Data Economy Report - Digital Realty

Data Economy

Report2018

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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.

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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


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.

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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.



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


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.


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


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.


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.


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.


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


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.


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


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


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.


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.


.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.


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%


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


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.


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.


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


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


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


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 %


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


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 %


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 %


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.


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


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 %


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 %


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 %


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


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


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 %


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


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.


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%


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%


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.


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.


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.


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 %


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).



GVA 2025Worsened Constraints

Change 2016-2025 (£millions)

Change 2016-2025 (%)

Reduction in GVA (£millions) cf Main

Case

Reduction in GVA (%) cf Main Case


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 %


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 %


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 %


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%.


RegionGVA2016

GVA 2025

Worsened Constraints


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.


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.





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.



5% and 11% higher (varying by sector) compared to levels



staff is assumed to be between 4% and 9% higher (varying


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.


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.


GVA 2025Eased Constraints


Change 2016-2025 (%)


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 %


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%


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 (%)


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%


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


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


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 %


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 %


3 3 0 . 3 %

S Other services 1 8 0 . 2 %

Total 9,9 62 1 0 0.0 %

Ireland Data Economy Results4


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.



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.


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 (%)


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%


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 %


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%



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,




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%


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



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 %


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 %


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 %


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.


€millions

GVA 2025

€millions

Increase in GVA

(€millions)

Increase in GVA (%)


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 %


M Professional services 5 6 4 9 0 9 3 4 5 61 %


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 %


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%


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




5% per annum lower under this scenario compared to the




6% and 13% lower (varying by sector) compared to levels



staff is assumed to be between 5% and 10% lower (varying


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.


The below table sets out the levels of GVA attributable to


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%).


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 %


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 %


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%


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.





5% per annum higher under this scenario compared to the




6% and 13% higher (varying by sector) compared to levels



staff is assumed to be between 5% and 9% higher (varying


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.


The table below sets out the levels of GVA attributable to


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.


GVA 2025 Eased Constraints

Change 2016-2025 (€millions)

Change 2016-2025 (%)

Increase in GVA (€millions)

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%


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 %


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 %


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.


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.


% of total


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 %


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%


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


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


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 %


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


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



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.


GVA 2016€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 %


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 %


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 (%)

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 %


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


• 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%).


attributable to the German Data Economy is estimated to

have increased from about 2.91% to 3.24%.



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).


The estimates for the extent of the achievement of Data

Economy actualisation are presented in the table below,




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


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 %


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.


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.


% of total


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%


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 %


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


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


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


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



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.


GVA 2016€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 %


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 %


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%).


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 (%)

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 %



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,




to represent.


Sector (Sections)2016 Actual GVA

€millions2016 Full Potential GVA

€millions

Full Potential GVA minus Actual GVA

€millions


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%


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).


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


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 %

D A T A E C O N O M Y R E P O R T 2 0 1 8D A T A E C O N O M Y R E P O R T 2 0 1 88 0


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


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


.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.


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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successfully, by allowing them to focus on innovating,

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We support the digital strategies of more than 2,300

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and third sector clients. Services include labour market

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Documents

Data Economy Report - Digital Realty