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Data Economy
Report2018
1D A T A E C O N O M Y R E P O R T 2 0 1 8
Contents
Executive summary 2
Introduction 7
Chapter 1: Drivers of the Data Economy 14
Chapter 2: Overview of National Data Economy results 26
Chapter 3: UK Data Economy results 35
Chapter 4: Ireland Data Economy results 52
Chapter 5: Germany Data Economy results 63
Chapter 6: Netherlands Data Economy results 72
Chapter 7: Emergence of data centres as key players in the Data Economy 79
Chapter 8: How to unlock the potential of data 82
About 85
2
The creation and sharing of data has always been an
important driver of human social and economic progress,
but over the past 20 years there has been an explosion
in the rate at which new data is being created. Given the
enormous increase in the amount of data and the increasing
range of uses that businesses and Governments are finding
for this data, the purpose of this report is to highlight and
quantify the already large and increasing role that business
data plays in the economies of four European countries: the
UK, Ireland, Germany and the Netherlands.
For the purposes of this report the Data Economy is defined
as the financial and economic value created by the storage,
retrieval and analysis – via sophisticated software and other
tools – of large volumes of highly detailed business and
organisational data at very high speeds.
The research undertaken for this report has included a
large-scale review of documents and data, followed by
targeted consultations with stakeholders. However, the main
source of new information has come from detailed economic
modelling of the current contribution of the Data Economy,
coupled with forecasting of the potential future contribution
of data for the UK and Irish economies.
Drivers of the Data Economy
Advances in digital technology and the increasing ubiquity
of connected devices and sensors means that there has
been a huge increase in the global rate at which data is
being generated. Moreover, the rate at which data is created
is currently accelerating, driven by the rapidly rising number
of household and business applications.
The growth of consumer data is further boosted by the
demand for digital entertainment and communication,
ranging from video and music streaming to online computer
gaming and the sharing of pictures, videos and other
information on social media.
Simultaneously, business use of data has also increased
exponentially, driven by the increasing number of digital
devices and sensors used on production lines, and in energy,
transportation and telecommunications infrastructure, as
well as in vehicles used for moving freight and passengers.
Although the generation of data by consumers in future will
remain very significant, it is expected that an increasingly
large proportion of data will be created by businesses. This
growth will be driven by the demand for data: to improve
business decision-making; to create opportunities for cost-
efficiencies and revenue growth; and from opportunities for
product and service innovation.
However, a review of national and sector-level documentary
evidence has revealed concerns and challenges regarding
the extent to which businesses and other organisations
are able to identify and exploit the efficiency and service
innovation benefits that stand to be gained from the
analysis of data. The most important of these constraints
include the following:
• Under-developed awareness in some businesses of the
potential benefits of data analytics
• Resistance to, and fear of, potential organisational
change entailed by data analytics
• Absence of available resources regarding the ability to
integrate and manage large datasets
• Shortages of sufficient skilled staff
• The lack of financial means to make the technological
and staffing investments, particularly in the case of
smaller businesses and organisations.
D A T A E C O N O M Y R E P O R T 2 0 1 8
Executive Summary
3D A T A E C O N O M Y R E P O R T 2 0 1 8
Overview of results
In terms of the value of economic output (as measured by
Gross Value Added), the largest Data Economy among the
four countries considered by this research is that of Germany
(€108 billion). However, as a proportion of the overall
national economy the German Data Economy is estimated
to be the smallest (3.8%). The largest Data Economy in
proportionate terms is the UK (4.2%) followed by
Ireland (4.0%).
In terms of direct jobs, the UK is the largest Data Economy,
with 3.3% of national employment accounted for by this
category. However, the difference between the UK and
the other countries is quite small: in the Netherlands and
Germany the proportion is 3.2%.
Indicator UK Ireland Germany Netherlands
2016 GVA €millions (2016 prices) 1 8 9, 8 26 9,9 62 1 0 8 , 3 2 7 24 ,6 3 7
2016 GVA as % of national economy
4 . 2 % 4 .0 % 3 . 8% 3 .9 %
2016 Data Economy employment
(direct, ‘000s)1 ,1 47 61 1 , 3 2 3 247
2016 Data Economy jobs as % of total workforce jobs
3 . 3 % 3 .0 % 3 . 2 % 3 . 2 %
1 | Note: to enable comparison the estimated value of economic output (GVA) across the four countries is provided in terms of millions of Euros.
UK€90 billion
Netherlands€25 billion
Germany€108 billion
Ireland€10 billion
D A T A E C O N O M Y R E P O R T 2 0 1 84
However, it should be recognised that in some other
advanced economies the scale of the employment and
economic output contribution of data is larger than for any
of the four countries listed in the previous table. For example,
in the USA the Data Economy is estimated to already
contribute 5.1% of output and 4.1% of jobs, while in Canada
the proportions are 4.3% and 3.4% respectively.
Among the four countries that are the principal focus of this
report, the largest contribution by sector (in proportionate
terms) is made by the ICT sector, with this proportion
ranging from 34% in the Netherlands to nearly 50% in
Ireland. In the UK the most significant contributions from
other sectors come from the Financial and Professional
services sectors, whereas in the other countries (especially
in Germany) the Manufacturing sector is also a major
component. In both the Netherlands and Ireland, Financial
and Professional services are also important contributors, as
is the Transport sector.
Although the UK has the most significant Data Economy
in proportionate terms, there is evidence that the other
countries are closing the gap. In the most recent five-year
period (2012-2016), economic output attributable to the Data
Economy is estimated to have grown the fastest in Ireland
(64%), followed by Germany (51%) and the Netherlands
(41%). The equivalent growth in the UK was 33%.
Despite the strong rate of growth of the Data Economy in all countries, there remains untapped growth potential.
The UK is estimated to be currently achieving only 58% of
its current potential, with Germany achieving 55%. The worst
performing country on this basis is the Netherlands, which
is estimated to be currently achieving only about 49% of
its potential.
D A T A E C O N O M Y R E P O R T 2 0 1 8 5
How to unlock the potential of data
While it is expected that the future value of the Data
Economy will continue to grow strongly in each country,
there is a danger that – unless significant constraints and
barriers are not addressed – a large proportion of the
potential value of the Data Economy will remain unrealised.
The following is a generic set of actions focused on
individual businesses, business networks and Government
that are relevant to all four of the countries considered in
this report.
Actions for industry groups and individual businesses
.01Businesses have a lot of work to do to build confidence
and trust with respect to the handling of customers’ data.
Distrust and concerns about privacy and security must be
resolved by industry (and Government) if the full value of
the Data Economy is to be realised.
.02There are unrealised opportunities for businesses of all
sizes to utilise the data they hold across all areas of their
operations. Senior management in large businesses must
therefore lead and fully integrate digital transformation in
their companies as a key backbone of long-term business
development strategy. This is especially important for
businesses operating in sectors that have hitherto been
slower at making significant investments in data analytic
capabilities, including investment in infrastructure,
equipment and software to enable advanced data analytics
capabilities, but also in terms of investing in both managerial
capacity and technical skills that are needed to realise the
opportunities more fully.
.03All large businesses (i.e. more than 250 employees) should
appoint a Chief Data Officer reporting to the CEO to
coordinate strategy and ensure full integration with wider
business objectives.
.04Large businesses also have a potential role to play in
helping to encourage and mentor SMEs to investigate and
develop data analytics infrastructure and applications.
There is an opportunity for larger businesses to provide
support for SMEs who are members of their supply chain,
by defining standards and by sharing best practice
experience and expertise.
.05There is also a major opportunity for a larger number of
SMEs to begin to secure business growth and productivity
gains that are available from analysis of their own data.
Essentially, the availability of analytical functionality via
Cloud computing means that tools and infrastructure
previously only available to larger companies are now within
the scope of smaller businesses.
.06There is an urgent need for further investment by the
private sector in recruiting workers and developing training
programmes – such as digital apprenticeships – targeting
school leavers and returners to the workforce.
.07Peak industry bodies should pool resources to campaign for
greater awareness of the value of data among businesses
large and small.
.08Sector network groupings should each devise sector-specific
programmes designed to raise awareness and address
sector-specific constraints such as skills shortages.
.09It is vital that telecommunications infrastructure providers
(many of whom are private sector) continue to invest in
telecoms infrastructure, both in terms of ultrafast broadband
and in the emerging fifth generation of mobile phone
networks (5G).
6
Actions for Government
.01Government has a role in continuing to improve the
curriculum and in enhancing the quality and relevance of
teaching of subjects such as mathematics, statistics and
computer science in secondary, further and higher education.
.02Government can also help to promote the Data Economy
as a career destination for young people, especially
among groups (such as females) who are traditionally
under-represented in computer science and
similar occupations.
.03Government also has a potentially important role in
helping to retrain older workers (including those who
have had a period of absence from the workforce) and
in providing incentives for smaller businesses to invest
in workforce training.
.04Government has a key role to play in making its own data
Open Data, available and shared for others to use. Even
in the UK (which is ranked top globally for openness of
Government data) there is still more to do. In Ireland this is
a particularly pertinent issue as Ireland has a relatively low
ranking (27th) in the global Open Data Barometer rankings
(albeit its position has improved – by four places – in the
most recent ratings).
.05Government has a role to play in providing the
regulatory framework for the next generation of fixed and
mobile telecoms infrastructure. There is also a specific
planning policy issue with the future mobile network as 5G
will require a much denser physical coverage of masts and
relay stations compared to the current 4G network. This
isn’t just relevant to rural areas. Investment will be needed
to ensure good quality of coverage within and between
buildings in more densely populated urban areas.
.06There are opportunities to improve the performance of
Government as data-led service providers: Government
needs to continually rethink the way that services are
delivered and truly embrace a Data Economy approach.
D A T A E C O N O M Y R E P O R T 2 0 1 86
D A T A E C O N O M Y R E P O R T 2 0 1 8 7
Purpose of the study
The creation and sharing of information and knowledge
has played a crucial role in the development of societies
throughout the entirety of human history. Over the past
200 years, the sharing of information between humans and
businesses has played an increasingly important role in
the economic and social development of our civilisations.
Over the past 20 years, huge advances in technology have
led to an explosion in the rate at which new data is being
created: according to some sources, 90% of all data created
throughout human history has been created in just the past
two years.2
Moreover, the pace of data creation is constantly increasing.
Many experts expect the amount of data generated daily to
increase by at least 10% per annum over the next decade.
Given the enormous increase in the amount of data and the
increasing range of uses that businesses and Governments
are finding for this data, the purpose of this report is to
highlight and quantify the already large and increasing role
that business data plays in the economies of four European
countries: the UK, Ireland, Germany and the Netherlands.
For businesses, the gathering, storage and analysis of large
quantities of data from different parts of their operations
is widely understood to be already generating significant
opportunities for production and supply chain cost savings.
Moreover, many businesses are now also using data analysis
to understand and spot patterns of behaviour, so enabling
the creation of improved products and services.
Although the contribution of the Data Economy is already
thought to be very substantial – manifested, for example,
in terms of contributions to cost savings, revenue growth
and the generation of economic output in the form of Gross
Value Added (GVA)3 – up-to-date and comprehensive
quantified estimates of the scale of this contribution in each
of the four countries have yet to be produced.
To fill this gap the purpose of this report is to quantify the
scale of the contribution of the Data Economy in terms of
both GVA and in terms of the levels of direct employment in
each country.
The specific objectives of the research are:
• To provide a quantification of the Data Economy
for each country researched (UK, Ireland, Germany,
Netherlands)
• To analyse current trends in the growth of the Data
Economy in each country
• For the UK and Ireland, also to generate predictions of
the potential scale of the future Data Economy over a
medium-term timeframe (up to 2025)
• To provide insights into the factors affecting the
current and potential future value of the Data Economy,
including identification of potential constraints and
hindrances that may affect the extent to which growth
opportunities are realised in full
• To provide disaggregated comparisons of the Data
Economy on a sector-by-sector basis in each country
• For the UK, Germany and Netherlands, also to provide
a disaggregated assessment on a regional basis.4
2 | Åse Dragland from SINTEF is the most frequently cited source of this calculation.
3 | Gross Value Added is essentially the difference between the value of output minus the costs of intermediate consumption. GVA is also used to assess the contribution of individual businesses, industrial sectors and sub-national areas to the overall value of production in an economy (GDP).4 | The standard regions are defined by the NUTS2 system of classification developed and maintained by the European Union (NUTS = Nomenclature d’Unités Territoriales Statistiques).
Introduction
D A T A E C O N O M Y R E P O R T 2 0 1 88
What we mean by the Data Economy
In this report we define the Data Economy as the financial
and economic value created by the storage, retrieval and
analysis – via sophisticated software and other tools – of
large volumes of highly detailed business and organisational
data at very high speeds (so-called Big Data).
Opportunities for the creation of financial value for individual
businesses through the analysis and interpretation of
different types of Big Data include:
• Potential for the realisation of enhanced levels of
operational efficiency
• Efficiencies in the management of business
procurement and supply chains
• The making of improved strategic and tactical
business decisions
• Innovation in the form of new types of products or
services that can be sold to existing or new customers.
The efficiencies, improved decision-making and innovations
that result from the analysis of Big Data can lead to increases
in business revenues and/or cost savings, resulting in
enhanced profitability for individual businesses.
Over time, as these gains become recognised and more
widely adopted by other businesses, they can lead to the
growth of sectors and the creation of benefits for customers
in the form of lower prices.
Also included in the definition of the Data Economy is the
Internet of Things (IoT). The IoT is essentially the linking
of devices, sensors and other technologies to the internet
which leads to the generation of very large volumes of
data of great relevance to business operations. The advent
of the IoT therefore creates further opportunities for the
production and sharing of business relevant data.
The economic value for the economy delivered by the Data
Economy derives from several sources, including:
• Widespread adoption of Big Data and related
technologies (such as the IoT), leading to the
maintenance or acceleration of sector and economy-
wide productivity growth
• Opportunities for efficiency-driven reductions in the
price of goods and services offered to customers
• Increased potential for domestically-based businesses
and sectors to be successful in the face of international
competition, either in export or home markets
• Opportunities for the creation of improvements in the
quality and specification of goods and services
• Analysis of data by and for Government departments
and agencies can lead to improved public services and/
or cost efficiencies in the delivery of services to users
• The rising demand for Data Economy services (such
as the design and maintenance of data analytics
storage and retrieval infrastructure and applications)
also creates opportunities for a growing ICT services
segment of the economy stimulating growth of existing
providers and opportunities for the formation of new
businesses to supply these services
• The creation of additional and high-value employment
opportunities from the demand for highly skilled labour
required by businesses to undertake Big Data analytics
or to provide other types of Data Economy services.
D A T A E C O N O M Y R E P O R T 2 0 1 8 9
Approach
The study has involved four
main stages, as follows:
Mobilisation
The study mobilisation stage served to clarify the study
objectives and identify sources of data, documents and other
sources of insight.
Data and document review
The mobilisation phase was followed by the literature review
stage, which provided a detailed and extensive review of
the academic and non-academic literature covering the
characteristics, growth drivers and potential constraints
on the Data Economy. Although country-specific sources
were obtained where possible, the process also involved the
review of documents produced by the European Union and
other pan-European entities, and there was a large number
of materials identified from the United States and other non-
European sources. In total, over 200 relevant documents
were identified and reviewed as part of this process.
.01MOBILISATION
.02DATA AND DOCUMENT REVIEW
.03 CONSULTATIONS
.04ECONOMICMODELLING
D A T A E C O N O M Y R E P O R T 2 0 1 81 0
Consultations
The consultation phase was originally conceived as
a mechanism to fill any gaps that remained after the
completion of the literature review stage. However, because
the data and document review process resulted in the
identification of a much larger number of highly useful
resources than was originally expected, the consultations
were designed to be much more targeted and involved
interviews with four organisations:
• The UK’s Digital Catapult
• The Data Science Institute, Imperial College London
• Tech UK
• The Open Data Governance Board (Ireland).
The focus of the consultation interviews was to obtain
additional insights (and to test our interim conclusions)
on topics such as:
• The role that data is playing in the modern, knowledge-
driven economy
• The general extent to which businesses and public
organisations are currently exploiting the full potential
of data analytics infrastructure and applications to
achieve operational efficiencies and contribute to other
corporate objectives
• Drivers for the growth of the Data Economy
• Potential future constraints on the growth of the Data
Economy and what needs to be done to mitigate these
potential hindrances
• Views of the future evolution of the Data Economy
• Views about how the utilisation of data varies across
sectors and national geographies.
Economic modelling
Insights gathered during the document review stage –
supplemented with additional insights gained through the
consultation process – were helpful in the development of a
sector-based model used to estimate the current extent of
the Data Economy contribution in each of the four countries.
The model developed for each country was disaggregated
by 19 sectors based on standard industrial classifications. In
the case of the UK, Germany and the Netherlands the model
was also disaggregated by standard regional geographies.
The base year for the development of estimates of measures
of economic performance (such as GVA and employment)
was 2016, which is the most recent year for which the
relevant data is available across all geographies. Equivalent
datasets were also assembled for the years 2012-2016 so
that the evolution of the trajectory of growth over the most
recent 5-year period could be assessed.
The types of data captured for each country included
the following:
• Levels of business turnover and GVA
• Employment data, defined in both sectoral terms
(Standard Industrial Classifications) and occupational
terms (Standard Occupational Classifications)
• Productivity trend data
• Labour market data (such as estimates of technical
skills gaps and skills shortages in key sectors)
• Business demographic data: the number of business
units and establishments involved in the provision of
Data Economy services
• Data sourced from regular or ad-hoc business surveys.
The sources of data included the central statistics agency
for each country, with additional data obtained from
sources including Eurostat and the OECD (Organisation for
Economic Co-operation and Development).
To enable estimates of the future growth of the Data
Economy it was also necessary to obtain or develop
economic forecasts for each country disaggregated by sector
for the period 2016-2025. In the case of the UK, Germany
and the Netherlands, sector-based regional growth forecasts
were also developed.
The Data Economy models used for each country are
satellites of independent forecasting model to which we
subscribe and can manipulate through sensitivity testing and
scenario modelling.
D A T A E C O N O M Y R E P O R T 2 0 1 8 1 1
The underlying econometric model for each country
provides historic trend data (from 1981) for key economic
indicators: its structure quantifies relationships between
factors such as consumption spending, business investment
and public spending, labour market indicators and
international trade.
The forecast model subscription allows the introduction
of additional assumptions and variables to generate variant
scenarios and forecasts that are not constrained to the
model’s central forecasts. We have used data and insights
generated through a series of Data Economy assignments
for various clients to develop alternative scenarios for
each economy predicated on a (positive) shock
generated through:
.01 Gains for business productivity: additional investment in data
analytics infrastructure and applications by businesses can
be expected to generate additional productivity for factors
deployed; for example, in agriculture, better use of data
can reduce the need for treatment of crops with pesticides
and fungicides, thereby reducing costs and increasing
efficiency. Likewise, in logistics, data analytics can increase
the productivity of vehicle fleets. However, the extent of this
productivity boost varies by sector
.02Opportunities for business revenue generation: new
products and services utilising data and data analytics
(e.g. Big Data opens opportunities for viable treatments for
treatments affecting relatively small populations of patients)
.03Net gains for the net rate of new business formation, i.e. new
businesses emerging to take advantage of the opportunities
for new products and services created by the advent of the
Data Economy
.04Because of 1-3, there may also be potential for net new job
creation, but in some cases these gains may be offset to
some degree both within companies or in supply chains.
The sources of data and insights include:
.01National and regional economic datasets published by
Government or other statistics agencies
.02Data from bespoke business surveys
.03Document review: over 200 academic and other documents
have been reviewed that pertain to the international
Data Economy.
D A T A E C O N O M Y R E P O R T 2 0 1 81 2
Time frame for the assessment and
scenarios
A key objective for this study was to
quantify the current value of the Data
Economy in each country included within
its scope. The most up-to-date year for
which the relevant data is available across
all four countries is 2016. The estimates
of current value therefore relate to the
calendar year 2016.
The study also provides an analysis of the
recent trajectories and sources (by sector)
of Data Economy growth in each country.
The time frame for this analysis is the
period 2012-2016.
For two of the countries covered by the
report – the UK and Ireland – there are also
forward estimates of the potential future
growth of the Data Economy under three
alternative scenarios.
The timeframe for the development of
these scenarios is the period 2017-2025,
with the focus for reporting on the contrast
between current (2016) levels of output
and employment associated with the Data
Economy, compared to levels expected to
be achieved under each future
scenario during 2025.
.01A CENTRAL SCENARIO
The growth expected if current macro-economic forecasts for the
two countries are achieved, and if the currently expected trajectory of growth
of the Data Economy is maintained.
.02A PESSIMISTIC SCENARIO Constraints on the future growth of the
Data Economy (such as skills shortages or a slow-down in business appetite for investment in workforce training or technology) prove to
be greater obstacles to growth than is expected under the central case scenario.
.03AN OPTIMISTIC SCENARIO
The trajectory of growth is stronger because hindrances on the further development of the
Data Economy are addressed. For example, this scenario explores what would be expected
to happen if there were accelerated levels of investment in Data Economy technologies and
capabilities on the part of businesses, compared to the trajectories expected under the
central scenario.
D A T A E C O N O M Y R E P O R T 2 0 1 8 1 3
Structure of the report
The remainder of this report is structured as follows:
CHAPTER 1:DRIVERS OF THE DATA ECONOMY Provides an overview of the changes that are driving the
growth of the Data Economy, including technology change
and the development of business and organisational
applications. The chapter also discusses the mechanisms
through which businesses and organisations can generate
value through the more widespread utilisation of data, and
it also identifies the factors that are potentially inhibiting
greater levels of extraction of value from the use of this
data across a range of key sectors such as Manufacturing,
Transportation and Financial Services.
CHAPTER 2:OVERVIEW OF NATIONALDATA ECONOMY RESULTSProvides a summary of the country-level estimates of the
current scale of the Data Economy and the sources of this
contribution by industrial and service sector. This chapter
also provides a brief basis of comparison of the performance
of the four countries with major economies in Europe (i.e.
France and Italy) and elsewhere (specifically, with the USA,
Canada and Japan).
CHAPTER 3:UK DATA ECONOMY RESULTSProvides estimates of the current and expected future
trajectory of growth of the Data Economy in the UK, as well
as an assessment of the growth trend in the recent past
(2012-2016). Estimates for key metrics are presented on both
a sectoral and regional basis.
CHAPTER 4:IRELAND DATA ECONOMY RESULTSProvides a similar assessment for the Data Economy of
Ireland. As is the case for the analysis of the UK, current and
future estimates are presented on a sectoral basis for Ireland.
However, unlike for the UK, the analysis for Ireland in this
chapter does not include any sub-national estimates.
CHAPTER 5: GERMAN DATA ECONOMY RESULTSProvides an assessment of the current scale of the Data
Economy of Germany, both on a national and regional basis
and disaggregated by sectors.
CHAPTER 6: NETHERLAND DATA ECONOMY RESULTSProvides a similar type of analysis for the Netherlands to that
for Germany described above.
CHAPTER 7 & 8: Provides recommendations on what businesses and
Governments need to do to increase the likely future growth
trajectory of the Data Economy and to ensure that a greater
proportion of the available growth potential is converted
into reality.
D A T A E C O N O M Y R E P O R T 2 0 1 81 4
Introduction
Advances in digital technology and the increasing ubiquity
of connected devices and sensors means that there has
been a huge increase in the global rate at which data is
being generated. By one reckoning, 90% of all the data
generated during human history has been created in just the
past two years.5
The rate at which data is created is currently accelerating,
driven by the rapidly rising number of household and
business applications. Consumer generation of data in
advanced economies is driven by high levels of penetration
of smartphones and tablet computers and is further boosted
by the increasing usage of wearable health monitoring
devices (the latter, for example, generate large amounts of
data every minute of each day ranging from an individual’s
location and their movement, to their energy usage, heart
rate, sleep patterns and other data).
The growth of consumer data is further boosted by the
demand for digital entertainment and communication,
ranging from video and music streaming to online computer
gaming and the sharing of pictures, videos and other
information on social media.
Simultaneously, business use of data has also increased
exponentially, driven by the increasing number of digital
devices and sensors used on production lines, in energy,
transportation and telecommunications infrastructure and in
vehicles used for moving freight and passengers.
Moreover, the rate at which data is being generated shows no sign of abating: one prediction is that the daily amount of data generated globally will increase tenfold over the next eight years, from around 16ZB6 per day in 2017 to over 160ZB per day by 2025.7
This global growth will be driven by ever larger numbers
of people connecting to digital devices for an ever larger
number of applications. The amount of data generated each
day is expected to be further boosted as novel technologies
such as virtual reality and autonomous vehicles are
introduced and become widespread in their usage.
Although the generation of data by consumers in future
will remain very significant, it is expected that in future at
least 60% of data generated globally will be created by
businesses.8 The growing importance of data to businesses
will be driven by at least four mechanisms, although there is
potential for overlaps between them. The mechanisms are
as follows:
.01 Improved business intelligence and decision-making
.02Cost-efficiencies and revenue growth
.03Opportunities for product and service innovation and new
forms of enterprise
.04Opportunities for new business creation.
It is worthwhile exploring each of these themes briefly in
turn, as they are applicable to most if not all sectors of the
economy and irrespective of whether a business is primarily
serving consumer or business-to-business markets.
Later in this chapter we then turn to consider some specific
issues and opportunities facing some of the most important
sectors of advanced modern economies (such as Financial
services and Manufacturing) before moving on to consider
potential constraints on growth.
5 | 10 Key Marketing Trends for 2017, IBM.
6 | ZB = zettabyte, or roughly one trillion gigabytes.
7 | What Will We Do When The World’s Data Hits 163 Zettabytes In 2025?, Forbes, 13 April 2017.
8 | Data Age 2025, IDC, 2017.
Drivers of the Data Economy1
D A T A E C O N O M Y R E P O R T 2 0 1 8 1 5
Data as a driver of business growth
Business intelligence and decision-making
It is axiomatic that the more information a business has
about its operations and the markets within which it is active,
the better placed it will be to make sound business decisions.
The generation of ever greater volumes of data provides
the potential for the development of more detailed insights
into a wide range of issues and challenges facing businesses,
such as the following non-exhaustive list of opportunities
and themes:
• Better insights into customer behaviour and market
trends (for example, greater levels of anticipation
of seasonal trends and opportunities for increased
revenues via more precise targeting of incentives
to customers)
• More efficient procurement and management of supply
chains and inventories
• Improved environmental performance (for example,
a reduced carbon footprint from energy savings via
improved vehicle fleet management, more efficient
heating and lighting of intelligent buildings, and
reduced use of resources)
• More cost-effective compliance with labour market,
environmental and other forms of regulation
• Identification and management of business threats
and risks.
Cost-efficiencies and revenue growth
The generation of large amounts of data on business
operations and processes creates large opportunities to
achieve cost savings in production.
In sectors such as Manufacturing and Construction,
such efficiencies can be achieved through more efficient
procurement, better utilisation of machines and vehicles,
and the identification and elimination of wasted resources
and energy used in production. Greater adoption of data
analytics infrastructure and applications and the IoT also
enables greater levels of adoption of so-called Industry 4.0
technologies creating sustainable advances in productivity
potential.9
Productivity advances and revenue growth opportunities
through the greater use of data analytics are also available
in consumer-focused areas of activity such as Wholesale
& retail and Accommodation & food. For example, analysis
of patterns of seasonal or cyclical patterns of customer
behaviours can enable hotels, restaurants and retail
businesses to be better prepared (via stock levels or other
forms of capacity) to anticipate future increases or decreases
in customer demand for certain services or products, leading
to increased revenue and lower levels of wastage.
Product and service innovation
Linked to the previous point, there are also opportunities
for companies to use business, market and other data to
create entirely new products and services to meet the
needs of customers.
A powerful example of the new types of innovation that
is possible via data analytics comes from the field of life
science research and development. For example, McKinsey
has identified that in the United States alone the greater use
of Big Data analytics in pharmaceutical R&D could generate
$US100 billion in additional value annually, by allowing
the development of new treatments and medicines, by
improving the success rate of research and clinical trials and
creating new approaches for more individualised treatments
for patients.10 Many chronic and serious ailments are
suffered by relatively small populations of patients, thereby
making R&D into treatments for these populations very
expensive: productivity advances offered by Big Data creates
opportunities for significant advances in treatments at a cost
that healthcare systems can potentially afford.
9 | Industry 4.0 is a term used to describe the trend of advanced automation (including robotics) and data exchange in manufacturing processes. It includes cyber-physical systems, the Internet of Things, cloud computing and cognitive computing.
10 | How big data can revolutionize pharmaceutical R&D, McKinsey, April 2013.
D A T A E C O N O M Y R E P O R T 2 0 1 81 6
Enterprise opportunities
While some larger companies may choose to develop
in-house data storage and analytics infrastructure and
expertise, there are clear opportunities for the emergence
and growth of specialist companies providing data services
for businesses.
According to analysis published by the European
Commission, in 2016 there were 120,000 companies involved
in providing services relevant to the Data Economy in the
UK alone.11 Between 2008 and 2016, the population of these
companies grew by an average of 4% per annum.
Analysis of employment data in the UK also reveals a strong recent rate of growth of Data Economy employment: rising from around 0.97 million in 2012, to nearly 1.15 million by 2016.12
This implies an average rate of growth of over 3% in the
employment base of the industry over this period. Similar
rates of growth are also evident in other European countries,
including in Ireland, Germany and the Netherlands. More
specific details of these trends are found in later chapters of
this report.
A key feature of the Data Economy business ecosystem is
companies such as Digital Realty (the client for this report),
who delivers Data Centre services and provides a range of
data analytics support services to businesses around
the world.
11 | European Commission, May 2017
12 | Labour Force Survey, ONS
D A T A E C O N O M Y R E P O R T 2 0 1 8 1 7
Focus on sectors
Having introduced in broad terms the drivers of growth
and the benefits for business offered by the emergence
of the Data Economy, the next step is to examine some
of these issues at a business sector level. Although this
assessment isn’t comprehensive (i.e. not every sector
of the economy is discussed in detail), individual
assessment is provided for the following:
.01MANUFACTURING
.02TRANSPORT
.03HEALTHCARE
.05MEDIA AND ENTERTAINMENT
.04RETAIL
.06FINANCIAL SERVICES
D A T A E C O N O M Y R E P O R T 2 0 1 81 8
Manufacturing
Much of the recent discussion regarding the influence of Big
Data is set within the context of a so-called fourth industrial
revolution, termed Industry 4.0. There is no single definition
of Industry 4.0, but commentators generally agree that it
centres on cyber-physical links and the application of the
Internet of Things in industry. It is also related to terms
such as ‘industrial internet’ and the ‘digital factory’. Various
aspects have been cited as important to the Industry 4.0
concept, including:
• Connecting production line machines and sensors
• Big Data and analytics
• Improved data transfer and storage.
A McKinsey report on this topic surveyed over 300
manufacturers in advanced economies.13 It found that only
about half of manufacturers were ready for Industry 4.0.
The executives surveyed estimated that 40-50% of today’s
machines will need to be replaced or upgraded to make
them suitable for Industry 4.0 processes. Such levels of
investment pose a significant challenge to manufacturers.
To take advantage of digitisation, the research suggested
that companies will need to gather much more data and
make better use of it, take digitisation into account when
planning the future of the company, and to prepare for
digital transformation through investing in management
and workforce training and skills development.
An international survey of 2,000 businesses undertaken by
PWC in 2016 found that increased digitisation was expected
to both reduce costs and increase sales significantly. Some
33% of survey participants reported that they have already
achieved advanced digitisation while 72% expect to have
achieved it by 2020. Based on the survey findings, globally
manufacturers are expecting to invest over $US900 billion
in digitisation by 2020.14
Boston Consulting Group published a report in January
201715 which sets out international comparisons of how
prepared industry is for Industry 4.0. The research involved
a survey of 1,500 business managers across five countries
(UK, France, Germany, the United States and China). The
study found a range of levels of preparedness for Industry
4.0: for example, while 80% of UK companies reported they
had made some progress, this proportion was lower than in
the other countries, such as Germany (where the equivalent
proportion was 90%) and France (89%).
13 | Manufacturing’s next act, McKinsey, June 2015.
14 | Industry 4.0: Building the digital enterprise, PwC, 2016.
15 | Is UK Industry ready for the Fourth Industrial Revolution?, BCG, January 2017.
D A T A E C O N O M Y R E P O R T 2 0 1 8 1 9
Transport
The transportation sector is a vast generator of data.
Analysis and decision-making based on this data has the
potential to achieve significant efficiencies and save time
and costs for drivers, passengers, freight hauliers and those
depending on the timely arrival of goods.
Vast amounts of transport-related data are gathered
by information-sensing mobile devices, remote sensing,
software logs, cameras, microphones and wireless sensor
networks. Global technological information per-capita
capacity has approximately doubled every 40 months
since the 1980s. Some predictions show that transportation
data production will be 44 times greater in 2020 than it
was in 2009.16
The increase in transport data is manifest in the availability
of day-to-day road traffic information available to users
in their vehicles, such as through satellite navigation
systems. Similar passenger information applications provide
departure and journey management information for public
transport users. Payment for transport (ticketing and tolling)
is increasingly reliant on data-dependent technology,
applications and services.
In all modes of transport, there are already large quantities
of data available for operators to improve performance,
efficiency, service provision, safety and security. Data also
enables operators to manage demand conflicts, customer
service, environmental impacts and innovation. This can
be seen in systems such as traffic signal co-ordination,
trains reporting track defects, on-line flight check-ins and
cargo tracking.
The potential present and future benefits of Big Data for
transportation have been identified as follows:
.01Improved information for improved transport system
efficiency and capacity. This includes:
.a
Improved transport system planning through use of
technologies such as GIS (geographic information systems),
traffic analytics, predictive analytics, fleet analytics and
improved transport system modelling
.b
Enhanced transport system design through improved
modelling and simulation
.c
Vehicle management systems (including vehicle-to-vehicle,
vehicle-to-infrastructure, integrated supervision systems,
driver assistance systems and parking management
systems, etc.)
.d
Infrastructure management systems (including vehicle-to-
infrastructure, network connectivity, traffic management,
infrastructure monitoring, and weather management, etc.)
.e
Intelligent fleet and logistics management, including for
logistics and distribution but also for postal services and
emergency services
.f
Improved risk management including safety, security, system
resilience. For example, for public transport operators the
advent of smart ticketing has already produced reductions
in the incidence of lost revenues from ticket evasion (or
customers simply not having the right ticket for the journey).
16 | Deriving Transport Benefits from Big Data and the Internet of Things in Smart Cities, Womble Bond Dickinson, 2017.
D A T A E C O N O M Y R E P O R T 2 0 1 82 0
.02Improved levels of customer service and experience,
including the delivery of more reliable and punctual public
transportation services:
.a
Improved passenger information systems allow users to
manage their journeys better (for example information about
a late service could allow users to plan and implement an
alternative service)
.b
Integrated payment systems cut down on lost revenue
.c
In addition, smart ticketing saves users’ time and provides
opportunities for value added services (e.g. discounts on
refreshments or offers from partner retailers, etc.)
.03Improved safety performance, such as trains and buses
reporting emerging faults or trains reporting potential rail
tracks defects
.04Improved environmental performance – more efficient
vehicle running reduces fuel consumption, reduces
production of carbon and emissions of other gases
and particulates
The further development of transportation data systems
married to a 5th generation mobile network offers
opportunities for the development of driverless cars and
other autonomous vehicles.
Healthcare
It has already been mentioned in this chapter that McKinsey
has identified very large savings and anticipated substantial
productivity advances from greater use of Big Data analytics
in pharmaceutical R&D. Big Data creates opportunities for
affordable development of new treatments and medicines in
fields such as cancer, cardio-vascular health and dementia by
improving the success rate of research and clinical trials and
creating new approaches for more individualised treatments
for patients.17
A key challenge for life science industries is translating
scientific discovery into commercially viable medicines and
treatments for people with various or multiple illnesses
and conditions. The process is tending to become more
challenging, time consuming, risky and expensive because
of tightening regulations and an extended timetable for
realisation which can take 15 years or more, and with
an increasing proportion of potential new products not
receiving regulatory approval along the way.
The emergence of technologies such as Big Data analytics
make it easier to design better clinical trials accessing
national and international drug trial and healthcare data and
by targeting specific patient sub-populations.
McKinsey and other commentators have forecast that Big
Data analytics could substantially reduce R&D costs for
pharmaceutical companies and increase the chances of
drugs under development gaining approval.18 Big Data in life
sciences is driven by the opportunity to deliver new drugs
for specific patient populations, and has arisen because
of the combined impacts of low cost genome sequencing,
the availability of electronic medical records, increasing
personalised medical treatments, and the collection of
ongoing data once treatments have entered the market.
A linked technological development is the emergence of
a range of wearable and other devices that can monitor
patients’ usage of medicines, monitor symptoms and
treatment progress and outcomes.
The faster and more certain translation of scientific discovery
into viable healthcare treatments will, in turn, generate better
healthcare outcomes for patients whose conditions and
illnesses are benefited by the delivery of new medicines
and treatments.
17 | How big data can revolutionize pharmaceutical R&D, McKinsey, April 2013.
18 | Big Data in pharmaceuticals, The Manufacturer, October 2014.
D A T A E C O N O M Y R E P O R T 2 0 1 8 2 1
Retail
Larger retailers have developed a wide range of data-driven
demand forecasting tools to help them anticipate sales
trends and to anticipate the evolution of customer demand
patterns. Many larger retailers have also invested in card
schemes and other programmes that help to track customer
purchasing behaviours and to personalise discounts and
other offers designed to maximise customer revenue and
reward customer loyalty trends.
One of the most significant trends affecting the retail
sector over the past decade has been the huge growth in
online sales. For example, in the UK over 63% of the adult
population has bought an item online, and the annual value
of goods bought online in the UK is worth over $US84
billion.19 Moreover, an increasing proportion of online sales
is made by customers using devices such as smartphones.
However, there are significant challenges: the conversion
rate from ‘adds to basket’ to actual sales is typically only
3%-6%, with smartphone conversion rates at the low end
of this range. Greater use of Big Data analytics on the part
of retailers may help retailers significantly increase their
conversion rates thereby adding to revenues
and profitability.
Many larger retailers and specialist online retailers have
developed highly effective online strategies, but some larger
retailers, many regional and medium-sized, and most smaller
retailers have not kept up with these developments. Long
term competitiveness requires them to develop some form
of online presence, even if it is simply click and collect. On
this basis, greater use of data by a wider range of retailers
(in terms of their size) is likely to be important in helping
small to medium sized retailers stay competitive in an
increasingly online world.
Media and entertainment
Media and entertainment business have generally been at
the forefront of implementing new technologies including
digital technology. The advent of digital platforms has
reduced barriers to entry to the industry and therefore
created a more competitive environment, with new
opportunities such as digital advertising threatening
traditional revenue sources.
Drivers for change in these industries linked to Big
Data include:
• Opportunities for businesses to develop a highly
detailed understanding of their customers based on
different types of interaction (such as product usage,
social media interactions, etc. as well as customer
preferences and attitudes) and to use that data to build
highly engaged relationships with customers
• Products and content: Big Data provides opportunities
to produce new types of content in more sophisticated
ways tailored to the preferences of individual
consumers; it can also be used to identify and to tailor
content suited to the personalised needs of customers
based on their histories of previous interactions
• Media industry customers are also often a source of
content supply for the media industry
• Big Data can be used to track changing customer
interests and preferences in a fast-moving world.
*$84 billion converted £61 billion, with exchange rate as at April, 2018.
19 | Northern Europe report, We are Social, January 2017.
£61 billion*
63%
D A T A E C O N O M Y R E P O R T 2 0 1 82 2
Financial services
The advent of Big Data has created new opportunities
for the growth of the financial services sector and the
achievement of significant operational efficiencies.
The financial technology sub-sector was an enthusiastic
early adopter of Big Data technology. Uses include the
combination of trader performance data, market data,
unstructured news, user data, and general ledger data to
gain previously impossible insights. This enables the creation
of much more powerful real time analytical and decision-
making power.20
Survey research undertaken by Accenture reveals that
globally 71% of firms in the financial services sector are
developing Big Data and predictive analytics, and that a
similar proportion state that Big Data is critically important to
their firms.21
Based on survey responses, Accenture calculates that annual investment in data-related capabilities is already likely to be in the order of $US9 billion annually, and that this investment is currently increasing at around 25% per annum.
Opportunities for the better utilisation of data analytics
centres on the following themes:
• Enhanced decision-making: improved decisions
based on evidence, enabling more efficient and faster
identification of business problems and opportunities,
including for enhanced productivity and cost-efficiency
• Service and product innovation: Big Data creates
opportunities for new services and products based on
the intelligent interpretation of customer behaviour and
market trends
• Enhanced risk and regulatory management: over the
last decade the finance sector has faced a large increase
in regulation and reporting requirements, but now
Big Data is improving the detection of non-compliant
behaviours by staff as well as improving financial
institution resilience (using simulation tests, stress-
testing and other data-driven models)
• Another role of Big Data in financial services is to help
detect patterns of investment or insurance fraud on the
part of customers.
20 | The Big Data dilemma, House of Commons Science and Technology Committee, 2016.
21 | Exploring Next Generation Financial Services: The Big Data Revolution, Accenture, 2016.
25%
$9 billion
71% globally
D A T A E C O N O M Y R E P O R T 2 0 1 8 2 3
Constraints and barriers to growth of the Data Economy
The review of national and sector-level documentary
evidence reveals several concerns and challenges regarding
the extent to which individual businesses, industrial sectors
and Governments are in a position to secure the efficiency
and service innovation benefits that stand to be gained –
both currently and in the future – as a result of the advent of
the Data Economy.
The most important of these constraints have been
identified by the European Commission as being:22
• A lack of general awareness of the functioning and the
potential benefits of data analytics
• Resistance to and fear of potential organisational
change entailed by Big Data analytics
• Absence of available resources regarding the ability to
integrate and manage large datasets
• Shortages of skilled data-savvy staff
• The lack of financial means to make the technological
and staffing investments – this is a challenge especially
for SMEs.
Key points regarding each of these constraints are
provided below.
Public acceptance of data gathering and sharing
Open data is important to the growth of the Data Economy
because it dramatically reduces the time and resources
needed to understand what Government is doing. Because
Open Data is made available in bulk and in formats that
simple computer programmes can analyse, comparing and
combining data from different sources becomes faster and
easier. This greatly enhances the ability of policymakers and
others to find solutions to complex development problems
and for businesses to identify and develop new commercial
opportunities.
The benefits of data sharing are already evident. For example, it is estimated the value of time saved resulting from the release of Transport for London’s Open Data is c.£58 million per year, from an annual spend of less than £1m.23
Although the UK was ranked top in the 2016 global Open
Data Barometer, the country’s rate of progress on Open
Data is reported to have slowed, signalling that new political
will and momentum may be needed as difficult elements of
Open Data are tackled.24 Of the countries considered by the
2016 report, only the Netherlands (7th) ranks in the top ten
countries on this measure.
With respect to private data, there is some evidence that
there may be growing unease or distrust with respect to
the ability of companies and Governments to protect the
security of their data following several well-reported data
security lapses on the part of financial service providers,
retailers and telecommunications service providers.
Some companies are also concerned about what they regard
as a lack of clarity in the regulatory framework with respect
to data privacy, data security and protection.25
The introduction of the General Data Protection Regulation
(GDPR) across the European Union in 2018 may lead to
significant improvements in both public confidence and
clarity over regulation. However, it also comes with risks,
and so the net benefits of the regulation are not currently
possible to forecast with confidence.
In particular, any increase in restrictions on the accessibility
of Open Data or the ability of private companies to store and
utilise customer data – whether from GDPR or other causes
– threatens to restrict the development of data applications
and innovations and is a significant threat to the future
growth of the Data Economy.
22 | Enter the Data Economy EU Policies for a Thriving Data Ecosystem, European Commission, January 2017.
23 | Deriving Transport Benefits from Big Data and the Internet of Things in Smart Cities, Womble Bond Dickinson, 2017.
24 | Open Data Barometer Third Edition, World Wide Web Foundation, April 2016.
25 | EU Policies for a thriving data ecosystem, European Commission, 2017.
D A T A E C O N O M Y R E P O R T 2 0 1 82 4
Investing in data analytical systems: cultural and
financial barriers
Sector-based business surveys referred to earlier in this
chapter reveal the very significant level of recent, current and
expected future levels of investment required by companies
to gather and use business data.
Keeping abreast of required investment in data storage
and analytical capability may require expensive investment
in computer and data analytical technologies, as well as
complementary investment in staff recruitment and/or staff
training and development. Obviously for all businesses there
are competing areas for business investment, so attaching
the appropriate priority to enhance data capabilities may
be a challenge for some businesses, especially smaller and
medium-sized businesses.
Responses from surveys reveal that many SMEs struggle
to secure the finance they need to invest in fast-evolving
technologies, including advanced computer systems.
Even companies that recognise the longer-term competitive
imperative of increasing levels of investment in advanced
technology may struggle to adequately prioritise investment
in sophisticated computing and data analytics capabilities
and infrastructure.
Infrastructure and standards
In some cases, there may also be infrastructure constraints
or concerns about the reliability of data. The conditions
necessary for the full exploitation of the possibilities of the
Data Economy include:
• Availability of high quality, reliable and trusted data
from large datasets
• Availability of robust standards and interoperability
of data
• Enabling infrastructure such as fast broadband,
large and flexible computing resources, deployment
of smart connected sensors and availability of
abundant bandwidth.
In modern economies the successive roll-out of the next
generation of advanced communications technology
(such as high-speed broadband and high-capacity mobile
telecommunications infrastructure) tends to favour more
densely populated urban areas as these provide a higher
density of household and business customers. Would-be
users in less densely populated areas (and/or areas with
topographical challenges) may face significant delays in
receiving services and, in some cases, services may never
be made available.
This creates the danger that the economic and social
advantages that stand to be created by the Data Economy
may not be fully shared, with businesses and households in
rural or semi-rural areas facing significant disadvantage. In
addition, some industries that operate in these areas (such
as agriculture) or across these areas (such as the distribution
and logistics sectors) may also be unable to exploit fully the
potential advantages offered by the Data Economy because
of spatially uneven infrastructure investment. Moreover,
the advent of new opportunities such as autonomous
vehicles could be significantly delayed or constrained by
the patchy nature of telecoms infrastructure in less densely
populated areas.
D A T A E C O N O M Y R E P O R T 2 0 1 8 2 5
Skills
The availability of digital skills is reported to be approaching
a crisis.26 There are two dimensions to the skills deficit:
• Digital skills shortages – the ability of companies
and organisations to recruit sufficient numbers of
appropriately skilled staff to carry out work that is
needed to grasp the opportunities created by the
digital economy
• Digital skills gaps – any deficiencies in technical or
managerial skills amongst existing employees that
may constrain organisations from recognising and
implementing strategies to take the opportunities
the Data Economy offers.
Realisation of the full potential value of the Data Economy
requires access to the right skills: data engineering skills to
develop a robust data infrastructure, data analysis skills to
extract valuable insights from data, and business skills to
apply them.27
Previous work undertaken by Development Economics on
behalf of telecoms provider O2 quantified that nationally
an additional 153,000 digitally skilled workers per annum
would be needed by the UK economy over the 2015-2020
period alone. However, the shortage of digitally skilled
workers is not just confined to the UK: the growth in
demand across Europe and other advanced economies
has been documented by the European Union and other
commentators.
The competition for workers with the necessary skillsets
is intense, as these workers are also sought for other
knowledge-economy activities and applications across a
wide range of economic activity.
Moreover, the shortage of skills is expected to grow
remorselessly as Big Data reaches further into the economy.
This shortage creates economic implications but also
potentially puts the quality and security of this data at risk.
There is a range of initiatives to help develop computing and
digital skills, but the danger is that the wider set of Big Data
skills is not being strategically addressed.
The ultimate risk is that businesses and organisations are
unable to grow the Big Data sector at the fastest possible
pace, and as a result value and job-creating opportunities
are squandered.
26 | For example, House of Commons Science and Technology Committee, The Big Data Dilemma, 2016.
27 | Skills of the Datavores, Nesta, 2015.
153,000digitally skilled
workers
D A T A E C O N O M Y R E P O R T 2 0 1 82 6
Introduction
The purpose of this chapter is to provide an overview and
key points of comparison of the data economies of four
European countries: the UK, Ireland, Germany and the
Netherlands. The metrics used in this assessment are:
• The GVA associated with the Data Economy in each
country in the most recent year for which data is
available (2016), and the proportion of the country’s
overall economic output that this represents
• The level of direct employment (measured by
workforce jobs) associated with the Data Economy in
each country in the most recent year for which data is
available (2016)
• The composition of the overall Data Economy by
industry, using standard industrial classifications,
thereby enabling international comparisons to be made
• The extent to which the Data Economy in each country
has grown since 2012
• The extent to which the Data Economy in each country
was delivering against its full potential in 2016.
In addition to the comparisons between the four countries,
the chapter also provides summary Data Economy statistics
(for GVA and direct employment) enabling comparisons
with five other major economies (the USA, Canada, France,
Italy and Japan). Together with Germany and the UK,
these additional five countries comprise the G7 group of
major economies.
Overview of National Data Economy Results
2
D A T A E C O N O M Y R E P O R T 2 0 1 8 2 7
Current (2016) size of the national data economies
This sub-section provides estimates of the scale of the Data
Economy in each of the four European countries that are the
principal focus of this report. The metrics used to assess the
scale of the Data Economy in each country are as follows:
.01 Value of economic output – measured by GVA
.02The proportion of the overall size of the national economy
attributable to the Data Economy
.03The amount of direct employment – measured by workforce
jobs – accounted for by the Data Economy
.04The proportion of the overall employment in each economy
accounted for by the Data Economy
In each case the estimates are for the year 2016, which is the
latest year for which relevant data is available.
To make it easier to compare the absolute size of the
GVA attributable to the Data Economy in each country, in
this chapter the UK result is expressed in Euros.28 In the
UK-specific chapter which follows, UK financial results are
presented using Pounds Sterling.
The largest Data Economy by value among the four
countries assessed here is that of Germany (€108 billion).
However, as a proportion of the overall national economy the
German Data Economy is the smallest (3.8%). The largest
Data Economy in proportionate terms is the UK (4.2%),
followed by Ireland (4.0%).
In employment terms the UK remains the largest Data
Economy, with 3.3% of national employment accounted for
by this category. However, the difference between the UK
and the other countries is quite small: in the Netherlands and
Germany the proportion is 3.2%.
28 | This conversion has used the yearly average £:€ exchange rate for 2016 (1:1.225), sourced from https://www.ofx.com/en-gb/forex-news/historical-exchange-rates/yearly-average-rates/
Indicator UK Ireland Germany Netherlands
2016 GVA €millions (2016 prices)
8 9, 8 26 9,9 62 1 0 8 , 3 2 7 24 ,6 3 7
2016 GVA as % of national economy
4 . 2 % 4 .0 % 3 . 8% 3 .9 %
2016 Data Economy employment
(direct, ‘000s)1 ,1 47 61 1 , 3 2 3 247
2016 Data Economy jobs as % of total workforce jobs
3 . 3 % 3 .0 % 3 . 2 % 3 . 2 %
D A T A E C O N O M Y R E P O R T 2 0 1 82 8
Comparison with other countries
The table below sets out some key metrics for the estimated
size of the respective Data Economy in 2016 for the four
countries that are the principal focus of this report. The
benchmarks that are provided are the United States, Canada,
France, Italy and Japan (which, together with Germany and
the UK comprise the G7 countries).
When a wider set of international countries is used,
the largest Data Economy in proportionate terms
when GVA is considered is that of the United States
(5.1%), whereas the smallest is that of Italy (3.1%).
The extent of the Data Economy in Canada and
Japan is similar to that of the UK in terms of both
output and direct employment.
CountryData Economy GVA
€millions (2016 prices)Data Economy GVA as % of national economy
RankingData Economy em-ployment (direct)
Data Economy jobs as % of total workforce
jobsRanking
United States 8 5 8 , 3 49 5 .1 % 1 6 ,69 8 4 .1 % 1
Canada 59,4 4 3 4 . 3 % 2 67 7 3 .4% 2
Japan 1 8 7,49 9 4 . 2 % 3 2 ,1 26 3 . 2 % 4
UK 8 9, 8 26 4 . 2 % 3 1 ,1 47 3 . 3 % 3
Ireland 9,9 62 4 .0 % 5 61 3 .0 % 7
Netherlands 24 ,6 3 7 3 .9 % 6 247 3 . 2 % 4
France 8 0 , 2 0 6 3 .6% 7 8 59 2 . 8% 8
Germany 1 0 8 , 3 2 7 3 . 8% 7 1 , 3 2 3 3 . 2 % 4
Italy 51 , 8 2 5 3 .1 % 9 62 0 2 .4% 9
Indicator France ItalyUnited States
Canada Japan
2016 GVA €millions (2016 prices)
8 0 , 2 0 6 5 1 , 8 2 5 8 5 8 , 3 49 59,4 4 3 1 8 7,49 9
2016 GVA as % of national economy
3 .6% 3 .1 % 5 .1 % 4 . 3 % 4 . 2 %
2016 Data Economy employment (direct)
8 59 62 0 6 ,69 8 67 7 2 ,1 26
Data Economy jobs as % of total workforce
2 . 8% 2 .4% 4 .1 % 3 .4% 3 . 2 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 2 9
Disaggregation of GVA by industry (2016)
The composition of the Data Economy – in terms of
economic output – can also be assessed according to the
contributions made by business and organisation sectors.
The breakdown of these contributions in proportionate
terms is summarised in the table below.
The largest contribution in proportionate terms in each country is made by the ICT sector, with this proportion ranging from 34% in the Netherlands to nearly 50% in Ireland.
In the UK the most significant other contributions come from
the Financial and Professional services sectors, whereas in
the other countries the Manufacturing sector is also a major
component. In the Netherlands, Financial services is also
important, as is the Wholesale & retail distribution sector.
Sector (Sections)UK
% of totalIreland
% of totalGermany% of total
Netherlands% of total
A Agriculture, forestry, fishing 0 .1 % 0 .1 % 0 .1 % 0 .4%
B Mining & quarrying 1 . 2 % 0.4% 0. 5 % 0.6%
C Manufacturing 6 .4% 1 6 .7 % 1 9. 5 % 9.7 %
D Electricity 1 . 7 % 0. 8% 1 . 8% 1 .6%
E Water supply 0 .4% 0. 2 % 0. 5 % 0.6%
F Construction 2 . 3 % 0.9 % 2 .1 % 2 .0 %
G Wholesale, retail 4 . 7 % 2 .4% 4 .9 % 6 . 3 %
H Transport 2 .9 % 1 . 2 % 2 .4% 2 .6%
I Accommodation & food 0 .1 % 0 .1 % 0 .1 % 0 . 2 %
J ICT 41 . 3 % 49.7 % 3 4 .7 % 3 4 . 2 %
K Financial services 1 5 . 8% 1 3 . 5 % 9.7 % 1 7.1 %
L Real estate activities 5 .0 % 2 .1 % 4 . 8% 2 . 5 %
M Professional services 7.0 % 5 .7 % 6 .1 % 8 .6%
N Business support services 2 . 3 % 1 . 8% 2 .9 % 3 . 5 %
O Public administration 3 . 2 % 1 . 5 % 3 . 8% 3 .4%
P Education 2 .0 % 1 .1 % 1 . 8% 2 .1 %
Q Health 1 . 8% 1 . 2 % 2 .4% 3 .1 %
R Arts, entertainment, recreation 1 .0 % 0. 3 % 0.9 % 1 .0 %
S Other services 0 . 8% 0. 2 % 1 .1 % 0 .6%
Total 1 0 0.0 % 1 0 0.0 % 1 0 0.0 % 1 0 0.0 %
D A T A E C O N O M Y R E P O R T 2 0 1 83 0
Country2012 Data Economy jobs as %
of National Workforce2016 Data Economy jobs as %
of National Workforce2012-2016 Change (%)
2012-2016 Change (percentage points)
UK 3 .0 6% 3 . 3 1 % 8 . 3 % 0. 2 5 p p
Ireland 2 . 3 3 % 3 .0 5 % 3 0.9 % 0.7 2 p p
Germany 2 .9 1 % 3 . 24% 1 1 . 3 % 0. 3 3 p p
Netherlands 2 . 8 1 % 3 . 2 0 % 1 3 .9 % 0. 3 9 p p
Country2012 GVA(millions)
2016 GVA(millions)
2012-2016 Change (%)
UK (£) 5 5 , 2 8 4 7 3 , 3 2 7 3 3 %
Ireland (€) 6 ,0 6 5 9,9 62 6 4%
Germany (€) 7 1 , 741 1 0 8 , 3 2 7 51 %
Netherlands (€) 1 7,494 24 ,6 3 7 41 %
Growth of Data Economy: 2012-2016
The trajectory of growth of the Data Economy can be
measured in terms of both employment and economic
output. Depending on which measure is used, the messages
about which country has been growing most strongly over
the 2012-2016 period varies slightly.
The first table provides inflation-adjusted data on value of
economic output associated with the Data Economy in both
2012 and 2016. From this perspective, the fastest growing
data economies are in Ireland (64% growth between 2012
and 2016) and Germany (51%). The UK grew at 33%, which
was nearly half the rate at which the Irish Data Economy
grew over this period.
Another perspective is offered by comparison of the
changing proportion of overall employment in each country
contributed by the Data Economy. In the UK this grew by
around 8.3% between 2012 and 2016, whereas in Ireland it
grew by nearly 31%. On this basis the German job growth
performance wasn’t as strong as was the case with GVA, with
the proportion of overall employment accounted for by the
Data Economy growing by just over 11% compared to nearly
14% in the Netherlands.
D A T A E C O N O M Y R E P O R T 2 0 1 8 3 1
Change in composition: 2012-2016
The change in the size of the GVA contribution of the Data
Economy can be viewed in several ways:
• First, the proportionate contribution made to the
overall change occurring between 2012 and 2016
in each economy
• Second, the proportionate change in the absolute size
of the contributions made by each business sector in
each country over the same period.
The first approach is useful because it reveals where the
largest sources of growth have occurred. The second
approach is also useful because it highlights the sectors
that are growing the fastest (albeit in some cases from a
low base).
Data summarising the proportionate contributions to overall
change occurring between 2012 and 2016 (with adjustments
made for inflation) are set out in the table on the next page.
The data indicates that the ICT sector was the largest
contributor to growth in each country, ranging from 32%
in the Netherlands to nearly 50% in Ireland. Notable other
contributions include:
• Manufacturing – accounting for nearly 24% of growth in
Ireland and 17% in Germany
• Financial services – accounting for 10% of growth in the
Netherlands and nearly 12% in the UK
• Professional services – accounting for between 7% and
10% of growth in each country.
24%+ Ireland
17%+ Germany
Manufacturing
10%+ Netherlands
12%+ UK
Financial services
Professional services7-10%
in each country
D A T A E C O N O M Y R E P O R T 2 0 1 83 2
Some notable variations are also evident:
• Transportation made a much larger contribution to the
growth of the Data Economy in the UK compared to its
contribution to the other three countries
• Wholesale and retail trade made a significant
contribution to Data Economy growth in the
Netherlands but only a very small contribution in
Ireland, and a similar pattern also occurred with
respect to real estate services
• Business support services made a significant
contribution to the growth of the Data Economy in the
Netherlands but much less so elsewhere
• Education and Health made much more significant
contributions in Germany and the Netherlands
compared to the UK and Ireland.
Sector (Sections)UK
2012-2016 % Ireland
% of totalGermany% of total
Netherlands% of total
A Agriculture, forestry, fishing 0 . 2 % 0. 2 % 0. 2 % 0. 8%
B Mining & quarrying 1 .7 % 0. 5 % 1 .0 % 1 . 2 %
C Manufacturing 4 . 3 % 2 3 . 5 % 1 6 .9 % 9. 5 %
D Electricity 2 . 8% 0. 5 % 1 . 2 % 0. 8%
E Water supply 0 .9 % 0. 3 % 0.9 % 0.6%
F Construction 4 .1 % 1 . 5 % 3 .4% 3 . 2 %
G Wholesale, retail 6 . 3 % 1 .7 % 6 .4% 9. 5 %
H Transport 4 .9 % 1 .0 % 3 .1 % 3 .4%
I Accommodation & food 0 . 3 % 0. 2 % 0. 2 % 0. 3 %
J ICT 41 .7 % 49.6% 3 4 . 2 % 3 2 .6%
K Financial services 1 1 . 5 % 8 .4% 7.6% 1 0 .0 %
L Real estate activities 5 .1 % 1 .7 % 4 . 8% 3 .6%
M Professional services 7.7 % 7. 2 % 7.1 % 9. 2 %
N Business support services 2 .7 % 2 . 3 % 2 .9 % 5 . 3 %
O Public administration 1 .0 % 0. 3 % 3 . 2 % 2 .6%
P Education 0 .9 % 0. 2 % 1 . 8% 2 .1 %
Q Health 2 . 2 % 0. 5 % 2 .9 % 3 .4%
R Arts, entertainment, recreation 0 .7 % 0.1 % 0 .9 % 1 .1 %
S Other services 1 .1 % 0 .1 % 1 .4% 0. 8%
Total 1 0 0.0 % 1 0 0.0 % 1 0 0.0 % 1 0 0.0 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 3 3
The second table shows the relative extent of growth in each
sector. Some of the largest growth sectors in proportionate
terms including Agriculture, Accommodation & food services
and Construction, albeit in most cases these sectors are
growing from a comparatively small base.
Sector (Sections)UK Contributions to overall change
2012-2016 (%)
Ireland Contributions to overall change
2012-2016 (%)
Germany Contributions to overall change
2012-2016 (%)
Netherlands Contributions to overall change
2012-2016 (%)
A Agriculture, forestry, fishing 1 0 1 % 1 59 % 1 1 3 % 1 5 4%
B Mining & quarrying 5 2 % 9 3 % 1 6 4% 1 3 7 %
C Manufacturing 2 0 % 1 2 3 % 41 % 4 0 %
D Electricity 7 1 % 3 3 % 2 8% 1 8%
E Water supply 9 5 % 8 4% 1 47 % 4 0 %
F Construction 8 2 % 1 8 6% 1 1 9 % 8 9 %
G Wholesale, retail 49 % 3 9 % 8 1 % 7 7 %
H Transport 7 2 % 4 8% 7 9 % 59 %
I Accommodation & food 1 0 1 % 1 0 0 % 9 5 % 1 0 9 %
J ICT 3 3 % 6 4% 5 0 % 3 8%
K Financial services 2 2 % 3 2 % 3 6% 2 0 %
L Real estate activities 3 3 % 47 % 5 2 % 7 3 %
M Professional services 3 7 % 9 8% 6 5 % 4 5 %
N Business support services 41 % 1 0 0 % 51 % 7 9 %
O Public administration 8% 9 % 3 9 % 2 9 %
P Education 1 2 % 1 0 % 51 % 41 %
Q Health 41 % 2 0 % 6 8% 47 %
R Arts, entertainment, recreation 2 2 % 1 9 % 5 2 % 4 8%
S Other services 4 8% 2 5 % 7 9 % 70 %
Total 3 3% 6 4% 5 1 % 41 %
D A T A E C O N O M Y R E P O R T 2 0 1 83 4
Current size of the Data Economy versus potential
A further basis of comparison is an assessment of the
extent to which the data is being utilised to its full potential
in each country.
This assessment involves the production of current (2016)
estimates of the size of the Data Economy in each country,
which is then compared to the estimated size it could have
reached if constraints (both on demand side and the supply
side) were not in place. For example, if issues such as skills
gaps and skills shortages were no longer a factor limiting the
size of the Data Economy in each country.
The table below sets out estimates for 2016 of both the
current actual size and estimated full potential size of the
Data Economy of each country. The final column is simply
the proportion of actual size compared to potential size.
On this basis, the UK is estimated to be currently achieving
58% of its potential, with Germany achieving 55%. The worst
performing country on this basis is the Netherlands, which
is estimated to be currently achieving only about 49% of
its potential.
Country2016
Data economy GVA
2016 Data Economy full
potential GVA
2016 Data Economy as
% of potential
UK (€millions)
8 9, 8 26 1 5 3 ,9 3 6 5 8%
Ireland (€millions)
9,9 62 1 9,1 0 8 5 2 %
Germany (€millions)
1 0 8 , 3 2 7 1 9 6 , 269 5 5 %
Netherland (€millions)
24 ,6 3 7 49, 8 3 8 49 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 3 5
Introduction
The focus of this chapter is the production of estimates
of the current and potential future size of the UK’s Data
Economy. The most recent year for which data is available
is 2016. The recent trajectory of change over the 2012-2016
period is also assessed. The principal metric is GVA, although
there are also estimates for employment and business
turnover/cost savings generated through the utilisation
by businesses and organisations of their data. Future
estimates are provided for the year 2025. All financial values
are provided in terms of millions of Pounds Sterling using
a 2016 price base. As well as providing current estimates
and future predictions on a sectoral basis, the chapter also
provides a sub-national spatial assessment using standard
UK regional geographies.
Current (2016) size of the UK Data Economy
The UK Data Economy is estimated to have generated
economic output (GVA) worth £73.3 billion in 2016.
The largest contributors to this total were provided by the
ICT, Financial services and Professional services sectors:
these together accounted for 64% of the total.
Sector (Sections)2016 GVA £millions
(2016 prices)% of total
A Agriculture, forestry, fishing
7 1 0 .1 %
B Mining & quarrying 8 8 1 1 . 2 %
C Manufacturing 4 ,70 2 6 .4%
D Electricity 1 , 2 3 6 1 . 7 %
E Water supply 3 1 5 0 .4%
F Construction 1 ,6 5 3 2 . 3 %
G Wholesale, retail 3 ,4 4 0 4 .7 %
H Transport 2 ,1 1 1 2 .9 %
I Accommodation & food
1 0 0 0 .1 %
J ICT 3 0 , 267 41 . 3 %
K Financial services 1 1 ,6 0 5 1 5 . 8%
L Real estate activities 3 ,670 5 .0 %
M Professional services 5 ,1 3 6 7.0 %
N Business support services
1 , 70 9 2 . 3 %
O Public administration 2 , 3 2 9 3 . 2 %
P Education 1 ,4 69 2 .0 %
Q Health 1 , 3 2 7 1 . 8%
R Arts, entertainment, recreation
70 2 1 .0 %
S Other services 6 0 2 0 . 8%
Total 7 3 , 3 2 7 1 0 0 %
£73.3 billion in 2016
UK Data Economy Results3
D A T A E C O N O M Y R E P O R T 2 0 1 83 6
The scale of the estimated contribution varies significantly
by sector. This pattern is influenced by several variables,
including:
• The relative importance of each of the sectors to the
economy as a whole: all other things being equal, the
contribution of large sectors such as Financial services
will exceed that of smaller sectors such as Water supply
• The extent to which sectors have been growing relative
to the rest of the economy in recent years: fast-
developing sectors such as Professional services are
more likely to have been leaders in expanding their use
of technologies such as data analytics
• Related to the last point, the absorption rate of new
technologies varies across sectors. Generally, more
knowledge-intensive sectors (such as advanced
manufacturing, pharmaceuticals, media industries and
ICT) have a greater propensity to invest in advanced
technologies such as data analytics. Most of these are
also the fastest growing parts of the economy referred
to in the previous point, but there are some exceptions.
The estimated overall GVA generated by the UK economy in
2016 is approximately £1,747 million. On this basis, the Data
Economy accounted for approximately 4.2% of the national
total for economic output in 2016.
The UK Data Economy can also be estimated by region. The largest contributors are London (32.3%) and the South East of England (17.2%), reflecting in part the greater importance of ICT, financial and professional services in those areas.
Note: Ex-regio is the relatively small amount of GVA that
cannot be allocated to specific UK regions.
Region2016 GVA £millions
(2016 prices)% of total
North East 1 , 8 1 8 2 . 5 %
North West 5 ,7 3 6 7. 8%
Yorkshire & Humber 3 ,6 67 5 .0 %
East Midlands 3 , 2 3 5 4 .4%
West Midlands 4 , 26 8 5 . 8%
East of England 5 ,6 0 0 7.6%
London 2 3 ,6 6 4 3 2 . 3 %
South East 1 2 ,6 3 4 1 7. 2 %
South West 4 , 5 42 6 . 2 %
Wales 1 , 7 2 2 2 . 3 %
Scotland 4 ,6 07 6 . 3 %
Northern Ireland 1 ,0 9 9 1 . 5 %
Ex-regio 7 3 4 1 .0 %
Total 7 3 , 3 2 7 1 0 0.0 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 3 7
The pattern of contribution by region is influenced by a few
factors, particularly:
• The relative scale of the underlying economy in
each region: for example, the economy of London is
significantly larger than that of North East England
or Northern Ireland. As a result, the size of the Data
Economy in London would also be expected to be
larger even if all other things were equal
• Of course, the distribution of activity is not evenly
spread across all regions. The knowledge intensity of
regions indeed varies significantly, with the economies
of London, South East England and the East of England
hosting an above-average share of knowledge-driven
sectors such as Financial and Professional services and
ICT. The North of England, the West Midlands, Wales
and Northern Ireland on the other hand possess above
average representation of other types of industry. In
addition, outside of London and the ‘greater South East’
(including the East of England region), sectors such as
Construction form a larger relative proportion of the
economy as a whole
• Another important factor that favours London and
the South East is that the distribution of higher-order
corporate functions is more concentrated in that area.
That is, high-knowledge activities even for companies
that are not operating in what is traditionally thought
of as the knowledge-intensive sectors (e.g. retailing).
This further implies that a greater proportion of
corporate command and control activities (including
data analytical functions) are likely to be located in
those regions.
The total number of direct jobs associated with the Data
Economy in 2016, based on second quarter data sourced
from the ONS (Office for National Statistics) Labour Force
Survey, was 1.147 million. This estimate is based on national
data disaggregated by occupational category, so cannot be
set out by region or industry.
By 2016, direct employment in the Data Economy was
estimated to account for 3.31% of the overall number of
workforce jobs estimated to be present in the UK economy.
In addition to direct jobs, additional employment stimulus is
created via indirect (procurement) and induced (multiplier)
effects. The overall number of additional jobs in the UK
economy supported through indirect and induced effects
in 2016 is estimated to be 505,000. (Note: this estimate
excludes the potential effects of double-counting of ICT
sector supply chain jobs generated by demand for Data
Economy services by the rest of the UK economy).
The combined direct, indirect and induced employment stimulus attributable to the Data Economy is estimated to be 1.652 million jobs in 2016.
1.652 million jobs
D A T A E C O N O M Y R E P O R T 2 0 1 83 8
Growth trajectory since 2012
The UK Data Economy has grown significantly over the past
five years, from £55.28 billion in 2012 to £73.33 billion in 2016.
This change amounts to an average annual rate of growth of
7.3% p.a. over this period.
Comparing the estimates for 2012 to those for 2016,
the most significant increases (in absolute terms) have
occurred in ICT, Financial and Professional services, and in
the Distribution sector (Wholesale & retail trade). However,
in proportionate terms the most significant increases
have occurred in the Agriculture, forestry & fishing and
Accommodation & food sectors, albeit from a comparatively
low base.
Sector (Sections)GVA 2012(£millions)
GVA 2016(£millions)
Change(£millions)
Change (%)
A Agriculture, forestry, fishing 3 6 7 1 3 6 1 0 1 %
B Mining & quarrying 5 7 9 8 8 1 3 0 2 5 2 %
C Manufacturing 3 ,9 3 3 4 ,70 2 769 2 0 %
D Electricity 7 2 2 1 , 2 3 6 5 1 4 7 1 %
E Water supply 1 62 3 1 5 1 5 4 9 5 %
F Construction 9 1 0 1 ,6 5 3 74 3 8 2 %
G Wholesale, retail 2 , 3 0 2 3 ,4 4 0 1 ,1 3 8 49 %
H Transport 1 , 2 3 0 2 ,1 1 1 8 8 1 7 2 %
I Accommodation & food 5 0 1 0 0 5 0 1 0 1 %
J ICT 2 2 ,7 3 5 3 0 , 267 7, 5 3 2 3 3 %
K Financial services 9, 5 3 8 1 1 ,6 0 5 2 ,0 67 2 2 %
L Real estate activities 2 ,7 5 0 3 ,670 9 2 1 3 3 %
M Professional services 3 ,742 5 ,1 3 6 1 , 3 94 3 7 %
N Business support services 1 , 2 1 6 1 , 70 9 49 3 41 %
O Public administration 2 ,1 5 2 2 , 3 2 9 1 7 7 8%
P Education 1 , 3 0 8 1 ,4 69 1 61 1 2 %
Q Health 9 3 9 1 , 3 2 7 3 8 8 41 %
R Arts, entertainment, recreation 5 7 5 70 2 1 2 7 2 2 %
S Other services 4 0 6 6 0 2 1 9 6 4 8%
Total 5 5 , 2 8 4 7 3 , 3 2 7 1 8 ,0 4 3 3 3%
Annual rate of growth of 7.3% p.a.
D A T A E C O N O M Y R E P O R T 2 0 1 8 3 9
These trends may have occurred in part because of a
relative slow-down in the rate of absorption of data analytical
technologies amongst some of the ‘traditional’ leading
sectors in the Data Economy field, such as Financial services.
However, there is some survey-based evidence from the
UK which has detected a noticeable reluctance on the part
of UK manufacturers (compared to major international
competitors) to invest in advanced technologies including
advanced automation, robotics, data analytics and other
Industry 4.0 developments.29 It is also notable that
Manufacturing is one of the slowest growth sectors in the
table preceding. The reasons for the technology investment
gap in UK manufacturing are complex and relate in part to
the above-average proportion of SMEs in UK manufacturing
compared to other international economies.
It is also worthwhile to consider the differential growth of the
Data Economy in terms of the UK regions. The table below
sets out the relevant data for 2012 and 2016.
Whereas the increase in the size of the Data Economy for
the UK as a whole over the past five years was 33% (i.e.
an average annual growth rate of 7.3% per annum), the
performance of individual UK regions has differed markedly,
with the East of England growing the strongest (37%) and
Northern Ireland the slowest (19%).
The out-performance of the East of England region may be
influenced in part by the growth of a world-class healthcare
and pharmaceuticals R&D hub centred on Cambridge. The
pharmaceutical sector alone accounts for about one fifth of
all UK commercial R&D activity, and Cambridge is emerging
as the centre of a leading world-class life science R&D hub.
As discussed above, data analytics offers considerable
productivity and value generating growth potential for
healthcare, which includes not only pharmaceutical research,
but also the development and manufacture of medical
devices and technologies which are directly linked to the
growth of the Data Economy.
As mentioned earlier in this chapter, the UK Data Economy
is estimated to have contributed 4.2% of UK economic
output (GVA) in 2016. The equivalent proportion in 2012 is
estimated to be 3.7%. The current size of the Data Economy
has also been assessed in terms of the impact on business
and organisation turnover and cost savings. It is estimated
that the effect on business/organisation turnover and costs
in 2016 was worth a total of £165 billion. The equivalent
figure for 2012 is estimated to be just under £123 billion,
which is an overall increase of about 35%. The sectoral and
spatial breakdowns of this benefit for business is very similar
to that for GVA set out in the tables preceding.
The increase in the number of direct jobs associated with the Data Economy over the 2012-2016 period (sourced from the ONS Labour Force Survey) was 166,000.
This was equivalent to an increase of 16.9% over this period.
Over the 2012-2016 period the proportion of workforce jobs
attributable to the UK Data Economy is estimated to have
increased from 3.06% to 3.31%.
29 | The evidence comes from unpublished surveys of European manufacturers undertaken during 2015, 2016 and 2017 by Development Economics on behalf of Barclays Bank.
Region 2012 2016Change
(£millions)Change
(%)
North East 1 , 3 5 6 1 , 8 1 8 4 6 3 3 4%
North West 4 ,4 0 1 5 ,7 3 6 1 , 3 3 5 3 0 %
Yorkshire & Humber
3 ,0 47 3 ,6 67 62 1 2 0 %
East Midlands 2 ,4 8 3 3 , 2 3 5 7 51 3 0 %
West Midlands 3 , 2 8 9 4 , 26 8 9 7 9 3 0 %
East of England
4 ,0 9 5 5 ,6 0 0 1 , 5 0 6 3 7 %
London 1 7, 5 2 7 2 3 ,6 6 4 6 ,1 3 8 3 5 %
South East 9 , 5 07 1 2 ,6 3 4 3 ,1 2 8 3 3 %
South West 3 ,4 0 1 4 , 5 42 1 ,1 4 0 3 4%
Wales 1 , 3 8 0 1 , 7 2 2 3 4 3 2 5 %
Scotland 3 ,4 3 9 4 ,6 07 1 ,1 69 3 4%
Northern Ireland
9 2 0 1 ,0 9 9 1 7 9 1 9 %
Ex-regio 4 8 2 7 3 4 2 5 2 5 2 %
Total 5 5 , 2 8 4 7 3 , 3 2 7 1 8 ,0 4 3 3 3%
D A T A E C O N O M Y R E P O R T 2 0 1 84 0
Current size of Data Economy versus current potential
Estimates have also been produced of the current (2016)
size of the UK Data Economy compared to the extent it
could have reached by this point if all constraints (both on
the demand side and the supply side) had been addressed.
For example, if the awareness and preparedness of business
to utilise data solutions and implement them to their full
current potential were at optimum levels (i.e. match those of
the best performing companies in their respective sectors)
and if issues such as skills gaps and skills shortages were no
longer a factor.
These estimates are presented in a table below,
disaggregated by business sector. The table shows current
levels of performance (in terms of GVA) and the proportion
of overall potential value generation that this is estimated
to represent.
Sector (Sections)2016
Actual GVA2016
Full Potential GVAFull Potential GVA minus Actual GVA
Actual GVA as % of Full Potential
A Agriculture, forestry, fishing 7 1 1 7 8 1 07 4 0 %
B Mining & quarrying 8 8 1 1 ,6 0 3 7 2 2 5 5 %
C Manufacturing 4 ,70 2 9 , 2 1 9 4 , 5 1 7 5 1 %
D Electricity 1 , 2 3 6 2 ,1 6 8 9 3 2 5 7 %
E Water supply 3 1 5 5 8 4 269 5 4%
F Construction 1 ,6 5 3 3 ,7 5 6 2 ,1 0 3 4 4%
G Wholesale, retail 3 ,4 4 0 6 ,49 1 3 ,0 51 5 3 %
H Transport 2 ,1 1 1 4 , 3 9 8 2 , 2 8 7 4 8%
I Accommodation & food 1 0 0 2 2 8 1 2 8 4 4%
J ICT 3 0 , 267 41 ,74 8 1 1 ,4 8 0 7 3 %
K Financial services 1 1 ,6 0 5 2 0 , 3 59 8 ,7 5 4 5 7 %
L Real estate activities 3 ,670 7,6 4 6 3 ,9 76 4 8%
M Professional services 5 ,1 3 6 9 ,69 1 4 , 5 5 5 5 3 %
N Business support services 1 , 70 9 3 , 8 8 4 2 ,1 7 5 4 4%
O Public administration 2 , 3 2 9 4 ,6 5 8 2 , 3 2 9 5 0 %
P Education 1 ,4 69 2 , 8 2 5 1 , 3 5 6 5 2 %
Q Health 1 , 3 2 7 3 ,0 8 7 1 , 76 0 4 3 %
R Arts, entertainment, recreation 70 2 1 ,6 3 3 9 3 1 4 3 %
S Other services 6 0 2 1 , 5 0 5 9 0 3 4 0 %
Total 7 3 , 3 2 7 1 2 5 ,6 6 0 5 2 , 3 3 5 5 8%
D A T A E C O N O M Y R E P O R T 2 0 1 8 4 1
58%
Overall, the UK economy is estimated to be currently utilising only around 58% of the full potential of data to boost revenues and productivity.
However, some sectors, such as Agriculture, forestry &
fishing appear to perform significantly worse than the overall
UK average.
The reasons why the UK economy operates well-within the
levels of possibility currently offered by the full extent of the
Data Economy relate to the following:
.Under-investment by businessesEspecially (but not exclusively) small and medium sized
companies. This under-investment is linked in many cases to
a failure to fully recognise the competitive advantages and
cost efficiencies that stand to be gained through analysis
of their operational and customer data. That is, a failure
on the part of some businesses to appropriately prioritise
investment in their data analytics capability (including
infrastructure, technical skills and business expertise to
grasp the opportunities fully). However, there is also
evidence that in some cases business have recognised the
potential advantages and gains that stand to be realised,
but they have struggled to make a successful case for
financial resources to lenders so that their business plans
can be implemented.
.Inadequate infrastructureIn some cases, business development potential may be
stymied by inadequate telecommunications infrastructure.
For example, it is reported that the implementation of
precision farming technologies (involving use of more
precise applications of fertiliser, pesticides, fungicides and
other inputs) is constrained in many areas due to poor levels
of 4G mobile telecommunications (as these approaches
rely on location mapping using GIS technologies). Poor
telecommunications infrastructure can also restrict the
market appeal of accommodation and food service providing
businesses.
.Skills gaps and shortagesA more significant issue for many businesses is the difficulty
in recruiting or retaining workers with the skills needed to
develop and maintain data analytical systems. At a national
level, the UK is already facing a severe digital skills shortage
which has been acknowledged in the findings of Parliamentary
committees and in reports produced by leading advisory
agencies such as the UK Commission on Employment and Skills.
The UK was already expected to experience an annual digital
skills shortage of over 150,000 digital workers per annum
up to 2020, and it is important to note that these forecasts
pre-date the decision of the UK to leave the European
Union. The decision to leave the EU is likely to affect the
future ability of the UK to attract talent in the form of
mathematicians, statisticians, computer scientists and other
expertise required to build and develop a Data Economy.
D A T A E C O N O M Y R E P O R T 2 0 1 84 2
Expected future size of the Data Economy
The future size of the UK Data Economy focusing on the
period 2017-2025 has also been assessed. This has involved
the production of annual breakdowns of the future value
of the UK Data Economy across all regions and business
sectors. However, for purposes of brevity we report here only
regional and national sector totals for the final year of the
forecasting period (2025).
Continuation of current trends scenario
Under the first scenario, current trends are expected to
continue with no improvement to or worsening of existing
constraints. The scenario is predicated on the expected
underlying growth trends for each sector on a region-by-
region basis, plus a continuation of the annual rates of
penetration of Data Economy services in each region and
sector as evident in the 2012-2016 data described earlier in
this chapter.
The next table sets out the levels of GVA generated by
the UK Data Economy attributable to each sector that are
expected to be generated annually under this scenario by
2025. It should be noted that a 2016 price base is used, so
that increases in the value of production are expressed in
real terms (i.e. the effect of future inflation is excluded).
The conclusion of this assessment is that the UK Data
Economy can be expected – on the basis of current
trajectories – to be worth £94.6 billion per annum by
2025 (2016 prices). Apart from the ICT sector, the largest
contributors to this growth, in absolute terms, are expected
to be Financial services and Professional services.
D A T A E C O N O M Y R E P O R T 2 0 1 8 4 3
Sector (Sections)GVA2016
GVA 2025
Increase in GVA (£millions)
Increase in GVA (%)
A Agriculture, forestry, fishing 7 1 74 3 5 %
B Mining & quarrying 8 8 1 9 0 5 2 3 3 %
C Manufacturing 4 ,70 2 5 ,4 6 8 767 1 6%
D Electricity 1 , 2 3 6 1 , 5 1 5 2 7 9 2 3 %
E Water supply 3 1 5 3 7 1 5 6 1 8%
F Construction 1 ,6 5 3 1 ,9 7 7 3 2 5 2 0 %
G Wholesale, retail 3 ,4 4 0 4 , 2 3 6 7 9 6 2 3 %
H Transport 2 ,1 1 1 2 ,4 62 3 51 1 7 %
I Accommodation & food 1 0 0 1 24 24 24%
J ICT 3 0 , 267 42 , 8 1 9 1 2 , 5 5 2 41 %
K Financial services 1 1 ,6 0 5 1 3 , 5 3 9 1 ,9 3 4 1 7 %
L Real estate activities 3 ,670 4 ,62 7 9 5 6 26%
M Professional services 5 ,1 3 6 7,1 61 2 ,0 2 5 3 9 %
N Business support services 1 , 70 9 2 , 2 9 8 5 8 9 3 4%
O Public administration 2 , 3 2 9 2 ,42 8 9 9 4%
P Education 1 ,4 69 1 , 5 7 7 1 0 8 7 %
Q Health 1 , 3 2 7 1 , 5 2 7 2 0 0 1 5 %
R Arts, entertainment, recreation 70 2 8 3 0 1 2 8 1 8%
S Other services 6 0 2 6 6 3 61 1 0 %
Total 7 3 , 3 2 7 94 ,6 0 2 2 1 , 2 74 2 9 %
Despite this growth, the UK Data Economy is by 2025
still expected to be operating well within its potential full
capacity and capability. The main reasons why the economy
is expected to continue to operate sub-optimally to a
significant extent include the following:
.Inadequate business investmentAs of 2017 many businesses had not recognised the
competitive advantages of data analytics, and this situation
is not expected to be completely remedied in the future
either. Even in knowledge-driven sectors such as financial
services, around 30% of companies do not appear to
have accorded investment in data analytics the level of
prioritisation that would appear to be appropriate. In sectors
such as Manufacturing, the proportion of UK companies
that are prioritising investment in Industry 4.0 technologies
and capabilities lags significantly behind international
benchmarks. On this basis, there is significant evidence that
the underlying trajectory of investment will continue to be
sub-optimal.
.Skills deficits expected to continue to exert an influenceThe UK has a widely acknowledged digital skills deficit
that is not expected to lessen over the 2017-2025 period.
Indeed, the potential effect of the decision to leave the EU
is expected by some to worsen the shortage as, despite
Government assurances that a system to encourage skilled
migration will be put in place, there is still a danger that the
UK could be increasingly perceived to be an unwelcoming
destination for skilled immigrants.
D A T A E C O N O M Y R E P O R T 2 0 1 84 4
The regional breakdown of the expected future size of the
UK Data Economy by 2025 has also been estimated. The
forecasts are set out in the table below, with the 2016 levels
also set out for ease of reference.
The overall average increase expected is 29%, but some
areas (notably London and the South East) are expected to
grow their regional data economies at a faster rate than this.
As previously noted, the principal reasons for the expected
above-average performance of London and the other
‘greater South East’ regions are related to:
• Above average representation of knowledge economy
sectors such as Financial services, Professional services,
Life Science R&D, Media and Creative industries
• Above average representation of high-order corporate
command and control functions across a range of
business sectors (and also some public services)
• Above average rate of business formation in the ICT
sector and specifically in the delivery of digital
economy services
• Greater density of advanced communications
infrastructure
• Above average densities of highly skilled workers.
On the other hand, the data economies of the North East,
Wales and Northern Ireland are expected to grow at a
significantly slower rate than that expected for the UK
as a whole. These are the regions which have the lowest
proportion of knowledge-driven sectoral and business
functional activity, and also the lowest proportion of skilled
Data Economy workers and the lowest birth rate for digital
economy businesses.
Based on current rates of job growth, it is expected that
the total number of direct jobs attributable to the UK Data
Economy will increase from 1.147 million in 2016, to about 1.52
million by 2025 (i.e. an overall increase of about 371,000).
In addition, there is expected to be a further 668,000 jobs
supported throughout the rest of the UK economy via
indirect (i.e. procurement) and induced (multiplier) effects.
The overall level of employment attributable to the UK
Data Economy by 2025 under the central case scenario is
therefore expected to be 2.127 million.
RegionGVA2016
GVA 2025
Increase in GVA
(£millions)
Increase in GVA (%)
North East 1 , 8 1 8 2 , 2 3 5 41 7 2 3 %
North West 5 ,7 3 6 7, 2 7 9 1 , 5 4 3 2 7 %
Yorkshire & Humber
3 ,6 67 4 , 5 61 8 94 24%
East Midlands 3 , 2 3 5 4 ,07 8 8 4 3 26%
West Midlands 4 , 26 8 5 , 3 5 1 1 ,0 8 3 2 5 %
East of England
5 ,6 0 0 7, 2 5 7 1 ,6 5 6 3 0 %
London 2 3 ,6 6 4 3 1 , 3 9 5 7,7 3 1 3 3 %
South East 1 2 ,6 3 4 1 6 , 8 0 4 4 ,1 69 3 3 %
South West 4 , 5 42 5 ,7 2 8 1 ,1 8 6 26%
Wales 1 , 7 2 2 2 ,1 1 3 3 9 1 2 3 %
Scotland 4 ,6 07 5 ,694 1 ,0 8 7 24%
Northern Ireland
1 ,0 9 9 1 , 3 5 5 2 5 7 2 3 %
Ex-regio 7 3 4 7 51 1 7 2 %
Total 7 3 , 3 2 7 94 ,6 0 2 2 1 , 2 74 2 9 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 4 5
Alternative scenario 1: Constraints worsen
The first alternative scenario models a hypothetical situation
in which existing constraints on the growth of the UK Data
Economy (such as skills gaps and shortages, and/or a
failure of potential business users to recognise the potential
productivity and/or revenue growth opportunities offered by
more extensive and efficient use of data) become a greater
hindrance to the growth of this segment of the economy
than is expected to be the case under the central scenario.
The differential assumptions that are made under this
scenario include the following:
.Absorption rates of data analytics and IoTThe overall proportion of businesses developing capabilities
or using data analytics services is assumed to be, on average,
7% per annum lower under this scenario compared to the
current trends scenario. By 2025 the overall proportion of
businesses using some form of Data Economy approach
across the economy as a whole under this scenario is
expected to reach only 69%, compared to 74% under the
current trends scenario.
.Business capital investmentLevels of annual aggregate business capital investment
in data analytics infrastructure is assumed to be between
8% and 17% lower (varying by sector) compared to levels
expected under the current trends scenario.
.Business human resource investmentAverage annual expenditure in training and development of
staff is assumed to be between 6% and 11% lower (varying
by sector) compared to levels expected under the current
trends scenario.
.Skills shortagesThe average national deficit of skilled workers is assumed
to be 15% worse than is the case under the current trends
scenario.
The next table sets out the levels of GVA attributable to
each sector that is expected to be generated annually
under this lower growth scenario by 2025. A 2016 price
base is used, so that increases in the value of production are
expressed in real terms (i.e. the effect of future inflation is
excluded).
D A T A E C O N O M Y R E P O R T 2 0 1 84 6
Sector (Sections)GVA2016
GVA 2025Worsened Constraints
Change 2016-2025 (£millions)
Change 2016-2025 (%)
Reduction in GVA (£millions) cf Main
Case
Reduction in GVA (%) cf Main Case
A Agriculture, forestry, fishing
7 1 7 3 2 2 . 7 % 1 1 . 8%
B Mining & quarrying 8 8 1 8 9 2 1 1 1 . 2 % 1 3 1 .4%
C Manufacturing 4 ,70 2 4 ,94 3 242 5 .1 % 5 2 5 9 .6%
D Electricity 1 , 2 3 6 1 , 3 8 3 1 47 1 1 .9 % 1 3 2 8 .7 %
E Water supply 3 1 5 3 4 5 3 0 9 .4% 26 7.0 %
F Construction 1 ,6 5 3 1 , 8 4 8 1 9 5 1 1 . 8% 1 3 0 6 .6%
G Wholesale, retail 3 ,4 4 0 3 ,941 5 0 0 1 4 . 5 % 2 9 6 7.0 %
H Transport 2 ,1 1 1 2 , 3 1 6 2 0 5 9 .7 % 1 4 6 5 .9 %
I Accommodation & food
1 0 0 1 1 3 1 3 1 2 .6% 1 1 9 .1 %
J ICT 3 0 , 267 3 7,9 7 9 7,7 1 2 2 5 . 5 % 4 , 8 4 0 1 1 . 3 %
K Financial services 1 1 ,6 0 5 1 2 , 8 3 1 1 , 2 2 7 1 0 .6% 707 5 . 2 %
L Real estate activities 3 ,670 4 ,1 7 2 5 0 1 1 3 .7 % 4 5 5 9 . 8%
M Professional services 5 ,1 3 6 6 , 2 7 9 1 ,1 42 2 2 . 2 % 8 8 2 1 2 . 3 %
N Business support services
1 , 70 9 2 ,0 1 4 3 0 5 1 7. 8% 2 8 4 1 2 .4%
O Public administration 2 , 3 2 9 2 , 3 8 2 5 3 2 . 3 % 4 6 1 .9 %
P Education 1 ,4 69 1 , 5 2 7 5 8 4 .0 % 49 3 .1 %
Q Health 1 , 3 2 7 1 ,4 3 4 1 07 8 .0 % 9 3 6 .1 %
R Arts, entertainment, recreation
70 2 7 7 7 74 1 0 .6% 5 4 6 .4%
S Other services 6 0 2 6 3 5 3 3 5 . 5 % 2 8 4 . 2 %
Total 7 3 , 3 2 7 8 5 , 8 8 3 1 2 , 5 5 6 1 7.1 % 8 ,7 1 9 9. 2 %
Under this scenario the UK Data Economy is expected to
grow from £73.3 billion in 2016 to about £85.9 billion by 2025.
The overall scale of reduction across the UK economy as a
whole under this scenario would be expected to be just over
£8.7 billion, which is a reduction of about 9% compared to
the central case defined by the current expected trajectory
of change. However, the impact across the different sectors
of the economy is much more varied, with sectors such
as Mining and Public Administration comparatively little
affected, but with sectors such as Business support
services and Professional services much more
significantly constrained.
The regional breakdown of the expected future size of the
UK Data Economy by 2025 under this more constrained
hypothetical scenario has also been estimated, with results
set out in the next table.
Compared to the central case future scenario, the average
overall reduction across the UK is 9.2%, but Scotland (7.9%)
and North East England (7.8%) face slightly lower reductions.
The most significant erosion of potential growth under this
scenario is expected to occur in the South East and London,
both around 10%.
D A T A E C O N O M Y R E P O R T 2 0 1 8 4 7
RegionGVA2016
GVA 2025
Worsened Constraints
Change 2016-2025 (£millions)
Change 2016-2025
(%)
Reduction in GVA (£millions)
cf Main Case
Reduction in GVA (%) cf Main Case
North East 1 , 8 1 8 2 ,0 62 24 3 1 3 .4% 1 7 3 7. 8%
North West 5 ,7 3 6 6 ,6 3 1 8 9 5 1 5 .6% 6 4 8 8 .9 %
Yorkshire & Humber 3 ,6 67 4 ,1 8 2 5 1 5 1 4 .0 % 3 7 9 8 . 3 %
East Midlands 3 , 2 3 5 3 ,7 1 1 476 1 4 .7 % 3 67 9 .0 %
West Midlands 4 , 26 8 4 , 8 9 0 62 2 1 4 .6% 4 62 8 .6%
East of England 5 ,6 0 0 6 , 5 69 9 6 8 1 7. 3 % 6 8 8 9 . 5 %
London 2 3 ,6 6 4 2 8 , 3 1 3 4 ,6 49 1 9 .6% 3 ,0 8 2 9 . 8%
South East 1 2 ,6 3 4 1 5 ,1 2 5 2 ,49 1 1 9 .7 % 1 ,67 8 1 0 .0 %
South West 4 , 5 42 5 , 2 3 0 6 8 8 1 5 . 2 % 49 8 8 .7 %
Wales 1 , 7 2 2 1 ,94 3 2 2 1 1 2 . 8% 1 70 8 .0 %
Scotland 4 ,6 07 5 , 242 6 3 5 1 3 . 8% 4 5 2 7.9 %
Northern Ireland 1 ,0 9 9 1 , 24 6 1 47 1 3 .4% 1 0 9 8 .1 %
Ex-regio 7 3 4 74 0 6 0 . 8% 1 1 1 . 5 %
Total 7 3 , 3 2 7 8 5 , 8 8 3 1 2 , 5 5 6 1 7.1 % 8 ,7 1 9 9. 2 %
Under this more pessimistic scenario, we expect the total
number of direct jobs attributable to the UK Data Economy
to increase from 1.147 million in 2016, to about 1.48 million
by 2025. This would represent a reduction in the overall
increase in employment (compared to the predicted level
under the central case) of just over 42,000 jobs; i.e. the
overall expected increase in jobs in this more scenario is
about 2.8% less than the gain expected under the central
case scenario.
In addition, under this scenario there is expected to be
a reduced figure of a further 650,000 jobs supported
throughout the rest of the UK economy via indirect (i.e.
procurement) and induced (multiplier) effects.
The overall level of employment attributable to the UK Data
Economy by 2025 under this scenario is therefore expected
to be 2.127 million.
D A T A E C O N O M Y R E P O R T 2 0 1 84 8
Alternative scenario 2: Constraints relaxed
The second alternative scenario models a more optimistic
situation, in which some constraints on the growth of the
UK Data Economy are eased through policy initiatives
(e.g. designed to address skills shortages and skills gaps)
or through accelerated investment by businesses in Data
Economy technology, or both.
The differential assumptions that are made under this
scenario include the following:
.Absorption rates of data analytics and IoTThe overall proportion of businesses developing capabilities
or using data analytics services is assumed to be, on average,
4% per annum higher under this scenario compared to the
current trends scenario. By 2025 the overall proportion of
businesses using some form of Data Economy approach is
expected to reach 81% under this scenario, compared to 74%
under the current trends scenario.
.Business capital investmentLevels of annual aggregate business capital investment
in data analytics infrastructure is assumed to be between
5% and 11% higher (varying by sector) compared to levels
expected under the current trends scenario.
.Business human resource investmentAverage annual expenditure in training and development of
staff is assumed to be between 4% and 9% higher (varying
by sector) compared to levels expected under the current
trends scenario.
.Skills shortagesThe average national deficit of skilled workers is assumed
to be 10% lower than is the case under the current
trends scenario.
D A T A E C O N O M Y R E P O R T 2 0 1 8 4 9
The above table sets out the levels of GVA attributable to
each sector that are expected to be generated annually
under this increased growth scenario by 2025. Once again, a
2016 price base is used.
Under this more optimistic scenario, the UK Data Economy
is expected to grow from £73.3 billion in 2016 to about
£101.6 billion by 2025. The overall scale of increase in the
Data Economy across the UK economy (compared to the
central case) under this more scenario is expected to be just
over £7.0 billion, representing an increase of around 7.4%
compared to the central case. Sectors such as Professional
Services and Manufacturing are expected under this more
scenario to experience enhanced rates of growth compared
to the central case scenario.
The regional breakdown of the expected future size of
the UK Data Economy by 2025 under the higher growth
scenario has also been estimated, with results set out in
the next table.
Sector (Sections)GVA2016
GVA 2025Eased Constraints
Change 2016-2025 (£millions)
Change 2016-2025 (%)
Increase in GVA (£millions)
cf Main Case
Increase in GVA (%) cf Main Case
A Agriculture & forestry 7 1 7 5 4 5 .7 % 1 1 .1 %
B Mining & quarrying 8 8 1 9 1 5 3 4 3 .9 % 1 1 1 . 2 %
C Manufacturing 4 ,70 2 5 ,9 2 5 1 , 2 2 3 26 .0 % 4 5 6 8 . 3 %
D Electricity 1 , 2 3 6 1 , 59 6 3 6 0 2 9.1 % 8 1 5 .4%
E Water supply 3 1 5 3 8 6 7 1 2 2 .4% 1 5 4 .0 %
F Construction 1 ,6 5 3 2 ,07 8 42 5 2 5 .7 % 1 0 1 5 .1 %
G Wholesale, retail 3 ,4 4 0 4 , 5 24 1 ,0 8 3 3 1 . 5 % 2 8 7 6 . 8%
H Transport 2 ,1 1 1 2 , 5 61 4 5 0 2 1 . 3 % 9 9 4 .0 %
I Accommodation & food
1 0 0 1 3 0 3 0 3 0 .1 % 6 5 .0 %
J ICT 3 0 , 267 4 6 ,9 2 0 1 6 ,6 5 3 5 5 .0 % 4 ,1 0 1 9 .6%
K Financial services 1 1 ,6 0 5 1 4 , 2 1 6 2 ,61 1 2 2 . 5 % 67 7 5 .0 %
L Real estate activities 3 ,670 4 , 8 76 1 , 2 0 6 3 2 . 8% 249 5 .4%
M Professional services 5 ,1 3 6 7,767 2 ,6 3 1 5 1 . 2 % 6 0 6 8 . 5 %
N Business support 1 , 70 9 2 ,4 5 7 74 8 4 3 . 8% 1 59 6 .9 %
O Public administration 2 , 3 2 9 2 ,4 5 2 1 2 3 5 . 3 % 24 1 .0 %
P Education 1 ,4 69 1 ,6 0 3 1 3 4 9 .1 % 26 1 .6%
Q Health 1 , 3 2 7 1 , 5 8 2 2 5 4 1 9 .1 % 5 4 3 . 5 %
R Arts, entertainment, etc.
70 2 8 67 1 6 5 2 3 .4% 3 7 4 .4%
S Other services 6 0 2 67 7 7 5 1 2 . 5 % 1 4 2 .1 %
Total 7 3 , 3 2 7 1 0 1 ,6 0 6 2 8 , 2 7 9 3 8 .6% 7,0 0 5 7.4%
D A T A E C O N O M Y R E P O R T 2 0 1 85 0
Compared to the central case future scenario, the average
overall increase in Data Economy GVA across the UK under
the more optimistic scenario is 7.4%, but some regions
– most notably London (8.1%) and South East England
(8.1%) – would be expected to experience more pronounced
increases in economic activity.
Under this more scenario we also expect the total number
of direct jobs attributable to the UK Data Economy to
increase from 1.147 million in 2016, to about 1.55 million by
2025. This would be a gain of just over 31,000 jobs (i.e. an
overall increase of 2.1%) over the anticipated central case
scenario outcome.
In addition, under this scenario there is expected to be a
further 682,000 jobs supported throughout the rest of the
UK economy via indirect (i.e. procurement) and induced
(multiplier) effects.
The overall level of employment attributable to the UK Data
Economy by 2025 under this more optimistic scenario is
therefore expected to be 2.233 million.
RegionGVA2016
GVA 2025Eased Constraints
Change 2016-2025 (£millions)
Change 2016-2025 (%)
Increase in GVA (£millions)
cf Main Case
Increase in GVA (%) cf Main Case
North East 1 , 8 1 8 2 , 3 7 1 5 5 3 3 0 .4% 1 3 6 6 .1 %
North West 5 ,7 3 6 7,7 94 2 ,0 5 8 3 5 .9 % 51 5 7.1 %
Yorkshire & Humber 3 ,6 67 4 , 8 6 3 1 ,1 9 5 3 2 .6% 3 0 2 6 .6%
East Midlands 3 , 2 3 5 4 , 3 6 6 1 ,1 3 1 3 5 .0 % 2 8 8 7.1 %
West Midlands 4 , 26 8 5 ,7 1 6 1 ,4 4 8 3 3 .9 % 3 6 5 6 . 8%
East of England 5 ,6 0 0 7, 8 0 2 2 , 2 0 2 3 9. 3 % 5 4 5 7. 5 %
London 2 3 ,6 6 4 3 3 ,9 1 3 1 0 , 249 4 3 . 3 % 2 , 5 1 8 8 .0 %
South East 1 2 ,6 3 4 1 8 ,1 6 4 5 , 5 3 0 4 3 . 8% 1 , 3 6 0 8 .1 %
South West 4 , 5 42 6 ,1 1 9 1 , 5 7 7 3 4 .7 % 3 9 1 6 . 8%
Wales 1 , 7 2 2 2 , 24 6 5 2 3 3 0 .4% 1 3 3 6 . 3 %
Scotland 4 ,6 07 6 ,0 5 0 1 ,4 4 3 3 1 . 3 % 3 5 6 6 . 3 %
Northern Ireland 1 ,0 9 9 1 ,4 42 3 4 3 3 1 . 2 % 8 7 6 .4%
Ex-regio 7 3 4 76 0 26 3 .6% 9 1 . 2 %
Total 7 3 , 3 2 7 1 0 1 ,6 0 6 2 8 , 2 7 9 3 8 .6% 7,0 0 5 7.4%
D A T A E C O N O M Y R E P O R T 2 0 1 8 5 1
Conclusions
The current scale of the contribution of the UK Data
Economy is estimated to amount to some £73.3 billion per
annum. The contribution has grown from around £55 billion
in 2012 with a recent growth rate of over 7% per annum, well
ahead of the annual growth rate for the economy as a whole.
Already by 2016 the Data Economy accounted for over 4% of national economic output and over 3% of national employment.
The main sources of this contribution in sector terms are
ICT services, Financial services and Professional services.
Geographically, well over 50% of the UK Data Economy is
in just three regions: London, the South East and the East
of England. This is primarily because of the above-average
representation of knowledge-economy activities and high-
order corporate command and control functions located in
those areas.
Despite the impressive growth of the Data Economy over
the past five years, there is abundant evidence that the UK
Data Economy is operating well-within its full potential. It is
estimated that 42% of potential value remains unrealised.
The main causes of the squandered potential for additional
business turnover, economic output and growth of
employment are considered to be:
• Underinvestment by businesses in Data Economy
capabilities, influenced in part by a failure of some
businesses to recognise the relevance and potential
of data analytics for their business, but also in some
cases an inability to access business finance to allow
the implementation of cogent business plans
• Infrastructure issues affecting some areas and sectors
• Skills deficits, in the form of unfilled vacancies for
digitally skilled workers and also in some cases skills
gaps on the part of workers with responsibilities for
undertaking data analytics tasks for their employers.
The chapter has also looked at a range of future scenarios
for the growth of the UK Data Economy. Under the currently
expected trajectory of growth, the value of the Data
Economy is expected to reach nearly £95 billion (in real
terms) by 2025, which is growth of 30% compared to current
levels. However, this contribution could be significantly lower
if skills deficits and other potential constraints (including the
general business appetite for investment in Data Economy
capabilities) turn out to be worse than currently expected.
On the other hand, the performance by 2025 could be
significantly raised if business investment in technology
and skills runs ahead of currently anticipated trends, and if
infrastructure constraints are addressed (for example, if the
expected roll out of a national 5G network happens more
quickly than expected under the current trends scenario).
4%of national economic
output
D A T A E C O N O M Y R E P O R T 2 0 1 85 2
Introduction
The focus of this chapter is the production of estimates
of the current and potential future size of the Republic of
Ireland’s Data Economy. As was the case with the previous
chapter which focused on the UK, the most recent year for
which data for Ireland is available is 2016. Growth trends over
the 2012-2016 period are also assessed in this chapter, and
future estimates are provided for the year 2025.
All financial values reported in this chapter are provided in
terms of millions of Euros using a 2016 price base. It should
be noted that because of the limitations imposed by the
availability of data, the assessment in this chapter focuses on
the national economy only; i.e. there is no disaggregation by
regions or other types of sub-national geography.
Current (2016) size of the Republic of Ireland Data Economy
Based on a similar modelling approach to that used for the
UK, it is estimated that the Republic of Ireland (hereafter,
Ireland) Data Economy generated economic output
(Gross Value Added) worth €9.96 billion in 2016. The
largest contributors to this total were provided by the ICT,
Manufacturing and Financial services sectors, which together
accounted for 80% of the total Irish Data Economy.
Sector (Sections)2016 GVA €millions
% of total
A Agriculture, forestry, fishing
1 4 0 .1 %
B Mining & quarrying 41 0 .4%
C Manufacturing 1 ,6 62 1 6 .7 %
D Electricity 8 2 0 . 8%
E Water supply 24 0 . 2 %
F Construction 9 1 0 .9 %
G Wholesale, retail 2 3 9 2 .4%
H Transport 1 2 0 1 . 2 %
I Accommodation & food 1 4 0 .1 %
J ICT 4 ,9 5 6 49.7 %
K Financial services 1 , 3 49 1 3 . 5 %
L Real estate activities 2 0 4 2 .1 %
M Professional services 5 6 4 5 .7 %
N Business support services
1 7 8 1 . 8%
O Public administration 1 4 8 1 . 5 %
P Education 1 0 8 1 .1 %
Q Health 1 1 8 1 . 2 %
R Arts, entertainment, recreation
3 3 0 . 3 %
S Other services 1 8 0 . 2 %
Total 9,9 62 1 0 0.0 %
Ireland Data Economy Results4
D A T A E C O N O M Y R E P O R T 2 0 1 8 5 3
The pattern of the contribution by sector is influenced by
various factors:
• The contribution of dominant sectors (such as
Manufacturing and Financial services) is naturally larger
than small sectors such as Utilities and Minerals
• Sectors with a higher level of knowledge-intensity (such
as ICT and Financial services) have a greater propensity
to invest in data analytics infrastructure and skills.
At first glance the scale of the contribution of the
Manufacturing sector in Ireland is perhaps surprising,
but the contribution of the sector to the Irish economy is
very significant: the sector accounts for nearly a quarter
of national economic output. In addition, one of the most
important sub-sectors is pharmaceuticals, which includes
important players such as Pfizer and Shire. It was noted
in chapter 1 of this report that life sciences (which falls
within the Manufacturing category) is a key generator of
commercial R&D and is increasingly reliant on data analytics.
On the other hand, the scale of the contribution of the
Financial services sector is not surprising: over the past
several decades Ireland has received significant levels
of inward investment from international financial
services companies.
The overall amount of GVA generated by the Irish economy
in 2016 is estimated to be just over €247 million. On this
basis, the Irish Data Economy accounted for approximately
4.0% of Irish economic output in 2016.
The total number of direct jobs associated with the Data
Economy in 2016 (based on data sourced from Eurostat)
was just over 61,000. By 2016, direct employment in the Data
Economy was estimated to account for 3.05% of the overall
number of jobs in the Irish economy.
In addition to the direct jobs, the Data Economy also
supports jobs via supply chain and multiplier effects. These
indirect and induced effects are estimated to amount to an
additional 23,200 jobs across Ireland in 2016.
The amount of total employment attributable to the Data Economy in 2016 is estimated to amount to just over 84,000 jobs.
D A T A E C O N O M Y R E P O R T 2 0 1 85 4
Growth trajectory since 2012
Overall, the financial value (in terms of GVA) of the Irish Data
Economy is estimated to have grown from €6.07 billion in
2012 to €9.96 billion in 2016.
This implies an average annual increase of around 13.2% over this period, which is nearly double the rate estimated for the UK in the previous chapter.Comparing the Irish estimates for 2012 to those for 2016, the
most significant increases (in absolute terms) have occurred
in ICT, Manufacturing, Financial services and Professional
services. However, in proportionate terms above-average
increases have occurred in a range of other sectors, including
Construction, Business support services and Agriculture,
forestry & fishing.
One factor that may have influenced the growth trend since
2012 is the high level of importance to the Irish economy
of international investment, especially from the United
States. Over the past few decades Ireland has become
a major European business headquarters location for
large numbers of multi-national corporations (including
technology manufacturers) across a range of knowledge
economy sectors. For example, it is estimated that the level
of US foreign investment in Ireland exceeds that which has
flowed to the BRIC countries (Brazil, Russia, India and China)
combined. It is also estimated that over 700 US companies
now have significant operations in Ireland. These companies
include major knowledge-economy players such as Dell,
Intel, Hewlett Packard, IBM, Pfizer, Google and Facebook.
Many of these companies will have located high-level
corporate command and control functions within their Irish
operations. This is likely to have played a significant role
in accelerating the level of investment of Data Economy
functions and activities within the Irish economy.
The current size of the Irish Data Economy has also
been assessed in terms of the impact on business and
organisation turnover and cost savings. The effect on
Irish business/organisation turnover and costs in 2016 is
estimated to have been worth a total of €22.4 billion.
The equivalent figure for 2012 is estimated to be around
€13.3 billion (in terms of 2016 prices). This implies an overall
increase in value of about 68%. The sectoral breakdown of
this increase is very similar to that for GVA set out in the
table below.
The increase in the number of jobs associated with the Data
Economy over the 2012-2016 period (using data sourced
from Eurostat and the Central Statistics Office (CSO) of
Ireland) is estimated to amount to approximately 18,500
jobs. This is an increase of around 43% compared to the
2012 position.
Over the 2012-2016 period the proportion of workforce jobs
attributable to the Irish Data Economy is estimated to have
increased from about 2.3% to just over 3.0%.
Sector (Sections)GVA 2012€millions
GVA 2016€millions
Change (€millions)
Change (%)
A Agriculture, forestry, fishing
5 1 4 8 1 59 %
B Mining & quarrying 2 1 41 2 0 9 3 %
C Manufacturing 74 5 1 ,6 62 9 1 7 1 2 3 %
D Electricity 61 8 2 2 0 3 3 %
E Water supply 1 3 24 1 1 8 4%
F Construction 3 2 9 1 59 1 8 6%
G Wholesale, retail 1 7 1 2 3 9 67 3 9 %
H Transport 8 1 1 2 0 3 9 4 8%
I Accommodation & food 7 1 4 7 1 0 0 %
J ICT 3 ,0 2 1 4 ,9 5 6 1 ,9 3 5 6 4%
K Financial services 1 ,0 2 0 1 , 3 49 3 2 9 3 2 %
L Real estate activities 1 3 9 2 0 4 6 5 47 %
M Professional services 2 8 5 5 6 4 2 7 9 9 8%
N Business support services
8 9 1 7 8 8 9 1 0 0 %
O Public administration 1 3 5 1 4 8 1 2 9 %
P Education 9 8 1 0 8 1 0 1 0 %
Q Health 9 8 1 1 8 2 0 2 0 %
R Arts, entertainment, recreation
2 8 3 3 5 1 9 %
S Other services 1 4 1 8 4 2 5 %
Total 6 ,0 6 5 9,9 62 3 , 8 9 7 6 4%
D A T A E C O N O M Y R E P O R T 2 0 1 8 5 5
Current size of Data Economy versus current potential
Estimates of the current (2016) size of the Irish Data
Economy have also been compared to the extent it could
have reached by this point if all constraints (both on the
demand side and the supply side) were not operating.
These estimates are presented in the next table,
disaggregated by business sector. The table shows current
levels of performance (in terms of GVA) and the proportion
of overall potential value generation that this is estimated
to represent.
The estimates suggest that whereas, in 2016, the Irish Data
Economy was worth around €9.96 billion, the full potential
value that could have been generated that year was just over
€19.14 billion.
Therefore, in 2016 the Irish Data Economy was estimated to be currently worth about 52% of its full potential in terms of contributions to revenue generation and productivity.
However, some sectors, such as Health and Business
support services perform significantly worse than the overall
Ireland average whereas sectors such as Financial services
appear to be achieving a greater-than-average proportion
of the existing potential (albeit with plenty of scope for
improvement remaining).
The principal factors that hinder full exploitation of the
Data Economy in Ireland are similar to the ones that were
discussed regarding the UK economy in the previous
chapter. These are:
• Under-investment by businesses, in particular SMEs:
this may because some businesses do not fully grasp
the growing importance of data analytics to the future
competitiveness of their business but, in some cases,
companies may understand its importance yet lack
the expertise or financial resources to realise the
opportunity
• Skills gaps and shortages: a significant issue for many
businesses is the recruitment and/or retention of
workers with the skills needed to develop and maintain
data analytical systems.
52%
D A T A E C O N O M Y R E P O R T 2 0 1 85 6
An additional factor that is very likely to be relevant are the
policies of the Irish Government towards Open Data.
It is notable that the global Open Data Barometer accords Ireland a much lower ranking (27th) compared to the UK (1st). The ranking is also lower than the other countries considered in this report: the Netherlands (7th) and Germany (11th).
The relative lack of openness of data in Ireland may be
hindering the development of public sector efficiency
(as well as accountability) and may also be limiting the
development of commercial opportunities and efficiencies
derived from the analysis of this data.
Sector (Sections)2016
Actual GVA€millions
2016Full Potential
GVA€millions
Full Poten-tial GVA
minus Actu-al GVA
€millions
Actual GVA as % of Full Potential
A Agriculture, forestry, fishing
1 4 3 6 2 2 3 8%
B Mining & quarrying 41 7 8 3 7 5 3 %
C Manufacturing 1 ,6 62 3 ,42 2 1 ,76 0 49 %
D Electricity 8 2 1 5 1 69 5 4%
E Water supply 24 47 2 3 51 %
F Construction 9 1 2 1 7 1 26 42 %
G Wholesale, retail 2 3 9 47 3 2 3 4 51 %
H Transport 1 2 0 262 1 42 4 6%
I Accommodation & food 1 4 3 3 1 9 4 3 %
J ICT 4 ,9 5 6 8 ,9 7 1 4 ,0 1 5 5 5 %
K Financial services 1 , 3 49 2 ,4 8 5 1 ,1 3 6 5 4%
L Real estate activities 2 0 4 4 47 24 3 4 6%
M Professional services 5 6 4 1 ,1 1 8 5 5 4 5 0 %
N Business support services
1 7 8 426 24 8 42 %
O Public administration 1 4 8 3 1 0 1 62 4 8%
P Education 1 0 8 2 1 8 1 1 0 5 0 %
Q Health 1 1 8 2 8 8 1 70 41 %
R Arts, entertainment, recreation
3 3 8 1 4 8 41 %
S Other services 1 8 4 6 2 8 3 9 %
Total 9,9 62 1 9,1 0 8 9,1 4 6 5 2 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 5 7
Expected future size of the Data Economy
Estimates for the potential future size of the Data Economy
of Ireland have been developed for the period 2017-2025.
This has involved the production of annual breakdowns of
the future value of the Irish Data Economy for all business
sectors. However, for purposes of brevity the estimates
produced for the final year of the forecasting period (2025)
only are reported here.
In short, three alternative scenarios have been assessed:
.01A continuation of current trends (i.e. the current trajectories
of change are maintained)
.02A more pessimistic scenario, using the same set of macro-
economic trend assumptions as for the first scenario, but
whereby current constraints on the operation of the Data
Economy (such as skills gaps and shortages) are assumed to
become more of a hindrance to growth in future
.03A more optimistic scenario, whereby current constraints and
restrictions on the future growth of the Data Economy are
assumed to be eased (but not removed entirely).
Continuation of current trends scenario
Under the first scenario, current macro-economic and Data
Economy growth trends affecting Ireland are expected to
continue with no improvement to or worsening of existing
constraints. The scenario is predicated on the expected
underlying growth trends for each sector on a sector-by-
sector basis.
The right hand table sets out the levels of GVA generated
by the Data Economy in Ireland under this scenario by 2025
disaggregated by sector. It should be noted that a 2016 price
base is used, so that increases in the value of production
are expressed in real terms (i.e. the effect of future inflation
is excluded).
Under this scenario the Irish Data Economy is expected to
grow in real terms from €9.96 billion in 2016 to around €13.75
billion by 2025. This is an increase of about €3.79 billion, or
38% in proportionate terms.
Sector (Sections)GVA2016
€millions
GVA 2025
€millions
Increase in GVA
(€millions)
Increase in GVA (%)
A Agriculture, forestry, fishing
1 4 1 7 4 2 7 %
B Mining & quarrying 41 49 9 2 2 %
C Manufacturing 1 ,6 62 2 ,0 4 5 3 8 3 2 3 %
D Electricity 8 2 1 1 2 3 0 3 7 %
E Water supply 24 3 3 9 3 8%
F Construction 9 1 1 2 8 3 7 41 %
G Wholesale, retail 2 3 9 3 4 0 1 0 2 4 3 %
H Transport 1 2 0 1 59 3 9 3 2 %
I Accommodation & food 1 4 1 9 6 42 %
J ICT 4 ,9 5 6 7,0 2 3 2 ,0 6 8 42 %
K Financial services 1 , 3 49 1 , 8 26 47 7 3 5 %
L Real estate activities 2 0 4 2 8 8 8 3 41 %
M Professional services 5 6 4 9 0 9 3 4 5 61 %
N Business support services
1 7 8 2 8 8 1 1 0 61 %
O Public administration 1 4 8 1 5 4 6 4%
P Education 1 0 8 1 3 3 2 5 2 3 %
Q Health 1 1 8 1 5 6 3 8 3 3 %
R Arts, entertainment, recreation
3 3 4 5 1 2 3 6%
S Other services 1 8 2 3 5 2 8%
Total 9,9 62 1 3 ,749 3 , 7 8 7 3 8%
D A T A E C O N O M Y R E P O R T 2 0 1 85 8
Despite this increase, the Irish Data Economy is still expected
to be operating well within its potential full capacity and
capability over this future period. The principal reasons why
the Irish Data Economy is expected to continue to operate
sub-optimally in this future period are:
.Under-investment by businesses and data-generating organisationsSignificant numbers of businesses are expected to under-
value the business competitiveness implications of data
analytics. However, even where businesses do have a high
level of awareness, it is anticipated that large numbers of
businesses (especially SMEs) will either lack the managerial
expertise to develop an appropriate strategy or, where
the need for such strategies is recognised, in some cases
businesses (especially SMEs) will be unable to accumulate
sufficient financial and/or technical resources to implement
an appropriate strategy successfully.
.Skills shortages and skills gapsSkills deficits are expected to continue to exert a significant
negative influence on the ability of Irish companies and
organisations to make full use of their data.
.The relative reluctance of the Irish Government to embrace and implement Open Data policies Although this situation is expected to improve (the 2016
ranking was an increase over the previous year’s ranking,
by four places) under the current trends scenario Ireland is
not expected to become a top ten performer with respect to
openness of data.
Notwithstanding the expected influence of these constraints,
the contribution of the Data Economy to some sectors is
expected to be significantly greater than the overall average,
with Business support services, Professional services and
Wholesale & retail trade expected to experience the greatest
increases in GVA.
Under this ‘maintained trajectory of change’ scenario, it is
anticipated that the total number of direct jobs attributable
to the Irish Data Economy will increase from 61,100 in 2016,
to around 72,000 by 2025 (i.e. an overall increase of about
11,000, which is equivalent to an 18% increase).
In addition to the direct jobs, it is expected that a further
27,000 jobs are supported via indirect (procurement) and
induced (multiplier) effects.
The overall number of attributable jobs expected under this
central case scenario by 2025 is therefore 99,500.
Alternative scenario 1: Constraints worsen
The first alternative scenario models a more pessimistic
potential situation, in which existing constraints on the
growth of the Irish Data Economy (such as the business
appetite, or ability, to invest) become an even greater
hindrance to the growth of this segment of the economy
than is expected to be the case under the central scenario.
The specific assumptions that are made under this scenario
include the following:
.Absorption rates of data analytics and IoTThe overall proportion of businesses developing capabilities
or using data analytics services is assumed to be, on average,
5% per annum lower under this scenario compared to the
current trends scenario.
.Business capital investmentLevels of annual aggregate business capital investment
in data analytics infrastructure is assumed to be between
6% and 13% lower (varying by sector) compared to levels
expected under the current trends scenario.
.Business human resource investmentAverage annual expenditure in training and development of
staff is assumed to be between 5% and 10% lower (varying
by sector) compared to levels expected under the current
trends scenario.
.Skills shortagesThe average national deficit of skilled workers is assumed to
be 12% worse than is the case under the current
trends scenario.
.Open DataThe recent improvement in the relative ranking of Ireland in
terms of Open Data is assumed to stall.
D A T A E C O N O M Y R E P O R T 2 0 1 8 5 9
The below table sets out the levels of GVA attributable to
each sector that are expected to be generated annually
under this this lower growth scenario by 2025. A 2016 price
base is used, so that increases in the value of production are
expressed in real terms (i.e. the effect of future inflation
is excluded).
Under this more pessimistic scenario – in which constraints
on future growth are exacerbated – the Irish Data Economy
is still expected to grow significantly, from €9.96 billion in
2016 to just under €12.4 billion by 2025.
However, compared to the central case the scale of overall
growth is expected to be lower under this scenario. The
overall scale of reduction across the Irish economy as a
whole under this scenario is expected to be around €1.35
billion. Expressed another way, the size of the Irish Data
Economy would be expected to be around 10% smaller
(compared to the current trends scenario) under this more
pessimistic scenario in which constraints such as skills
shortages and skills gaps exert a greater influence on the
future growth trajectory of the Data Economy sector.
In industrial terms, the sectors that are comparatively most
likely to be adversely affected (compared to the situation
expected under the central case scenario) are Business
support services and Professional services.
Under this more pessimistic scenario, it is expected that
the total number of direct jobs attributable to the Irish Data
Economy will increase from just over 61,000 in 2016, to about
67,000 by 2025. This represents an increase over 2016 levels
of about 5,800 jobs (9.4%).
Sector (Sections)GVA 2016€millions
GVA 2025€millions
Change 2016-2025(€millions)
Change 2016-2025 (%)
Decrease in GVA cf Main case(€millions)
Decrease in GVA cf Main case
(%)
A Agriculture & forestry 1 4 1 6 -1 -7.9 % 2 1 7 %
B Mining & quarrying 41 4 6 -3 - 6 .6% 6 1 4%
C Manufacturing 1 ,6 62 1 , 8 8 3 -1 62 -7.9 % 2 2 1 1 3 %
D Electricity 8 2 9 9 -1 3 -1 1 . 7 % 1 7 2 1 %
E Water supply 24 2 9 - 4 -1 2 .0 % 5 2 2 %
F Construction 9 1 1 1 5 -1 4 -1 0 .6% 24 26%
G Wholesale, retail 2 3 9 3 0 6 -3 4 -1 0 .0 % 6 8 2 8%
H Transport 1 2 0 1 4 4 -1 5 - 9 . 3 % 24 2 0 %
I Accommodation & food
1 4 1 7 -3 -1 2 .9 % 3 2 3 %
J ICT 4 ,9 5 6 6 , 3 3 5 - 6 8 9 - 9 . 8% 1 , 3 7 9 2 8%
K Financial services 1 , 3 49 1 ,6 69 -1 5 7 - 8 .6% 3 2 0 24%
L Real estate activities 2 0 4 2 51 -3 6 -1 2 .6% 47 2 3 %
M Professional services 5 6 4 7 7 2 -1 3 8 -1 5 .1 % 2 07 3 7 %
N Business support 1 7 8 2 3 8 - 49 -1 7. 2 % 6 0 3 4%
O Public administration 1 4 8 1 5 1 -2 -1 . 5 % 3 2 %
P Education 1 0 8 1 2 3 -1 1 - 8 .0 % 1 5 1 4%
Q Health 1 1 8 1 4 0 -1 7 -1 0 .6% 2 2 1 9 %
R Arts, entertainment, etc.
3 3 41 - 4 - 8 .7 % 8 24%
S Other services 1 8 2 0 -2 - 9 . 3 % 3 1 6%
Total 9,9 62 1 2 , 3 9 6 - 1 , 3 5 3 - 9. 8% 2 ,4 3 5 2 4%
D A T A E C O N O M Y R E P O R T 2 0 1 86 0
However, compared to the central case scenario, this
predicted outcome represents a reduction in the overall
increment in employment (compared to the predicted
level under the central case) of just over 5,000 jobs (i.e.
the overall expected increase in jobs under this more
pessimistic scenario is about 8% less than the gain expected
under the central case scenario). In addition to the direct
jobs, it is anticipated that there would be a further 25,400
jobs supported via indirect (procurement) and induced
(multiplier) effects. The overall number of attributable jobs
expected under this scenario by 2025 is therefore 92,300.
Alternative scenario 2: Constraints relaxed
The second alternative scenario models a more optimistic
situation, in which some of the existing constraints on the
growth of the Irish Data Economy are eased through policy
initiatives (e.g. designed to address skills shortages and skills
gaps) or through accelerated investment by businesses in
Data Economy capabilities, or both.
The differential assumptions that are made under this
scenario include the following:
.Absorption rates of data analytics and IoTThe overall proportion of businesses developing capabilities
or using data analytics services is assumed to be, on average,
5% per annum higher under this scenario compared to the
current trends scenario.
.Business capital investmentLevels of annual aggregate business capital investment
in data analytics infrastructure is assumed to be between
6% and 13% higher (varying by sector) compared to levels
expected under the current trends scenario.
.Business human resource investmentAverage annual expenditure in training and development of
staff is assumed to be between 5% and 9% higher (varying
by sector) compared to levels expected under the current
trends scenario.
.Skills shortagesthe average national deficit of skilled workers is assumed
to be 8% lower than is the case under the current
trends scenario.
D A T A E C O N O M Y R E P O R T 2 0 1 8 6 1
The table below sets out the levels of GVA attributable to
each sector that are expected to be generated annually
under this increased growth scenario by 2025. Again, a 2016
price base is used.
Under this more optimistic scenario, the Irish Data Economy
is expected to grow from €9.96 billion in 2016 to about
€14.87 billion by 2025, equivalent to a 49% increase. The
overall scale of increase in the Data Economy across the Irish
Data Economy as a whole (compared to the central case)
under this scenario is expected to be just over €4.9 billion,
representing an increase of around 8% compared to the
central case.
The largest proportionate increases (on a sector-by-sector
basis) under this optimistic scenario (compared to the
central case) are expected to occur in the Professional
services, Financial services and Wholesale & retail
trade sectors.
Under this more optimistic scenario, it is expected that
the total number of direct jobs attributable to the Irish
Data Economy will increase from 61,100 in 2016, to about
75,900 by 2025, which would represent a gain of just under
14,800 jobs compared to 2016 levels. This level of outcome
represents additional growth in employment of around 3,800
compared to the central case scenario, which is an additional
job growth uplift of around 5.3%.
In addition to the direct jobs, it is expected that a further
28,800 jobs would be supported via indirect (procurement)
and induced (multiplier) effects. The overall number of
attributable jobs expected under this scenario by 2025 is
therefore 104,800.
Sector (Sections)GVA2016
GVA 2025 Eased Constraints
Change 2016-2025 (€millions)
Change 2016-2025 (%)
Increase in GVA (€millions)
cf Main Case
Increase in GVA (%) cf Main Case
A Agriculture & forestry 1 4 1 9 1 7. 2 % 5 3 6%
B Mining & quarrying 41 5 2 3 6 .0 % 1 2 2 9 %
C Manufacturing 1 ,6 62 2 ,1 5 2 1 07 5 . 2 % 49 0 2 9 %
D Electricity 8 2 1 2 3 1 1 9 .6% 41 5 0 %
E Water supply 24 3 7 3 1 0 .0 % 1 3 5 2 %
F Construction 9 1 1 4 3 1 5 1 1 .4% 5 2 5 7 %
G Wholesale, retail 2 3 9 3 8 4 4 4 1 2 .9 % 1 4 5 61 %
H Transport 1 2 0 1 7 2 1 4 8 .6% 5 2 4 4%
I Accommodation & food
1 4 2 1 2 9 .6% 8 5 5 %
J ICT 4 ,9 5 6 7, 5 1 9 49 5 7.1 % 2 , 5 6 3 5 2 %
K Financial services 1 , 3 49 2 ,0 2 5 1 9 9 1 0 .9 % 676 5 0 %
L Real estate activities 2 0 4 3 1 5 2 7 9 .4% 1 1 0 5 4%
M Professional services 5 6 4 1 ,0 4 0 1 3 0 1 4 . 3 % 47 5 8 4%
N Business support 1 7 8 3 2 5 3 7 1 2 . 8% 1 4 6 8 2 %
O Public administration 1 4 8 1 5 5 1 1 .0 % 7 5 %
P Education 1 0 8 1 41 8 5 . 8% 3 3 3 1 %
Q Health 1 1 8 1 69 1 3 8 .0 % 51 4 3 %
R Arts, entertainment, etc.
3 3 49 4 9 .1 % 1 6 4 8%
S Other services 1 8 24 1 6 . 2 % 6 3 6%
Total 9,9 62 1 4 , 8 6 5 1 ,1 1 5 8 .1 % 4 ,9 0 3 49 %
D A T A E C O N O M Y R E P O R T 2 0 1 86 2
Conclusions
The current scale of the contribution of the Irish Data
Economy is estimated to amount to nearly €10 billion per
annum. The contribution has grown from just over €6 billion
in 2012.
The recent growth trajectory is therefore over 13% per annum.
This is well ahead of the annual growth rate for the Irish
economy as a whole, and it is about double the growth rate
experienced by the UK economy over the same period.
By 2016 the Data Economy accounts for over 4% of
Irish national economic output and over 3% of national
employment. Nevertheless, the Irish Data Economy
continues to operate well-within its full potential. It is
estimated that nearly 50% of potential value remained
unrealised in 2016.
The main causes of the lost potential for additional business
turnover, economic output and growth of employment from
the analysis of data are:
• Underinvestment by business in
Data Economy capabilities
• National skills deficits, in the form of unfilled vacancies
for digitally skilled workers and skills gaps on the part of
currently employed workers
• A relative slowness of the Irish Government to embrace
and implement Open Data policies in comparison with
many European and other advanced economies.
The chapter has also looked at a range of future scenarios
for the growth of the Irish Data Economy. Under the
currently expected trajectory of growth, the value of Data
Economy is expected to reach nearly €14 billion (in real
terms) by 2025, which would be growth of around 40%
compared to current levels. However, this contribution could
be significantly lower if skills deficits and other potential
constraints (including a stalling of a recent improvement
in the Irish Open Data ranking) turn out to be worse than
currently envisaged.
On the other hand, the performance by 2025 could be
significantly raised if business investment in technology
and skills runs ahead of currently anticipated trends, and
if the Irish Government accelerates its progress towards
implementing Open Data policies.
D A T A E C O N O M Y R E P O R T 2 0 1 8 6 3
Introduction
The focus of this chapter is the production of estimates of
the current and potential future size of the Data Economy of
Germany and its 16 regions. As is the case with the assessment
of the other European countries, the focus is on assessing the
current contribution for the year 2016 and the trajectory of
change over the 2012-2016 period.
As was the case for the UK, the assessment also provides a
regional breakdown of estimated Date Economy economic
output using GVA calculations.
The financial value of the Data Economy of Germany is
expressed in terms of millions of Euros using a 2016
price base.
Current (2016) size of the German Data Economy
It is estimated that the German Data Economy generated GVA
worth just over €108 billion in 2016. The largest contributors to
this total were provided by the ICT, Manufacturing and Financial
services sectors, which together accounted for 63.9% of the total
German Data Economy. The contribution of the Manufacturing
sector (19.5%) is especially notable and reflects (in part) the
great importance of this sector to the German economy.
There is evidence (based on a number of business surveys)
that the level of absorption of advanced production
technologies in sectors such as Manufacturing and Transport
are generally higher in Germany compared to most other
European countries, especially the UK and Italy. For example,
the total population of advanced robots operating in Germany
is estimated to be about 10 times greater than the number
of robots operating in the UK.30 Investment in advanced
robotics can be taken as a proxy for investment in Industry 4.0
technologies generally (a term, incidentally, which is believed to
have originated in Germany), which in terms of the production
economy (i.e. the non-service sector part of the economy) is
also linked to the emergence of the Data Economy.
Sector (Sections)2016 GVA €millions
% of total
A Agriculture, forestry, fishing
1 2 8 0 .1 %
B Mining & quarrying 5 6 0 0 . 5 %
C Manufacturing 2 1 ,1 5 5 1 9 . 5 %
D Electricity 1 ,9 3 9 1 . 8%
E Water supply 5 6 4 0 . 5 %
F Construction 2 , 2 8 5 2 .1 %
G Wholesale, retail 5 , 2 59 4 .9 %
H Transport 2 , 5 76 2 .4%
I Accommodation & food 1 42 0 .1 %
J ICT 3 7, 5 3 6 3 4 .7 %
K Financial services 1 0 , 5 1 6 9 .7 %
L Real estate activities 5 ,1 62 4 . 8%
M Professional services 6 , 59 1 6 .1 %
N Business support services
3 ,0 9 8 2 .9 %
O Public administration 4 ,1 2 0 3 . 8%
P Education 1 ,9 5 5 1 . 8%
Q Health 2 , 5 70 2 .4%
R Arts, entertainment, recreation
9 8 6 0 .9 %
S Other services 1 ,1 8 3 1 .1 %
Total 1 0 8 , 3 2 7 1 0 0 %
30 | International Federation of Robotics, 2017.
Germany Data Economy Results5
D A T A E C O N O M Y R E P O R T 2 0 1 86 4
Such investment has helped to raise the level of
Manufacturing productivity (and productivity in other
production sectors) to levels that are significantly higher
than European averages.
The overall amount of GVA generated by the German
economy in 2016 is estimated to be just over €2.82 trillion.
Given that the size of the Data Economy is currently
estimated to be €108 billion, the implication is that the Data
Economy accounted for approximately 3.8% of German
national economic output in 2016.
The German Data Economy can also be disaggregated
across its 16 standard regions, and this data is set out in the
table below.
The three largest German regions – Nordrhein-Westfalen, Baden-Württemberg and Bayern – together account for just over 53% of the German national Data Economy.
Region2016 GVA €million
(2016 prices)% of total
Baden- Württemberg
1 6 ,4 8 3 1 5 . 2 %
Bayern 1 9,4 6 3 1 8 .0 %
Berlin 6 ,1 1 4 5 .6%
Brandenburg 2 , 8 2 9 2 .6%
Bremen 76 0 0.7 %
Hamburg 3 ,0 1 1 2 . 8%
Hessen 8 ,9 0 6 8 . 2 %
Mecklenburg- Vorpommern
1 ,4 41 1 . 3 %
Niedersachsen 8 ,7 59 8 .1 %
Nordrhein-Westfalen 2 2 ,0 5 3 2 0 .4%
Rheinland-Pfalz 5 ,0 8 4 4 .7 %
Saarland 1 ,1 1 4 1 .0 %
Sachsen 4 ,61 9 4 . 3 %
Sachsen-Anhalt 2 ,0 5 6 1 .9 %
Schleswig-Holstein 3 , 3 42 3 .1 %
Thüringen 2 , 2 9 3 2 .1 %
Total 1 0 8 , 3 2 7 1 0 0.0 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 6 5
The distribution of the German Data Economy across the 16
regions is influenced by a number of factors:
• The relative scale of the underlying economy in each
region. For example, the economies of Bavaria (Bayern)
and Nordrhein-Westfalen are considerably larger than
those of city-regions such as Bremen or smaller regions
such as Saarland
• The distribution of economic activity by sector is
unevenly spread. One differentiator is that between
the city-region economies such as Berlin, Bremen and
Hamburg compared to regions with a mixture of major
cities and rural hinterland (such as Bayern) or smaller
cities with a large rural hinterland (such as
Schleswig-Holstein)
• There is also a distinction to be made with the regions
of the former DDR (East Germany) which are still in
the process of post-reunification restructuring and
regeneration. Metrics of economic development
(such as GDP per capita) indicate that regions such
as Brandenburg, Thüringen, Sachsen and Sachsen-
Anhalt are still lagging significantly behind German
national averages. For example, GDP per capita levels
in Brandenburg are around 38% lower than equivalent
levels in Bayern.
The total number of direct jobs associated with the German
Data Economy in 2016 (based on data sourced from
Eurostat) was approximately 1.32 million. Employment in the
Data Economy in Germany is estimated to account for 3.24%
of the overall number of jobs in the economy as a whole
in 2016.
In addition to these direct jobs, the Data Economy also
supports jobs via supply chain and multiplier effects. These
indirect and induced effects are estimated to amount to an
additional 629,000 jobs across the German economy in 2016.
Therefore, in total, the amount of employment attributable to the German Data Economy in 2016 is estimated to amount to just over 1.952 million jobs.
1.952 million jobs
D A T A E C O N O M Y R E P O R T 2 0 1 86 6
Growth trajectory since 2012
Overall, the financial value (in terms of GVA) of the German
Data Economy is estimated to have grown from just over
€74.7 billion in 2012 to just over €108.3 billion in 2016.
This is an overall increase of 51%, and implies an average annual increase of 10.9% in the size of the German Data Economy over this period.
The German growth rate therefore exceeds that achieved
by the UK but is lower than that estimated for Ireland.
Comparing the sector-level estimates for 2012 to those for
2016, the most significant increases (in absolute terms) have
occurred in ICT, Manufacturing, Professional services and
Wholesale & retail trade.
However, significant increases (more than 100%) have
occurred in sectors including Agriculture, forestry & fishing,
Mining & quarrying, Water supply and Construction.
Sector (Sections)GVA 2012€millions
GVA 2016€millions
Change (€millions)
Change (%)
A Agriculture, forestry, fishing 6 0 1 2 8 6 8 1 1 3 %
B Mining & quarrying 2 1 2 5 6 0 3 4 8 1 6 4%
C Manufacturing 1 4 ,9 7 5 2 1 ,1 5 5 6 ,1 8 0 41 %
D Electricity 1 , 5 1 7 1 ,9 3 9 42 2 2 8%
E Water supply 2 2 8 5 6 4 3 3 6 1 47 %
F Construction 1 ,0 4 4 2 , 2 8 5 1 , 241 1 1 9 %
G Wholesale, retail 2 ,9 0 5 5 , 2 59 2 , 3 5 4 8 1 %
H Transport 1 ,4 3 7 2 , 5 76 1 ,1 3 9 7 9 %
I Accommodation & food 7 3 1 42 69 9 5 %
J ICT 2 5 ,0 2 5 3 7, 5 3 6 1 2 , 5 1 1 5 0 %
K Financial services 7,7 3 1 1 0 , 5 1 6 2 ,7 8 5 3 6%
L Real estate activities 3 ,4 0 0 5 ,1 62 1 , 762 5 2 %
M Professional services 3 ,9 9 9 6 , 59 1 2 , 59 2 6 5 %
N Business support services 2 ,0 4 8 3 ,0 9 8 1 ,0 5 0 51 %
O Public administration 2 ,9 5 4 4 ,1 2 0 1 ,1 6 6 3 9 %
P Education 1 , 2 9 8 1 ,9 5 5 6 5 7 51 %
Q Health 1 , 5 26 2 , 5 70 1 ,0 4 4 6 8%
R Arts, entertainment, recreation 6 47 9 8 6 3 3 9 5 2 %
S Other services 6 61 1 ,1 8 3 5 2 2 7 9 %
Total 7 1 , 741 1 0 8 , 3 2 7 3 6 , 5 8 6 5 1 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 6 7
The distribution of overall Data Economy growth by sector
reflects several trends:
• The underlying growth rates of some sectors (such as
Professional services and Business services) is above
the average rate for the economy as a whole; such
sectors can naturally be expected to have grown the
Data Economy component of their output faster than
the economy-wide average
• The absorption rate of Data Economy technologies and
the recruitment of Data Economy workers is generally
faster in knowledge-driven sectors such as advanced
manufacturing and ICT.
A regional disaggregation of growth over the 2012-2016
period can also be estimated for Germany. This analysis –
set out in the table below – reveals that although the largest
shares of the growth of the Data Economy (in absolute
terms) is occurring in the large regions (such as Baden-
Württemberg and Nordrhein-Westfalen), the fastest growth
in the German Data Economy is occurring in city-regions
such as Berlin, Bremen and Hamburg
It is also notable that while Nordrhein-Westfalen still
accounts for the largest single share of the German Data
Economy, between 2012 and 2016 this region had a below-
average level of growth in this segment of the economy.
Region2012 GVA (€millions)
2016 GVA (€millions)
Change (€millions)
Change (%)
Baden-Württemberg 1 0 , 3 9 8 1 6 ,4 8 3 6 ,0 8 5 59 %
Bayern 1 2 ,7 8 0 1 9,4 6 3 6 ,6 8 3 5 2 %
Berlin 3 ,7 3 1 6 ,1 1 4 2 , 3 8 3 6 4%
Brandenburg 1 ,9 1 8 2 , 8 2 9 9 1 1 47 %
Bremen 4 5 2 76 0 3 0 8 6 8%
Hamburg 1 , 8 6 5 3 ,0 1 1 1 ,1 4 6 61 %
Hessen 5 ,9 9 2 8 ,9 0 6 2 ,9 1 4 49 %
Mecklenburg-Vorpommern
1 ,0 2 0 1 ,4 41 42 1 41 %
Niedersachsen 5 ,7 7 2 8 ,7 59 2 ,9 8 7 5 2 %
Nordrhein-Westfalen 1 5 ,0 8 1 2 2 ,0 5 3 6 ,9 7 2 4 6%
Rheinland-Pfalz 3 ,41 0 5 ,0 8 4 1 ,674 49 %
Saarland 7 51 1 ,1 1 4 3 6 3 4 8%
Sachsen 3 ,1 9 0 4 ,61 9 1 ,42 9 4 5 %
Sachsen-Anhalt 1 ,42 2 2 ,0 5 6 6 3 4 4 5 %
Schleswig-Holstein 2 , 3 61 3 , 3 42 9 8 1 42 %
Thüringen 1 , 59 7 2 , 2 9 3 69 6 4 4%
Total 7 1 , 741 1 0 8 , 3 2 7 3 6 , 5 8 6 5 1 %
D A T A E C O N O M Y R E P O R T 2 0 1 86 8
The trend towards faster growth in the German city-regions
highlights that the Data Economy may be largely (but not
exclusively) an urban phenomenon. Factors that are likely
to be encouraging the growth the of Data Economy in more
urbanised areas of advanced economies such as Germany
include the following:
• Major customers for the ICT sector itself come from
other knowledge-driven sectors such as Financial
services, Professional services, Media and creative
industries, as well as often hosting the major
headquarters functions of companies (across all
sectors) and Government, all of which tend to be
located in cities
• Linked to the previous point, cities also tend to possess
the largest density of knowledge economy businesses
and workers compared to their host countries as
a whole
• The most advanced networks of telecommunications
and other necessary infrastructure is found in cities
• Major universities are often located in cities, providing
businesses with a source of highly skilled graduate and
post-graduate workers.
All of the above are factors in the observed tendency for
knowledge-intensive activities to increasingly aggregate
as clusters in major cities such as London, Berlin, Munich,
Amsterdam, London and Dublin. In Germany, it is apparent
that cities such as Hamburg and Bremen are also
enjoying growth.
Clusters are widely acknowledged to be particularly
important to knowledge-intensive and high value business
activities, such as ICT, telecoms, advertising and the financial
sector. These are all industries driven by innovation and the
constant creation of new services, products and applications
and as a result, as has already been discussed, are sectors
driving the growth of the Data Economy.
The recent trend (2012-2016) in the growth of the German
Data Economy in terms of the impact on business and
organisation turnover and cost savings has also been
assessed. The estimated effect on German business/
organisation turnover and costs in 2016 was worth just
over €262 billion in total. The equivalent figure for 2012 is
estimated to be worth approximately €183 billion (in terms
of 2016 prices). This implies an overall increase in value of
about 43%. The sectoral breakdown of this increase is very
similar to that for GVA set out in the preceding table.
The number of jobs estimated to be associated with the German Data Economy is estimated to have increased from about 1.132 million in 2012 to 1.323 million jobs by 2016, based on data sourced from Eurostat.
Therefore, the increase in the number of jobs associated
with the German Data Economy over the 2012-2016 period is
estimated to be around 191,000 (17%).
Over the 2012-2016 period the proportion of workforce jobs
attributable to the German Data Economy is estimated to
have increased from about 2.91% to 3.24%.
D A T A E C O N O M Y R E P O R T 2 0 1 8 6 9
Current size of Data Economy versus current potential
Estimates of the current (2016) size of the German Data
Economy compared to the extent it could have reached by
this point if all constraints (both on the demand side and the
supply side) were addressed have also been produced.
The main types of constraints that are pertinent in Germany
are as follows:
.Under-investment by businesses Although in general German businesses appear to have an
above-average appetite for investment in new technology,
clearly this is not true for all businesses. One of the notable
features of the German economy is the relative strength
and importance of medium sized companies (the so-called
Mittelstand) across a range of sectors, many of whom are
family-owned. Many of these companies are also active in
exporting. However, there are some concerns that some
medium sized companies that are competing internationally
may find it more difficult to identify and implement data
analytics strategies compared to larger competitors both
domestically and internationally.
.Skills gaps and shortages A significant issue for many German businesses is difficulty
recruiting or retaining workers with the skills needed to
develop and maintain data analytical systems.
.Open dataThere is some evidence that the relative German ranking for
openness of data is falling. Based on the update of the most
recent global Open Data Barometer, Germany is no longer a
top ten country for Open Data (it is now ranked 11th).
D A T A E C O N O M Y R E P O R T 2 0 1 87 0
The estimates for the extent of the achievement of Data
Economy actualisation are presented in the table below,
disaggregated by business sector. The table shows current
levels of performance (in terms of GVA) and the proportion
of overall potential value generation that this is estimated
to represent.
The estimates suggest that whereas, in 2016, the German
Data Economy was worth around €108 billion, the full
potential value that could have been generated that year
was approximately €196 billion. Therefore, in 2016 the actual
German Data Economy was only operating at around 55%
of its full potential in terms of contributions to revenue
generation and productivity.
Moreover, sectors such as Health and Arts, entertainment
& recreation are operating at a level that is significantly
lower (in terms of unfulfilled potential) than the economy-
wide average. On the other hand, sectors such as Mining
& quarrying and Manufacturing appear to be achieving a
greater-than-average proportion of their existing potential.
Sector (Sections)2016 Actual GVA
€millions2016 Full Potential GVA
€millions
Full Potential GVA minus Actual GVA
€millions
Actual GVA as % of Full Potential
A Agriculture, forestry, fishing 1 2 8 2 8 7 1 6 0 4 4%
B Mining & quarrying 5 6 0 9 1 7 3 5 7 61 %
C Manufacturing 2 1 ,1 5 5 3 3 ,1 8 5 1 2 ,0 2 9 6 4%
D Electricity 1 ,9 3 9 3 ,4 0 2 1 ,4 6 3 5 7 %
E Water supply 5 6 4 1 ,0 4 4 4 8 0 5 4%
F Construction 2 , 2 8 5 5 ,1 94 2 ,9 0 9 4 4%
G Wholesale, retail 5 , 2 59 9 ,9 2 3 4 ,6 6 4 5 3 %
H Transport 2 , 5 76 5 , 3 6 8 2 ,7 9 1 4 8%
I Accommodation & food 1 42 3 2 3 1 8 1 4 4%
J ICT 3 7, 5 3 6 6 4 ,7 1 8 2 7,1 8 1 5 8%
K Financial services 1 0 , 5 1 6 1 8 ,4 4 8 7,9 3 3 5 7 %
L Real estate activities 5 ,1 62 1 0 ,7 5 4 5 , 59 2 4 8%
M Professional services 6 , 59 1 1 2 ,4 3 7 5 , 8 4 5 5 3 %
N Business support services 3 ,0 9 8 7,0 41 3 ,94 3 4 4%
O Public administration 4 ,1 2 0 8 , 241 4 ,1 2 0 5 0 %
P Education 1 ,9 5 5 3 ,7 59 1 , 8 0 4 5 2 %
Q Health 2 , 5 70 5 ,9 7 8 3 ,4 07 4 3 %
R Arts, entertainment, recreation 9 8 6 2 , 2 9 2 1 , 3 07 4 3 %
S Other services 1 ,1 8 3 2 ,9 59 1 ,7 7 5 4 0 %
Total 1 0 8 , 3 2 7 1 9 6 , 2 69 8 7,94 2 5 5 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 7 1
Conclusions
The current scale of the contribution of the German Data
Economy is estimated to amount to just over €108 billion per
annum. The contribution has grown from around €71 billion
in 2012. The recent growth trajectory is therefore nearly 11%
per annum. This is well ahead of the annual growth rate for
the German economy as a whole, and it is also significantly
faster than the most recent growth rate for the UK economy
over the same time period.
Despite this impressive rate of growth, the German Data
Economy continues to operate well-within its full potential.
It is estimated that nearly 45% of available value-adding
potential remained unrealised during 2016.
The main causes of the lost potential for additional business
turnover, economic output and growth of employment from
the analysis of data are:
• Inadequate investment by many businesses –
particularly SMEs – in developing and grasping the
potential value to be created through the analysis of
their operational, market and other data
• Skills deficits, in the form of an inability to fill vacancies
for digitally skilled workers in good time, along with
underdeveloped technical and/or managerial skills on
the part of some currently employees
• The continued relative under-development of some
regions of the German economy, notably the areas
(other than Berlin) that were formerly part of the DDR
prior to reunification
• A slight but growing concern that Germany is starting
to fall behind some other European and international
countries in terms of Open Data policies.
D A T A E C O N O M Y R E P O R T 2 0 1 87 2
Introduction
The focus of this chapter is the production of estimates of
the current and potential future size of the Data Economy of
the Netherlands and its four regions. As with the other country
chapters, the focus of the assessment is on the year 2016, and
also the growth trend for 2012-2016. The main metric used in
the assessment is GVA, but there are also estimates provided
for employment and business turnover/cost savings generated
by the business use of data.
All financial values are provided in terms of millions of Euros
using a 2016 price base.
Current (2016) size of the Netherlands Data Economy
It is estimated that the Netherlands Data Economy generated
GVA worth just over €24.6 billion in 2016. The largest contributors
to this total were provided by the ICT, Financial services,
Manufacturing and Professional services sectors, which together
accounted for 69.6% of the total Netherlands Data Economy.
The level of the contribution by sector is driven by
several factors including:
• The absolute size of the sector compared to the economy
as a whole
• Businesses operating within sectors that possess a higher
level of knowledge-intensity (such as pharmaceuticals and
the media) generally have a greater propensity to invest in
data analytics infrastructure and skills
• One notable feature of the sector breakdown for the
Dutch Data Economy (compared to the other three
countries considered in this report) is the relatively
larger contribution from agriculture. The use of data
in agriculture is increasingly important driven by the
advent of precision farming technologies which allow for
much more targeted use of agri-chemical inputs such as
fertilisers, pesticides and fungicides.
Sector (Sections)2016 GVA €millions
% of total
A Agriculture, forestry, fishing
8 9 0 .4%
B Mining & quarrying 1 49 0 .6%
C Manufacturing 2 , 3 8 8 9 .7 %
D Electricity 3 94 1 .6%
E Water supply 1 5 4 0 .6%
F Construction 49 2 2 .0 %
G Wholesale, retail 1 , 5 5 7 6 . 3 %
H Transport 6 5 2 2 .6%
I Accommodation & food 4 6 0 . 2 %
J ICT 8 ,41 8 3 4 . 2 %
K Financial services 4 , 2 2 5 1 7.1 %
L Real estate activities 61 6 2 . 5 %
M Professional services 2 ,1 2 1 8 .6%
N Business support services
8 6 0 3 . 5 %
O Public administration 8 2 8 3 .4%
P Education 5 0 9 2 .1 %
Q Health 7 5 2 3 .1 %
R Arts, entertainment, recreation
241 1 .0 %
S Other services 1 4 6 0 .6%
Total 2 4 ,6 3 7 1 0 0.0 %
Netherlands Data Economy Results6
D A T A E C O N O M Y R E P O R T 2 0 1 8 7 3
The overall amount of GVA generated by the Dutch
economy in 2016 is estimated to be just over €627 billion.
On this basis, the Netherlands Data Economy accounted for
approximately 3.9% of national economic output in 2016.
The Netherlands Data Economy can also be disaggregated
by its four standard regions. The largest contributors to the
Data Economy in spatial terms is West-Nederland (53%),
reflecting not only the greater size of that region but also
the greater concentrations of ICT, Financial services and
Professional services activity in that area.
This spatial distribution of activity is almost certainly linked
to the location within West-Nederland of some of the
country’s largest cities, including Amsterdam, Rotterdam,
The Hague and Haarlem. There is strong evidence that
the Data Economy is growing fastest in the largest cities of
advanced economies, for the following reasons:
• Clustering of the ICT sector in major cities, driven by
the presence in those places of demand for ICT services
on the part of other knowledge-driven sectors such
as Financial services, Professional services, Media and
creative industries and Government - these sectors
tend to be concentrated in major cities
• As a corollary, major cities also tend to possess the
largest density of knowledge economy businesses and
workers compared to their host countries as a whole
• The most advanced and densest networks of
telecommunications and other necessary infrastructure
is usually found in cities, and the largest cities tend to
be where the next generation of telecoms technology
tends to be rolled out first
• Major universities are often located in cities, providing
knowledge-driven businesses with a source of highly
skilled graduate and post-graduate workers.
The total number of direct jobs associated with the
Netherlands Data Economy in 2016 (based on data sourced
from Eurostat) was slightly over 247,000. Employment in the
Data Economy in the Netherlands during 2016 is estimated
to account for 3.20% of the overall number of jobs in the
Dutch economy as a whole.
In addition to these direct jobs, the Data Economy also
supports jobs via supply chain and multiplier effects. These
indirect and induced effects are estimated to amount to over
101,000 jobs across the Netherlands in 2016.
Therefore, the total amount of employment attributable to the Netherlands Data Economy in 2016 is estimated to amount to just over 349,000 jobs.
Region2016 GVA €millions
(2016 prices)% of total
Noord-Nederland 2 ,0 6 5 8 .4%
Oost-Nederland 4 ,67 7 1 9.0 %
West-Nederland 1 3 ,0 6 8 5 3 .0 %
Zuid-Nederland 4 , 8 2 7 1 9.6%
Total 2 4 ,6 3 7 1 0 0.0 %
349,000 jobs
D A T A E C O N O M Y R E P O R T 2 0 1 87 4
Growth trajectory since 2012
Overall, the financial value (in terms of GVA) of the
Netherlands Data Economy is estimated to have grown from
just under €17.5 billion in 2012 to just over €24.6 billion in
2016.
This implies an average annual increase of around 8.9% in the size of the Data Economy over this period.
Comparing the estimates for 2012 to those for 2016, the
most significant increases (in absolute terms) have occurred
in ICT, Financial services, Manufacturing, Professional
services and Wholesale & retail sectors. However, in
proportionate terms significant increases have occurred in a
range of other sectors including the Agriculture, forestry &
fishing and the Mining & quarrying sectors.
Sector (Sections)GVA 2012€millions
GVA 2016€millions
Change (€millions)
Change (%)
A Agriculture, forestry, fishing 3 5 8 9 5 4 1 5 4%
B Mining & quarrying 6 3 1 49 8 6 1 3 7 %
C Manufacturing 1 ,70 8 2 , 3 8 8 6 8 0 4 0 %
D Electricity 3 3 5 3 94 59 1 8%
E Water supply 1 1 0 1 5 4 4 4 4 0 %
F Construction 26 0 49 2 2 3 2 8 9 %
G Wholesale, retail 8 7 8 1 , 5 5 7 67 9 7 7 %
H Transport 4 0 9 6 5 2 24 3 59 %
I Accommodation & food 2 2 4 6 24 1 0 9 %
J ICT 6 ,0 9 2 8 ,41 8 2 , 3 26 3 8%
K Financial services 3 , 5 1 3 4 , 2 2 5 7 1 2 2 0 %
L Real estate activities 3 5 7 61 6 2 59 7 3 %
M Professional services 1 ,4 6 6 2 ,1 2 1 6 5 5 4 5 %
N Business support services 4 8 1 8 6 0 3 7 9 7 9 %
O Public administration 6 4 3 8 2 8 1 8 5 2 9 %
P Education 3 61 5 0 9 1 4 8 41 %
Q Health 5 1 2 7 5 2 24 0 47 %
R Arts, entertainment, recreation 1 6 3 241 7 8 4 8%
S Other services 8 6 1 4 6 6 0 70 %
Total 1 7,494 2 4 ,6 3 7 7,1 4 3 41 %
D A T A E C O N O M Y R E P O R T 2 0 1 8 7 5
A regional disaggregation can also be estimated for both
2012 and 2016. This analysis – set out in the table below –
reveals that the West-Nederland region is growing its share
of the Dutch Data Economy, from 51.5% in 2012 to 53.0% by
2016. Moreover, this region accounted for 56% of the overall
growth of the Dutch Data Economy over this period.
These trends again underline the clustering trend for the
Data Economy in major urban areas that have the densest
networks of customers, suppliers, advanced telecoms
infrastructure and the availability of highly skilled workers
and graduates.
The current size of the Netherlands Data Economy in terms
of the impact on business and organisation turnover and
cost savings has also been estimated. The estimated effect
on Dutch business/organisation turnover and costs in 2016
was worth a total of €56.9 billion. The equivalent figure for
2012 is estimated to be around €39.9 billion (in terms of 2016
prices). This implies an overall increase in value of about
43%. The sectoral breakdown of this increase is very similar
to that for GVA set out in the table above.
Over the same period the number of direct jobs associated
with the Data Economy is estimated (using data sourced
from Eurostat) to have increased from about 210,000 to just
over 247,000. Therefore, the increase in the number of direct
jobs attributable to the Netherlands Data Economy over the
2012-2016 period is estimated to be around 38,000 (18%).
Over the 2012-2016 period the proportion of workforce jobs
attributable to the Netherlands Data Economy is estimated
to have increased from about 2.8% to 3.2%.
Region 2012 GVA (€millions)
2016 GVA (€millions)
Change (€millions)
Change (%)
Noord-Nederland
1 , 5 6 5 2 ,0 6 5 5 0 0 3 2 %
Oost-Nederland
3 , 5 2 7 4 ,67 7 1 ,1 5 0 3 3 %
West-Nederland
9,0 2 2 1 3 ,0 6 8 4 ,0 4 6 4 5 %
Zuid-Nederland
3 , 3 8 1 4 , 8 2 7 1 ,4 47 4 3 %
Total 1 7,494 2 4 ,6 3 7 7,1 4 3 41 %
D A T A E C O N O M Y R E P O R T 2 0 1 87 6
Current size of Data Economy versus current potential
Estimates have also been produced of the current (2016)
size of the Netherlands Data Economy compared to the
extent it could have reached by this point if all constraints
(both on the demand side and the supply side) had been
addressed.
The main constraints that hinder the growth of the Dutch
Data Economy are as follows:
.Under-investment by businesses In particular SMEs who may not recognise the potential that
is offered by data analytics, or who may struggle to access
financial resources and expertise needed to design and
implement appropriate data strategies.
.Skills gaps and shortages A significant issue for many businesses is difficulty recruiting
or retaining workers with the skills needed to develop and
maintain data analytical systems.
The estimates of the actual versus full potential of the
Netherlands Data Economy are presented in the next table,
disaggregated by business sector. The table shows current
levels of performance (in terms of GVA) and the proportion
of overall potential value generation that this is estimated
to represent.
D A T A E C O N O M Y R E P O R T 2 0 1 8 7 7
Sector (Sections)2016 Actual GVA
€millions2016 Full Potential GVA
€millions
Full Potential GVA minus Actual GVA
€millions
Actual GVA as % of Full Potential
A Agriculture, forestry, fishing 8 9 241 1 5 2 3 7 %
B Mining & quarrying 1 49 2 9 2 1 4 3 5 1 %
C Manufacturing 2 , 3 8 8 5 ,0 5 6 2 ,6 6 8 47 %
D Electricity 3 94 747 3 5 3 5 3 %
E Water supply 1 5 4 3 0 8 1 5 4 5 0 %
F Construction 49 2 1 , 2 0 8 7 1 6 41 %
G Wholesale, retail 1 , 5 5 7 3 ,1 7 2 1 ,61 5 49 %
H Transport 6 5 2 1 ,4 6 8 8 1 6 4 4%
I Accommodation & food 4 6 1 1 3 67 41 %
J ICT 8 ,41 8 1 5 ,67 5 7, 2 5 7 5 4%
K Financial services 4 , 2 2 5 8 ,0 0 5 3 ,7 8 0 5 3 %
L Real estate activities 61 6 1 , 3 8 5 7 70 4 4%
M Professional services 2 ,1 2 1 4 , 3 2 3 2 , 2 0 1 49 %
N Business support services 8 6 0 2 ,1 1 1 1 , 2 5 1 41 %
O Public administration 8 2 8 1 , 7 9 0 9 61 4 6%
P Education 5 0 9 1 ,0 5 8 5 49 4 8%
Q Health 7 5 2 1 , 8 8 9 1 ,1 3 7 4 0 %
R Arts, entertainment, recreation 241 6 0 5 3 6 4 4 0 %
S Other services 1 4 6 3 94 24 8 3 7 %
Total 2 4 ,6 3 7 49, 8 3 8 2 5 , 2 0 1 49 %
The estimates suggest that whereas, in 2016, the
Netherlands Data Economy was worth around €24.64 billion,
the full potential value that could have been generated that
year was approximately €49.8 billion.
Therefore, in 2016 the actual Netherlands Data Economy was only operating at around 49% of its full potential in terms of contributions to revenue generation and productivity.
Moreover, sectors such as Agriculture, forestry & fishing
and Health are operating at a level that is significantly
worse (in terms of unfulfilled potential) than the economy-
wide average. On the other hand, sectors such as Financial
services and Electricity appear to be achieving a greater-
than-average proportion of the existing potential (albeit
with substantial scope for improvement still remaining).
D A T A E C O N O M Y R E P O R T 2 0 1 87 8
Conclusions
The current scale of the contribution of the Netherlands
Data Economy is estimated to amount to over €24 billion per
annum. The contribution has grown from €17 billion in 2012.
The recent growth trajectory is therefore nearly 9% per annum.
This is well ahead of the annual growth rate for the Dutch
economy as a whole, and it is also significantly faster than
the most recent growth rate for the UK economy over the
same time period. It is however, slightly slower than the
equivalent growth rate for Germany (8%).
Despite this level rate of recent growth, the Netherlands
Data Economy currently operates well within its full potential.
It is estimated that slightly over half of the available value-
adding potential remained unrealised during 2016. The main
causes of the lost potential for additional business turnover,
economic output and growth of employment from the
analysis of data are:
• Under-investment by many businesses – particularly
SMEs – in developing and utilising the potential value
to be created through the analysis of their operational,
market and other data
• Skills shortages (the inability of businesses to fill
vacancies for digitally skilled workers in good time)
• Skills gaps: a deficit in technical and/or managerial skills
on the part of some currently employed workers (which
could be addressed through workforce development
and training).
9%per annum
D A T A E C O N O M Y R E P O R T 2 0 1 8 7 9
The earlier chapters have concentrated on how much
value is being, or could be, garnered from data. Data itself,
however, also has its needs: it needs to be stored, managed,
accessed and analysed. Data centres are crucial to doing
that at the scale at which the modern Data Economy needs
to operate. This chapter, therefore, considered their role in
more detail.
Data centres are specialised buildings primarily housing
computer equipment combined with high-capacity
telecommunications infrastructure, storage systems and
energy supply. Data centres enable the receipt, storage,
transmission and processing of very large quantities of
digital data generated because of the increasing number of
digital devices and functions across a range of applications.
Data centres have emerged over the past 20 years or so
as a consequence of the huge increase in the creation of
digital data across a range of sources, including telecoms,
financial services, retail, transport, health, entertainment and
social media. The emergence of data centres also reflects
the increasing tendency to house IT resources in specialist
purpose-built facilities with resilient power supply and high
capacity fibre connectivity rather than on company premises
(such as in server rooms or basements).
In the early days of the data industry data hosting was
typically carried out on-site, with a business (or Government
department) accommodating its own data on servers located
within their own buildings. As the demand for data grew and
services became more diverse, data hosting increasingly
became outsourced. This was coupled with the emergence
of high-capacity data storage providers operating out of
their own large and resilient data centres.
Data centres are increasingly important economic assets in
their own right. Firstly, they require substantial investment to
build and fit out, with often over £100 million of investment
required for each data centre. Data centres also require a
highly skilled workforce to maintain and operate the
equipment provided within each centre.
In the UK context, ONS data indicates that UK-based
data centres have become a significant feature of the
UK economic landscape (in terms of turnover and GVA
generated) in recent years. For example, between 2008
and 2015, companies operating data centres in the UK have
experienced increases in the following metrics:
.TurnoverIncreased from £5.3 billion to £8.9 billion (67% overall
increase, CAGR31 = 7.6% p.a.).
.GVAIncreased from £3.4 billion to £6.2 billion (80% overall
increase, CAGR = 8.8% p.a.)
.WorkforceIncreased from approximately 39,000 to 46,000 (18% overall
increase, CAGR = 2.1% p.a.)
However, the main economic contribution of data centres
is indirect rather than direct: this indirect economic
contribution is driven by the role data centres play in
providing vital IT services for many other businesses that
use their services. The provision of these services enables
other businesses to operate more efficiently and with higher
degrees of productivity than would be possible otherwise.
31 | Compound Annual Growth Rate.
Emergence of Data Centres as Key Players in the Data Economy
7
Data centres tend to be located in key clusters, primarily
in the world’s major financial and business service centres,
including London, Frankfurt, Amsterdam and Tokyo.
Important drivers influencing the location of data centres in
Europe include the following:
.CustomersMajor cities tend to have the densest concentration of
major sources of demand, including major financial
institutions, media and entertainment activities, and
professional services activities.
.InfrastructureMajor cities tend to have the highest density of high capacity
telecommunications infrastructure.
.SkillsMajor cities have the largest and deepest labour markets
and they also have the ability to attract and retain highly
skilled knowledge-industry workers. They also tend to host
major universities providing an important source of skilled
graduates and post-graduates and therefore provide a
constant source of new workers helping to expand the
workforce of a growing industry.
The European Data centres Marketview identifies four
European cities as ‘Tier 1’ locations for data centres in
the European context. These cities are London, Frankfurt,
Amsterdam and Paris. The latest data on the relative
capacity of the four Tier 1 centres and the changes
occurring since 2015 are summarised below:
The largest increase in capacity in absolute terms between
2015 and 2017 occurred in London, but the relative capacity
of Frankfurt and Amsterdam increased at a faster rate over
this period. The data centre capacity located in Amsterdam
increased the fastest of all, at a rate of 46% between 2015
and 2017.
Location Market
capacity 2017Q3 (MW)
Market capacity 2017
Q3 (MW)
Change 2015-2017
(MW)
Change 2015-2017
(%)
Vacant capacity
(MW)
Vacant capacity
(%)
London 4 3 7 3 5 4 8 3 2 3 % 74 1 6 .9 %
Frankfurt 24 0 1 8 4 5 6 3 0 % 42 1 7. 5 %
Amsterdam 24 0 1 6 4 76 4 6% 41 1 7.1 %
Paris 1 5 6 1 4 0 1 6 1 1 % 2 7 1 7. 3 %
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Nevertheless, London still provided 41% of overall Tier 1
capacity in 2017, only slightly down from 42% in 2015.
According to the European Datacentres Marketview (CBRE,
3rd quarter, 2017) there are over 500 data centres located
in the UK, with 70% of these located within or around the
M25. London is identified by this source as the second
largest data centre market in the world, providing nearly
twice as much capacity as the next largest European centres
(Frankfurt and Amsterdam). Within the UK, the next most
important location after London is identified as Manchester.
Data available for the UK also enables the contribution of each data centre to be calculated: the average amount of GVA per data centre is estimated to lie been £291 million and £320 million p.a. This range is significantly higher for newly built data centres, which add between £397 million and £436 million p.a.
The capacity of data centres in Ireland is not covered by the
CBRE annual Marketview report. However, there are separate
estimates of the size of the Irish data centre sector provided
within a specific report produced by Host in Ireland. This
report, dating from 2017, identifies a total of around 100-
120 MW of co-location data centres in Ireland. This type of
facility is comparable to the data in the previous table for
London, Frankfurt, Amsterdam and Paris.
In addition, there is estimated to be around 300MW of so-
called hyperscale private data centres located in Ireland and
operated by such household names as Amazon, Google and
Facebook.
£397-£436million p.a.for newly built data centres
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The assessment in the preceding chapters identifies that
between 50% and 60% of the potential of the Data Economy
is not being realised in each of the four countries that have
been considered. While it is expected that the value of the
Data Economy will continue to grow in each country, there
is a danger that – unless significant constraints and barriers
are not addressed – a larger proportion of the increasing
potential value of the Data Economy will remain unrealised.
Across all four countries the types of constraints and
hindrances are similar in nature, although they vary in
intensity to some extent both between countries and
between sectors and regions within individual countries.
Provided below is a set of actions focused on individual
businesses, business networks and Government that are
relevant to all four of the countries considered in this report.
Additional points that are particularly relevant to the UK and
Ireland are highlighted in a small number of instances.
Actions for industry groups and individual businesses
.01 Business have a lot of work to do to build confidence and
trust with respect to the handling of customers’ data.
Distrust and concerns about privacy and security must be
resolved by industry (and Government) if the full value of
the Data Economy is to be realised. In particular:
.a
Companies that hold customer data must minimise or
prevent cyber-attacks by investing in infrastructure,
software, staff training and other safeguards to protect
the integrity of customer data
.b
Companies should also work with suppliers and through
business networks to share best practice experience and
information to help raise the standard of data security
and protection
.c
Companies that hold customer data also need to have a
robust set of policies in place that safeguard customer
data from illegal, unethical or inappropriate.
.02There are unrealised opportunities for businesses of all
sizes to utilise the data they hold across all areas of their
operations, from data held on customers through to all
relevant areas of operations, such as (depending on the
nature of the individual business): production lines, logistics,
management of premises, R&D, etc. Many companies have
achieved excellent results in some operational and customer
focused areas, but there may be other parts of the business
where opportunities for efficiency and/or enhanced revenue
generation remain. Senior management in large businesses
must therefore lead and fully integrate digital transformation
in their companies as a key backbone of long-term business
development strategy. This is especially important for
businesses operating in sectors that have hitherto been
slower at making significant investments in data analytic
capabilities, including investment in infrastructure,
equipment and software to enable advanced data analytics
capabilities, but also in terms of investing in both managerial
capacity and technical skills that are needed to grasp the
opportunities more fully.
.03All large businesses (i.e. more than 250 employees) should
appoint a Chief Data Officer reporting to the CEO to
coordinate strategy and ensure full integration with wider
business objectives.
.04Large businesses also have a potential role to play in
helping to encourage and mentor SMEs to investigate and
develop data analytics infrastructure and applications.
There is an opportunity for larger businesses to provide
support for SMEs who are members of their supply chain,
by defining standards and by sharing best practice
experience and expertise.
How to Unlock the Potential of Data8
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.05There is also a major opportunity for a larger number of
SMEs to begin to secure business growth and productivity
gains that are available from analysis of their own data within
what data protection rules permit. Essentially, the availability
of analytical functionality via Cloud computing means that
tools and infrastructure previously only available to larger
companies are now within the scope of smaller businesses.
.a
SMEs can start by identifying all sources of customer and
business performance data generated by their business.
.b
The next step is to consolidate the data into a single tool,
such as a customer relationship management system.
.c
The small or medium sized business is then able to use
the data to produce analytical reports and performance
dashboards so that useful information can be produced
and acted upon. For example, analysis of customer
contact data can be used to better predict future
patterns of customer behaviour and/or to deal better
with customer feedback and complaints.
.06There is an urgent need for further investment by the
private sector in recruiting workers and developing training
programmes – such as digital apprenticeships – targeting
school leavers and returners to the workforce.
.07Peak industry bodies - such as the CBI, the Institute of
Directors and the Federation of Small Businesses (FSB) in
the UK - should pool resources to campaign for greater
awareness of the value of data (and the reasons why this
value will increase in the next few years) among businesses
large and small.
.08Sector network groupings - such as the Sector Skills Councils
in the UK context - should each devise sector-specific
programmes designed to raise awareness and address
sector-specific constraints such as skills shortages.
.09It is expected that the advent of a fifth generation (5G) of
mobile telephony and ultrafast broadband will facilitate a
further acceleration of data opportunities, ranging from video
and audio streaming, through to online computer gaming,
virtual and augmented reality applications, and autonomous
vehicles. It is therefore vital that telecommunications
infrastructure providers (many of whom are private sector)
continue to invest in telecoms infrastructure capacity,
both in terms of ultrafast broadband and in the emerging
5G networks.
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Actions for Government
Investing in education and skills
Realising the full value of the Data Economy requires access
to the right technical and professional skills, including data
engineering skills to develop a robust data infrastructure,
data analysis skills to extract valuable insights from data, and
business skills to apply them.
.01
Government has a role in continuing to improve the
curriculum and in enhancing the quality and relevance
of teaching of subjects such as mathematics, statistics
and computer science in secondary, further and
higher education.
.02
Government can also help to promote the Data Economy
as a career destination for young people, especially
among groups (such as females) who are traditionally
under-represented in computer science and similar
occupations.
.03
Government also has a potentially important role in
helping to retrain older workers (including those who
have had a period of absence from the workforce) and
in providing incentives for smaller businesses to invest
in workforce training.
Open Data policies
Government has a key role to play in making its own data
Open Data, available and shared for others to use. Even
in the UK (which is ranked top globally for openness of
Government data) there is still more to do. In Ireland this is
a particularly pertinent issue as Ireland has a relatively low
ranking (27th) in the global Open Data Barometer rankings
(albeit its position has improved – by four places – in the
most recent ratings).
Telecommunications infrastructure
Government has a role to play in providing the regulatory
framework for the next generation of fixed and mobile
telecoms infrastructure. There is also a specific planning
policy issue with the future mobile network as 5G will require
a much denser physical coverage of masts and relay stations
compared to the current 4G network. This isn’t just relevant
to rural areas: investment will be needed to ensure good
quality of coverage within and between buildings in more
densely populated urban areas.
Better use of data by Government in delivering services
There are opportunities to improve the performance of
Government as data-led service providers: Government
needs to continually rethink the way that services are
delivered and truly embrace a Data Economy approach. For
example, in the UK it is estimated that only about 10% of
central government workloads have moved to cloud storage,
and in parts of the health sector or local government it is
estimated to be as a low as 2%.
About Digital Realty
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