copy the linklink copied!3. Economic and social benefits of data access and sharing
This chapter presents the available evidence of the direct and indirect economic and social benefits of data access and sharing. It then analyses the different types of benefits in more details. These include greater transparency and empowerment of users, new business opportunities, competition and co-operation within and across sectors and nations, crowdsourcing and user-driven innovation, and increasing efficiency.
The use, and in particular the re-use, of data across the economy underline the importance of data as a new form of capital for 21st-century knowledge economies. Data cannot be depleted as it can be re-used for a theoretically unlimited range of purposes (OECD, 2015[1]).1 This can create beneficial spill-overs, where data can be re-used to open up significant growth opportunities, or to generate benefits across society in ways that could not be foreseen when the data were first created.2 For instance, the spill-over benefits of public-sector data across the economy has motivated a range of government initiatives that have made public-sector data more openly accessible and free of costs to users. These initiatives have not only contributed to enhancing trust in governments but also have enabled data-driven innovation across the economy. Where data linkages are possible, data access and sharing can also boost spill-over benefits by enabling “super-additive” insights that may be greater than the sum of insights from isolated parts (data silos), leading to increasing returns to scope (OECD, 2015[1]).
The re-use of data as a public, private, or public-private platform to support a range of upstream social and economic activities has led some experts to consider data as an infrastructural resource under certain conditions. Examples could include health care quality data that is re-used to assess health care system efficiencies and performance and to support health-related research activities. That said, not all data can be considered an infrastructure and certainly not a public infrastructure, as experts also made clear during the Copenhagen Expert Workshop.3
Evidence shows that data access and sharing can generate positive social and economic benefits for data providers (direct impact), their suppliers and data users (indirect impact), and the wider economy (induced impact). However, quantifying the overall benefits of data access and sharing is difficult.4 Recent available studies by sector (public vs. private sector) further discussed below provide a rough estimate of the magnitude of the relative effects of data access and sharing. They suggest that data access and sharing can increase the value of data to holders (direct impact), but it can help create 10 to 20 times more value for data users (indirect impact), and 20 to 50 times more value for the wider economy (induced impact). In some cases, however, data access and sharing may also reduce the producer surplus of data holders. Overall, these studies suggests that data access and sharing can help generate social and economic benefits worth between 0.1% and 1.5% of gross domestic product (GDP) in the case of public-sector data, and between 1% and 2.5% of GDP (in few studies up to 4% of GDP) when also including private-sector data.
copy the linklink copied!Impact assessment studies on the economic and social benefits
Two main groups of studies are discussed in dedicated subsections, within which studies can be compared: i) studies focussing on the impact of public-sector data; and ii) those focussing on the impact of data from both the public and private sector.
Enhancing access to public-sector data
The former UK Office of Fair Trading (2006[2]) surveyed more than 400 UK public-sector information holders (PSIHs) and 300 UK businesses buying or licensing data from PSIHs. The data-sharing arrangements employed by the PSIHs were based on open data and/or paid-for licences, which accounted for most of the cases surveyed. The study estimates that the direct impact of public sector information (PSI) (i.e. the producer surplus generated by the PSIHs) in the United Kingdom was around GBP 66 million (USD 86 million) per annum,5 and the indirect impact (including the consumer surplus of PSI re-use) was around GBP 518 million (USD 674 million). The study shows that the high price of paid-for licences is a major barrier to data access. It suggests that reducing costs to the level of cost recovery would increase overall surplus. It also identified the distortion of downstream competition in the private sector through restricted access to raw data that PSIHs used themselves to provide added-value services (crowding out). The study estimates that if these issues were addressed, the producer surplus would vanish in favour for an increase of the indirect impact by GBP 585 million (USD 761 million), leading to an overall economic value of GBP 1.1 billion (USD 1.43 billion) or 0.1% of the GDP.
A report by Deloitte for the UK Department for Business, Innovation and Skills (Deloitte, 2013[3]) focusses particularly on Trading Funds such as the HM Land Registry, the Registers of Scotland, the Companies House, the Ordnance Survey, the UK Hydrographic Office, the Environment Agency, the Met Office, and the Office of National Statistics. It estimates that the direct economic impact (as revenues of PSIHs) is around GBP 0.1 billion (USD 0.13 billion), while the indirect impact on data users and suppliers of data PSIHs is between GBP 1.2 billion (USD 1.6 billion) and GBP 1.8 billion (USD 2.4 billion) per year.6 The wider indirect and induced impact of PSI was conservatively estimated to be around GBP 5 billion (USD 6.5 billion) per year. This included time saved as a result of access to real-time travel data, which is valued at between GBP 15 million (USD 19.5 million) and GBP 58 million (USD 75 million). This led to an overall estimate of GBP 6 billion (USD 8 billion) to GBP 7 billion (USD 9 billion), or around 0.5% of GDP.
In two studies, ACIL Tasman (Tasman, 2008[4]; Tasman, 2009[5]) estimate the economic contribution of geospatial data to the economy in Australia and New Zealand respectively. The studies conclude that geospatial data provided by the public sector at costs above the cost of production and distribution and issues around data formats and licensing schemes have impeded access, mainly by smaller firms. The aggregated turnover of the geospatial data service industry in Australia was estimated to be around AUD 1.4 billion (USD 1 billion) per year, with a gross value added of AUD 682 million (USD 484 million) thanks to geospatial data from the public sector (Tasman, 2008[4]). The indirect impact of geospatial data is estimated to have increased productivity across the economy,7 leading to an aggregated induced impact between AUD 6.4 billion (USD 4.5 billion) and AUD 12.6 billion (USD 9 billion) in 2008, which corresponds to 0.6% and 1.2% of GDP.8 (Tasman, 2008[4]) concludes that further 5% to 15% productivity gains could be unleashed if barriers to geospatial data would be removed, with the largest impacts most likely to occur in sectors such as agriculture, transport, asset management and property.
Recent studies on PSI re-use in 27 EU countries estimate that the value of the market for PSI was of the order of EUR 28 billion (USD 33 billion) in 2008 and EUR 32 billion (USD 38 billion) in 2010 (Vickery, 2011[6]; Vickery, 2012[7]). The aggregate indirect and induced economic impact from PSI across the whole EU27 economy is estimated to be of the order of EUR 140 billion (USD 165 billion) annually – roughly 1.5% of GDP. A similar study focussing on the OECD PSI market estimates the value of PSI to be around USD 97 billion in 2008 and USD 111 billion by 2010 (OECD, 2015[8]). The aggregate indirect impact is estimated to be around USD 500 billion in 2008.9
Estimates by Deloitte (2017[9]) based on open data provided by Transport for London (TfL) strongly confirm the positive net benefits of open data. The study shows that the re-use of TfL’s open data was generating annual economic benefits and savings of up to GBP 130 million a year for TfL customers, road users, London, and TfL itself (Table 3.1). This includes a gross value added of GBP 12 million to GBP 15 million per year for businesses which also directly created more than 500 jobs. But it does not account for the significant contribution TfL’s open data has made to improving societal outcomes, facilitating innovation and improving the wider environment (e.g. air quality and lower emissions).
Enhancing access to and sharing of public and private-sector data
A study by the McKinsey Global Institute (2013[10]) looks at the benefits of the re-use of public and private-sector data in seven areas of the global economy (education, transportation, consumer products, electricity, oil and gas, health care and consumer finance). It estimates that the re-use of data across these seven areas could help create value worth USD 3 trillion per year worldwide.10 By scaling the results of the McKinsey Global Institute (2013[10]) to the Group of Twenty (G20) economies, Lateral Economics (2014[11]) estimates that open data could increase G20 output by around USD 13 trillion over the next five years. “This would boost cumulative G20 GDP by around 1.1 percentage points of the 2% growth target over five years” (Lateral Economics, 2014[11]). Similar scaling for Australia suggest that “more vigorous open data policies could add around AUD 16 billion per annum to the Australian economy” (this would represent almost 1% of GDP or USD 13 billion).
A study conducted by Mitsubishi Research Institute (2017[12]) focusses on the economic roles and impacts of data platforms in Japan, in particular, their role as integrator of various forms of data and thus facilitator of data re-use across organisations. The study is based on case studies assessing the business model of major data platforms in Japan, including public-sector data such as geospatial data as well as private-sector data such as Internet of Things (IoT) data, traffic data (ranging from pedestrians to cars), consumer behaviours, and language translation data.11 Data platforms are estimated to improve data re-use of firms, which face a barrier to data access. Based on the results of Nomura Research Institute (2014[13]), the study estimates the contribution of data platforms with the assumption that the proportion of the respondents having barriers to data use is lowered by data platforms and that their contribution is proportionate to the gross value added created from data re-use. The overall gross value added attributed to data platforms in Japan was estimated to be between JPY 604 billion (USD 5 billion) and JPY 1.45 trillion (USD 13 billion) in fiscal year 2014.
The IDC and Lisbon Council (2018[14]) study assesses the data market size and the GDP impact of the data economy in EU28 countries, focussing on the value added created from data re-use, including the provision of data and its exploitation in the private sector. The data market is defined as the marketplace where digital data are exchanged as “products” and “services” as a result of the (re-)processing of raw data. The impact on the data economy is defined more broadly as the overall effects of the data market on the economy, involving generation, collection, storage, processing, distribution, analysis elaboration, delivery, and exploitation of data enabled by digital technologies. Therefore, the overall impact is estimated by summing up the direct, indirect, and induced impact.12 The direct impact is estimated by the volume of the data market as a proxy (i.e. revenues of data suppliers and adjusted through including imports and excluding exports). According to the study, the data market volume in EU28 countries is estimated to be EUR 59 billion in 2016 and EUR 65 billion in 2017 (an increase of roughly 20% year-on-year). The indirect impact (i.e. the impact on data suppliers and the impact on data users through innovation and efficiency gains) was above 50% of total impact in 2017. Overall, the study suggests an overall impact of the data economy impact on GDP of 2.2% (EUR 306 billion) in 2016 and 2.4% (EUR 336 billion) in 2017.
The impact on GDP in 2025 is forecasted based on three different scenarios related to the concentration of power in data access, control and exploitation. These scenarios affect the composition of direct, indirect and induced impacts:
The Baseline scenario, which “is characterised by a healthy growth of data innovation, a moderate concentration of power by dominant data owners with a data-governance model protecting personal data rights, and an uneven but rather wide distribution of data innovation benefits in the society”, is forecasted to lead to an overall impact of 4.2% of GDP (EUR 669 billion) in 2025.
The High Growth scenario (Data-driven Reality), “characterised by a high level of data innovation, low data power concentration, an open and transparent data-governance model with high data sharing, and a wide distribution of the benefits of data innovation in the society”, is forecasted at 6% of GDP (EUR 1 trillion).
The Challenge scenario (Digital Maze), which “is characterised by a low level of data innovation, a moderate level of data power concentration due to digital markets fragmentation, and an uneven distribution of data innovation benefits in the society”, is forecasted at 3.0% of GDP (EUR 470 billion).
copy the linklink copied!Main categories of economic and social benefits
The different types of benefits and cost savings highlighted in previous sections can be clustered around the following five categories: i) greater transparency, accountability and empowerment of users, for instance, when open data are used for cross-subsidising the production of public and social goods; ii) new business opportunities, including the creation of start-ups and in particular for data intermediaries and mobile app developers; iii) competition and co-operation within and across sectors and nations, including the integration of value chains; iv) crowdsourcing and user-driven innovation; and v) increased efficiency thanks to linkage and integration of data across multiple sources.
These benefits suggest that data access and sharing is a major enabling condition for open innovation, a concept that according to the OECD Innovation Strategy (OECD, 2010[15]; OECD, 2015[16]) describes the “use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation”. This includes proprietary-based business models that make active use of licensing, collaborations, joint ventures, etc. “Here ‘open’ is understood to ‘denote the arms’ length flow of innovation knowledge across the boundaries of individual organisations” (OECD, 2013[17]).
The following subsections discuss these five categories of economic and social benefits in greater detail.
Transparency, accountability and empowerment of users
Enhanced access and sharing is a key means for improving transparency and empowering users, including small and medium-sized enterprises and consumers. In the private sector, open data initiatives like the Open Banking initiative demonstrate how data can be used to help people transact, save, borrow, lend and invest their money. By increasing transparency in the financial market, the initiative can empower consumers so they become able to better compare existing offerings. This in turns can contribute to a higher level of competition in the market. It is estimated that in the United Kingdom alone, consumers could save up to GBP 70 (USD 90) per year by switching to a bank account that would better fit their needs (Staff, 2017[18]).
In the case of data portability, customers are empowered to retrieve their data and move more easily to an alternative supplier, which puts competitive pressure on suppliers to keep prices low and compete on features, including privacy-enhancing features. As noted in the subsection “Data portability” in Chapter 2, this can: i) reduce information asymmetries between individuals and the providers of goods and services; ii) limit switching costs for individuals and lower lock-in effects; and iii) potentially reduce barriers to market entry. In doing so, data portability can contribute to more vigorous competition among vendors and greater consumer choice. For these reasons, data portability is considered not only a means to strengthening the control rights of individuals over their personal data, but also as a way to increase competition among providers of data-driven products (OECD, 2015[1]).
In science, enhanced access and sharing, typically through open data, is critical for transparency and for scrutinising and replicating scientific results. This remains challenging, for instance, when test results of drug interventions need to be validated by the scientific community. Evidence suggests that the quality of scientific research depends on the extent to which the underlying data can be accessed by other scientists, which is not always the case. At the Copenhagen Expert Workshop, it was emphasised that the poor availability of data required to scrutinise and replicate research results was one of the main causes for the significant share of false scientific results, and for the risk of the erosion of trust in science.
The CSTP-GSF Workshop concluded that researchers need to share the data, software, workflows and details of the computational environment in open repositories to facilitate reproducibility of their research results (OECD, 2018[19]). Participants stressed that published articles would need to include persistent links to the underlying digital artefacts, including data and software code, to enable discoverability, and that scientific journals should include “reproducibility checks” as part of their publication process.
Citizens’ use of open data as provided by governments through their open data initiatives can also help increase openness, transparency and accountability of government activities and thus boost public trust in governments. According to the McKinsey Global Institute (2011[20]), full use of data analytics in Europe’s 23 largest governments might reduce administrative costs by 15% to 20%, creating the equivalent of EUR 150 billion to EUR 300 billion in new value, and accelerating annual productivity growth by 0.5 percentage points over the next ten years. The main benefits would be greater operational efficiency (due to greater transparency), increased tax collection (due to customised services, for example) and fewer frauds and errors (due to automated data analytics).
All these benefits show that by empowering data users enhanced access and sharing can be used to cross-subsidise economic activities, including the production of public and social goods (such as science and research) that would otherwise require picking winners (users or applications). Governments can support the production of public goods i) by directly producing these goods; or ii) by supporting private firms’ production of public and social goods through research grants, procurement programmes, contracted research and tax incentives. But all these strategies raise several issues, including difficulties in picking winners and losers, and the fact that resources are limited.
Enhanced access and sharing can be a more efficient and politically attractive “indirect intervention” to support economic activities relying on data re-use.13 While this has been regarded as an important feature, some authors, such as Johnson et al. (2017[21]) or Onsrud (2007[22]), have considered this as a risk and hidden cost to society.14 Especially where data are provided largely for private-sector consumption without significant spill-over benefits for citizens, or where there are insufficient complementary investments in skills and infrastructures needed for the effective re-use of data (see section “Trust and empowerment for the effective re-use of data across society” in Chapter 4). Johnson et al. (2017[21]) therefore warn that open data initiatives could be (mis-)used as “a kind of ‘smoke and mirrors’ that obscures a government’s actual commitment to citizen participation, transparency and accountability”.
Business opportunities including for data intermediaries and start-ups
Enhanced access can also create new business opportunities for smaller and larger firms. Better access to open government data, for instance, can allow entrepreneurs to develop innovative commercial and social goods and services. An example is RowdMap, an analytics company using open data to help health care plans, physician groups and hospital systems identify, quantify, and reduce low-value care. In July 2017, the company was acquired for USD 70 million by Cotiviti, a provider of analytics-driven payment accuracy solutions.
Enhanced access and sharing enables many business opportunities for data intermediaries, including data brokers, mobile apps and personal information management systems. This is because most end users, consumers and businesses included, often do not directly use raw data. They rather rely on data intermediaries that access raw data to extract and present the embedded information in more user-friendly ways, sometimes enriched through additional, inferred, data. These intermediaries typically provide added-value services including advanced data analytic services. While businesses tend to use data brokers as main data intermediaries, consumers often access added-value information services via apps (the Copenhagen Expert Workshop). Overall, this has led to new demand for added-value services and thus to new business opportunities for new and old intermediaries, including data brokers and app developers, but also for some incumbents in information and communication technology (ICT) and non-ICT industries (e.g. telecommunication and financial services firms).
For example, a major part of the benefits of open data by TfL were realised thanks to the development of apps that used TfL open data to provide real-time traffic information for more accurate navigation systems (Table 3.1). More than 80 data feeds were made available for developers through a free unified application programming interface (API),15 which ensured accurate real-time data for over 13 000 registered developers and more than 600 apps. This generated a gross value added of GBP 12 million to GBP 15 million per year for businesses and led to the direct creation of more than 500 jobs and more than 230 indirect jobs across the supply chains and the wider London economy.
Co-operation and competition across sectors and countries
Enhanced access and sharing can facilitate joint production or co-operation with suppliers, customers or even competitors. This is not a new phenomenon. Joint research ventures or patent pools are well-known examples, where firms share common resources under non-discriminatory access regimes. This is “because independent research efforts are inhibited by complexity, expense, strategic concerns, transaction costs, or other impediments” (Frischmann, 2012[23]). Sharing agreements are very often an important part of these collaboration efforts. In the case of data, access does not need to be open to the public, but may be limited to the partners who share their data to “overcome collective action problems, sometimes mere co-ordination problems and sometimes more difficult prisoner’s dilemma problems” (see subsection “Other restricted data-sharing arrangements” in Chapter 2).
At the Copenhagen Expert Workshop, experts presented a cases confirming that the re-use of data enabled the integration of value chains across sectors and even across national borders. The data provided by TfL through open data, for example, enabled the integration of transport and navigation services across different means of transport (i.e. multimodal transport and navigation information), a condition for the deployment of smart transportation services (Alissa Walker, 2016[24]). Thanks to access to TfL open data, services such as Google Maps could provide more accurate multimodal navigation information including for the first and last miles.
Another example of the integration of value chains across sectors through enhanced access and sharing is the Industrial Data Space (IDS), a platform for the commercialisation of data in a business-to-business (B2B) context. The development of the IDS was motivated by the recognition that the value of data was growing when combined to deliver added-value services. The main benefits and strengths of the IDS was its ability to link and integrate data from multiple sources and of different types (e.g. product data and environment data of production) to enable the creation of “smart services”. Table 3.2 lists a few IDS use cases in logistics, where data needed to be shared across the supply chain. Another IDS use case is in mobility services. Here, different types of data (such as geolocation data of the means of transport, data on traffic flows, and maintenance data) need to be combined for innovative smart mobility services and for added-value services such as new insurance models (e.g. pay as you drive) and just-in-time maintenance services.
In science and research, for instance, data-sharing platforms (research data repositories) can reduce the cost of conducting research by enabling collaboration among researchers across disciplines. As OECD (2017[25]) shows, citing Beagrie and Houghton (2012[26]; 2014[27]; 2013[28]; 2013[29]; 2016[30]), “there is substantial additional re-use of the stored data, with between 44% and 58% of surveyed users across the studies saying they could neither have created the data for themselves nor obtained them elsewhere”. In areas where co-operation across countries is needed to tackle global challenges, such as infectious diseases, and improve early detection and warning of emerging threats and events, data sharing is often crucial.
Uber allows users to request and pay for a ride with just one click via an app. The app’s simplicity has been one of Uber’s major success factors. As Uber expanded internationally, it needed a payment gateway to simplify the complexities of international mobile payment. During its initial expansion into Paris, for example, Uber had to charge passengers in US dollars and display euros on-screen. This was a large source of confusion and customer complaints.
Uber wanted a payment gateway that was specifically created for smartphones, so it would be consistently fast. But most importantly, the gateway needed to offer 100% data portability if Uber ever decided to switch providers. With many providers, merchants are not able to quickly retrieve their data, creating significant switching costs for merchants, like Uber, who rely on ease of payment to satisfy and retain customers.
Uber switched to Braintree (a PayPal company) in February 2011 for its entire international and US-based payments. After a cost-intensive process to extract its users’ data from their previous payment provider, Uber was able to quickly and easily integrate Braintree’s technology into its existing service with no visible effects on the customer end. This enabled Uber to expand into other international cities, while using local currencies for local rides.
Source: Braintree (n.d.[31]), Case Study: Uber, www.braintreepayments.com/en-fr/learn/braintree-merchants/uber.
Examples include the Program for Monitoring Emerging Diseases (ProMED), established in 1993, which has demonstrated the power of data sharing through networks and the feasibility of designing effective, low-cost global reporting systems. ProMED has also encouraged the development of additional electronic-surveillance data-sharing networks – such as the Global Public Health Intelligence Network (GPHIN)16 and HealthMap.17 Influenza surveillance is another well-developed global surveillance and monitoring systems enabled through data sharing. Established by the World Health Organization in 1948, it has developed over the years into a highly successful global partnership now including 110 collaborating laboratories in 82 countries that constantly monitor locally isolated influenza viruses and provide real-time streams of data on the emergence and spread of different strains.
Besides co-operation, enhancing access to and sharing of data is also seen as a major enabler and even driver of competition. This is particularly true for personal data portability, which, as was previously noted, is expected to increase competition between providers of digital goods and services, such as social networking service providers, and in analogue markets, such as utilities markets (see subsection “Data portability” in Chapter 2). Depending on their relative market power, some firms may therefore view data portability in some cases as beneficial (see Box 3.1 on Uber) and in other cases as contrary to their business interests (see Box 3.2 on Google and Facebook).
Online platforms such as Facebook and Google have made it possible for users to extract their personal data in machine-readable formats, even prior to the General Data Protection Regulation’s right to data portability (Art. 20). The situation remains challenging, however, when considering the connections, or “friends,” that a user may have on these platforms. As an illustration, porting a user’s entire Facebook identity “construed as both the data that she/he has uploaded and the friends that she/he has acquired” (Becker, 2012[32]) is still very complex. Looking back at the history of Facebook, Becker (2012[32]) notes that the company may have used a lack of data portability to strengthen its market position: “In short, a lack of portability might have helped the site to obtain its current share of the market as well as help it to preserve its current market position.”
The competition challenges related to data portability were strongly apparent in a public disagreement between Facebook and Google back in 2010, which prevented the interoperability of two of the largest online platforms. Horizontal interoperability was clearly viewed by the two platforms as contrary to their respective business interests and in particular lack of reciprocity a disincentive to engage in data portability. As Becker (2012[32]) further explains:
Facebook aided users in identifying additional friends in their social networks by accessing the Gmail contacts application programming interface (API). However, in late 2010, Google altered its terms of use to prevent Facebook from accessing a user’s Gmail contacts, ostensibly in retaliation for Facebook’s failure to reciprocate […]. Facebook’s response was to implement a two-step workaround that required users to export their Gmail contacts to their computers and then upload them to Facebook.
More recently these platforms have reconsidered their positions. For example, in 2010 Facebook begun to allow its users to download their personal data (including profile information, photos, videos, wall posts, event information, and a list of friends) (Tsotsis, 2010[33]). Similarly, Google featured a service called “Google Takeout” in 2011 (now called “Download your Data”) that also allowed users to download all their personal data (Willard, 2018[34]). Most recently, Google announced the Data Transfer Project, a collaborative initiative together with Microsoft, Twitter, and Facebook to promote interoperability standards (Willard, 2018[34]).
Policy makers and competition authorities are also looking at non-personal data portability as a possible additional remedy to address competition barriers in a B2B context. The Japan Fair Trade Commission, for instance, issued a report on “Data and Competition Policy” in June 2017, which stressed that “it may fall under the antitrust violation (abuse of superior position) if a large enterprise forced a smaller business partner to provide data which is gathered independently” (Japan Fair Trade Commission, 2017[35]). The report concluded that some practices that unjustly denied access to data could be an antitrust violation classified as “unfair enclosure”.18
However, regulatory interventions imposing access and sharing to private-sector data for competition purposes would have to be assessed carefully on a case-by-case basis. The risk of abuse of market power would typically depend not only on access to data, but also on other factors. These include the market segment under consideration, in particular its rate of technological change;19 the data sources used; the degree of detriment to consumer welfare; the potential barriers to entry, including the level of investments required for building comparable data sets; and last but not least, other control points such as APIs and intellectual property rights used sometimes in combination with data. Furthermore, it may also depend on the available means to escape the control of the dominant actor, including in particular the availability of open standards and personal data portability.
Crowdsourcing new insights and user-driven innovation
For data providers, enhanced access and sharing can provide significant economic and social benefits, even when data are made available free of costs. It can for instance enable new strategic partnerships, where organisations agree to share, cross-licence the re-use and mutually enrich their data sets, or where a community emerges that creates additional value that a single organisation would not be able to create.
Data access and sharing, and open data in particular, can be an optimal strategy for organisations “when they recognise that users may be best positioned to create value” (Frischmann, 2012[23]). Where users are granted access to their personal data through data portability, they can gain “better visibility into their own consumption, often revealing information that can lead to changes in behaviour” (McKinsey Global Institute, 2013[10]). In its most extreme form, where access is granted to the public through open data, users (including individuals and businesses) are empowered to “provide input to improve the quality of goods and services” (McKinsey Global Institute, 2013[10]).20
Supporting and engaging a community of data users via enhanced access and sharing can thus be in the genuine interest of a business. This is in particular true in the era of artificial intelligence, where no single organisation can expect to meet unilaterally all application and customer data needs. For Thomson Reuters, for example, it was the following three reasons which led to the decision to engage in data-sharing partnerships via open data:
Encouraging a community response to scale – inclusion, specialisation: This would include i) helping others (customers) to add value to business solutions by lowering barriers to participation and co-operation in data sharing and re-use (in the case of PermID, helping make data sharing and re-use easy and valuable for others); and ii) giving others a reason to include the business in their solutions.
Identifying where to collaborate, new ways to compete, how to foster an ecosystem: This includes i) technical and commercial experimentation (inside and outside of the business); ii) learning by converting capabilities into products (e.g. Thomson Reuters Labs); and iii) using customers as signposts.
Building partnerships and collective action, powering user-driven innovation while maximising the option value of investments: This includes the development of open standards and, in the case of PermID, understanding the key barriers to data sharing and re-use and the role of identity.
Enhanced access and sharing can also maximise the option value of data and related investments (i.e. the value of allowing flexibility in reaping the benefits of the investments by enabling multiple use options) when there is high uncertainty regarding sources of future market value (OECD, 2006[36]). This is in particular the case where organisations know that users are best placed to create future value. They adopt enhanced access strategies, taking advantage of the increased value of experimentation by users, market selection of the best services and learning over time about user preferences and possible paths for continued development. The advantage for the organisation is that it “maintains flexibility and avoids premature optimisation or lock-in to a particular development path or narrow range of paths” (Frischmann, 2012[23]).
Increased efficiency across society through data linkage and integration
Enhanced access and sharing is an enabler of increasing returns to scope where data linkage across organisations and sectors is possible. This is because data linkage enables “super-additive” insights, leading to increasing returns to scope. Linking data is a means to contextualise data and is thus a source for insights and value that are greater than the sum of isolated parts (data silos) (OECD, 2015[1]). The benefits of data linkage within organisations have been described for instance by Newman (2013) in the case of Google: “It’s not just that Google collects data from everyone using its search engine. It also collects data on what they’re interested in writing in their Gmail accounts, what they watch on YouTube, where they are located using data from Google Maps, a whole array of other data from use of Google’s Android phones, and user information supplied from Google’s whole web of online services.”21 These diverse data sets enable profiling hardly possible otherwise.
Data linkage across institutions has been recognised as key for monitoring and increasing the efficiency and quality of the health care system (OECD, 2015[37]). These include the development of health care quality and system performance indicators to measure care co-ordination and outcomes of care pathways and compliance with national health care guidelines; and to produce indicators of health care utilisation and costs, and disease prevalence, by socio-economic status. In the United Kingdom, for example, administrative hospital records have been linked (via unique patient health service number) with a number of cancer screening registries and used to improve how and when cancer is diagnosed (to increase early detection and survival) (Productivity Commission, 2017[38]).
Data linkage and integration may also be critical for deploying smart applications across sectors, such as for smart cities. The data produced and collected in these cities are created by multiple actors. Key among them are citizens and consumers, innovators and entrepreneurs, governments and utilities, data brokers and platforms, and infrastructure and system operators. Each of these groups is in principle connected to all the others through a digital layer and in multiple possible combinations. The extent to which data can be exchanged and linked among these actors and across systems, as well as the extent to which they can easily be re-used for different purposes, determines the ability to integrate the different types of applications and to enable synergies. Integrating different applications via the IoT, for example, can multiply the systems, machines, devices and services connected via electricity grids and information systems – such as solar cells on roofs, detailed weather forecasts, home heating systems and air conditioning, and supermarket stocks.
There are, however, various reasons why linking data across different data silos and organisations might be challenging. There may be legal, cultural and technical barriers to data access and sharing as described above. Other barriers may be related to skills. As the OECD (2013[39]) states: “Even though techniques for record linkage are now well developed, and are used by numerous organisations regularly, the capacity with which to carry out successful linkages may be in short supply.”
Some of the “barriers” to data linkage are legitimate, however, since data linkage is not only a source for great insights but may increase the risk of re-identification (see subsection “The violation of privacy, intellectual property rights, and other interests” in Chapter 4). In addition, data aggregation, when leading to one big data set, can create a single point of failure that a digital security threat can exploit to disrupt the availability, integrity, or confidentiality of the data on which economic and social activities rely (see subsection “Digital security risks and confidentiality breaches in particular” in Chapter 4). In many jurisdictions, the separation of linkage and analysis processes is therefore considered as best practice for confidentiality, meaning that those conducting the linkage (often a “trusted third party”) only have access to a set of identifiers, while those analysing the linked data only have access to de-identified data.
References
[24] Alissa Walker (2016), “Los Angeles Has Invented the Multimodal Navigation App of My Dreams”, https://gizmodo.com/la-has-invented-the-multimodal-navigation-app-of-my-dre-1756497278 (accessed on 2 March 2018).
[30] Beagrie, N. and J. Houghton (2016), The Value and Impact of the European Bioinformatics Institute, https://beagrie.com/static/resource/EBI-impact-report.pdf.
[27] Beagrie, N. and J. Houghton (2014), The Value and Impact of Data Sharing and Curation: A Synthesis of Three Recent Studies of UK Research Data Centres, JISC, Bristol and London, http://repository.jisc.ac.uk/5568/.
[28] Beagrie, N. and J. Houghton (2013), The Value and Impact of the Archaeology Data Services, Joint Information Systems Committee, Bristol and London.
[29] Beagrie, N. and J. Houghton (2013), The Value and Impact of the British Atmospheric Data Centre, Joint Information Systems Committee and the Natural Environment Research Council UK, Bristol and London, http://www.jisc.ac.uk/whatwedo/programmes/di_directions/strategicdirections/badc.aspx.
[26] Beagrie, N. and J. Houghton (2012), Economic Evaluation of Research Data Infrastructure (ESDS), Economic and Social Research Council, London, https://esrc.ukri.org/files/research/research-and-impact-evaluation/economic-impact-evaluation-of-the-economic-and-social-data-service/.
[32] Becker, M. (2012), “Interoperability Case Study: Cloud Computing”, The Berkman Center for Internet & Society Research Publication Series, Research Publication No. 2012-11, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=204.
[41] Boston Children’s Hospital (n.d.), About, http://www.diseasedaily.org/about (accessed on 5 February 2019).
[31] Braintree (n.d.), Case Study: Uber, http://www.braintreepayments.com/en-fr/learn/braintree-merchants/uber (accessed on 1 October 2018).
[40] Corrado, C., C. Hulten and D. Sichel (2009), “Intangible capital and U.S. economic growth”, Review of Income and Wealth Series 55, No.3, http://www.conference-board.org/pdf_free/IntangibleCapital_USEconomy.pdf.
[9] Deloitte (2017), Assessing the value of TfL’s open data and digital partnerships, http://content.tfl.gov.uk/deloitte-report-tfl-open-data.pdf (accessed on 2 March 2018).
[3] Deloitte (2013), Market Assessment of Public Sector Information: A Report to the Department for Business, Innovation and Skills.
[42] Department of Industry, Innovation, Science, Research and Tertiary Education (Australia) (2012), APS200 Project: The Place of Science in Policy Development in the Public Service, https://apo.org.au/node/31390.
[23] Frischmann, B. (2012), Infrastructure: The Social Value of Shared Resources, Oxford University Press.
[14] IDC and Lisbon Council (2018), Updating the European Data Market Monitoring Tool, http://datalandscape.eu/study-reports/first-report-facts-and-figures-updating-european-data-market-monitoring-tool.
[35] Japan Fair Trade Commission (2017), “Report of Study Group on Data and Competition Policy”, http://www.jftc.go.jp/en/pressreleases/yearly-2017/June/170606_files/170606-4.pdf.
[21] Johnson, P. et al. (2017), “The Cost(s) of Geospatial Open Data”, Transactions in GIS, Vol. 21/3, https://doi.org/10.1111/tgis.12283.
[11] Lateral Economics (2014), Open for Business: How Open Data Can Help Achieve the G20 Growth Target, http://www.omidyar.com/sites/default/files/file_archive/insights/ON%20Report_061114_FNL.pdf.
[10] McKinsey Global Institute (2013), Open data: Unlocking innovation and performance with liquid information, http://www.mckinsey.com/insights/business_technology/open_data_unlocking_innovation_and_performance_with_liquid_information.
[20] McKinsey Global Institute (2011), Big data: The next frontier for innovation, competition and productivity, http://www.mckinsey.com/~/media/McKinsey/dotcom/Insights%20and%20pubs/MGI/Research/Technology%20and%20Innovation/Big%20Data/MGI_big_data_full_report.ashx.
[12] Mitsubishi Research Institute (2017), De-ta ryutuu Purattofo-mu ni kannsuru tyo-sajigyo [Study on platforms for data sharing], http://www.meti.go.jp/meti_lib/report/H28FY/000467.pdf.
[13] Nomura Research Institute (2014), データの高度な利活用による業務・サービス改革が我が国経済及び社会に与える波及効果に係る調査研究 [Research on Spillover Effects of Evolution in Operation and Services by Data Use on the Economy and Society].
[19] OECD (2018), OECD Science, Technology and Innovation Outlook 2018: Adapting to Technological and Societal Disruption, OECD Publishing, Paris, https://dx.doi.org/10.1787/sti_in_outlook-2018-en.
[25] OECD (2017), “Business models for sustainable research data repositories”, OECD Science, Technology and Industry Policy Papers, No. 47, OECD Publishing, Paris, https://doi.org/10.1787/302b12bb-en.
[8] OECD (2015), “Assessing government initiatives on public sector information: A review of the OECD Council Recommendation”, OECD Digital Economy Papers, No. 248, OECD Publishing, Paris, https://doi.org/10.1787/5js04dr9l47j-en.
[1] OECD (2015), Data-Driven Innovation: Big Data for Growth and Well-Being, OECD Publishing, Paris, https://doi.org/10.1787/9789264229358-en.
[37] OECD (2015), Health Data Governance: Privacy, Monitoring and Research, (policy brief), OECD, Paris, https://www.oecd.org/health/health-systems/Health-Data-Governance-Policy-Brief.pdf.
[16] OECD (2015), The Innovation Imperative: Contributing to Productivity, Growth and Well-Being, OECD Publishing, Paris, https://doi.org/10.1787/9789264239814-en.
[17] OECD (2013), “Knowledge networks and markets”, OECD Science, Technology and Industry Policy Papers, No. 7, OECD Publishing, Paris, https://doi.org/10.1787/5k44wzw9q5zv-en.
[39] OECD (2013), New Data for Understanding the Human Condition: International Perspectives, OECD, Paris, http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf.
[15] OECD (2010), The OECD Innovation Strategy: Getting a Head Start on Tomorrow, OECD Publishing, Paris, https://doi.org/10.1787/9789264083479-en.
[36] OECD (2006), “Quasi option value”, in Cost-Benefit Analysis and the Environment: Recent Developments, OECD Publishing, Paris, https://doi.org/10.1787/9789264010055-11-en.
[2] Office of Fair Trading (2006), The Commercial Use of Public Information.
[22] Onsrud, H. (2007), Research and theory in advancing spatial data infrastructure concepts, Esri Press; Redlands, CA.
[38] Productivity Commission (2017), Productivity Commission Inquiry Report: Data Availability and Use, Productivity Commission, https://www.pc.gov.au/inquiries/completed/data-access/report/data-access.pdf (accessed on 19 March 2018).
[18] Staff, P. (2017), What is the Open Banking Standard? How a global system of banking APIs can help consumers, businesses, and banks, https://fin.plaid.com/articles/what-is-the-open-banking-standard (accessed on 2 March 2018).
[5] Tasman, A. (2009), Spatial Information in the New Zealand economy, http://cnig.gouv.fr/wp-content/uploads/2014/05/spatial-information-in-the-new-zealand-economy-2009.pdf.
[4] Tasman, A. (2008), The Value of Spatial Information, https://www.crcsi.com.au/assets/Resources/7d60411d-0ab9-45be-8d48-ef8dab5abd4a.pdf.
[33] Tsotsis, A. (2010), “Facebook Now Allows You To “Download Your Information””, TechCrunch, https://techcrunch.com/2010/10/06/facebook-now-allows-you-to-download-your-information/.
[7] Vickery, G. (2012), Review of recent studies on PSI reuse and related market developments, http://www.scb.se/statistik/_publikationer/NR9999_2012A01_BR_X76BR1201.pdf.
[6] Vickery, G. (2011), Review of recent studies on PSI reuse and related market developments, http://ec.europa.eu/information_society/policy/psi/facilitating_reuse/economic_analysis/index_en.htm.
[34] Willard, B. (2018), “Introducing Data Transfer Project: an open source platform promoting universal data portability”, Google Open Source, https://opensource.googleblog.com/2018/07/introducing-data-transfer-project.html.
Notes
← 1. This property is at the source of significant spill-overs, which provide the major theoretical link to multi-factor productivity growth according to a number of scholars including Corrado, Hulten and Sichel (2009[40]).
← 2. The OECD (2015[1]) suggests that data can be considered as the new “research and development” (R&D), i.e. as input to innovation for 21st-century innovation systems. Both, data and R&D share a number of common properties: both are intangible assets that can be combined with other innovation investments like training, software, organisational change, etc.; both enable the creation of knowledge with positive externalities or spill-overs across society; and both face the challenge of these externalities possibly negatively impacting on incentives to invest. Organisations may well be able to capture the private benefits of their investment in data, but do not yet always see the larger benefits that the data can bring to society.
← 3. See findings of the Copenhagen Workshop (www.oecd.org/internet/ieconomy/expert-workshop-enhanced-access-to-data-reconciling-risks-and-benefits-of-data-re-use.htm) for more details and for a discussion of the economic conditions under which data can be considered an infrastructural resource.
← 4. Current studies significantly differ in terms of the scope of the sectors (e.g. whether the public sector and/or the private sector was included), the types of data (e.g. whether personal, proprietary or public data was included), and the degrees of data openness (and the arrangements included such as open data) as well as the methodologies, including in particular the different level of the impact assessed (i.e. whether the effects were assessed at the organisational, sectoral or macroeconomic level).
← 5. These can be decomposed in the following categories of data: i) economic, business and legal information (GBP 24 million); ii) geographical information (GBP 25 million); iii) environmental and scientific information (GBP 16.5 million); and iv) other information.
← 6. These are based on 2011 data and include around GBP 100 million in revenues generated from sales of PSI, GBP 100 million through supply chain effects from increased jobs and related consumer spending from the production of PSI, and GBP 1.6 billion through consumer surplus from direct use and consumption PSI related products.
← 7. Tasman (2008[4]) estimates that 4.00% to 5.14% of total factor productivity gain in fisheries are based on the re-use of geospatial data from the public sector, around 1.93% in forestry, 1.40% to 1.53% in road transport, 1.35% to 1.50% in sheep/cattle, 0.98% to 1.32% in communication and 0.93% to 1.08% in agriculture.
← 8. The induced impact of the use and re-use of geospatial data in New Zealand is estimated to generate values worth NZD 1.2 billion in 2009, which also corresponds to 0.6% of GDP (Tasman, 2009[5]).
← 9. The OECD (2015[8]) concludes that there could be close to USD 200 billion of additional gains to the indirect benefits if barriers to use of data were removed, skills enhanced and the data infrastructure improved.
← 10. Altogether, over 50% of the total potential value of open data is estimated to be generated from consumer and customer surplus (McKinsey Global Institute, 2013[10]). The largest share of the total benefits of open data is attributed to better benchmarking, “an exercise that exposes variability and also promotes transparency within organisations” (McKinsey Global Institute, 2013[10]). Better benchmarking would enable “fostering competitiveness by making more information available and creating opportunities to better match supply and demand” as well as “enhancing the accountability of institutions such as governments and businesses [to] raise the quality of decision [making] by giving citizens and consumers more tools to scrutinise business and government” (McKinsey Global Institute, 2013[10]).
← 11. The objective of the study is to assess the contribution of data platforms in the increase of data re-use, irrespective of whether public- or private-sector data is used. Different types of data platforms are also included in the study, ranging from data markets (Windows Azure Marketplace, Datamarket and EverySense), market-based data services (Sakura IoT Platform and G Space Information Center) and open data platforms (G Space Information Center).
← 12. For the estimation of data market and the data economy, IDC and Lisbon Council (2018[14]) identifies data companies which consist of data suppliers and data users. Data suppliers have, as their main activity, the production and delivery of digital data-related products, services and technologies, while data users are organisations that generate, exploit, collect and analyse digital data intensively to improve their business activities.
← 13. As Frischmann (2012[23]) highlights such an approach “is not a direct subsidy to […] users who produce public or social goods, but it effectively creates cross-subsidies and eliminates the need to rely on either the market or the government to ‘pick winners’ – that is, to prioritise or rank […] users worthy of access and support”.
← 14. Referring to open government data, Johnson et al. (2017[21]), for instance, note that: “Given the benefits for the private sector in using open data instead of generating or purchasing similar data from other sources […], this raises questions as to what degree the public sector is subsidising private-sector business models by opening data. […] The rhetoric of open data, which often refers to it as data for which taxpayers have already paid […], may obscure the real costs to government of making data open, masking the true value of the subsidy to the private sector.”
← 15. An open API can provide both easy access to openly available data (such as a bank’s product offerings) and secure shared access to private data (such as a third party’s access to a user’s transaction history). These APIs would be established by banks and could be integrated with third-party technologies to carry out specific functions related to the banking data. For instance, apps could allow customers to compare banking services to choose what products best suits their needs (Staff, 2017[18]).
← 16. The GPHIN, developed by Health Canada in collaboration with the World Health Organization, is a secure Internet-based multilingual early-warning tool that continuously searches global media sources such as news wires and websites to identify information about disease outbreaks and other events of potential international public health concern. See www.who.int/csr/alertresponse/epidemicintelligence/en/ (accessed 7 May 2015).
← 17. HealthMap, developed at Boston Children’s Hospital in 2006, uses online informal sources for disease outbreak monitoring and real-time surveillance of emerging public health threats (Boston Children's Hospital, n.d.[41]).
← 18. The extent to which data control can raise competition issues has been discussed in OECD (2015[1]).
← 19. Markets featuring a series of disruptive innovations can lead to patterns in which firms rise to positions of temporary monopoly power but are then displaced by a competitor with superior innovation.
← 20. For example, as the public sector makes its data available for science and research, new scientific insights can be used as evidence for informing policy makers and regulators. Australia’s Department of Industry, Innovation, Science, Research and Tertiary Education, through its APS200 Project on “the place of science in policy development in the public service” has identified a number of practical and useful strategies to maximise the use of science in policy development based in particular on public-sector data (Department of Industry, Innovation, Science, Research and Tertiary Education (Australia), 2012[42]).
← 21. The “super-additive” nature of linked data is of course not without its challenges as well. In particular, linked datasets can undermine confidentiality and privacy protection measures such as anonymisation and pseudonymisation.
