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Report for SASJune 2013

Data on the Balance Sheet

M a k i n g B u s i n e s s S e n s e

Data

Capital

Economies

Reg

ulation

Fram

ewor

k

Competitors

Legal

Real time analysis

Skills

Organisation

Centre for Economics and Business Research

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DisclaimerWhilst every effort has been made to ensure the accuracy of the material in this document, neither the Centre for Economics and Business Research Ltd (Cebr) nor the report’s authors will be liable for any loss or damages incurred through the use of the report.

Authorship and AcknowledgementsThis report has been produced by Cebr, an independent economics and business research consultancy established in 1993, providing forecasts and advice to City institutions, government departments, local authorities and numerous blue-chip companies throughout Europe. The study was led by Shehan Mohamed, with direction from Oliver Hogan, Head of Microeconomics.

This study was commissioned by SAS UK & Ireland, the leader in business analytics software and services, and the larg-est independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 60,000 sites globally improve performance and deliver value by making better decisions faster.

The report has utilised data available in the public domain from a variety of public domain sources.

London, June 2013

Centre for Economics and Business Research LtdUnit 1, 4 Bath Street, London EC1V 9DXTel: 020 7324 2850 Web: www.cebr.com

Cover image created at Tagxedo.com Copyright © 2013 Tagxedo


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Contents

Executive Summary ................................................................................................................................................................. 1

Introduction .............................................................................................................................................................................. 2

The present: how is data valued and why is this problematic? .......................................................................................... 2

How to value data? .................................................................................................................................................................. 4

How long is data valuable for? ............................................................................................................................................... 5

Other factors affecting valuation of data .............................................................................................................................. 5

Competitive forces .............................................................................................................................................................. 5

Legal and regulatory ............................................................................................................................................................ 6

Technological change .......................................................................................................................................................... 7

Human capital ...................................................................................................................................................................... 8

A potential solution: integrated reporting .............................................................................................................................8


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Executive SummaryData that enable a company to improve customer relations, streamline production or develop new products are providing future economic benefit and should be regarded as assets.

It is increasingly important that firms are able to account for their data. Firstly, regulatory and compliance initiatives are putting greater emphasis on the quality of data and resulting decision-making in the aftermath of the financial crisis. Secondly, for financial reasons, data that play an increasingly important role in value creation must be recognised if they are to be accorded appropriate priority by company decision-makers.

At economy level, valuing data would provide investors with better information on relative returns, allowing capital to be allocated to activities with the highest expected returns. More efficient allocation of capital within an economy would quicken the pace of economic progress.

Recognising the value of data is also vital in valuing national economies. Current national accounting methods do not capture the growing importance of software and information services – the relative size of the sector has been broadly stationary at around 5 per cent over the last 10 years. This lack of awareness of the size and importance of data in mod-ern economies may, in turn, be preventing governments from recognising growth industries and tailoring regulation and taxation regimes to support them.

Several features of data, however, make them difficult to value within a traditional balance-sheet accounting framework.

• Datasetsareheterogeneous,meaningmarketvaluationisnotalwaysappropriate.

• Estimationsofthereturnoninvestmentindata(theprofitderivedwhenfirmsinvestindataandusethemintheirbusi-ness) can be highly uncertain, as a range of other factors including the behaviour of competitors and customers, legal and regulatory conditions, technology and the human resources available to analyse data all influence the value a firm can draw from them.

• Thecostsofgatheringandmanagingdatamaybedifficulttodistinguishfromthecostsofdoingbusinessandthenon-rival nature of data (zero marginal cost to make more widely available) makes it difficult to attribute costs across users.

• Datadoesnothaveaphysicalpresenceandthereforemaybeconsideredtohaveaninfinitelifewhencomparedalongside physical assets. However, data can depreciate quickly if it is readily outdated (e.g. unstructured social media and financial trading data).

• Somedatahasadditivevalue,thatis,thevalueoftheoriginaldataincreasesasmoredataisaccumulated(e.g.clinical,DNA and climate data).

• Whiletherateofdepreciationtendstobehigh,thereisvalueintheoptiontoputthedatatounforeseencommercialusages in the future, provided that they are well-maintained. But the valuation of such options is not straightforward either.

Other factors can also have a dramatic impact on the value of data

• Thebehaviourofcompetitorsandconsumerscanchangethevalueofdata.Inthehighly-competitivesoft-drinksmar-ket place, a company collecting data on consumer flavour preferences has an advantage, which can be eroded if a competitor gains access to similar data.

• Legalandregulatoryconditionscanaffectthevalueofdata.Legally-mandatedaccesstopublicsectordatacancreatean opportunity for other companies to create value. Information released by the Department for Transport in the UK, for example, is used to develop navigation apps. Changes in regulation can reduce the value of consumer data held by online retailers and used to generate advertising and transactions revenue.

• Dataonlyhavevalueiftheycanbeaccessedandanalysedbycurrenttechnologies.Forlargedatasetsorreal-timeanalysis this may require investment in new data-infrastructure. Use of technology to consolidate data across a major retailer can release value by reducing the time taken to create personalised promotions.

• Humaninputandunderstandingisneededtoaskquestionsofdata,analyseitanddeviseresponsestoitsinsights.These skills are in short supply, and companies facing these limitations are increasingly training staff in house.

Nevertheless, data must be accounted for. Given the challenges in measuring the value of data, we suggest an integrated reporting framework, providing investors and other interested parties with a more comprehensive view of a company’s value by dealing with the factors and risks that can boost or depress the value of data.


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IntroductionIn this report by Centre for Economics and Business Research (Cebr), we discuss the economic arguments for placing ‘data’ as an asset on the company balance sheet and in the wider financial reporting framework. Firms increasingly rely on data to generate value. Our report, ‘Data equity – unlocking the value of big data’ assessed the economic benefits of big data and forecast this to rise from £27.6bn in 2012 to £40.7bn per year by 2017 in the UK alone1. Data can benefit businesses by improving customer intelligence, supply chain management, quality control, fraud detection, risk man-agement and employee performance. With data playing a more important role in business strategy and operations, it is becoming increasingly important that their value is properly accounted for.

The first section of this report discusses how the current standard accounting system struggles to value data, and the consequences of this. The report then discusses methods of valuing data, and potential difficulties associated with these. The final section of the report offers a potential solution to these problems which is based on a form of integrated reporting.

The present: how is data valued and why is this problematic? An asset is defined as any resource controlled by a company which generates future economic benefits and has an associated cost or value which can be reliably measured2. As it can generate value for firms, data can be identified as intangible assets - a ‘non-monetary asset without physical substance’3. There has been significant growth in this asset class in recent years. The private intangible assets held in the United States and United Kingdom were worth £353.9bn ($565.5bn) and £59.5bn ($95.4bn) respectively in 20114. In both countries the value of this asset class has risen by 25 per cent in cash terms over the last five years. By comparison, the value of tangible assets has only grown by 7 per cent over the same period. The increasing worth of intangible assets has largely been fuelled by rises in computer software spend-ing and this trend is expected to continue as the developed world becomes increasingly knowledge-based.

As Figure 1 illustrates, the proportion of private assets that are intangible has increased substantially since 2001 as firms have invested in R&D, computer software and patents.

Figure 1: Percentage of total private assets which are intangible

Source: UK Office for National Statistics, US Bureau of Economic Analysis, Cebr analysis

1 Cebr (2012), “Data equity: Unlocking the value of big data”, SAS.2 International Financial Reporting Standards, (January 2012), IAS 38 Intangible Assets.3 International Financial Reporting Standards, (January 2012), IAS 38 Intangible Assets.4 Intangible assets include computer software spending, patents and artistic originals. Data for the US is based on computer software spending, which

accounts for the majority of asset value.


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There are two broad trends that are prompting businesses to classify their data as an economic resource and place them on their balance sheets, explaining some of the rise in the proportion of intangible assets.

Firstly, regulatory and compliance initiatives place an onus on businesses to manage their data quality in order to improve decision making and the flow of information to investors. It is possible that a lack of data governance in the lead up to the 2008 global financial crisis was one element that contributed to the collapse of the international banking system. A parliamentary review of the collapse of the British bank HBOS revealed shortcomings in the management information used to inform credit provision decisions5. The Financial Services Authority (FSA) indicated that the ‘quality and timeliness of data use for risk management needs improving’.

The second trend is financially-motivated. In an age where cloud, mobile, social media and big data dominate, business-es are handling vast volumes of information. These data are becoming increasingly important in determining business value. As customer data are shared and analysed within firms, it is likely that firms that can provide an interactive cus-tomer experience and personalise their products and services will see their brands strengthen. According to Interbrand, the companies that saw the largest rises in brand value in 2012 were Apple, Amazon, Google, Samsung and Burberry6 – all firms associated with data-intensive research7. Data are also increasingly valuable in allowing firms to optimise their operations, cut costs and maximise profits. In other companies, data are intrinsic to the firm’s existence - digital media companies trade in information and data are a core part of their output. Existing international financial accounting frameworks struggle to incorporate these assets, leading to growing discrepancies between market and book values of firms as the number of companies with substantial data operations expands and digital-intensive firms grow in size and economic importance8. LinkedIn, for example, held assets of $1.38 billion in 20129 but has a market valuation of around $20 billion10.

Given these two trends, companies are increasingly keen to formally assess the value of their data. Doing so, however, is far from simple. The economic value of data depends on both the intrinsic characteristics of the data and the environ-ment in which they are to be used. As such, defining their value and demonstrating it in monetary terms can be difficult. Predicting the value of data over time is more difficult than that of fixed assets and as markets for data are less well-developed, assessing the value of data is not straightforward. While firms may be able to place some data on their bal-ance sheets, many will also have significant stores of data which they are unable to appropriately value. The initial public offering of Facebook illustrated this clearly: the company was initially valued by the market at $104 billion, despite having recorded assets worth only $6.3 billion11. These stores of data are, as such, not reported in financial statements.

Most accounting standards provide a set of conditions under which it is appropriate to place data on a company’s bal-ance sheet. These standards, however, do not give specific advice on how data should be valued. Data that have histori-cally been sold or purchased at specific market values can be placed on the balance sheet (e.g. patents and computer software). However, firms do not in most cases wish to sell their data in the first place, instead gaining an advantage from the exclusivity of their ownership and use. Accounting standards enable firms that generate proprietary data internally to place data onto the balance sheet once it is sufficiently ‘developed’ and economic benefits become palpable. The cur-rent accounting frameworks, therefore, only assign data a value when those data are expected to produce future benefits and the value of these benefits can be estimated with reasonable certainty.

These problems with existing accounting methodologies affect national accounts, as well as the accounts of compa-nies. National accounting frameworks at present classify the collection, storage and analysis of data as a cost of doing business, despite the fact that data can have many of the characteristics of a fixed asset. Existing GDP measures were designed for an economy in which goods and assets are largely physical, and suggest that the information sector (software, publishing, motion picture and sound recording, broadcasting, telecoms and information and data process-ing services) makes up the same share of the US economy as it did 25 years ago (about 4 per cent) despite the dramatic increase in the number of firms operating in these markets12.

5 Parliamentary Commission on Banking Standards (March 2013), “‘An accident waiting to happen’: The failure of HBOS”.6 Interbrand (2012), Best Global Brands 2012, Interbrand.7 Although, unlike the other brands in this list, Burberry is not primarily a technology company, it has been noted as having a well-developed data strategy:

http://www.forbes.com/sites/netapp/2013/04/01/burberry-caesars-big-data/.8 Mayer-Schonberger and Cuiker, K. (2013), “Big Data: A Revolution That Will Transform How We Live”, Work and Think, John Murray.9 LinkedIn financial statement 2012, http://investors.linkedin.com/financials-statements.cfm.10 Nasdaq valuation, www.nasdaq.com/quotes.11 Mayer-Schonberger and Cuiker, K. (2013), “Big Data: A Revolution That Will Transform How We Live”, Work and Think, John Murray. Although the company’s

value fell quickly after the IPO, there remains a significant gap between the market and “book” value of the company, which is at least partially due to the firm’s inability to record the value of their data in their accounts.

12 Brynjolfsson and Saunders (2009), “What the GDP Gets Wrong (And Why Managers Should Care)”, MITSloan Management Review.


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The same argument can be made in the UK, where the proportion of gross value added accounted for by information and communications industries has remained at 6 per cent since 2002, despite

continuous technological progress. With the growth of the service-based industries and of digital production, changes in accounting measures are necessary to ensure different types of output are accurately recorded13. This will, in turn, help ensure that government policies are informed by a better understanding of the relative contributions of these sectors.

The United States Bureau of Economic Analysis announced, in March 2013, the decision to recognise expenditures by business, government and non-profit institutions serving households on research and development as fixed investments in the national accounts from July 2013 onwards. The UK is expected to follow suit in 2014. This move to new internation-al accounting standards will bring billions of dollars’ worth of intangible assets, including data, onto the balance sheet. Research and development is defined as “creative work undertaken on a systematic basis to increase the stock of knowl-edge, and use this stock of knowledge for the purpose of discovering or developing new products, including improved versions or qualities of existing products, or discovering or developing new or more efficient processes of production.”14 Data are often used by companies to improve existing products or processes of production or to develop new products meaning that they clearly fall within this definition.

Recognising research and development, including big data, as assets will also improve company profit figures, as R&D spending will no longer appear as a cost of doing business15. This, in turn, should prevent data from being undervalued, and increase the potential for further developing big data.

How to value data?There are three ways of attempting to quantify the value of data. Firstly, data may be evaluated through its market value. Data are then considered to be worth as much as they could be sold for on an open market. The usefulness of this measurement is limited, however, by the variety of data types and sources. Data are not standardised assets like gold or bonds, and so have limited liquidity. This heterogeneous nature of data means companies have little way of knowing the market value of their data without attempting to sell it. Security and privacy concerns have also dampened enthusiasm for sales of data.

Secondly, data could potentially be valued based on a quantification of the earnings and profits from their use. This is analogous to a return on investment model, data being valued by identifying the earnings a firm gains by investing in data. This is closely related to the first type of valuation, as market prices should align with the value firms are able to derive from data. This approach, however, emphasises the value of data to the firms who collect it and may be particu-larly useful for novel forms of data that do not yet have a market valuation. However, it can be difficult to determine the potential earnings that data will generate in the future due to the range of other factors that determine them, such as competitor behaviour, technological progress, legal and regulatory constraints and the availability of human resources.

Where data give a firm a competitive advantage, for example, their valuation will depend upon the reaction of other firms in the market and may decline if those competitors find a way to respond. The earnings accruing from data may also be less than expected if the business is unable to recruit the staff needed to analyse them. Furthermore, changes in legisla-tion that regulates the use of data may prevent organisations from taking advantage of their knowledge. For example, if the law is changed to prevent automated analysis and marketing, companies would no longer be able to advertise to internet users on the basis of their page view history, reducing the valuation of these kinds of data16.

Thirdly, data could be valued at the cost of collecting it, in line with traditional accounting practices. But this only works where the costs of data collection can be clearly identified. This is simply not practical in many cases because data is collected automatically during the production and selling process. Furthermore, data collected for one purpose, with a certain set of fixed costs, may then have value in other applications. For example, data collected by a car manufacturer to alert drivers to the maintenance requirements of their vehicles (a customer service function) may be used to under-stand the driving habits of drivers, and fed into product development.

13 For example, OECD ICT, “R&D and Intangibles”, ICTNET Issue Paper 3, OECD.14 System of National Accounts 2008, 119 paragraph 6.207.15 Harding, R. (2013), “Data shifts to lift US economy 3 per cent”, http://www.ft.com/cms/s/0/52d23fa6-aa98-11e2-bc0d-00144feabdc0.html#axzz2Tof8Oa2r.16 The European Commission has proposed regulations that would mean all companies holding personal regulations would have to be able to show how and

why they are using these data – Pignal, S and Palmer, M, “New EU privacy rules worry business”, Financial Times 22nd January 2012.


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The attribution of the costs of data in situations such as these is difficult, as data are non-rival assets and there is no additional cost to making data more widely available. With traditional accounting frameworks, it is unclear whether the costs of gathering the data should be counted twice or whether this would be adequate to capture the additional value

in the potential second use of the data in the example above. Furthermore, technological progress is likely to mean that the marginal cost of collecting data will fall in future, while the potential to analyse that data and benefit from them increases. Costs are, as such, increasingly unlikely to provide an accurate valuation of data.

How long is data valuable for? There are further difficulties with valuing data using these different approaches. Specifically, it is difficult to account for depreciation of data as an asset class over time. Unlike physical assets, intangible assets do not suffer from wear and tear over time. However, while their lifespan is notionally infinite, it is limited in practice to the extent to which they can be put to productive use. The rate of depreciation in some instances can be high, which is can be the case for unstructured social media and point-of-sale data used for predicting consumer tastes. DNA data used to develop personalised medi-cines or climate data used to predict weather systems, by contrast may have an ‘additive’ value i.e. the value of the data grows as the data is accumulated.

The valuation of data must take into account the length of time over which they directly contribute to economic output and the profits of their owners. The value of data may be eroded over time as they are replaced with new data offering better insights into current circumstances. Understanding this process of decaying value is crucial in ensuring accuracy, but is likely to depend on the nature of the data concerned and their purpose. Moreover, unlike most physical assets, data are non-rival goods which can be used simultaneously by multiple users without losing value. The value of data can, therefore, be much greater than that derived from its initial use. Establishing this value, however, is very difficult as the uses for data cannot always be foreseen. Data in these instances may be seen as having an “option” value. That is, with an uncertain future and environment, collecting and storing data may offer the flexibility to decide to put the data to some currently unforeseen use in the future. This flexibility to use the data after some of the uncertainties are resolved has a value and option pricing provides the theoretical foundations for establishing it.

The quality of data is also vital in determining their value. Simple errors in inputting data are common and can undermine the value of a dataset, as can duplication, missing data, incomplete or inconsistent entries. Technology that can over-come common errors such as misspellings will help to ensure that data are useable and maintain their value. Time-series datasets involve metadata which describe the content and context of data files and which can be as important as the data themselves. Time series data can only be usefully analysed where there is continuity in measurement over time. But current systems often lack the capacity to store metadata, and this poses a risk to the value of data they hold17. The value of big data will crucially depend on the degree to which data are captured effectively and stored securely, as these determine the extent to which analytics can interrogate data rigorously and successfully.

Furthermore, in many cases data lacks inherent value and must be analysed and combined with other inputs (and data sources) to unlock insights which have value for a business. With assets such as these, value is highly contingent, depending on conditions that external to the firm (legal rules about data collection and analysis, the degree to which markets are competitive) and internal factors (technology and availability of the right kinds of human capital).

Other factors affecting valuation of data

Competitive forces The degree to which a market is competitive determines the importance of data in maintaining profitability and, there-fore, affects the valuation of data. Data that allow a company to target and maintain customers more effectively may be particularly valuable in a competitive market place. Where firms fight for customers, the ability to respond quickly to competitors’ price changes or to target consumers can be important in raising market share and revenue. Equally, where profit margins are tight due to price competition between firms, data that allow a company to streamline its operations and cut costs will be relatively more valuable.

17 Informatica (2012), “Balancing Opportunity and Risk in Big Data: A Survey of Enterprise Priorities and Strategies for Harnessing Big Data”, Informatica.


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The marketplace for soft drinks is highly competitive, and dominated by a few very large companies. Three players made up 66 per cent of the total market in the United States in 201018. Product differentiation is crucial to success and mar-ket share growth by creating new, innovative drinks to attract customers away from rivals. Data can provide important insights into consumer tastes, enabling companies to steer their research and development efforts towards products that are likely to receive the most favourable market reception. Coca-Cola has already found a novel way to collect data on consumers’ preferences. The company’s Freestyle soda fountains, launched in 2009, allow consumers to customise their soft

drinks by adding extra flavours to established products, with data on customer choices streamed back to the company’s headquarters19. This provides a wealth of real-time data about popular flavours and ordering habits in particular areas, which can provide Coca-Cola with a competitive advantage, allowing it to understand and react to consumer prefer-ences. These data will also have a higher market value due to the competitive advantage they confer upon the owner.

The value of these data in providing a competitive advantage, however, is affected by rivals’ access to similar insights. PepsiCo has recently announced plans to test similar machines20 and if these collect data in the same way as the Coca-Cola Freestyle, this could reduce the value of Coca-Cola’s data-stream. While these data would continue to allow the firm to meet consumer demand more effectively, they would no longer form a unique competitive advantage.

Big data can also provide a competitive advantage by distinguishing a company from its rivals. The Climate Corporation, for example, competes with other agricultural insurers by using big data to offer famers premiums that vary according to the crop and location, analysing data on weather patterns, climate trends and soil characteristics to provide individual-ised quotes21. Data enables Climate Corporation to tailor policies more precisely, protecting their returns and providing better value for customers. However, other companies may also have access to the same public data that businesses such as Climate Corporation draw on. The ability of another firm to access and analyse data like the Climate Corporation does, the valuation of the data in terms of earnings generation would be reduced as it would no longer provide a unique competitive advantage.

Legal and regulatoryThe legal and regulatory environment in which a firm operates can play a role in determining the value of company data. Changes in legislation that have the effect of liberalising data previously held by one or a select group of firms will have consequences for incumbent guardians of the data as well as for wider business.

This is particularly relevant in the case of the mandated open release of public sector information which, when placed into the hands of private business, can drive innovation and business creation22. The executive order by the White House requiring all US federal government data to be ‘open and machine-readable’ itself highlights the importance of manag-ing information as an asset, by making data accessible, discoverable and usable, insofar as this drives entrepreneurship, innovation and scientific discovery23. The White House cites a parallel from the 1980s, when the Federal Government made both weather data and Global Positioning System (GPS) data freely available to anyone. This spurred the creation of navigation systems, weather warning systems and location-based applications to name but a few.

The Open Data Institute (ODI) in the UK is an independent organisation helping businesses to use open data to create new products and services. The ODI wishes to see a “legal duty placed on public bodies to create and maintain their core reference data” which are, in turn, provided universally to private enterprises24. Commuters are already using apps based on real-time transport data released by rail and bus operating companies to plan their journeys and data from the Department for Transport (DfT) is being used to develop apps which enable people to navigate around cities25. These are cases in which access to public data has generated income for firms, while legislation requiring data to be public has dramatically reduced the market value of the data and the costs of acquiring them26. Legislation therefore plays a signifi-cant role in determining the value of data as an asset to both public and private organisations.

18 Sierra Services, Inc and Supply Chain Management Centre, Rutgers University (2012), “Breaking Down the Chain: a guide to the soft drink industry”, ChangeLab Solutions.

19 Scanlon, J. (2009), “Coca-Cola’s Freestyle”, Bloomberg Businessweek http://www.businessweek.com/innovate/next/archives/2009/09/coca-colas_free.html.20 CBC News (2013), http://www.cbc.ca/news/business/story/2013/05/10/business-pepsico-coca-cola-freestyle-touch-tower.html?cmp=rss.21 The Economist, 19th November 2012, http://www.economist.com/blogs/schumpeter/2012/11/weather-insurance.22 OECD (2013), “Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by ‘Big Data’”, OECD Digital Economy

Papers, no. 222, OECD Publishing.23 Open Data Policy (2013), “Managing Information as an Asset”, Memorandum for the heads of executive departments and agencies, Executive office of the

President.24 Open Data Institute (2013), http://theodi.org/news/odi-calls-government-act-fast-response-shakespeare-review.,25 UK Cabinet Office (2013), https://www.gov.uk/government/policies/improving-the-transparency-and-accountability-of-government-and-its-services.26 Companies will, however, still face some costs to store and analyse data.


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The valuations of data are not solely affected by changes in the law, but also by developments in the tax regime. For instance, there is a bill in the United States obliging online retailers to collect sales taxes on behalf of States27. Currently, sales tax is not collected where online retailers do not have a physical presence (offices or distribution centres) in the State where the online purchase is made. Such a bill, if passed, would impact the business models of companies such as eBay and Amazon which rely on their marketplace data to match consumers with trading businesses. According to a Survey results suggest that a total of 44 per cent of respondents said they would buy fewer products online if the bill was passed28. Such a change in the regulatory environment has implications for the usefulness of customer data held by online retailers, but also proprietary data held by online marketplaces which generate significant advertising and transac-tion-based revenues with their data.

Technological change Data must be analysed in order to yield value and this is often complicated by technological problems. Many companies’ existing stores of data are scattered across a variety of formats, creating difficulties in unifying data. Legacy systems often undermine the value of historical data, necessitating costly cleaning and reformatting before analysis is possible. Moreover, as the nascent market for big data technology remains fragmented it is likely that some consolidation will occur in the coming years29, meaning that some formats and file types may become obsolete.

For data to retain their value, they must always be stored in accessible formats using infrastructure which is robust enough to adapt to changes. Assuming data are accessible, technological developments may also boost their value, providing the potential for new forms of analysis. For example, real-time information on purchases and cash flows is likely to have substantial value in fraud-prevention, but at present there is a lack of technology able to analyse this data easily and cheaply30. The development of these systems could substantially increase the valuation of this data.

The scale of big data will also place pressure on technological infrastructure. Data often lacked value in the past simply because it was impossible to store and analyse them and, therefore, to derive earnings from them or to sell them on. The value of data is increasingly defined by the extent to which it can be exploited as these advances progress. Enormous datasets are needed to analyse trends over long periods of time or across whole populations, which will challenge exist-ing server provision. Analytics firm NPD Group, for example, found that they needed to replace their entire data-storage infrastructure to retain the value of their data as customer demand for real-time analysis increased31. To maintain the value of data, companies will need to invest in digital infrastructure to ensure data are readily available, that access is reliable, and that storage is secure. This is likely to increase the costs of collecting data initially, but technological prog-ress is likely to reduce the relative costs in the medium to long term. The market valuations of data are also likely to rise as firms become more sophisticated in analysing them and as demand rises.

The value of big data may also be limited by the fragmentation of data across a business. Departments each collate their data using separate systems, which has led to a system of information silos within a business. This can mean departments are unable to access data from elsewhere in the firm due to difficulties integrating different formats or due simply to a lack knowledge of the data available to them from other departments, preventing full exploitation of the data. Companies with information about customers scattered across different departments, for example, may miss oppor-tunities to sell related products or offer relevant support. Taking steps to unify and integrate these data can increase their value. Sears Holdings, a US retailer, collected a great deal of data, but this was scattered across many databases and warehouses maintained by different brands. By consolidating their data, the company avoided the need to collate data from different sources before undertaking any analysis. The time taken to design a set of promotions fell from eight weeks to one and the quality improved – promotions were timelier and more finely tailored to the consumer, increasing the chances of uptake and improving customer loyalty32. The value of the firm’s existing data in terms of potential earn-ings was thus multiplied by applying advances in data-storage technology.

Changes in technology in cases like these can add value to data by making it easier to work with. The valuation of data will be influenced by a firm’s IT systems and technological advances, whether measured in costs or market value.

27 Marketplace Fairness Act of 2013, http://www.gpo.gov/fdsys/pkg/BILLS-113s743es/pdf/BILLS-113s743es.pdf28 Endicia consumer survey, cited by Anderson, G. , “Will the Marketplace Fairness Act hurt online sales”, Retail Wire, 15/05/2013: http://www.retailwire.com/

news-article/16771/will-the-marketplace-fairness-act-hurt-online-sales29 Informatica (2012), “Balancing Opportunity and Risk in Big Data: A Survey of Enterprise Priorities and Strategies for Harnessing Big Data”, Informatica.30 Popular open source big data analytic system Hadoop, for example, lacks a real-time analysis capability: http://www.informationweek.com/big-data/

slideshows/big-data-analytics/5-big-wishes-for-big-data-deployments/240153214.31 Hewlett Packard press release, 6th May 2013. http://www8.hp.com/us/en/hp-news/press-release.html?id=1404342#.UZYBi0o4GZS.32 McAfee, A. and Brynjolfsson, E. (2012), “Big Data: The Management Revolution”, Harvard Business Review.


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Human capital Data are valuable inputs to production in their own right, but, like physical capital, they cannot create value on their own. Data need to be analysed and the resulting insights extracted and put to work. The valuation of data thus depends on the quality of the human capital they are combined with in the production of output. With the growing availability of data, the complements to them are becoming more valuable, particularly the human resources needed to work with data.

Several different types of skill are needed to draw value from data. Firstly, data scientists and statisticians are needed to distil information. With unstructured data (such as word documents, videos and photographs) becoming more important, data cleaning and organisational skills are also crucial. Data visualisation is also increasingly important, utilising a com-bination of data and design skills to ease the understanding of large data sets. Finally, the business implications of data insights need to be clearly conveyed to decision-makers.

While technologies to deal with big data have developed quickly over the last decade, the skills needed to use them are still relatively rare. People with these technical skills and the ability to derive relevant business insights are in short sup-ply. The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills, and 1.5 million manag-ers able to act on these findings33. More than a quarter of firms surveyed by Informatica in 2012 said they were chal-lenged by lack of skilled data scientists to perform analysis, while over 30 per cent reported difficulties recruiting skilled developers to manage big data34.

Recent surveys of business leaders by Harvard Business Review found that 69 per cent are offering training and devel-oping data skills within their existing workforces. Technology firm IBM has developed relationships with about 300 aca-demic institutions, including Yale, to develop big data analytical skills35. Leading business analytics company, SAS, has developed similar relationships with more than 2000 universities across the globe. However, it will be some time before businesses have a ready supply of people to the level required to capitalise on the potential value of big data. Without people to ask the right questions of data and interpret the answers, the valuation of a firm’s data is much reduced. Companies can overcome this constraint, to an extent, by outsourcing analytics.

Managers also need to acquire knowledge of data, and the skills to interpret it. While managers do not need to have the deep statistical knowledge of analysts, they must be able to reason mathematically and to understand how to interpret the results of statistical models to ensure appropriate business operational changes are made in response to the insights generated by data analysis. Proctor and Gamble has established a baseline digital skills inventory for each level of the organisation, along with a training facility, to ensure that all staff can competently analyse the data they work with36. Data literacy is increasingly important across the board and skill levels across companies are vital in maximising the value of data in terms of potential to generate income.

A potential solution: integrated reportingWith the growing importance of data to both the activities of individual firms and national economies, it is increasingly critical to find ways of expressing their value accurately. Without this innovation, decisions at both corporate and national level will be made on the basis of incomplete information, magnifying the risk of misallocation of resources and conse-quent economic inefficiency.

One potential solution is a shift to integrated reporting. Partly in response to the difficulties inherent in valuing modern firms and economies due to their dependence on intangible assets such as data, the International Integrated Reporting Council (IIRC), a global coalition of regulators, investors, companies, accounting professionals and non-governmental organisations, are calling for a new international accounting framework. The Council argues that current accounting frameworks overemphasise short-term financial information and neglect the sources of long-term value creation.

33 McKinsey Global Institute (2011), “Big data: The next frontier for innovation, competition and productivity”.34 Informatica (2012), “Balancing Opportunity and Risk in Big Data: A Survey of Enterprise Priorities and Strategies for Harnessing Big Data”, Informatica.35 Melgio, D (2013), “IBM Brings ‘Big Data’ to Business School”, Bloomberg Business Week, http://www.businessweek.com/articles/2013-05-01/ibm-brings-

big-data-to-business-school.36 Chui, M. and Fleming, T. (2011), “Inside P&G’s digital revolution”, McKinsey Quarterly. http://www.mckinsey.com/insights/consumer_and_retail/inside_p_and_

ampgs_digital_revolution.


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They propose that each firm should report all the inputs it uses and the business model it relies upon in transforming those inputs into outputs in order to present a clearer picture of the firm’s ability to create value in the short, medium and long term37. This means firms must refer not only to financial and physical capital in their reporting, but also to human, social and relationship capital and knowledge capital. The resulting reports would present a forward-looking picture of the firm’s prospects, rather than a review of past performance. The hope is that this form of reporting would more accurately represent the value of firms, reducing the likelihood that investors take ill-informed investment decisions and improving access to finance by minimising risk premiums on investments.

This form of reporting would encourage firms to make their reliance on data and its role in value-creation explicit. Firms would be free to choose the most appropriate way of valuing their data to reflect their individual circumstances. They would also be able to deal with the factors and risks that can boost or depress the value of data, making the assump-tions around data valuation explicit and providing investors with realistic information about the fluid values of their data assets. With the additional information this form of accounting would provide, companies and governments would be in a much better position to assess the value of data and create policies to support this vital new source of economic growth.

37 Integrated Reporting (2013), “Consultation Draft of the International <IR> Framework”, Integrated Reporting.

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