How Data Informs Business

Policies for Building Data as an Infrastructure Asset

Data as infrastructure has recently become a prominent topic for discussion in policy circles. Recognition is growing within many governments that, in the digital economy, data is a critical new infrastructure asset that enables new, often more efficient and inclusive delivery of other activities, particularly services. The value of the data and the potential for use expands with quantity and quality. Interest has surged in crafting policies to support wider use of data, while also recognizing the new risks and challenges it poses. This first section looks at policies to expand the sharing of data and the next looks at how to balance that with addressing concerns about privacy and security.

Greater access to data also has beneficial spillovers, and data can be used and reused to open up significant growth opportunities or to generate benefits across society in ways unforeseen when the data was created. The Organisation for Economic Co-operation and Development (OECD) therefore recommends that policy makers aim for an innovation policy mix that encourages investments in data (its collection, curation, and reuse), while addressing the low appropriation of returns to encourage data sharing. This calls for ensuring a relevant legal framework exists, with policies aiming for the extensive sharing, use, and development of public data sources and research data infrastructure. Policies governing business-to-business and business-to-government data also need to encourage appropriate sharing of data, spurring innovation while avoiding stifling competition.

Governments increasingly recognize government data as a strategic resource (the data management policy of Qatar's Ministry of Information and Communications Technology, for instance, explicitly identifies it as such). The New Zealand data and information management principles provide a useful set of principles stating that information should be open, readily available, well managed, reasonably priced, and reusable, unless there are necessary reasons for its protection. Personal and classified information will remain protected. Government data and information should also be trusted and authoritative.

In supporting strong data infrastructure, governments should consider policies focused both on management of data assets and on data governance. The next section considers these issues for three types of data sharing – the reuse of certain government data, business-to-business data, and business-to-government data.

Policies on the reuse of public sector information

Many now recognize public data in user-friendly formats freely available online for anyone to use and for any purpose as a major resource to aid economic growth. While social media, companies, and non-government organizations can all be sources of open data, the term is usually applied to data that comes from government and government-supported institutions – open government data. The data governments collect or generate, when freely available, is more than just a tool to hold governments accountable. It also drives innovation that can help launch new businesses, optimize existing companies' operations, create jobs, and improve the climate for foreign investment. Increased availability of data can fuel the private sector through access to new types of public and publicly funded data, including data held by utility companies and the transport sector, and research data.

The benefits connected to reusable public data, especially open government data, are diverse and yet largely untapped. Positive outcomes range from greater transparency, efficiency, and economic growth to broader social welfare. Although some countries are applying the "open-by-default" principle to public data sharing policies, particularly advanced economies, this does not imply that all data sets should be made available to the public. When thinking of open data policies, governments can consider that the same limits apply to open data as to access to information. In other words, the protection of privacy, personal data, or national security are common limits. In addition, for governments just beginning to open their data, opening certain data sets over others holds more potential value. Geospatial data, or data on weather, transport, and roads, can be particularly critical, and among the first any government should consider opening.

The Open Data Charter sets out six principles developed in 2015 by governments, civil society, and experts around the world to represent a globally agreed-on set of aspirational norms for how to publish data (table 6.1). So far, 57 national and local governments have adopted it for the development of open data policies. However, a vast amount of public information is still made available (if at all) in non-user-friendly formats (that is PDFs and JPEG), making this data suboptimal for creating value-added services and products.

Table 6.1 Open data principles

	1. Open by default This can represent a real shift in how governments operate and how they interact with citizens. The presumption is that governments need to justify data that is kept closed, for example, for security or data protection. To make this work, citizens must also feel confident that open data will not compromise their right to privacy
	2. Timely and comprehensive Open data is only valuable if it is still relevant. Getting information published quickly and in a comprehensive way is central to its potential for success. As much as possible, governments should provide data in its original, unmodified form. Maintaining historical data is important for keeping track of changes and evaluating the impact of reforms.
	3. Accessible and usable Ensuring data is machine readable and easy to find will make it go further. Portals are one way of achieving this. But it is also important to think about the user experience of those accessing data, including the file formats in which information is provided. Data should be free of charge, under an open license, for example, those developed by Creative Commons.
	4. Comparable and interoperable Data has a multiplier effect. The more quality data sets you have access to, and the easier it is for them to talk to each other, the more potential value you can get from them. Commonly agreed-upon data standards play a crucial role in making this happen.
	5. For improved governance and citizen engagement Open data has the capacity to let citizens (and others in government) have a better idea of what officials and politicians are doing. This transparency can improve public services and help hold governments to account.
	6. For inclusive development and innovation Finally, open data can help spur inclusive economic development. For example, greater access to data can make farming more efficient or it can be used to tackle climate change. Finally, we often think of open data as just about improving government performance, but a whole universe exists of entrepreneurs making money from open data.

Transparency has been an objective of many open data initiatives in the past decade, based on the principle that sunlight is the best disinfectant. The most important data sets that help enable the growth of an anticorruption culture have now become clearer: corporate registers, public contracting information, information on public officials, land registration information, government budget and spending data, and courts data are all helpful for this agenda. Moreover, the modalities of the publication of this data are also important. Data published in user-friendly, machine-readable formats helps governments fight corruption more effectively as it enables civil society to analyze and support government efforts to identify irregularities. Promotion of common standards, such as the Open Contracting Data Standard, enables sharing of toolsets so that local activists can build on the work of those in other jurisdictions.

Although anti-corruption has been an objective of many open data initiatives in the past decade, the supply of data alone is seldom sufficient – civil society actors who will use the data, as well as government willingness to respond, are needed; with these in place, a "virtuous circle" can be created in which some initial pressure leads to initial improvement and release of more data.

Data access policies are increasingly expanding to cover data generated by publicly funded research. "Open science" efforts rely on the premise that scientific information resulting from public funding should be accessible and reusable, with as few restrictions as possible. The opening of research processes, designs, workflows of dissemination of results, and methodologies can expand quality, avoid duplication, and facilitate reuse, which ultimately can help maximize the societal role of science. Research data policies need to ensure coherence and complementarity between open access and open data policies.

Governments can also promote business opportunities by mainstreaming the use of application programming interfaces (APIs) for more automatic access to dynamic data. This has important implications in supporting data ecosystems, as it saves costs and time to access data, facilitating practical usage. Sharing data through secure APIs can produce value added for data assets across the data value chain, particularly where potential is often unexploited by data holders. However, current public sector use of APIs is limited, particularly in developing countries – expanding use requires awareness raising and training.

Policies for private sector data as a driver of innovation and competitiveness

Data can be shared to support the creation of more than one new product, service, or production process.

This can allow companies to connect in different datasharing engagements with larger companies, small and medium enterprises (SMEs), and start-ups, or even the public sector. This way, data value can be maximized on several fronts simultaneously.

Data-sharing models have emerged to promote fair and competitive markets for products and services that rely on nonpersonal machine-generated data created, and to assist public agencies in accessing private sector data, to guide policy decisions or improve public services. The EC (2018) defines a set of key principles to be taken into account to improve data sharing for all parties involved, in business-to-business (B2B) and business-to-government situations. Access to and reuse of private sector data also constitute major cornerstones of a common data economy.

Business-to-business data sharing

An ever-increasing amount of data is created automatically by objects or processes based on disruptive technologies, such as sensors and the Internet of Things. These mainly relate to nonpersonal data generated by machines and open a new discussion and a dilemma around the privileged position of the producers of those devices in determining the access to and usage of the data they generate.

An EC public consultation with private sector stakeholders showed consensus that more B2B data sharing would be beneficial (EC 2018), where data can be reused without losing data quality or competitive advantage. The critical point in B2B data sharing might not rely on ownership, but on how data access is structured, managed, and approached. It could be argued that, at this initial stage of the development of data economies, it is too early for legislation requiring B2B data sharing. However, governments can consider non-regulatory measures to promote B2B data sharing:

• Fostering the adoption and use of APIs for easier and more systematic access to data. APIs can open up a data ecosystem of startups, exploiting unused data sets and supporting host organizations to adopt and create new data services and products. This has happened in the financial sector, leading to the emergence of financial technology ecosystems and new products and services that are already showing a relevant impact on banking the unbanked. The configuration and utilization of APIs requires the consideration of several principles: security, use of standards, user-friendliness, stability, and sustainability over time.

• Providing key guiding principles for good practices in B2B sharing agreements to ensure fair and competitive markets and to avoid excluding SMEs. Those crafted by the EC are an example, including (a) transparency, clearly identifying who will have access, to what type of data, at which level of detail, and usage purposes; (b) respect for the commercial interests of data holders and users; (c) ensuring undistorted competition when sharing sensitive data; and (d) minimizing data lock-in to enable data portability as much as possible.

• Promoting the development of trusted and secure platforms and privacy-minded analytical techniques to secure sharing of proprietary industrial data and personal data and ensure compliance with relevant legislation (data protection, IP rights, and so on). Data collaboratives have emerged as a potentially viable response to the data challenges companies face. They provide access to "verified" and useful data (open data or otherwise) from public and private sources, commercial models that reward data producers and consumers, legal and regulatory protections and guidance, data security infrastructure, network connectivity, analytics infrastructure, and literacy programs.

Business-to-government data sharing

Data that companies collect and produce – cellular data, utility companies, shared carpooling services (such as Uber), or social media – can lead to improved traffic, better urban planning, and so on. As with B2B data sharing, governments can consider using key principles to guide these exchanges. The EC has defined the following key principles: (a) proportionality in the use of private sector data justified by clear and demonstrable public interest – the cost and effort required for the supply and reuse of private sector data should be reasonable compared with the expected public benefits; (b) purpose limitation of business-to-government collaboration; (c) "do no harm" – protection of trade secrets and other commercially sensitive information; (d) conditions for data reuse; and (e) mitigate limitations of private sector data such as potential inherent bias – companies supplying the data should offer reasonable and proportionatedate support to help assess the quality of the data.