 0.29 0.13

Variance 0.04 0.04 0.05 0.03 0.08 0.02

Mean 0.22 0.26 0.23 0.30 0.59 0.35

Minimum 0.00 0.00 0.00 0.00 0.00 0.11

Maximum 1.00 1.00 1.00 1.00 1.00 0.70

Table 8 Innovation potential – descriptive statistics




54

3.4 Infrastructure

3.4.1 Broadband infrastructure

High speed broadband, which is the basic underlying infrastructure for the rapid exchange and free

dissemination of data, has to be reliable if machine-to-machine communication will be used for critical

tasks like traffic coordination. Along with the high speed fixed broadband Internet connections, mobile

broadband infrastructure is playing an increasingly important role for the data economy, since mobile

devices are often used for collecting and disseminating of data.

In the last 10 years, the situation with the broadband connectivity improved significantly. On average, in

2017, 85% of households have access to the broadband Internet, compared to 42% in 2007 (Figure 44).

Some European countries, including the Netherlands, Luxembourg and Iceland have broadband

connectivity rates that are close to 100%. High penetration levels of broadband connections manifest

households in Norway, Finland, Sweden, United Kingdom, Denmark and Germany. However, there are

still many regions within the EU, which lack the broadband access. This concerns in particular many

Eastern European countries, as well as Portugal and Greece.

Many of the upcoming technologies, like Cloud Computing, Internet of Things, and Industry 4.0, are

dependent on internet connections which are reliable, fast and high-capacity. One of the most important

infrastructure technology, which is currently capable to meet these permanently increasing requirements

for the next decades, are fibre connections. However, the expansion of fibre connections requires large

scale investments in the fibre infrastructure, so it remains a central challenge in many countries, like

Greece, Belgium, Ireland, Austria, Germany, and Italy. In these countries the share of fibre connections in

the total broadband connections is still extreme low (Figure 24), due to a well-established copper based

telecommunication infrastructure. In contrast, Japan with 75%, Korea - 74%, Latvia - 63% and Sweden -

55% have the highest penetration rate of fibre in total broadband connections.



55

Figure 24 Percentage of households' with the Internet fixed or mobile broadband connection33

Due to increasing demand for data transmission in real time, data economy requires fast Internet

connections. For the continuously growing number of Internet capable devices, there is a pressing need

of sufficient data transfer capacities. The type of the broadband technology determines the range of speed

at which data can be transmitted. The faster the Internet connection speed is, the faster data can be up-

and downloaded. Along with the download speeds, which are advertised by Internet access providers,

upload speeds are also an important issue for the data economy. Download speeds are usually much

faster than upload speeds, however for the uploading of large volumes of data adequate upload speed

standards will be needed.

Data on measured download speed reveals that Internet access speed varies significantly across countries.

Although speed requirements have been rapidly increasing in recent years, the average measured speed

in the most countries was still less than 15 megabits per second in the first quarter of 2016. Among all

observed countries, South Korea is leading in terms of average Internet speed, followed by Norway and

Sweden (Figure 28). Switzerland, Latvia, Japan, Netherlands, Czech Republic, Finland, and Denmark are

further countries with above-average Internet speed, which reflects the efforts of these countries in the

promotion of high speed Internet by investing in fibre or upgraded broadband connections.

33 Figure based on Eurostat, Digital Economy and Society Database.

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Although the number of enterprises using contracted download speed of at least 100 megabits per second

has continuously increased in the European countries in recent years, the share of such enterprises

remains relatively low (Figure 27).

Figure 25 Percentage of fibre connections in total broadband subscriptions, December 201634

3.4.2 Adoption of IPv6

All internet capable devices, computers as well as smartphones and devices capable of machine-to-

machine communication, need to have a unique IP address for their communication. The former standard,

IPv4, was able to distinguish between 2³² different addresses. Although this allows more than 3 billion

devices to connect, a further proliferation of the Internet of Things requires by far more IP address

capacities. To interconnect the Internet of Things devices, each of us may use in the near future, the

34 Figure based on OECD, Broadband statistics.

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Turkey

Hungary

Switzerland

Australia

Luxembourg

New Zealand

Denmark

Slovenia

Slovak Republic

Finland

Portugal

Iceland

Spain

Estonia

Norway

Sweden

Latvia

Korea

Japan



57

adoption of a new standard IPv6 is necessary. According to Google's metrics (Figure 26), Belgium ranked

first in terms of adoption rate of IPv6 in 2016, followed by the United States, Switzerland, Greece and

Germany. As of 2016, a large group of European countries displays a quite low level of the IPv6-enabled

networks.

Figure 26 Country adoption of IPv6 according to Google's metrics, 201635

35 Figure based on OECD, Digital Economy Outlook 2017.

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Figure 27 Maximum contracted download speed of the fastest fixed Internet connection in 201736



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Figure 28 Akamai's measured average speed, Q1 201637

3.4.3 Index on infrastructural conditions

The ICT sector in general and the data economy in particular build on infrastructure technologies. The

index that measures infrastructural conditions of the European countries focus on the following

fundamental building blocks: a total broadband connectivity of households, share of fibre connections in

total broadband subscriptions, download speed of Internet connections in enterprises, and countries'

adoption of IPv6.

Due to the limited data availability, only two of four individual indices (download speed of enterprises and

share of households with fixed or mobile broadband internet connection) can be calculated for two

periods of time: 2014 and 2017. Data on the two other indicators are available only for 2016 and therefore

do not permit comparisons over time.

37 Figure based on OECD, Digital Economy Outlook 2017.

0 5 10 15 20 25 30 35

MexicoTurkey

ChileGreece

ItalyAustralia

FranceIceland

New ZealandLuxembourg

EstoniaPoland

PortugalSpain

AustriaIsrael

HungarySlovak Republic

GermanyOECD average

CanadaIreland

SloveniaUnited Kingdom

BelgiumUnited States

DenmarkFinland

Czech RepublicNetherlands

JapanLatvia

SwitzerlandSwedenNorway

Korea

Megabits per second



60

In terms of the supply of households with fixed or mobile connectivity, there is a relatively low level of

dispersion across Europe (Table 9). The share of households with fixed or mobile connectivity increased

across all European countries. Many countries that originally displayed a below average proportion of

households with fixed or mobile broadband internet connections were moving faster ahead, so that

average of the original indicators improved while the variation declined in 2017 compared to 2014 as

indicated by measures of dispersion.

The data analysis for the download speed of enterprises is based on the weighted sum of the speed in

tiers where the download speed of at least 100 Mb/s is given the highest weight while the lowest speed

of less than 2 Mb/s is given the smallest weight correspondingly. The descriptive statistics reveal that

although the average download speed of enterprises in 2017 increased, a significantly higher coefficients

of variation and of quartile dispersion suggest that the download speed of enterprises across the selected

set of European countries became much more varied (Table 10).

Data on the share of fibre connections as well as of the adoption rate of the IPv6 display a very large

dispersion across countries, as reflected by the coefficients of quartile dispersion and of variance (Table

10).



61

Figure 29 Index on infrastructural conditions, 2016/201738

The average index based on these four individual indicators gives a good indication of the countries'

overall level of the necessary infrastructural environment on which the data economy relies. It helps to

assess whether the infrastructural conditions at country level are adequate for the establishment and

sustainable development of the data economy. As the best performing countries rank Sweden,

Luxembourg, Denmark, Netherlands, Finland and Belgium (Figure 29). They predominantly take first

places in all categories covered by the index. An above average performance in terms of creating

necessary infrastructural conditions for the uptake and development of the data economy demonstrate

Estonia, Norway, Iceland, Portugal and United Kingdom. However, in a group of European countries

including Greece, Romania, Croatia, Poland, Italy and Bulgaria the infrastructural conditions remain a key

bottleneck. These countries are grouped in the lower tail of distribution in almost all categories covered

by the index.


0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

BulgariaItaly

PolandGreece

RomaniaCroatiaAustriaFrance

SlovakiaHungary

CyprusIreland

Czech RepublicSlovenia

SpainLatvia

GermanyLithuania

United KingdomNorwayIceland

PortugalEstonia

BelgiumFinland

NetherlandsDenmark

LuxembourgSweden



62

Country Download speed of enterprises

Households with broadband internet connection

Fibre share of broadband connections

IPv6 usage Average index

2014 2017 2014 2017 2016 2016 2016-2017

Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank

Sweden 0.69 5 0.73 4 0.82 7 0.84 5 0.88 2 0.07 17 0.63 1

Luxembourg 0.64 7 0.59 7 0.97 2 0.97 2 0.32 11 0.45 4 0.58 2

Denmark 1.00 1 1.00 1 0.76 9 0.81 8 0.41 10 0.02 19 0.56 3

Netherlands 0.83 2 0.81 2 1.00 1 1.00 1 0.23 14 0.14 13 0.54 4

Finland 0.74 3 0.60 6 0.87 4 0.84 5 0.49 8 0.24 9 0.54 5

Belgium 0.71 4 0.57 8 0.66 10 0.55 13 0.00 21 1.00 1 0.53 6

Estonia 0.54 11 0.47 10 0.66 10 0.65 12 0.57 4 0.33 6 0.50 7

Portugal 0.55 10 0.75 3 0.18 27 0.29 23 0.51 7 0.43 5 0.50 8

Iceland 0.97 2 0.94 3 0.53 6 0.01 21 0.49 9

Norway 0.47 13 0.28 15 0.84 5 0.87 4 0.61 3 0.15 12 0.48 10

United Kingdom 0.26 20 0.22 18 0.84 5 0.84 5 0.32 7 0.46 11

Lithuania 0.66 6 0.63 5 0.24 25 0.26 26 0.44 12

Germany 0.36 16 0.34 13 0.82 7 0.81 8 0.03 18 0.55 3 0.43 13

Latvia 0.56 9 0.25 16 0.45 18 0.29 23 1.00 1 0.00 22 0.38 14

Spain 0.45 14 0.44 12 0.45 18 0.52 14 0.56 5 0.00 23 0.38 15

Slovenia 0.57 8 0.45 11 0.50 17 0.48 16 0.45 9 0.05 18 0.36 16

Czech Republic 0.40 15 0.32 14 0.53 15 0.52 14 0.27 13 0.22 10 0.33 17

Ireland 0.49 12 0.48 9 0.63 12 0.68 10 0.01 20 0.16 11 0.33 18

Cyprus 0.08 27 0.20 19 0.34 23 0.39 18 0.29 19

Hungary 0.25 21 0.23 17 0.45 18 0.48 16 0.29 12 0.10 15 0.28 20

Slovakia 0.15 23 0.10 24 0.53 15 0.39 18 0.24 21

France 0.28 19 0.15 21 0.55 14 0.39 18 0.12 16 0.27 8 0.23 22

Austria 0.31 17 0.09 25 0.61 13 0.68 10 0.02 19 0.10 14 0.22 23

Croatia 0.00 28 0.14 23 0.32 24 0.29 23 0.21 24

Romania 0.30 18 0.19 20 0.05 28 0.23 27 0.21 25

Greece 0.13 25 0.00 28 0.24 25 0.13 28 0.00 22 0.57 2 0.18 26

Poland 0.10 26 0.06 26 0.39 21 0.35 22 0.13 15 0.07 16 0.15 27

Italy 0.13 24 0.06 26 0.39 21 0.39 18 0.04 17 0.01 20 0.12 28

Bulgaria 0.18 22 0.15 21 0.00 29 0.00 29 0.08 29

Table 9 Infrastructure – indices



63

Statistical measure Download speed of enterprises

Households with broadband internet connection

Fibre share of broadband

IPv6 usage Average index

2014 2017 2014 2017 2016 2016 2016-2017

Quartile coefficient of dispersion 0.99 1.41 0.85 0.97 1.63 1.88 0.72

Upper quartile 0.62 0.58 0.82 0.82 0.53 0.33 0.50

Lower quartile 0.20 0.15 0.37 0.32 0.04 0.05 0.23

Median 0.43 0.30 0.53 0.52 0.30 0.15 0.38

Coefficient of variation 0.59 0.70 0.49 0.49 0.83 1.04 0.41

Standard deviation 0.25 0.26 0.27 0.27 0.28 0.24 0.15

Variance 0.06 0.07 0.07 0.07 0.08 0.06 0.02

Mean 0.42 0.37 0.55 0.55 0.34 0.23 0.37

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.08

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 0.63

Table 10 Infrastructure – descriptive statistics

3.5 Technology diffusion

3.5.1 RFID technologies

Radio Frequency Identification Technologies (RFID) is a technology to automatically identify and track tags

attached to different devices. There is a wide variety of applications of RFID technology in the digitized

world. One example is the identification and tracking of different objects. By means of RFID technology,

huge amounts of data can be generated, which when having been analysed, can be turned into value (e.g.,

the process of inventory of shops or storehouses can be dramatically speeded up).

In recent years, the number of products and technologies, which contain incorporated RFID tags has been

growing rapidly. Companies increasingly make use of RFID technologies and of collected data in order to,

for instance, monitor and optimise their internal processes, or to identify and analyse problems.

However, looking across all EU countries, the proliferation of RFID technologies still remains well below

under its potential. In Europe, only a small fraction of businesses has adopted them. Although in most

European countries the adoption of RFID technologies has grown in the last three years, on average, less

than 15% of all enterprises in the EU used RFID technologies in 2017 (Figure 30). In some countries, like

the Czech Republic, United Kingdom, Greece, Hungary and Romania, the adoption rates are particularly

low. Within the EU, Finland (23%) and Belgium (21%) have the highest proportion of enterprises using

RFID. In contrast, in 2013 about 42% of enterprises in South Korea have reported using RFID.



64

Figure 30 Percentage of enterprises using RFID39

Note: Data relate to all enterprises, without financial sector (10 persons employed or more). Data for

Korea refer to 2013. As of January 2018, data for 2017 for Korea and Iceland are not available.

3.5.2 Cloud computing

Cloud computing also plays a significant role as a key enabling technology for the data economy. Storage,

management and computation intensive analysis of huge amounts of data require substantial investments

in state-of-the-art hard- and software presenting a considerable challenge in particular to small and

medium businesses due to their budgetary restrictions. By providing the necessary infrastructure for data

storage, data management and data processing, cloud computing can decisively contribute to the uptake

and successful development of the data economy. On the basis of an IaaS (Infrastructure as a Service)

platform, which provides software for the storage and processing of data, companies can even generate

their own big data products. Hence, cloud computing services are critical to enable small businesses to

implement their big data projects at reasonable costs. Moreover, cloud computing helps to increase the

efficiency of data related operations to a large extend, since the required ICT resources can always be

made available quickly and in exactly the right quantity and scale. Thus, cloud computing is both the driver

and enabling technology for the data economy. However, significant aspects limiting the adoption of cloud

39 Figure based on Eurostat, Digital Economy and Society Database; OECD Statistics: ICT Access and Usage by Businesses.

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computing by businesses still exist, like low capacities to change of many businesses, privacy and security

issues, lack of appropriate standards (OECD, 2015).

In Europe, the diffusion of cloud computing has risen significantly in recent years. Among all countries

observed (Figure 31), Finland has the highest share of enterprises using cloud computer services, followed

by Sweden and Denmark. In the EU28 as a whole, about 20% of all enterprises without financial sector

with at least 10 employees in 2016 used cloud computing. In some countries, including Greece, Latvia,

Poland, Bulgaria and Romania, the diffusion of cloud computing is still low. In Italy, the proportion of

enterprises using cloud computer services even dropped by 45% in 2016, compared to 2014.

Figure 31 Share of enterprises buying cloud computer services40

Note: Data relate to all enterprises, without financial sector (10 persons employed or more). As of January

2018, data for 2017 for Sweden, Finland, Ireland, UK, Netherlands, Estonia, Malta, Italy, Luxembourg,

France, Germany are not available.

3.5.3 CRM software solutions

Customer Relationship Software (CRM) solutions are used for the management of customers', clients',

suppliers', and partners' data as well as for the processing and evaluation of this data. Companies

increasingly integrate CRM software solutions to use the data on their customers more effectively and to

optimise their internal processes, making data driven decisions. During the period from 2010 to 2017, a

40 Figure base on Eurostat, Digital Economy and Society Database.

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steady increase in the adoption of CRM software solutions can be observed in most European countries.

Countries with high proportion of enterprises using CRM solutions are Germany, Netherlands, Belgium,

Austria, Cyprus, Luxembourg and Finland (Figure 32).

Figure 32 Percentage of enterprises using CRM41


3.5.4 Machine-to-Machine subscriptions

In the time, when the Internet of Things (IoT) is growing rapidly, machine-to-machine (M2M)

communication plays an important role and is one of its fundamental applications. Embedded sensors in

IoT devices generate continuously data, which can be shared in real time between devices (e.g., cars) to

interact more cooperatively. Besides this decentralised usage, a centralised collection (e.g., for traffic

management) may produce huge amounts of data for further processing and analyses.

More and more companies are combining their M2M applications with big data analytics to optimise their

internal processes. Sensors are increasingly being used in transportation and many other domains like

agriculture, manufacturing, health and energy sector that are currently becoming important fields of

application for the Internet of Things.


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Since 2012, the number of M2M subscriptions increased by more than 130% in the OECD countries (OECD,

2017). Among the observed countries (Figure 33), Sweden has the highest penetration of M2M cards

(number of SIM cards embedded in machines). Other European countries including Norway, Netherlands,

Finland, Italy, Belgium, Estonia and France demonstrate in international comparison high levels of

diffusion of M2M application technology, reflecting the uptake of the Internet of Things in these countries.

Figure 33 M2M cards, per 100 inhabitants, 201642

3.5.5 Index on the diffusion of data-related technologies

The composite index on diffusion of data related technologies is calculated to indicate to which extent the

European countries assimilate and make use of core technologies that are important enablers and drivers

of the data driven economy. The assessment of each country's performance is based on four indicators:

share of enterprises using cloud computing services, share of enterprises using software solutions like

CRM, share of enterprises using radio frequency identification (RFID) technologies, and the number of

machine-to-machine cards per 100 inhabitants.

Due to the data availability, only three of four subindices (share of enterprises using cloud computing

services, share of enterprises using CRM, share of enterprises using RFID) can be calculated for two

periods of time: 2014 and 2016 or 2017. In terms of using cloud computing services, there is a slight

decrease of variation in the index distribution in 2016, compared to 2014 (Table 12). However, the

42 Figure based on OECD, Broadband Statistics.

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polarisation between countries with the highest index and group of countries with the lowest index is still

large (Table 11). Overall, there was no improving index scores within countries in 2016, which slightly

smaller mean displays (Table 12). Italy lost 9 positions in the ranking compared to 2014.

Less dispersion and hardly any changes in variation across countries in 2017 are observed in using CRM.

Still, even in the best performing countries less than 50 % of the companies are using these software tools.

RFID technologies are distributed more unevenly across the European countries with even more variance

in 2017 than in 2014, as indicated by the coefficients of dispersion (Table 12). The performance of several

countries declined over time, whereas other countries improved their performance in 2017, compared to

2014. But on the whole, there was only a minor increase in diffusion of the RFID technologies across

European countries.

A great dispersion around a small mean displays the distribution of the penetration with machine to

machine cards. Moreover, the analysis of quartiles shows that only 5 countries lie significantly above the

average penetration share, therefore indicating a wide variation in performance between countries with

the high scores (Sweden, Norway and the Netherlands) and the group of countries at the low end of

distribution with extremely low scores (Poland, Slovenia and Greece).

On the whole, the diffusion of data related technologies has not been widespread within the selected

group of countries so far.

With regard to the average index on the diffusion of the data related technologies based on the selected

indicators, Finland and Sweden stand out as best performers (Figure 34). Further countries that use data

related technologies heavily are the Netherlands, Belgium and Germany. On the other hand, a group of

countries including the Czech Republic, Poland, Latvia, Greece, Iceland, Hungary and Romania leverage

these technologies at an extremely low level.



69

Figure 34 Index on diffusion of data related technologies, 2016/201743


0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

RomaniaHungary

GreeceIceland

LatviaPoland

Czech RepublicPortugal

FranceEstonia

BulgariaSlovenia

CroatiaUnited Kingdom

LithuaniaItaly

SpainIreland

SlovakiaDenmark

NorwayLuxembourg

AustriaCyprus

GermanyBelgium

NetherlandsSwedenFinland



70

Country Enterprises using cloud computing services

Enterprises using CRM systems

Enterprises using RFID technology

Machine-to-machine cards per 100 inhabitants

Average index

2014 2016 2014 2017 2014 2017 2016 2016-2017


Finland 1.00 1 1.00 1 0.94 4 0.76 6 1.00 1 1.00 1 0.23 4 0.75 1

Sweden 0.74 4 0.82 2 0.84 5 0.64 10 0.29 16 0.31 14 1.00 1 0.69 2

Netherlands 0.50 7 0.56 6 1.00 1 1.00 1 0.47 9 0.69 4 0.25 3 0.62 3

Belgium 0.35 11 0.42 8 0.77 6 0.88 3 0.76 3 0.88 2 0.18 6 0.59 4

Germany 0.13 22 0.18 21 0.97 3 1.00 1 0.59 6 0.56 8

0.58 5

Cyprus 0.11 23 0.16 22 0.58 11 0.85 5 0.24 18 0.44 11

0.48 6

Austria 0.15 20 0.20 18 1.00 1 0.88 3 0.82 2 0.75 3 0.07 14 0.47 7

Luxembourg 0.17 17 0.24 13 0.74 7 0.76 6 0.65 5 0.69 4 0.15 10 0.46 8

Norway 0.52 6 0.66 4 0.74 7 0.64 10 0.24 18 0.19 19 0.25 2 0.43 9

Denmark 0.72 5 0.70 3 0.71 9 0.67 9 0.41 12 0.13 21 0.18 7 0.42 10

Slovakia 0.30 12 0.22 14 0.23 22 0.30 18 0.47 9 0.69 4

0.40 11

Ireland 0.50 7 0.58 5 0.58 11 0.58 12 0.12 25 0.25 16 0.14 11 0.39 12

Spain 0.20 16 0.22 14 0.71 9 0.70 8 0.41 12 0.50 9 0.08 13 0.38 13

Italy 0.76 3 0.30 11 0.55 13 0.52 15 0.41 12 0.38 13 0.21 5 0.35 14

Lithuania 0.17 17 0.20 18 0.45 14 0.58 12 0.41 12 0.19 19

0.32 15

United Kingdom 0.41 9 0.56 6 0.29 18 0.55 14 0.12 25 0.06 24 0.11 12 0.32 16

Croatia 0.37 10 0.32 9 0.13 26 0.18 22 0.47 9 0.44 11

0.31 17

Slovenia 0.22 13 0.30 11 0.35 17 0.33 17 0.53 8 0.50 9 0.01 19 0.29 18

Bulgaria 0.07 24 0.00 27 0.26 20 0.15 24 0.76 3 0.69 4

0.28 19

Estonia 0.22 13 0.32 9 0.26 20 0.30 18 0.24 18 0.31 14 0.18 8 0.28 20

France 0.15 20 0.20 18 0.39 15 0.42 16 0.18 23 0.25 16 0.18 9 0.26 21

Portugal 0.17 17 0.22 14 0.39 15 0.30 18 0.59 6 0.25 16 0.06 16 0.21 22

Czech Republic 0.22 13 0.22 14 0.16 24 0.15 24 0.12 25 0.06 24 0.07 15 0.13 23

Poland 0.02 27 0.02 25 0.29 18 0.27 21 0.12 25 0.13 21 0.05 18 0.12 24

Latvia 0.02 27 0.02 25 0.00 29 0.09 26 0.24 18 0.13 21

0.08 25

Iceland 0.83 2

0.19 23 0.09 26 0.29 16

0.06 17 0.07 26

Greece 0.07 24 0.04 24 0.13 26 0.18 22 0.00 29 0.00 26 0.00 20 0.06 27

Hungary 0.07 24 0.10 23 0.06 28 0.00 28 0.18 23 0.00 26

0.03 28

Romania 0.00 29 0.00 27 0.16 24 0.00 28 0.24 18 0.00 26

0.00 29

Table 11 Technology diffusion – indices




71

Statistical measure

Enterprises using cloud computing services

Enterprises using CRM systems

Enterprises using RFID technology

Machine-to-machine cards per 100 inhabitants

Average index

2014 2016 2014 2017 2014 2017 2016 2016-2017

Quartile coefficient of dispersion 1.75 1.64 1.38 1.06 0.86 1.70 0.97 0.94

Upper quartile 0.50 0.53 0.74 0.73 0.56 0.66 0.20 0.47

Lower quartile 0.12 0.17 0.21 0.18 0.21 0.13 0.06 0.17

Median 0.22 0.22 0.39 0.52 0.41 0.31 0.15 0.32

Coefficient of variation 0.85 0.81 0.63 0.63 0.62 0.75 1.18 0.58

Standard deviation 0.27 0.25 0.30 0.30 0.24 0.28 0.20 0.20

Variance 0.07 0.06 0.09 0.09 0.06 0.08 0.04 0.04

Mean 0.32 0.31 0.48 0.47 0.39 0.37 0.17 0.34

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.75

Table 12 Technology diffusion – descriptive statistics



72

3.6 Security status

Ensuring adequate security controls and being able to effectively deal with existing threats to security

belong to the most critical aspects of the data economy. The issues of privacy and security require to be

effectively addressed to ensure the overall acceptance of data reuse and to establish trust in the data

economy.

3.6.1 Global Cyber Security Index 2017

With continuous increases of data exchange via cyberspace, the threat of cyber attacks and the disruption

of network systems is also increasing. Cyber attacks lead to severe damages to individuals, businesses and

public authorities. Therefore, in many countries, cybersecurity has gained a particular strategic

importance in recent years.

To provide insights into the cyber security capacities of countries, the Cyber Security Index (CSI)

elaborated by the International Telecommunication Union (ITU), which is based on country-level survey

results, was included in the analysis. It is a composite index that measures the status of the ITU member

states' cybersecurity commitment with regard to five different dimensions44:

Legal: existence of legal institutions and legislations dealing with cybersecurity as well as cybercrime and insuring practices to address cybersecurity threats.

Technical: availability of technical institutions and frameworks dealing with cybersecurity-related threats.

Organizational: existence of policy coordination institutions and strategies for cybersecurity development at governmental level.

Capacity Building: availability of technical and human resources to fight cybercrime and to raise awareness about cybersecurity. This includes research and development, education and training programmes, certified professionals and public sector agencies fostering capacity building etc.

Cooperation: existence of national collaborative efforts, participation in international partnerships, cooperative frameworks and information sharing networks with the aim of combating cybersecurity intrusions.

The index scores reveal that the USA, Estonia, Australia, France and Canada belong to the countries with

the strongest commitment to cybersecurity in the world. High rankings were also achieved by a group of

European countries including the United Kingdom, the Netherlands, Finland, Sweden, Switzerland and

Spain (Figure 35). High index scores of these countries suggest that on the whole, they implement more

measures to address cyber security threats. Compared to that, European countries such Hungary,

Portugal, Lithuania, Cyprus, Greece, Malta, Iceland, Slovakia and Slovenia appear to have various

weaknesses with respect to their overall cybersecurity capacities implying that at present they are not

44 Global Cybersecurity Index 2017, ITU 2017, full report available at: https://www.itu.int/pub/D-STR-GCI.01-2017.

https://www.itu.int/pub/D-STR-GCI.01-2017



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particularly well prepared to respond adequately to cyber security attacks or to prevent them. However,

the index does not provide information on the effectiveness of these measures.

Figure 35 Global Cybersecurity Index, 201745

3.6.2 Addressing security risks by enterprises

Companies are increasingly becoming the target of cyber attacks. In the data economy, any data exchange

offers additional potential for cybercrime.

Every company is potentially threatened by cyber attacks and must implement company-specific

measures to effectively address and prevent attacks. A look at the data from Eurostat reveals that as of

2015, on average, only a small fraction of enterprises in Europe have ICT security policies capable of

addressing risks of destruction or corruption of data, disclosure of confidential data and unavailability of

ICT services due to an attack (Figure 36). Small businesses in Europe display a major security vulnerability.

The gap between large and small enterprises is particularly large in Cyprus, the United Kingdom, Malta,

France, Ireland and Norway.

It is expected that the frequency of reviews and updates of the ICT security policy by enterprises improves

their readiness to respond to newly arising security risks. In Europe, less than 25% of all enterprises

reported that they have defined or reviewed their ICT security policy within the last 12 months in 2015

(Figure 37). The highest share of such enterprises are located in Ireland (30%), Croatia (29%), Portugal

45 Figure based on GSI, elaborated by ITU.

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(29%), Italy (27%), Slovenia (27%), Malta (26%) and Sweden (26%). A particularly small percentage of

enterprises that recently defined or reviewed their ICT policy was reported in Greece and Estonia (8%

each), and Poland and Hungary (6% each).

3.6.3 Secure Internet servers

For the protection of confidential data and the prevention of misuse, secure Internet technologies are of

utmost importance. The number of secure Internet servers in a country indicates the extent by which

encrypted transactions over the Internet can be conducted. Data from the World Bank reveal that the

share of secure Internet servers using encryption technology increased significantly in many countries

since 2010 (Figure 38). To the countries with the highest prevalence of secure Internet servers belong

Iceland, Netherlands, Luxembourg, Korea, Norway, Malta, Finland and Sweden.

On the whole, in many European countries, the density of secure servers is among the highest ones in the

world. Between 2010 and 2016, the most marked rises were achieved in the Czech Republic (by 321%),

Poland (261%), France (187%), Slovenia (153%) and Latvia (134%). Many other European countries

showed a dynamic growth during this timeframe. However, there are still huge disparities within the EU

between the group of countries with the highest prevalence and those with the lowest prevalence of

secure Internet servers using encryption technology. Many European countries including Latvia, Spain,

Hungary, Portugal, Italy, Croatia, Lithuania, Greece, Bulgaria and Romania were starting from a very low

base and still have a long way to go.

Significant increases were also registered in China, although starting from an extremely low level in 2010.

Within the selected set of countries, China remains the country with the lowest density of secure Internet

technology.



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Figure 36 Percentage of enterprises whose ICT security policy addressed security risks, 201546

Note: The ICT risks relate to the risks of destruction or corruption of data, disclosure of confidential data

and unavailability of ICT services due to an attack or an accident. Data relate to all enterprises without

financial sector (10 persons employed or more).

46 Figure based on data from Eurostat.

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Figure 37 Percentage of enterprises whose ICT security policy was defined or reviewed within the last 12 months, 201547

Note: Data relate to all enterprises without financial sector (10 persons employed or more).


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Figure 38 Secure Internet servers per 1 million inhabitants48

3.6.4 Index on security-related aspects

It is very difficult to make a comprehensive quantitative estimation of the cyber security situation in

different countries due to the poor availability of data on the one hand and the complex nature of it on

the other. Nevertheless, the following measurement concept intends to provide insights into some

important aspects of cyber security in European countries. The index focuses particularly on the capacities

of countries to respond on the potential security risks.

The composite index is based on the following indicators: ITU's global CSI, measuring the level of the cyber

security commitment of countries, the share of enterprises that addressed data-related security risks49,

the density of secure Internet servers in each country and the share of enterprises that recently defined

or reviewed their ICT security policy. For the first three indicators, subindices can be calculated for two

different time points, thus allowing time comparisons during a given time period. The final average index

is calculated using each indicator data for the latest available year.

48 Figure based on World Bank Development Indicators.

49 Risks are associated with the risks of destruction or corruption of data, disclosure of confidential data

and unavailability of ICT services due to an attack or an accident.

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In terms of European countries' commitment to cyber security, considerable improvements in the

performance of countries can be observed in 2017 compared to 2014, as demonstrated by the increase

of the average scores from 0.51 in 2014 to 0.61 in 2017. The lower measures of spread (variance,

coefficient of variation, standard deviation, coefficient of quartile dispersion) of the input data indicate

the declining variation of performance across countries (Table 14). This suggests that on the whole, cyber

security issues are given higher priority in the majority of countries. To the most committed countries

within the selected group belong Estonia, France, Norway, the United Kingdom, the Netherlands, Finland,

Sweden and Spain. In 2017, the most significant improvements achieved Ireland gaining 17 ranks, France

- 12, Belgium - 8, Spain -5. At the same time, some countries lost their relative positioning because other

countries moved faster towards higher cyber security standards. The strongest drops experienced

Slovakia losing 18 ranks, Hungary - 15, Austria - 8, and Germany - 7. The relatively low scores of Cyprus,

Greece, Malta, Iceland, Slovakia and Slovenia suggest that these countries still have a lot of space for

improvements with respect to cyber security.

With respect to the share of enterprises with ICT security policy addressing security risks, there are

significant disparities within European countries. The descriptive statistics reveal an overall increase in the

percentage of enterprises and more equal distribution across countries in 2015, compared to 2010.

Ireland ranks first in 2015, followed by Malta, Croatia, Sweden, Portugal, Denmark, Finland, the United

Kingdom and Slovakia. Malta, Ireland, Croatia and Portugal achieved the most significant improvements

in their ranking in 2015. However, the share of the best performing country of 35% and of the worst

performing countries (Hungary and Poland) of 8% demonstrate a considerable polarization within the

selected group of countries.

The number of secure Internet servers is increasing steadily over time. In comparison to 2016, the

variation across countries decreased markedly (Table descriptive statistics) indicating some conversion

processes within the group of European countries. However, the distribution is still very polarized, which

high scores of coefficients of variation and of quartile dispersion confirm. Iceland, the Netherlands and

Luxembourg were ranked in 2016 as the best performing countries, followed by Norway, Malta, Finland,

Sweden and Denmark. Quite a large number of European countries is tightly bunched in the lower section

of distribution.

Considerable dispersion is observed between the countries with the highest scores in terms of the shares

of enterprises that recently defined or reviewed their ICT security policy and those with the lowest. A large

number of countries are positioned in the upper tail of distribution, whereas Estonia, Greece, Hungary

and Poland are lagging far behind. The best performing countries are Ireland, Croatia and Portugal.



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Figure 39 Index on security related aspects50

The average index based on four individual indicators provides insights into the positioning of countries

with regard to some aspects of their capacity to respond to cyber security threats. As the best performing

countries rank Sweden, Ireland, the Netherlands, Finland and the United Kingdom (Figure 39). They are

followed by a group of countries including Norway, Denmark, Malta and Luxembourg. On the whole, there

is less dispersion between groups of countries that reach above average index scores indicating that in

these countries the ICT security policy is becoming increasingly important. However, in the lower tail of

distribution the variation is more pronounced leading to considerable differences between the best

performing and worst performing countries. In particular, Greece and Hungary lag significantly behind

within the group of selected European countries. Their index scores and those of Latvia, Bulgaria, Poland

and Lithuania suggest that there is a considerable need for action to improve the ICT security standards

in these countries to be better prepared to security risks in the data economy.


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Country ITU's Global Cyber Security Index

Enterprises whose ICT security policy addressed security risks

Enterprises whose ICT security policy was defined or reviewed within the last 12 months

Secure Internet servers (per 1 million inhabitants)

Average index

2014 2017 2010 2015 2015 2010 2016 2015-2017


Sweden 0.84 8 0.78 7 0.77 4 0.81 4 0.83 6 0.49 8 0.54 7 0.74 1

Netherlands 0.89 5 0.83 5 0.45 16 0.44 15 0.58 16 0.90 2 0.91 2 0.69 3

Norway 1.00 1 0.88 3 0.87 2 0.48 14 0.63 14 0.65 4 0.64 4 0.66 6

Denmark 0.74 12 0.54 16 0.84 3 0.67 6 0.79 8 0.74 3 0.50 8 0.63 7

Finland 0.79 10 0.79 6 0.74 6 0.67 6 0.75 12 0.48 9 0.54 6 0.69 4

United Kingdom 0.95 2 0.87 4 0.58 10 0.67 6 0.79 8 0.54 6 0.42 11 0.69 5

Luxembourg 0.53 18 0.51 18 0.61 9 0.41 20 0.58 16 0.55 5 0.82 3 0.58 10

Malta 0.32 24 0.11 27 0.55 12 0.85 2 0.83 6 0.54 7 0.58 5 0.59 8

Germany 0.95 2 0.67 10 0.48 13 0.44 15 0.54 19 0.33 11 0.49 9 0.54 13

Iceland 0.05 27 0.08 28 0.48 13

1.00 1 1.00 1 0.54 11

Ireland 0.05 27 0.66 11 0.58 10 1.00 1 1.00 1 0.38 10 0.23 15 0.72 2

Austria 0.89 5 0.59 13 0.48 13 0.44 15 0.58 16 0.33 12 0.45 10 0.52 17

Spain 0.74 12 0.75 8 0.68 7 0.56 10 0.71 13 0.08 19 0.09 21 0.52 16

Slovakia 0.79 10 0.04 29 1.00 1 0.67 6 0.79 8 0.03 27 0.07 24 0.39 21

Belgium 0.47 20 0.65 12 0.45 16 0.56 10 0.63 14 0.18 14 0.29 14 0.53 15

Croatia 0.42 23 0.49 19 0.19 25 0.85 2 0.96 2 0.05 24 0.06 26 0.59 9

Czech Republic 0.58 17 0.53 17 0.23 22 0.44 15 0.79 8 0.11 16 0.40 12 0.54 12

France 0.74 12 0.95 2 0.35 20 0.41 20 0.33 22 0.10 18 0.23 16 0.48 19

Italy 0.68 15 0.56 14 0.39 19 0.52 13 0.88 4 0.05 26 0.06 25 0.50 18

Estonia 0.95 2 1.00 1 0.10 27 0.11 25 0.08 25 0.16 15 0.32 13 0.38 22

Cyprus 0.21 25 0.29 25 0.68 7 0.44 15 0.42 21 0.32 13 0.20 19 0.34 24

Portugal 0.21 25 0.33 23 0.23 22 0.78 5 0.96 2 0.05 23 0.07 23 0.53 14

Latvia 0.84 8 0.69 9 0.23 22 0.11 25 0.21 24 0.06 22 0.09 20 0.27 25

Slovenia 0.00 30 0.00 30 0.26 21 0.56 10 0.88 4 0.11 17 0.20 17 0.41 20

Romania 0.53 18 0.48 20 0.06 29 0.37 22 0.50 20 0.00 30 0.00 30 0.34 23




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Lithuania 0.47 20 0.32 24 0.42 18

0.06 21 0.04 27 0.18 28

Hungary 0.89 5 0.38 22 0.10 27 0.00 27 0.00 27 0.05 25 0.08 22 0.12 30

Poland 0.63 16 0.55 15 0.13 26 0.00 27 0.00 27 0.07 20 0.20 18 0.19 27

Greece 0.05 27 0.26 26 0.77 4 0.19 23 0.08 25 0.03 28 0.03 28 0.14 29

Bulgaria 0.47 20 0.47 21 0.00 30 0.15 24 0.29 23 0.01 29 0.00 29 0.23 26

Table 13 Security status – indices

Statistical measure ITU's Global Cyber Security Index

Enterprises whose ICT security policy addressed security risks

Enterprises whose ICT security policy was defined or reviewed within the last 12 months

Secure Internet servers (per 1 million inhabitants)

Average index

2014 2017 2010 2015 2015 2010 2016 2015-17

Quartile coefficient of dispersion 0.70 0.78 0.97 0.62 0.75 3.34 1.90 0.50

Upper quartile 0.86 0.75 0.68 0.67 0.82 0.50 0.51 0.60

Lower quartile 0.39 0.33 0.23 0.38 0.35 0.05 0.07 0.34

Median 0.66 0.55 0.47 0.46 0.63 0.14 0.23 0.53

Coefficient of variation 0.52 0.50 0.58 0.53 0.51 0.99 0.87 0.38

Standard deviation 0.30 0.27 0.26 0.26 0.30 0.28 0.28 0.18

Variance 0.09 0.07 0.07 0.07 0.09 0.08 0.08 0.03

Mean 0.59 0.54 0.46 0.49 0.59 0.28 0.32 0.48

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.74

Table 14 Security status – descriptive statistics



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3.7 Privacy protection

Along with challenges related to security issues, privacy and the protection of personal data is another

critical challenge, which has to be addressed adequately to establish trust in the data economy. The

privacy challenge associated with the loss of control over confidential information and the concomitant

risk of invasion of privacy or its potential misuse remains a major concern not only for individuals but also

for enterprises, institutions and governmental bodies. Privacy and security issues often overlap, since

privacy concerns are also related to possible security breaches. They therefore should be considered in

conjunction with each other.

In this section, light is indented to be shed on important measurable aspects of privacy protection.

3.7.1 Enterprises addressing privacy-related risks

Each enterprise is potentially exposed to the risk of the disclosure of its confidential and sensible data.

The capability of enterprises to protect their own and their customers' and partners' data will contribute

decisively to their competitive advantage in the data economy.

A growing awareness of privacy related risks results in the implementation of measures aimed at

preventing intrusion and protecting privacy by an increasing share of enterprises. However, there are big

discrepancies between different countries on the one hand, and by business size, on the other.

Data from Eurostat and OECD (Figure 40) reveal that within the country group, for which data are

available, the proportion of businesses with formal policy51 to effectively manage the ICT privacy risks

varies considerably. The highest proportion of enterprises capable of coping with privacy related risks is

registered for South Korea, followed by New Zealand, Ireland, Malta, Sweden and Portugal. The lowest

percentage of enterprises having a formal policy to manage ICT privacy risks were reported in Greece,

Bulgaria, Estonia, Latvia, Hungary and Poland. The average share in Hungary and Poland (9% each)

accounts for only 20% of the corresponding share in South Korea (44%). Furthermore, when broken down

by size of businesses, data provide a clear evidence of significant gaps between large and small companies

suggesting that the propensity of enterprises to implement policy measures for handling privacy related

risks depends very much on the size of enterprises. Among all European countries, the most significant

gap between the proportion of large and small enterprises capable of addressing privacy related risks are

observed in the United Kingdom (by 50 percentage points (p.p.)), France (by 47 p.p.), Ireland and Norway

(45 p.p.). The least discrepancies are reported in Slovenia (26 p.p.) and Greece (21 p.p.).

51 The ICT security policy relates to activities and measures of enterprises that addressed the risks of disclosure of confidential data due to intrusion, pharming, phishing attacks or by accident



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Figure 40 Percentage of businesses with formal policy to manage ICT privacy risks, 201552

Note: Data relate to all enterprises without financial sector. For South Korea, data are available for 2014,

for New Zealand data relate to 2016.

3.7.2 Awareness of privacy-related risks among individuals

The ability of individuals to control and manage their personal data is strongly associated with the level

of awareness of privacy-related risk that may arise from the disclosure of personal data. Moreover,

specific skills need to be developed that enable individuals to effectively apply privacy control practices

when using the Internet.

The indicator reflecting the proportion of individuals in different European countries that manage access

to their personal information on the Internet by doing at least one of a set of selected activities53 reveals

a somewhat differentiated picture. According to data, the residents in the Western European and

especially in the Nordic countries tend to be more aware of privacy-related risks, which results in taking

more measures to protect personal data on the Internet (Figure 41). This awareness seems to be much

52 Figure based on Eurostat, OECD statistics. 53 Activities that the indicator describes include: reading privacy policy statements before providing personal information, restricting access to their geographical location, limiting access to their profile or content on social networking sites, not allowing the use of personal information for advertising purposes, checking that the website where they needed to provide personal information was secure (e.g., https sites, safety logo or certificate), asking websites or search engines to access the information hold about them to be updated or deleted.

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less pronounced in the Eastern and Southern European countries, where the proportion of individuals

that undertake such activities is much lower. This requires additional mechanisms to promote privacy

protection awareness and the level of empowerment among individuals through targeted education and

training programmes.

Figure 41 Individuals who manage access to their personal information on the Internet, 201654

3.7.3 Privacy Control Index

The Privacy Control Index, which was elaborated by Nieuwesteeg (2017) from the Rotterdam Institute of

Law and Economics, gives a good idea about the extent to which regulatory framework provides

mechanisms for privacy control in each country.

The index is based on six basic characteristics of the data protection laws for different countries, which

have been quantified for the purpose of statistical analysis. These characteristics concern:

Data collection requirements: focus on different requirement levels ranging from no requirements at all to the information duty of individuals that their data are collected and the obligation to obtain consent from individuals prior to using their data. The latter represents the highest level of control with respect to data collection requirements.

Data breach notification requirements: reflects the existence of the obligation to notify data breaches to affected customers and supervisory authority.

Data protection authority: asks the question about existence of the data protection authority that enforces compliance with the Data Protection Law.


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Data protection officer: indicates whether national law provides an appointment of data protection officers for every organization to ensure compliance.

Monetary sanctions: reflects the maximum monetary sanction that can be imposed for privacy breaches. Sanctions of above one million Euro are given the highest scores.

Criminal penalties: describes whether possibilities to impose criminal penalties for non-compliance are in place.

The index is scored according to the presence of all these underlying characteristics. The analysis reveals

that Germany, Luxembourg, Norway, Italy and Poland are countries with the highest level of legal privacy

control mechanisms, whereas Japan, Australia, Hungary, Romania, the Netherlands, Switzerland and

China are among the countries with the lowest standards of privacy control provided at the regulatory

level.

Figure 42 Privacy control index55

However, the index does not allow an assessment of the effectiveness of enforcement of the privacy

control regulations in the countries. A further iteration of the index will be necessary to reflect the impact

of the EU General Data Protection Regulation, which comes into force in May 2018.

55 Figure based on Nieuwesteeg (2017).

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3.7.4 Privacy protection index

The average index is based on three indicators that reflect important privacy-related aspects.

Due to the limited time coverage of the data, only the subindex on the share of businesses having a formal

policy to manage ICT privacy risks can be calculated for two time periods: 2010 and 2015. The descriptive

statistics of the indicator as well as of the subindex thereof show an overall improvement in the relative

performance of countries in 2015, compared to 2010 (Table 15). The measures of spread display a clear

decrease of variation between countries indicating stronger efforts of some countries with originally lower

scores to improve their privacy policy and a drop in shares in some countries with originally high scores

(Greece, Norway, Luxembourg, Cyprus and Denmark). In 2015, the most marked improvements in their

relative positioning exhibit Ireland, Portugal, Croatia, Slovenia and the Czech Republic.

As for the indicator used to reflect awareness of privacy-related risks of individuals when using the

Internet as well as basic skills to manage them, among all European countries for which data are available,

the percentage of individuals that manages their personal information is highest in Luxembourg, Norway,

Denmark, Finland, Netherlands, Germany and United Kingdom. These countries are grouped in the upper

quartile of the subindex scores (Table 16). The country group including Lithuania, Cyprus, Greece, Italy,

Poland, Bulgaria and Romania captures positions in the lower tail of distribution indicating an overall lower

level of online privacy control among individuals to prevent privacy abuses.



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Figure 43 Privacy protection index, 2015/201656

The privacy control index based on six underlying characteristics of the data protection law at country

level shows significant disparities among European countries, as indicated by the coefficient of variation

and of quartile dispersion. There is a wide variation in performance between countries with highest

ranking (Germany, Norway and Luxembourg) and those with lowest rank (Hungary, Romania and the

Netherlands).

The average index based on the three indicators with a focus on privacy control mechanisms described

above is used as a proxy to indicate the level of some important privacy-protection-related issues in the

European countries. Due to data availability constraints, other relevant aspects of privacy could not be

captured by the index. However, with respect to these three privacy-control-related issues, Norway,

Luxembourg, Germany and Malta are in the top positions, whereas Hungary, Bulgaria, Iceland and

Romania are placed at the bottom of the list of selected European countries (Figure 43). A relatively good

performance is also found in Slovakia, Ireland, Finland, Italy, Denmark, Portugal and the United Kingdom.


0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

RomaniaIceland

BulgariaHungary

LithuaniaGreecePolandEstonia

LatviaCzech Republic

NetherlandsSlovenia

FranceSpain

CyprusAustria

BelgiumSwedenCroatia

United KingdomPortugal

DenmarkItaly

FinlandIreland

SlovakiaMalta

GermanyLuxembourg

Norway



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The Netherlands' poor positioning is attributable to its worst performance with regard to the index

assessing the regulatory level of privacy control, which is provided by the national data protection law.

On the whole, the privacy protection index is a less dispersed indicator.

Country

Awareness of privacy- related risks among individuals



Average index

2016 2010 2015 2016 2015/2016

Index Rank Index Rank Index Rank Index Rank Index Rank

Norway 0.92 2 1.00 1 0.57 14 0.94 3 0.61 1

Luxembourg 1.00 1 0.64 9 0.43 20 0.94 4 0.59 2

Germany 0.77 6 0.55 13 0.50 17 1.00 1 0.57 3

Malta 0.52 16 0.64 11 0.97 3 0.79 5 0.57 4

Slovakia 0.65 9 0.91 3 0.87 5 0.63 9 0.54 5

Ireland 0.32 21 0.61 12 1.00 1 0.66 8 0.49 6

Finland 0.89 4 0.79 6 0.70 9 0.35 18 0.49 7

Italy 0.23 25 0.55 14 0.77 8 0.94 2 0.48 8

Denmark 0.91 3 0.82 4 0.67 10 0.35 19 0.48 9

Portugal 0.52 17 0.27 22 0.93 4 0.42 12 0.47 10

United Kingdom 0.77 7 0.64 10 0.77 7 0.29 20 0.46 11

Croatia 0.50 18 0.24 23 0.83 6 0.44 12

Sweden 0.94 2 0.97 2 0.35 17 0.44 13

Belgium 0.62 11 0.48 18 0.57 15 0.57 10 0.44 14

Austria 0.70 8 0.52 17 0.50 18 0.51 11 0.43 15

Cyprus 0.24 23 0.73 7 0.53 16 0.77 6 0.39 16

Spain 0.56 15 0.70 8 0.67 11 0.29 21 0.38 17

France 0.62 12 0.39 19 0.43 21 0.29 23 0.34 18

Slovenia 0.33 20 0.24 24 0.63 12 0.32 19

Netherlands 0.82 5 0.52 16 0.47 19 0.00 26 0.32 20

Czech Republic 0.39 19 0.30 21 0.60 13 0.29 22 0.32 21

Latvia 0.65 10 0.21 25 0.17 25 0.27 22

Estonia 0.61 13 0.12 26 0.17 26 0.26 23

Poland 0.17 26 0.12 27 0.00 27 0.77 7 0.23 24

Greece 0.24 24 0.82 5 0.23 23 0.42 13 0.22 25

Lithuania 0.26 22 0.39 20 0.36 15 0.21 26

Hungary 0.58 14 0.09 28 0.00 28 0.14 25 0.18 27

Bulgaria 0.09 27 0.00 30 0.20 24 0.42 14 0.18 28

Iceland 0.52 15 0.35 16 0.18 29

Romania 0.00 28 0.06 29 0.40 22 0.14 24 0.14 30

Table 15 Privacy protection – indices



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Statistical measure

Awareness of privacy- related risks among individuals



Average index

2016 2010 2015 2016 2015/2016

Quartile coefficient of dispersion 0.85 0.90 0.63 1.13 0.57

Upper quartile 0.75 0.70 0.77 0.77 0.48

Lower quartile 0.27 0.24 0.41 0.29 0.25

Median 0.57 0.52 0.57 0.42 0.41

Coefficient of variation 0.50 0.56 0.51 0.54 0.36

Standard deviation 0.27 0.28 0.28 0.27 0.14

Variance 0.07 0.08 0.08 0.07 0.02

Mean 0.53 0.49 0.55 0.50 0.38

Minimum 0.00 0.00 0.00 0.00 0.14

Maximum 1.00 1.00 1.00 1.00 0.61

Table 16 Privacy protection – descriptive statistics

3.8 Societal participation

3.8.1 E-government participation

E-government services offer manifold advantages from which citizens can directly benefit: they make

administrative actions faster and more cost efficient, ensure greater efficiency and transparency of public

services, contribute to accountability and help prevent corruption.

E-government is particularly significant in the context of the data economy, because the participation in

e-government leads to the generation of huge amounts of data. Moreover, it enables public access to

open data, which can be equally re-used by citizens, civic or other non-profit organisations and businesses.

At the same time, government bodies are active users of data, increasingly relying on both public and

private data in order to improve the efficiency of public services and to be able to make an evidence-

based policy.

In many European countries, the majority of residents use e-government services. Nordic countries

display the highest percentage of residents interacting with public authorities via the Internet (Figure 44).

A sharp increase in using e-government services between 2010 and 2017 was found in Greece, where it

grew almost threefold, and in Lithuania and the Czech Republic, where the share of individuals using

digital administration doubled in 2017, compared to 2010. However, the differences between some

European countries are still significant. A particular low percentage of individuals using e-government

services were reported for Croatia, Poland, Italy, Bulgaria and, above all, Romania. This situation can be

related to different developments, like persistent challenges in the digitalization of public services and



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making them available online, poor digital skills as well as a low real income of broad population groups

(particularly in Bulgaria and Romania) diminishing people's access to and use of digital technologies.

Data activities in form of on-line consultations or e-voting enable citizens' participation in policy making,

contribute significantly to the democratization of political processes and promote political opinion

making. Data from Eurostat57 reveal that in a group of European countries comprising Iceland, Malta,

Denmark, Sweden, Finland, the United Kingdom, Germany and, above all, Luxembourg this kind of

participation in political processes is particularly high in European comparison (Figure 45). By contrast, in

Bulgaria, the Czech Republic, Hungary, Romania, Slovakia and Cyprus it remains far below the European

average. Moreover, the Netherlands and Latvia exhibit a marked downward trend in the participation of

their residents in online consultation or voting between 2011 and 2017. Also in Estonia, Austria, Iceland,

Greece, Slovenia and the Czech Republic the share has declined.

Figure 44 Percentage of individuals interacting with public authorities via Internet58

57 Data relate to the share of individuals that take part in on-line consultations or voting to define civic or political issues (e.g. urban planning, signing a petition etc.). 58 Figure based on Eurostat.

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Figure 45 Percentage of individuals taking part in on-line consultations or voting59

3.8.2 Participation in social or professional networks

Social and professional networks are one of the most important data sources enabling day-to-day

generation and free flow of vast amounts of data. They also provide a platform for the data exchange

between social network members. Data generated by social media holds a particularly large potential,

because they can be used to better understand and predict people's needs, requirements, desires and

behaviours.

The participation in social and professional networks increased considerably in the last 10 years across all

European countries. At present, the vast majority of individuals in most European countries participate in

social networks like Facebook and Twitter. The data show little variation between individual countries

(Figure 46). However, in Nordic countries, Belgium, the United Kingdom, Malta, Luxembourg, the

Netherlands and Hungary, the social networks seem to be more popular than in the rest of Europe.

However, the situation is different with respect to professional networks, which are more widespread in

Northern and Western Europe than in Southern and Eastern Europe.

3.8.3 Data-driven purchasing decisions

Digitization and data have fundamentally changed the purchasing behaviour of individuals. An increasing

number of consumers are relying on data available on the Internet to optimise their purchasing decisions.


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The data that are most frequently used prior to purchasing a good or service relate to price information,

customer ratings, brands, functionality of a product etc.

A glance at statistical data confirms that the broad majority of individuals in the European countries

consults data on products and services available on the Internet to be able to make better informed

purchasing decisions. Between 2010 and 2017, the share of such individuals constantly increased in most

countries (Figure 47). The highest level is recorded in Iceland accounting for 90% of all individuals, closely

followed by the Netherlands, Finland, Sweden, Germany, Norway and Luxembourg. Between 2010 and

2017, the overall increases were more pronounced in countries with initially lower levels of individuals

making data-driven purchasing decisions, which led to more homogeneity among countries in 2017.

However, Bulgaria, Italy and Romania constitute an exception of this overall development. In Romania,

the share of individuals making data-driven purchasing decisions is only 35% of the share in the

Netherlands.

Figure 46 Percentage of individuals participating in social and professional networks, 201760


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Participating in professional networks (LinkedIn, Xing, etc.)



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Figure 47 Percentage of individuals finding information about goods and services on the Internet prior to their purchase61

3.8.4 Sharing economy

Big data are an enabler and an important driver of the sharing economy, which makes the utilisation and

use of resources more efficient and cost-effective. Big data and big data algorithms make sure that that

individual demand and supply is brought together so that the participants get exactly what they require.

A considerable percentage of individuals already benefits from the sharing economy in accommodation

and transport. According to data, participating in the sharing economy in these two areas is most

widespread in the United Kingdom, followed at some distance by Luxembourg, Ireland, Malta, the

Netherlands, Belgium and Germany (Figure 48). Among the countries of Central and Eastern European,

Slovakia and Estonia are the countries where people are most involved in sharing economy activities. By

contrast, in a number of European countries including Portugal, Romania, Cyprus and, in particular, the

Czech Republic, the participation in the sharing economy is relatively weak.


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Figure 48 Participation in sharing economy, 201762

3.8.5 Index on society's participations in data-related activities

Society is the main participator and contributor to the data economy. Big data enables better decision

making of individuals, cost and resource savings through data-based sharing activities and it improves

transparency and efficiency of public services. There are many other important data-based social benefits

e.g. in the health sphere, security and safety control, education, energy consumption that could not be

considered for the index due to the lack of data for their measurement.

The index on social participation in data-related activities focus on selected fields for which metrics at

country level are available. The index consists of the following indicators: e-government participation,

political decision making of individuals, data-driven purchasing decisions, participation in social and

professional networks, and sharing economy activities of individuals. For the e-government participation,

participation in social networks as well as data driven purchasing decisions, data are available for 2014

and 2017, whereas the indicator for sharing economy activities refers to 2017. For participation in

professional networks and political decision making, time comparisons are possible for 2013 and 2017.

E-government participation becomes increasingly important across Europe, which higher average scores

in 2017 reflect. On the whole, the participation of individuals in the e-government was less varied in 2017

(Table 17). Significant improvements in the relative positioning show Estonia, climbing from the 16th up


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Individuals using a website or app to arrange an accommodation from another individual

Individuals using a website or app to arrange a transport service from another individual



95

to the 7th rank and Ireland gaining 5 positions in 2017, whereas Slovakia lost considerably, reaching the

20th rank in 2017 compared to the 10th rank in 2014.

A considerable polarization within the group of selected European countries is observed in terms of the

political decision making of individuals measured by the percentage of individuals taking part in on-line

consultations or voting to define civic or political issues (e.g., urban planning, signing a petition). Regarding

2017, the subindex scores demonstrate even wider variation between countries around a smaller mean,

as indicated by the coefficients of variation and of quartile dispersion (Table 18). There is particularly large

variation in the upper quartile of the subindex score distribution. A large number of countries is closely

bunched below the median showing much less disparities between individual countries.

Social media becomes consistently more important for individuals, as indicated by statistical data. The

share of individuals participating in social media increased significantly in all countries by 2017. Among all

observed countries, Iceland's first position remains unchanged in 2017. The most meaningful increases

are registered in Romania gaining 10 positions (from the 30th rank in 2014 to the 20th rank in 2017) and

Belgium gaining 8 positions reaching the 4th rank in 2017. Overall, the variation across countries declines,

as signaling by the coefficient of variation and of quartile dispersion. However, a relatively large dispersion

is observed between the three best performing countries. Also the participation of individuals in

professional networks like LinkedIn and Xing increases continuously in most European countries. Most

significant growth of individuals using professional networks between 2013 and 2017 display Cyprus

gaining 8 positions and Lithuania, gaining 5 positions compared to 2013, whereas the Czech Republic lost

considerably. The overall dispersion within the selected group of European countries decreased notably

indicating growing conversion between countries (Table 18). However, the score variation between the

best positioned countries (the Netherlands and Denmark) and a large group of countries closely bunched

at the low end of distribution is still large.

Meanwhile, a growing number of consumers all over Europe relies on data on the Internet to better inform

their purchasing decisions. The overall share of individuals consulting the Internet prior to purchasing a

product or service increased, whereas the variation decreases over time, as confirmed by the coefficients

of variation and quartile dispersion. However, there is still a considerable polarization between the group

of countries with highest scores and the group of countries with the lowest scores including Bulgaria, Italy

and Romania.

The subindex on the data-driven sharing economy was calculated as an equally weighted average of the

share of individuals who used any website or app to arrange an accommodation from another individual

as well as the share of individuals who arranged a transport service this way. The extent to which

individuals participate in these two fields of the sharing economy varies widely from country to country.

There is a great distance between the country that ranks first – the United Kingdom – and the second best



96

country Ireland. The level of participation of the majority of European countries in the sharing economy

is still well below its potential resulting in a low mean score of 0.32.

The average index based on these six individual indicators aims at giving an indication of the social

participation in data-related activities in the European countries. It helps to assess to which extent

individuals in different countries are engaged in or rely on data-driven activities in their everyday lives

(Figure 49). A look at the average index scores reveals a considerable variation between countries. Among

all countries observed, Iceland ranks first in terms of social participation in the data economy followed by

Luxembourg, Denmark, the Netherlands, the United Kingdom, Sweden and Norway suggesting a particular

important role of data in the everyday lives of residents of those countries. However, as indicated by the

descriptive statistics, there is a lot of variation across European countries. Even in the low 25% of

distribution, countries at the very low end (Romania and Bulgaria) and the upper end of the distribution

(Slovenia, Greece and Poland) vary widely in their relative positioning.



97

Figure 49 Index on social participation in data related activities63

63 Figure based on own claculations.

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

RomaniaBulgaria

ItalyCroatia

Czech RepublicPolandGreece

SloveniaCyprus

SlovakiaLithuaniaPortugal

LatviaAustria

HungaryFrance

SpainGermany

IrelandBelgium

MaltaEstoniaFinlandNorwaySweden

United KingdomNetherlands

DenmarkLuxembourg

Iceland



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Country Percentage of individuals making data based purchasing decisions

Percentage of individuals interacting with public authorities via Internet

Participation in social networks

Participation in professional networks

Percentage of individuals taking part in on-line consultations or voting (e.g., urban planning, signing a petition)

Partici-pation in sharing economy

Average index

2014 2017 2014 2017 2014 2017 2013 2017 2013 2017 2017 2016/2017

Index Rank Index R. Index R. Index R. Index R. Index R. Index R. Index R. Index R. Index R. Index R. Index R.

Iceland 1.00 1 1.00 1 1.00 1 0.95 2 1.00 1 1.00 1 0.41 8 0.38 11 1.00 1 0.80 2 0.55 4 0.78 1

Luxem-bourg

0.87 7 0.88 7 0.76 7 0.83 8 0.51 5 0.54 8 0.64 5 0.66 3 0.48 4 1.00 1 0.43 7 0.72 2

Denmark 0.91 6 0.83 8 0.99 2 1.00 1 0.64 3 0.70 3 0.77 2 0.88 2 0.39 5 0.40 4 0.25 17 0.67 3

Nether-lands

0.93 4 0.98 2 0.87 6 0.88 6 0.49 8 0.52 9 1.00 1 1.00 1 0.22 14 0.23 11 0.36 10 0.66 4

United Kingdom

0.80 9 0.76 9 0.55 15 0.50 17 0.51 5 0.61 5 0.59 6 0.63 5 0.26 12 0.37 6 1.00 1 0.64 5

Sweden 0.94 3 0.91 4 0.95 4 0.94 3 0.62 4 0.61 5 0.64 4 0.66 3 0.52 3 0.40 4 0.34 12 0.64 6

Norway 0.98 2 0.90 5 0.96 3 0.94 3 0.74 2 0.87 2 0.68 3 0.53 6 0.35 8 0.23 11 0.32 13 0.63 7

Finland 0.93 5 0.93 3 0.93 5 0.93 5 0.43 9 0.50 10 0.36 10 0.53 6 0.74 2 0.37 6 0.22 21 0.58 8

Estonia 0.76 10 0.76 10 0.55 15 0.86 7 0.32 13 0.37 13 0.50 7 0.41 8 0.17 17 0.20 14 0.58 3 0.53 9

Malta 0.52 19 0.67 14 0.40 24 0.45 24 0.36 10 0.59 7 0.23 15 0.38 11 0.30 11 0.50 3 0.50 5 0.51 10

Belgium 0.67 11 0.74 11 0.60 11 0.58 12 0.34 12 0.63 4 0.27 13 0.41 8 0.09 21 0.10 19 0.37 9 0.47 11

Ireland 0.61 15 0.67 13 0.55 17 0.58 12 0.30 15 0.35 15 0.36 11 0.38 11 0.04 23 0.07 21 0.61 2 0.44 12

Germany 0.81 8 0.90 6 0.57 13 0.55 14 0.13 21 0.17 21 0.27 14 0.28 17 0.39 5 0.33 8 0.31 14 0.42 13

Spain 0.57 16 0.55 18 0.52 18 0.54 15 0.32 13 0.30 17 0.32 12 0.38 11 0.35 8 0.30 9 0.39 8 0.41 14

France 0.63 12 0.62 16 0.72 8 0.74 10 0.06 27 0.00 29 0.23 17 0.22 19 0.35 8 0.17 15 0.44 6 0.36 15

Hungary 0.57 16 0.59 17 0.52 18 0.48 19 0.51 7 0.48 11 0.18 18 0.31 15 0.04 23 0.03 25 0.22 20 0.35 16

Austria 0.63 12 0.55 18 0.65 9 0.66 11 0.17 20 0.17 21 0.41 9 0.41 8 0.39 5 0.17 15 0.13 24 0.35 17

Latvia 0.33 24 0.45 26 0.59 12 0.75 9 0.36 10 0.37 13 0.05 23 0.13 24 0.09 21 0.10 19 0.24 18 0.34 18

Portugal 0.39 23 0.50 22 0.41 21 0.46 22 0.23 18 0.28 18 0.23 16 0.31 15 0.22 14 0.30 9 0.09 26 0.33 19

Lithuania 0.56 18 0.53 20 0.41 21 0.49 18 0.23 18 0.24 19 0.00 28 0.19 22 0.17 17 0.23 11 0.24 18 0.32 20




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Slovakia 0.46 22 0.47 24 0.63 10 0.48 19 0.30 15 0.35 15 0.00 27 0.06 27 0.04 23 0.03 25 0.36 11 0.29 21

Cyprus 0.50 20 0.52 21 0.41 21 0.41 25 0.30 15 0.43 12 0.00 26 0.22 19 0.04 23 0.00 30 0.05 28 0.27 22

Slovenia 0.50 20 0.66 15 0.57 13 0.51 16 0.13 21 0.04 28 0.05 25 0.16 23 0.26 12 0.07 21 0.12 25 0.26 23

Greece 0.31 25 0.45 26 0.47 20 0.48 19 0.11 23 0.15 23 0.05 24 0.09 26 0.13 20 0.07 21

0.25 24

Poland 0.28 26 0.47 24 0.23 27 0.28 27 0.02 28 0.11 25 0.14 22 0.25 18 0.00 29 0.07 21 0.31 15 0.25 25

Czech Republic

0.63 12 0.72 12 0.36 25 0.46 22 0.09 24 0.11 25 0.18 19 0.06 27 0.04 23 0.03 25 0.02 29 0.24 26

Croatia 0.17 27 0.48 23 0.29 26 0.29 26 0.09 24 0.09 27 0.18 20 0.13 24 0.22 14 0.17 15 0.19 22 0.22 27

Italy 0.00 30 0.12 29 0.17 28 0.20 28 0.00 29 0.00 29 0.18 21 0.22 19 0.17 17 0.13 18 0.30 16 0.16 28

Bulgaria 0.11 28 0.14 28 0.15 29 0.15 29 0.09 24 0.15 23 0.00 30 0.00 30 0.04 23 0.03 25 0.14 23 0.10 29

Romania 0.02 29 0.00 30 0.00 30 0.00 30 0.00 30 0.20 20 0.00 29 0.06 27 0.00 29 0.03 25 0.09 26 0.06 30

Table 17 Societal participation – indices



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Statistical measure

Percentage of individuals making data based purchasing decisions

Percentage of individuals interacting with public authorities via Internet

Participation in social networks

Participation in professional networks

Percentage of individuals taking part in on-line consultations or voting (e.g., urban planning, signing a petition)

Participation in sharing economy

Average index

2014 2017 2014 2017 2014 2017 2013 2017 2013 2017 2017 2016/2017

Quartile coefficient of dispersion 0.77 0.57 0.59 0.71 1.32 1.16 1.70 0.93 1.45 1.65 0.80 0.94

Upper quartile 0.83 0.84 0.73 0.83 0.49 0.55 0.43 0.44 0.36 0.34 0.41 0.59

Lower quartile 0.38 0.48 0.41 0.46 0.10 0.15 0.05 0.15 0.04 0.07 0.17 0.26

Median 0.59 0.64 0.55 0.53 0.30 0.35 0.23 0.31 0.22 0.17 0.31 0.36

Coefficient of variation 0.49 0.40 0.46 0.44 0.75 0.69 0.87 0.70 0.89 0.97 0.64 0.46

Standard deviation 0.28 0.25 0.26 0.25 0.24 0.25 0.26 0.24 0.22 0.22 0.20 0.19

Variance 0.08 0.06 0.07 0.06 0.06 0.06 0.07 0.06 0.05 0.05 0.04 0.04

Mean 0.58 0.62 0.56 0.58 0.31 0.36 0.30 0.34 0.25 0.23 0.32 0.41

Minimum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.06

Maximum 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.78

Table 18 Societal participation – descriptive statistics



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3.9 Data openness

Open data – i.e. data made available for free use by third parties – is key for the data economy. It holds a

huge economic potential as an important source of new innovative products and services developed by

start-ups and other companies. However, open data has an enormous value not only for businesses. It

can be equally used by civil society, science and the public administration providing valuable knowledge

and insights.

Due to the relevance of open data for the data economy, it was considered important to include its

measurements in the Observatory report. For the assessment of the level of data openness of European

countries, the results of the recent study from the European Data Portal64 on the Open data maturity were

used. The data openness of the European countries is assessed according to the two key dimensions: Open

Data Readiness and Open Data Portal Maturity.

3.9.1 Open data readiness

Open data readiness assessment is based on the examination of the following indicators65:

Open data policies: describes to which extent countries have a dedicated open data policy in place, signaling a stronger commitment of countries towards an open data society.

Use of open data: examines to which extent open data from national open data portals are used and re-used, or seen as important.

Impact of open data: measures political, social and economic impact of open data.

64 Open Data Maturity in Europe in 2017: Open Data for the European Data Economy. European Data Portal, last update: November 2017, full report available at: https://www.europeandataportal.eu/sites/default/files/ edp_landscaping_insight_report_n3_2017.pdf 65 The detailed description of methodology is discussed in: Open Data Maturity in Europe in 2017: Open Data for the European Data Economy. European Data Portal, last update: November 2017.

https://www.europeandataportal.eu/sites/default/files/%0bedp_landscaping_insight_report_n3_2017.pdf

https://www.europeandataportal.eu/sites/default/files/%0bedp_landscaping_insight_report_n3_2017.pdf



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Figure 50 Open data readiness66

The scores of the Open Data Readiness index measured as weighted average of the three indicators

described above, reveal that most European countries are putting a lot of efforts to improve their open

data readiness. In all three relevant fields, a lot of progress can be registered in many countries since 2015,

leading to significant increases of the Open Data Readiness scores in 2017. Many European countries

developed and implemented effective open data policies leading to increasing use and re-use of open

data. The data also provides a clear evidence for the increasing political, social and economic impact of

open data.

The most significant improvements in open data readiness were achieved by Luxembourg, Latvia, Ireland,

Slovenia and the Czech Republic (Figure 50). In 2017, Ireland, Spain, the Netherlands, France and Finland

are the best performers approaching the level of the highest possible open data readiness. However, the

performance varies widely across countries. Data suggest that greater efforts are still required to improve

open data readiness providing better opportunities for the development of data-related activities in many

European countries, particularly in Hungary, Switzerland, Portugal, Malta and Iceland.

3.9.2 Open data portal maturity

Portal maturity assesses the maturity of national open data portals in the European countries focusing on

the level of sophistication of the current infrastructure. Thereby, special attention is paid to the user

66 Figure based on European Data Portal.

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friendliness of open data portals with respect to both data publishers and data re-users. The portal

maturity assessment is based on the examination of the following indicators:

Usability of the portal: describes the extent to which the access to and download of datasets are available. The indicator also reflects the possibility for users to upload their own datasets and to provide feedback on existing datasets.

Re-usability of data: estimates to which extent published data can easily be re-used, which is highly dependent on the quality of data and its metadata. It focuses on the level of the machine-readability of datasets and technical requirements related to the format in which the data is made available or the structure of it.

Spread of data: describes the variety of categories, which datasets published on the national open data portals cover.

Data on portal maturity show that considerable progress has been made in terms of the usability of portals

and the re-usability of data as well as the variety of domains open data cover. The most significant

performance improvements manifest in Luxembourg, Latvia, Romania, Ireland, Estonia and Croatia

(Figure 51). The large leap forward of Luxembourg is attributable to launching a user-friendly open data

portal in 2016. Similarly, Latvia's significant improvement is due to the establishment of the national open

data portal in 2017. However, shortcomings still exist with regard to user-friendliness of many national

portals in Europe that hamper open data re-use. For example, many portals are still missing some

important features facilitating the access to and the re-use of data67. According to data of the European

Data Portal, the most serious weaknesses in terms of open data portal maturity exhibit Greece, Denmark,

Lithuania, Iceland and, particularly, Hungary and Malta.

67 Open Data Maturity in Europe in 2017: Open Data for the European Data Economy. European Data Portal, last update: November 2017.



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Figure 51 Open data portal maturity68

3.9.3 Index on data openness

European countries made significant progress to increase data openness since 2015, as displayed by

indicators on open data readiness and open data portal maturity. The average index based on these two

indicators reflects the overall data openness of the European countries. The measurements have been

conducted for three consecutive years (2015-2017), allowing for direct time comparisons.

With regard to open data readiness, the selected group of countries shows collectively visible

improvements in 2017 compared to 2015, as indicated by the higher mean and median scores (Table 20).

Although the variation across countries decreases, significant discrepancies still exist. Ireland ranks first in

2017 having improved its relative positioning considerably compared to the 19th rank in 2015 (Table 19).

Spain, the Netherlands, France and Finland also belong to the top performing countries in 2017. The most

significant improvements in open data readiness is registered in Luxembourg that jumped from the 28th

to the 7th rank, Slovenia gaining 17 positions (from 23rd to 6th), Norway moving from the 20th up to the 9th

rank, the Czech Republic and Italy gaining 8 positions each and Cyprus improving by 7 positions until 2017.

At the same time, a number of countries experienced significant drops in performance. These are

Hungary, losing 17 positions, Portugal and Poland, losing 16 ranks each, Austria, dropping from the 4th

rank in 2015 to the 16th rank in 2017, and Germany and Denmark loosing 9 positions each. Hungary,

68 Figure based on European Data Portal.

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Switzerland, Portugal, Malta and Iceland are grouped at the very low end of the distribution falling far

behind well performing countries.

Considerable progress has been made by most European countries in terms of open data portal maturity

achieving much higher scores in 2017. Lower measures of spread indicate lower variation and polarization

across the selected set of countries reflecting some convergence processes within it. Luxembourg was the

best performing country in 2017, having jumped from the 28th rank in 2015. The second place is shared

by Spain, Ireland, Germany and Romania followed by Finland and Croatia. A group of countries including

Romania, Ireland, Croatia, Latvia and Estonia improved their performance distinctly compared to 2015.

Lithuania increased its score more than sixfold, albeit from a very low base. Switzerland and Denmark, on

the other hand, plummeted by 21 and 20 places each. Significant performance drops are identified for

Greece (by 12 places), Italy (by 10), the United Kingdom (by 8), Bulgaria (by 6), Austria (by 5) and Hungary

(by 4). Many countries were losing their position because a number of others was moving ahead faster.

Malta retains its last rank in 2017, whereas Hungary occupies the second last and Iceland – at some

distance – the third last place.

The data openness index, calculated as the weighted average of the two subindices discussed above,

displays that although a lot of progress has been made, there are still large discrepancies in the level of

data openness between European countries. Different groups of countries can be identified that reached

different degrees of open data maturity. Ireland, Spain, the Netherlands, France and Finland have an

advanced open data ecosystem with respect to the open data policy, framework conditions for the use

and re-use of data as well as the sophistication of the open data portal. The large group of countries

comprising Luxembourg, Slovenia, Italy, Norway, the United Kingdom, Romania, Slovakia, Austria, Croatia,

Bulgaria and Cyprus exhibits an above-average performance in terms of data openness, however some

barriers still exist. This group of countries is closely followed by Germany, Latvia, Belgium, the Czech

Republic, Sweden and Poland. Further away from the European average are Estonia, Denmark and

Lithuania followed by the group of countries including Switzerland, Hungary, Portugal, Malta and, in

particular, Iceland that close the ranking list. The data reveal that these countries are in their early

development stage of data openness and still encounter various shortcomings in both dimensions.



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Figure 52 Data Openness Index, 201769

Country Open data readiness Open data portal maturity Average index

2015 2017 2015 2017 2015 2017

Index Rank Index Rank Index Rank Index Rank Index Rank Index Rank

Ireland 0.53 19 1.00 1 0.53 16 0.95 2 0.53 19 1.00 1

Spain 1.00 1 0.96 2 1.00 1 0.95 2 1.00 1 0.97 2

Finland 0.86 3 0.91 5 0.80 8 0.92 6 0.85 3 0.91 5

France 0.97 2 0.93 4 0.73 10 0.87 8 0.94 2 0.92 4

Luxembourg 0.21 28 0.80 7 0.00 28 1.00 1 0.18 28 0.83 6

Netherlands 0.73 8 0.95 3 0.77 9 0.85 9 0.74 7 0.94 3

Romania 0.57 14 0.71 13 0.40 21 0.95 2 0.54 17 0.75 11

Croatia 0.58 12 0.68 17 0.53 16 0.90 7 0.57 14 0.71 14

United Kingdom 0.83 5 0.76 10 0.93 3 0.82 11 0.85 4 0.76 10

Italy 0.54 16 0.78 8 0.95 2 0.79 12 0.60 13 0.78 8

69 Figure based on own calculations based on Open Data Maturity Index published by European Data Portal.

0.00 0.20 0.40 0.60 0.80 1.00 1.20

IcelandMalta

PortugalHungary

SwitzerlandLithuaniaDenmark

EstoniaPoland

SwedenCzech Republic

BelgiumLatvia

GermanyGreeceCyprus

BulgariaCroatiaAustria

SlovakiaRomania

United KingdomNorway

ItalySlovenia

LuxembourgFinlandFrance

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Norway 0.51 20 0.78 9 0.73 10 0.79 12 0.54 18 0.77 9

Austria 0.84 4 0.70 16 0.83 5 0.85 9 0.84 5 0.72 13

Slovenia 0.39 23 0.83 6 0.53 16 0.69 20 0.41 24 0.80 7

Germany 0.59 11 0.57 20 0.83 5 0.95 2 0.63 10 0.62 18

Slovakia 0.54 16 0.72 12 0.73 10 0.79 12 0.57 15 0.73 12

Bulgaria 0.68 9 0.71 13 0.73 10 0.72 18 0.69 9 0.70 15

Belgium 0.38 24 0.57 20 0.61 15 0.79 12 0.41 23 0.59 20

Cyprus 0.54 16 0.74 11 0.33 23 0.54 24 0.51 21 0.70 16

Sweden 0.48 21 0.53 22 0.53 16 0.74 16 0.49 22 0.55 22

Latvia 0.15 29 0.60 18 0.00 28 0.67 21 0.13 29 0.60 19

Czech Republic 0.27 27 0.60 19 0.33 23 0.64 22 0.28 27 0.59 21

Greece 0.79 6 0.70 15 0.72 14 0.46 26 0.78 6 0.65 17

Estonia 0.36 25 0.42 26 0.33 23 0.74 16 0.36 25 0.45 24

Poland 0.74 7 0.51 23 0.46 20 0.62 23 0.70 8 0.50 23

Portugal 0.57 13 0.25 29 0.37 22 0.72 18 0.55 16 0.29 29

Denmark 0.56 15 0.48 24 0.82 7 0.44 27 0.60 12 0.45 25

Lithuania 0.34 26 0.45 25 0.07 27 0.44 27 0.30 26 0.42 26

Switzerland 0.45 22 0.36 28 0.87 4 0.49 25 0.51 20 0.35 27

Hungary 0.67 10 0.37 27 0.27 26 0.18 30 0.61 11 0.30 28

Iceland 0.00 30 0.00 31 0.00 28 0.31 29 0.00 30 0.00 31

Malta 0.00 30 0.23 30 0.00 28 0.00 31 0.00 30 0.15 30

Table 19 Data openness – indices

Statistical measure Open data readiness

Open data portal maturity

Average index

2015 2017 2015 2017 2015 2017

Quartile coefficient of dispersion 0.65 0.43 0.88 0.45 0.52 0.47

Upper quartile 0.73 0.78 0.80 0.87 0.70 0.78

Lower quartile 0.38 0.48 0.33 0.54 0.41 0.45

Median 0.54 0.70 0.53 0.74 0.55 0.70

Coefficient of variation 0.46 0.37 0.56 0.34 0.46 0.38

Standard deviation 0.25 0.23 0.30 0.24 0.25 0.24

Variance 0.06 0.05 0.09 0.06 0.06 0.06

Mean 0.54 0.63 0.54 0.70 0.54 0.63

Minimum 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 1.00 1.00 1.00 1.00 1.00 1.00

Table 20 Data openness – descriptive statistics



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4 Discussion of findings

D5.3 provides a measurement concept for the data economy using nine composed indices reflecting key

categories, which are of great significance for the development of the data economy. Beyond that, it also

includes an in-depth analysis of individual indicators to uncover and better understand specific dynamics

behind the indices.

Moreover, this work attempts to benchmark European countries on their capacities and chances to

participate in the data economy and to build up ecosystems relevant for the data economy. It aims to

contribute to the understanding of whether and to which extent important framework conditions

necessary to unleash the potential of the data economy and to reap benefits from it are in place in

different European countries. It also helps to identify currents trends and developments that directly

affect the countries' capacities to build up the data economy and reveal individual strengths and

weaknesses of countries.

A total of 41 individual indicators distributed among 9 central categories were used to describe the

development of the data economy and to assess the profiles of individual countries.

Figure 53 Distribution of average index scores



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The results of the analysis indicate a considerable heterogeneity among countries implying that policies

should take into consideration the specific characteristics of each country to be able to provide more

tailored policy measures.

A large dispersion was identified between countries with regard to the data-driven business sector

activities indicating that at present, European countries reap to a very varying degree economic impact

from data-based activities (Figure 53). Big-data-related technologies as well as the underlying

infrastructure are also very unevenly distributed across European countries. Furthermore, the analysis

provides evidence that in most countries targeted efforts are needed to allocate more resources in the

research and skill capacities of the data economy to be able to improve its innovative potential and to

sustain in the international competition. Apart from that, there are significant cyber security gaps within

Europe. The variation in the capacities of countries to respond to cyber security threats is particularly high

in the lower two quartiles of the distribution indicating an increasing divide between individual groups of

European countries. Big discrepancies across countries were also found regarding data openness, as

reflected by the uneven distribution of index scores underneath their median score. Finally, the level to

which individuals participate in and benefit from data-related activities was found to differ considerably

across Europe.

Due to the importance of the aspects underlying the development of the data economy, it is essential to

investigate backgrounds and causes of the observed trends and uneven developments more closely in

future studies to be able to better identify policy priorities, which are best suited to respond to countries

needs and challenges.

Based on the average score results of composite indices, European countries can be classified into three

broad categories: high potential countries, countries with average capacities and below average

performing countries (Figure 54).



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Figure 54 Scores of European countries

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Social participation in data related activities Business environment Data openness

Mean



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4.1 High potential countries

This group comprises all countries whose average index score values are greater than or equal to 0.5, thus

lying well above the European average score of 0.43. To this group of countries belong Ireland, Finland,

Sweden, Norway, the Netherlands, the United Kingdom, Luxembourg, Belgium, Germany, Denmark and

Malta.

Among the selected set of countries, Ireland, Finland, Sweden, Norway and the Netherlands achieve the

highest average scores over all categories. To the best performing countries also belong Malta ranking in

six of the nine categories among the top five countries as well as the United Kingdom and Luxembourg,

each ranking among the top five countries in a total of four categories.

On the whole, countries of this group show a considerable potential in many central areas that affect the

development of the data economy, suggesting that they are better prepared to use opportunities big data

presents and to successfully sustain the international competition. They commit considerable resources

to research and innovation and concentrate a bulk of the data-economy-related knowledge creation.

Moreover, this country group makes considerable efforts to create a business-friendly environment and

takes more measures to ensure a better security and privacy control. Besides, many of these countries

are increasingly employing big-data-related technologies.

However, the results also reveal some areas of weaknesses in a number of countries. For example, there

are some evident shortcomings in the Netherlands in terms of innovation potential as well as privacy

control aspects. Apart from the Netherlands, Luxembourg, Belgium, Denmark and Spain are further

countries underperforming in data-economy-related innovation capacities. In the United Kingdom,

technologies related to big data remain relatively poorly diffused. Germany has to strengthen the capacity

of its business sector to better exploit opportunities that big data is offering, whereas Malta still has a lot

of room for improvement with respect to data openness.

Among the selected group of countries, Ireland represents a special case worth mentioning. In recent years, Ireland attracted a lot of investments in ICT services including data-related activities. The high concentration of both multinational and Irish companies has created a highly competitive environment that fosters innovations and provides good opportunities for the development of the data economy as indicated by the indices on business activities and innovation potential. Moreover, Ireland's data-driven companies take advantage of a very favourable business environment, as reflected by the highest possible ranking achieved by Ireland in this category. However, Ireland encounters challenges with regard to infrastructural conditions and the diffusion of big data technologies.

4.2 Countries with average capacities

This country group includes countries whose average index scores largely scatter around the mean value of 0.43 in all explored categories ranging between 0.46 and 0.38. This group of countries comprises Spain, Austria, France, Portugal, Estonia, Iceland, Slovakia and Cyprus. Spain, Austria and France are the best



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performing countries within this country group, whereas Slovakia and Cyprus are at the borderline between the second and third group of countries. On the one hand, some of these countries are performing very well in selected categories, for instance, Spain and France achieve top rankings in terms of data openness, and Cyprus stands out regarding the business environment. On the other hand, some countries demonstrate considerable polarisation in terms of their performance in individual categories. This is particularly true for Iceland that ranks fairly poor in the diffusion of big data technologies, data openness and privacy protection issues, performing at the same time very well regarding the social participation in data-related activities, cyber security aspects and business environment conditions. Austria and France perform poorly in terms of the infrastructural environment necessary for the establishment and sustainable development of the data economy and Cyprus displays a relatively low level of innovation potential in data-related activities. On the whole, countries making up the middle group have a good basis for establishing a competitive data economy. However, to be able to achieve this, continuous efforts to further build up necessary capacities and targeted measures aimed at overcoming existing barriers will be necessary.

4.3 Below average performing countries

This country group encompass Croatia, Italy, Slovenia, the Czech Republic, Latvia, Lithuania, Romania,

Bulgaria, Greece, Poland and Hungary. Their average index scores range between 0.37 for Croatia and

Italy and 0.25 for Hungary. While the performance of Croatia and Italy over all categories is much closer

to the European average, the low section of distribution is more polarised. This is particularly true for

Romania, Bulgaria, Greece, Poland and Hungary, which perform well below the European average, and

are grouped at the very low end of the index score distribution. However, with the exception of Hungary

and Latvia, all countries display a relatively good performance in data openness.

On the whole, this group of countries exhibits a relatively low level of engagement in data-related

activities of the business sector as well as limited capacities to exploit business opportunities big data is

offering. They also lag considerably behind in terms of innovative capability. Companies in many of these

countries - particularly Slovenia, Italy and Greece - face challenges regarding the business environment,

which constitutes an additional barrier to the development of a dynamic and innovative data economy.

In the Czech Republic, Poland, Latvia, Greece, Hungary and Romania, the leverage of big-data-related

technologies remains at a very low level. Some of the countries lack adequate infrastructural conditions.

They are particularly poor in Italy and Bulgaria. Furthermore, most countries encounter shortcomings in

terms of cyber security and privacy control mechanisms.

The results of this work indicate that many of these countries have a long way to go to establish a favourable ecosystem for the data-driven economy. Moreover, there is a considerable risk to be left behind in terms of participating in a knowledge-intensive data economy and contributing to its innovative potential in case they do not succeed in investing more in new technologies and building up their innovative capacities.



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5 Conclusions and outlook

This work provides a measurement concept to describe the current status of the data economy in Europe.

It aims to capture developments and achievements of countries in the central aspects that directly affect

their capacity to participate and to build up a competitive data economy. Measurement across different

categories carefully chosen on the basis of the previous project works was considered important to give

an extensive profile of countries. These categories are: business sector activities, business environment,

innovation potential of the data economy, infrastructural conditions, diffusion of big-data-related

technologies, security related aspects, privacy protection, social participation in data related activities and

data openness. Countries have been assessed according to their performance in these key categories

using composed indices. The results of the analysis allow a mapping of countries as well as classification

according to their characteristics in the key dimensions.

The analysis detected individual strengths of countries that contribute to their capacities to build up a

robust data driven economy as well as weaknesses that need to be overcome to be able to unleash the

full potential of the data economy and to reap its benefits. It shows existing gaps, which are particularly

wide between best performing countries and those grouped at the low end of the index score distribution.

The results of the analysis reveal a lot of dispersion across countries indicating their varying capabilities

and potentials to use business opportunities big data offers, generate innovations, adapt technologies

and techniques relevant for the data economy as well as to respond to security and privacy threats. To

the areas with the largest variation across countries belong business sector activities, the degree of data

openness, capacity to respond to security threats, diffusion of big-data-related technologies and society's

participation in the data related activities. Significant divides over the majority of indicators have been

identified between individual groups of countries. The gap is particularly large between scores of best

performing countries and those at the low end of the distribution. As big data related technologies are

rapidly involving, there is a considerable risk for countries exhibiting a well below average performance in

the selected dimensions to be left behind. Therefore, there is a urgent need for targeted measures in

these countries to foster their innovative capability, support business sector activities and the use of

advanced technologies.

The authors of this report are aware that important aspects underlying the development of the data

economy are more complex and cannot be captured adequately by a limited number of relevant

indicators. Moreover, the lack or incompleteness of reliable data make it difficult to fully reflect them. In

spite of limitations, this work nevertheless provides some valuable contributions. It reports data on

central dimensions of the data economy based on a careful scrutiny of indicators available in the official

data sources. It provides evidence based insights into the key dimensions of the data economy and gives

a realistic picture of the capabilities of individual European countries. The results can be very useful for

business stakeholders and academics alike to study the determinants of the data economy at the country

level and to help test assumptions discussed in the media as well as the relevant literature. Even more



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importantly, the study can serve as an empirical basis for policy makers to help them better understand

implications of the current developments of the data economy in the European countries and to inform

policy decisions. Furthermore, the analysis provides an important starting point for a more in-depth

exploration of the potentials of countries in different data economy relevant fields and a more close

investigation of the identified trends and uneven developments as well as of their causes and

backgrounds.

Further measurements in future will be necessary to observe the evolvement of the data economy in

Europe and to see whether the uneven developments increase or decrease over time. However, a

comprehensive analysis with a more targeted focus on data related activities in different countries

requires the overcoming of the present data constraints and the limited availability of common metrics

that would make international comparisons possible. We also hope that such data economy measurement

attempts would provide an additional incentive for the collection and making available data enabling a

more targeted and exhaustive assessments.



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6 List of references

Acatech, BDI, Fraunhofer ISI, & ZEW. (2017). Innovationsindikator 2017. Retrieved from

http://www.innovationsindikator.de/2017/

Archibugi, D., & Coco, A. (2004). A New Indicator of Technological Capabilities for Developed and

Developing Countries. World Development, 32(4), 629–654. doi:10.1016/j.worlddev.2003.10.008

IDC & OpenEvidence. (2017). European data market – SMART 2013/0063: Final report. Retrieved from

http://ec.europa.eu/newsroom/dae/document.cfm?doc_id=44400

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The Worldwide Governance Indicators: A Summary of

Methodology, Data and Analytical Issues (World Bank Policy Research Working Paper No. 5430).

Lundvall, B.-Å. (Ed.). (1995). National systems of innovation: Towards a theory of innovation and

interactive learning. London, New York (N.Y.): Pinter.

Nieuwesteeg, B. (2017). Quantifying Key Characteristics of 71 Data Protection Laws. Journal of

Intellectual Property, Information Technology and E-Commerce Law, 7(3), 182–203.

North, D. C. (1990). Institutions, institutional change, and economic performance. Political economy of

institutions and decisions. Cambridge: Cambridge University Press.

OECD. (2015). Data-driven innovation: Big data for growth and well-being. Paris.

OECD. (2017). OECD Science, Technology and Industry Scoreboard 2017: The Digital Transformation.

Paris.

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