Social and economic activities are increasingly migrating to the Internet. The cost of data collection, storage and processing continues to decline dramatically. Ever larger volumes of data will be generated from the Internet of Things, smart devices, and autonomous machine-to-machine communications. We are now at the cusp of a new era, in which “big data” will play a transformative role.

Early in 2011 the OECD began a project on New Sources of Growth: Knowledgebased Capital (KBC). The project was inspired by findings from the OECD’s Innovation Strategy, originally published in 2010 and now updated to 2015 (forthcoming). According to these findings, many innovating firms invest, beyond R&D, in a broader range of intangibles assets including i) intellectual property (e.g. patents, trademarks, copyrights, trade secrets, designs); ii) digital data and information (e.g. data and analytics); and iii) economic competencies (e.g. organisational capital and firm-specific skills). These intangible assets are referred to as knowledge-based capital (KBC).

Close to real-time analysis of large volumes of data (big data) – generated from a myriad of transactions, production and communication processes – is accelerating knowledge and value creation across society to unforeseen levels. Data-driven innovation (DDI) refers to significant improvement of existing, or the development of new, products, processes, organisational methods and markets emerging from this phenomenon.

This chapter provides a synthesis of the main findings of Phase II of the OECD project on New Sources of Growth: Knowledge-Based Capital, in particular its pillar which focuses on data-driven innovation (KBC2: DATA). It first presents available evidence on the increasing role of “big data” and data analytics, highlighting in particular the potential of data-driven innovation (DDI) for economic growth, development, and well-being. It then presents the context and policy issues related to the various aspects of DDI covered in this book, chapter by chapter. The discussion concludes by raising key challenges that most countries will face as DDI takes off and accelerates, and the policy considerations they will need to address.

In exploring the rapidly evolving data ecosystem, this chapter enumerates the key actors, their main technologies and services, and their business and revenue models. It uses a layer model to identify these actors as well as strategic points of control in the system. It goes on to discuss the interaction among actors, analysing in particular the relation between competition and collaboration for DDI, and how this “co-opetition” translates in terms of horizontal and vertical dynamics. The chapter analyses the degree to which data ecosystems are open, global and interconnected. Finally, it looks at the implications of DDI for global value chains (GVCs) and trade, taxation, and competition.

This chapter highlights the key drivers of data-driven innovation (DDI), today a widespread socio-economic phenomenon. It documents the key trends leading to the adoption of data and analytics across the economy, which are related to i) data generation and collection, ii) data processing and analysis, and iii) data-driven decision making. It also shows how the confluence of these trends is leading to the “industrialisation” of knowledge creation and a paradigm shift in decision making towards decision automation. The chapter then highlights the limitations of data-driven decision making, and concludes with a discussion of the key policy implications.

This chapter introduces the theoretical foundation for the economic potential of data and discusses key data governance issues that need to be addressed in order to maximise data’s potential and reuse across society. It begins by presenting data as an infrastructural resource and a non-rivalrous capital good. It goes on to discuss how data’s value depends entirely upon context, with reuse enabling multi-sided markets in which huge returns to scale and scope can lead to positive feedback loops. The often misunderstood notion of “ownership” is discussed, and data quality is seen as multifaceted and involving seven dimensions. The key aspects of data access, sharing, portability and interoperability are examined and presented as elements of a data governance framework that can help overcome barriers to the reuse of data.

This chapter provides an overview of emerging trust issues raised by the increasing use of data-intensive applications that impact individuals in their commercial, social and citizen interactions. Security issues are addressed first, with an examination of the traditional approach and its inherent limitations. Comparisons are then made with current digital security risk management, which views risks as the possible detrimental consequences for the objectives of, or benefits expected from, the data value cycle. The point is made that a certain level of risk has always to be accepted for the value cycle to provide some benefit – raising the question of who decides that level. The discussion then takes up privacy protection. Practical means for preventing information discovery are enumerated, and the dangers of information asymmetry, data-driven discrimination, and unanticipated uses of consumer data addressed. Attention then turns to potential policy approaches to help in addressing the issues raised.

This chapter discusses the implications of data-driven innovation (DDI) on skills and employment, focusing on two challenges in particular: one, DDI may further increase pressure on the labour market, and especially on middle income jobs, as it enables an increasing number of cognitive and manual tasks to be performed by data- and analyticsempowered applications; and two, the demand for data specialist skills may exceed supply on the labour market. The chapter first shows that DDI could lead to structural change in labour markets, and discusses the implications with regard to skills. It then focuses on data specialist skills and competence, the lack of which could prevent economy-wide adoption of DDI and the (re-)creation of jobs. Finally, the chapter discusses the policy challenges for promoting DDI while smoothing structural adjustments, focusing on challenges in i) addressing wage and income inequalities, and ii) satisfying skills and competence needs.

This chapter summarises the recent evolution of science – mainly thanks to the advent of data analytics – towards a more open and data-driven enterprise. It examines how new and evolving opportunities for interconnecting and sharing have led to what could be called citizen science. A discussion follows on the various impacts of open access to science, research and innovation on the business and science communities and on citizens. There are examples of organisations involved in open data efforts, and an exploration of the challenges and opportunities presented by data sharing. The focus then shifts to policies and practices in the OECD area and beyond, with the emphasis on infrastructure for data sharing. With unrestricted access to publications and data, firms and individuals may use and reuse scientific outputs to produce new products and services – but do scientists and researchers have the incentives or indeed the skills to perform these tasks?

This chapter examines how large and diverse health data sets are being used to improve population health and support patient-centred care, health system management, and human health research. Among the aspects considered are electronic health records, smart models of care, the role of social media and crowdsourcing. The chapter also looks at barriers that will need to be overcome to pave the way for widespread data-driven innovation (DDI) in the health sector, examining issues raised by the use of personal health data not discussed in previous chapters. It concludes with a list of success factors that will enable governments to provide the leadership needed to progress further toward data-driven health research and care.

This chapter provides an overview of data production and examples of opportunities for data-driven innovation in cities, as well as a discussion of related policy implications. The focus is on data-driven innovation i) that increases the efficiency of urban systems, including through system integration; ii) that enables new business opportunities, for example in urban mobility and accommodation markets; and iii) that improves urban governance. Examples in each of these areas show that the potential of data-driven innovation in cities has only begun to be tapped, and that the conditions to unleash it need to be improved. Issues to be addressed by policy makers to improve such conditions include interoperability, regulation, digital security risk management, and privacy.

This chapter examines the benefits and challenges of opening access to data from one of the economy’s most data-intensive sectors, the public sector. The potential of public sector information (PSI) including open government data (OGD) is discussed from several perspectives: use by government itself which, in tandem with data analytics, can make for better informed policy making and enable delivery of more innovative services; open access for citizens, which can greatly improve accountability through transparency and lead to citizens’ empowerment; and reuse in the private sector, a stimulus to innovation. The challenges in implementing open data strategies are also enumerated, including dissuasive pricing and licensing practices; differences in licensing systems across national institutions; lack of information and standards and poor interoperability; organisational and cultural obstacles within the public sector; and legal constraints. The chapter concludes with a number of recommended policy options.