8.We are living in the data age. Since Sir Tim Berners-Lee proposed his “vague but exciting” plan for a ‘distributed information system’ at CERN—and in the process inadvertently launched the information revolution—the amount of data we share has exploded (paragraph 2). Properly exploited, this data should be transformative, increasing efficiency, unlocking new avenues in life-saving research and creating as yet unimagined opportunities for innovation across all sectors and industries.
9.As TechUK put it, “big data is a UK success story—underpinning the digital transformation across sectors and industries including retail, media and fintech, and is a key driver in enabling digital entrepreneurialism”. As Nesta highlighted, the UK—as ‘the connected kingdom’—is well placed to capitalise on this opportunity. In the year to June 2015, eight UK tech businesses reached a valuation of at least $1bn—so called ‘unicorns’. But, as TechUK noted, “we are only at the beginning of the evolution of big data technologies”. Existing datasets are nowhere near fully exploited, with most companies surveyed estimating that they are analysing just 12% of their data.
10.The Centre for Economic and Business Research estimated in 2012 that big data could create 58,000 new jobs over the period 2012–2017, and contribute £216 billion to the UK economy, or 2.3% of GDP, over that period (Figure 1). It expected two-thirds of this contribution to come from business efficiency (£149 billion), with the rest accounted for by business creation (£42 billion) and business innovation (£24 billion). The manufacturing sector was expected to be the biggest contributor, with £45 billion over the five year period (see Example 1). The opportunities for central government, as we discuss below, were also seen as significant (paragraph 34).
Figure 1: Economic value of big data, by sector, 2012–2017
Transport & logistics
Energy & utilities
Example 1: Modelling in automotive design
High-powered computing is being used to run computer simulations that model components of a product before the manufacturing process begins. Engineers from Bentley Motors used one such system to create virtual models of vehicles. This enabled faster product development times, decreased the number of prototypes required, reduced costs and eliminated the need for late-stage modification.i
i House of Commons Parliamentary Office of Science and Technology, Big data in business, POST Note 469 (July 2014)
11.Nesta told us that “data–driven companies are over 10% more productive than ‘dataphobes’—firms that don’t exploit their data”. They estimate that if all such dataphobes were to make good use of data in driving their business decisions, it would produce a 3% increase in UK productivity.
12.Big data is a “key driver in enabling digital entrepreneurialism”. The Government has sought to promote this through initiatives such as the Digital Catapult (which we discuss at paragraph 53) and Tech City UK. Tech City UK was launched in 2010 to support the East London tech cluster known as Silicon Roundabout, though the organisation has since extended its support to Greater London and other UK cities. Tech City’s budget from Innovate UK is £2.2 million for 2015–16, which covers “programmes, policy informing and championing work”. Baroness Shields, minister for internet safety and security but also a former chief executive of Tech City, told us that its budget “does not sound much, but the impact of that initiative, putting a spotlight on technology in this country, has led to enormous investment and innovation around this area”. Indeed, the number of London’s digital technology sector companies grew by 92% between 2010 and 2013, and its Tech companies now employ over 250,000 people—a 17% increase on five years ago compared to a 7.8% rise in overall employment.
13.The Government has identified the financial services technology—’fintech’—as a potential growth technology sector. In a 2014 report commissioned by UK Trade & Investment, consultants EY estimated that the UK fintech sector generates £20 billion in annual revenue. Imram Gulamhuseinwala from EY gave evidence to us on the factors which were making “the UK market one of the most attractive markets in Europe” for fintech. Experian believed the role of big data in financial services was to:
detect patterns of financial or insurance fraud, to combine trader performance data, market data, unstructured news, user data, and general ledger data to gain previously impossible insights. This enables the ‘real time’ decision-making power that makes a difference between winners and losers in the financial markets.
James Meekings, the co-founder of Funding Circle, stressed the “huge potential that big data holds for the fintech industry” (see Example 2). He told us that:
The more data we can access about the small businesses that come to us, and the better we can analyse it, the greater the benefit we can have. We already carry out extremely thorough checks on every business that applies for a loan, using the same systems as the banks. However, greater access to businesses’ data would allow us to speed up these checks—and increase the overall number of businesses that we can assess.
Example 2: Fintech
Funding Circle is an online peer-to-peer lending network, identified by TechCity as one of its ‘Future Fifty’ii—the UK’s top 50 growth-stage digital companies. The Government’s British Business Bank has partnered with Funding Circle as part of its Investment Programme, and has invested a total of £60 million in smaller businesses since 2012 via the company’s lending platform.iii
ii Funding Circle, Future fifty
iii BIS, New £40 million investment by British Business Bank to support £450 million of lending to smaller businesses, News item, 25 February 2014
14.The potential benefits of big data are also significant in healthcare and medical research—both in terms of efficient delivery of services and discovery of more effective, personalised treatment of patients (see Example 3). Professor John Williams of the Royal College of Physicians illustrated the possibilities of big data for stratified medicine, using the example of targeted treatments for irritable bowel disorders. He concluded that:
If we had large datasets, where we could analyse the physical and genetic make-up of the patient and their disease, we would be able to predict from the large dataset which of those treatments the patient was most likely to respond to. We would avoid putting the patient through a series of very dangerous treatments and end up precisely with the one most likely to benefit them.
Example 3: Cancer diagnosis routes
The National Cancer Intelligence Network ‘Routes to Diagnosis’ study examines different routes to cancer diagnosis, including delays in diagnosis, and their impacts on survival. It links data from Hospital Episode Statistics, cancer waiting times and cancer screening to data from the National Cancer Data Repository. Personal identifiers are used to link these datasets at patient level and to look at the effects of factors such as socio-economic status, age, gender and ethnicity on Routes to Diagnosis and patient outcomes. Results have informed public awareness campaigns, such as Public Health England’s ‘Be Clear On Cancer’ campaign, seeking to help patients to spot symptoms of cancer earlier.iv
iv House of Commons Parliamentary Office of Science and Technology, Big data and public health, POST Note 474 (July 2014)
15.Other scientific disciplines such as experimental physics also make extensive use of big data techniques. The Data Centre for the Large Hadron Collider at CERN (the world’s largest and most powerful particle accelerator) “processes about one petabyte of data every day—the equivalent of around 210,000 DVDs”, and distributes this data across the world via a grid which “gives a community of over 8,000 physicists near real-time access to LHC data”. In the future, the Square Kilometre Array (the world’s largest radio telescope, run from the UK’s Jodrell Bank Observatory) will “require supercomputers faster than any in existence in 2015, and network technology that will generate more data traffic than the entire Internet”. The computer power it will need will be about three times more powerful than the most powerful supercomputer available in 2013, equivalent to the processing power of about 100 million 2013-era PCs.
16.The Meteorological Office uses supercomputers to model climate change and its impacts. In a more everyday application, it collects and analyses a massive amount of data every day to produce weather forecasts, as well as advising energy and retail sectors, for example, about weather that might affect “consumer trends”. Others have used Met Office data, along with other datasets, to provide additional big data commercial outputs (Example 4).
Example 4: Using big data to plan for extreme weather events
UK company, KnowNow Information Ltd, provides information for emergency services to plan for, and respond to, extreme weather conditions. Using the big data analysis capabilities provided by the Science and Technology Facilities Council’s Hartree Centre, it can predict the probability of certain types of emergency, based on location and weather conditions. Its flood event model combines existing open data generated by the emergency services, the Met Office, Ordnance Survey, the British Geological Survey and the Environment Agency.v
v STFC, Big Data predicts extreme weather blackspots for UK emergency services, news item (5 September 2015)
17.The Centre for Economic and Business Research estimated that there could be £20 billion of benefit from big data over a five-year period for central government (Paragraph 10). The Government described how big data can “help cut costs, increase productivity, and improve the delivery of services”. By analysing 800 million monthly credit and debit card payments, for example, and matching these with other datasets, HMRC has been able “to more effectively target tax enforcement activity”. Paul Maltby, Director of data at the Government Digital Service, described the opportunities for better use of data:
There is huge opportunity … [to use data] to segment audiences and think about predictive analytics and tailor interventions. That is one of the very large potential gains in this field for public services.
18.Elsewhere in the public sector there are opportunities for efficiency savings in transport (see Example 5). Transport for London (TfL) told us how they were using big data to “transform transport services”:
Twelve million daily public transport trips make Oyster and contactless payment cards a significant source of big data. Nineteen million daily ‘taps’ from these systems allow travel patterns to be studied, bringing a depth to our understanding of customer profile and behaviour. The additional 18 million car, cycling, and walking trips provide a phenomenal 30 million daily journeys on the TfL network that are fit for big data analysis.
London’s richly detailed travel data feeds into transport planning models to predict the impact of development in our city. TfL has long used station entry and exit data for network planning. We can now also infer where people are leaving a bus through a big data tool … that combines bus location and ticketing data to create origin and destination pairs. This creates a comprehensive picture of travel patterns which network planning teams can use to minimise the impacts of closures or diversions.
Example 5: Modelling the rail network assets
Network Rail’s £330 million ‘ORBIS’ programme aims to create a detailed digital model of the UK’s rail network in order to improve the organisation’s asset management. This uses geographical data collected by maintenance staff using tablets and smartphones to generate a spatial model of the railway infrastructure, containing information about how assets are used and their capability and performance.vi
vi Royal Academy of Engineering and Institution of Engineering & Technology, Connecting data: Driving productivity and innovation (November 2015
19.The UK is a world leader in big data research across disciplines and our Tech sector, especially in London, dramatically outperforms the rest of the economy on growth and productivity indicators. By identifying big data as one of the Eight Great Technologies, and investing significant financing in large scale data infrastructure, the Government has signalled that realising the full potential of big data is a priority. However, investing in capital infrastructure projects alone will not deliver this. Urgent action on the digital skills crisis, overcoming public distrust over data sharing, further progress on ‘open data’ and greater clarity over prospective data protection legislative changes are essential if the UK is to set the pace on big data. We discuss these pre-requisites in the following chapters.
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13 Boston Consulting Group, The Connected Kingdom: How the Internet Is Transforming the UK (2010)
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15 The Telegraph, , 15 June 2015
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23 Oxford Economics, as reported by The Telegraph, It’s taken years but the UK is finally building a great technology industry, 15 June 2015
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35 GLA and TfL ()
Prepared 11 February 2016