In summer 2022, the Open Data Institute (ODI) launched the research project “Understanding the social and economic value of sharing data”, with the aim of learning how to better support organisations that want to unlock the value of the data within their ecosystems. To that aim, we are currently conducting research in order to develop training materials and tools, we’ve launched a call as part of the ODI Fellowship programme for researchers interested in examining topics related to the value of data, and we have started exploring collaboration opportunities with private sector organisations.
To support this ongoing work, we're now launching a collaborative annotated bibliography. This is a curated, living repository of research papers, reports, websites and other resources compiling some of the work of researchers and organisations from around the world interested in the economics of data and on unlocking the social and economic value of data more broadly. The repository will be useful for researchers interested in the value of data from an academic perspective, but also for organisations interested in learning more about how they can unlock and measure the value of data within their ecosystems. It will also be useful for policymakers interested in learning more about how to enhance and quantify the contributions of data use and sharing to local and national economies.
The bibliography is currently organised around four main topics:
- the economics of data
- data ecosystems and incentives
- empirical methods of data valuation
- practical case studies
In the first section we explore how data, as an economic asset or good, has particular characteristics that differentiate it from other goods or assets that organisations normally hold or have access to: it is non-rival, it may have varying levels of excludability, it involves externalities, it may have increasing or decreasing returns, and it has a large option value. Because of these particular characteristics, traditional market pricing mechanisms don’t necessarily provide an efficient and effective way of approximating the value of datasets, as there is a gap between the private value that actors exchanging data through markets are able to realise, and the value that sharing and using data can create for the wider economy.
Likewise, the value of data is strongly linked to access: as more actors are able to access and use data, its value increases. The second category, called ‘Data ecosystems and incentives for sharing’ focuses on data and value flows. This category includes all the resources that place special attention to the role of ecosystems and data flows for unlocking or increasing the value of data, including some that explore governance frameworks and systems of incentives for increasing data availability and use.
The third section focuses on methods for data valuation. Because of the economic characteristics outlined earlier, measuring the value of data both for private actors and for wider society is challenging. Different methods may yield different results which may be more or less appropriate depending on the intended purpose of the measurement. Existing methods can be broadly grouped into cost-based, income-based and market-based. Each of these approaches is subject to specific assumptions and has different limitations. Additionally, recent literature has started new methodologies borrowed from other fields, such as impact-based methods and real options methods. We’ve grouped articles that outline and compare existing methods, and articles that develop specific methods more deeply under the category ‘Empirical methods for data valuation’.
Finally, the last category groups some case studies and practical examples that showcase the economic outcomes associated with enhancing access to data. These cases use diverse methodologies and cut across multiple industries and sectors. We believe that these use cases are useful because they show both the ways by which data becomes valuable, but also the limitations that exist for measuring the value that data can have. Learning from practical examples may be useful for organisations wanting to better understand how the data they have can become more valuable, or how they can estimate the impact that investments on data and data infrastructure have had in the past or could have in the future.
We are keen to hear what you think of the bibliography! Please email us at [email protected] to let us know if we’re missing something important or if you think we could do something to make the collaborative literature review an even more useful resource. If you have any articles, books, podcasts or other pieces of content that you would like to be added to the bibliography, please submit them through the community suggestions form.