Define the value proposition, benefits and purpose

“People think the actual flow of data can be a barrier. It’s not really. As soon as people decide … the problem is getting to the decision of sharing the data.”
(Head of Startup and Innovation Programmes, Open Data Institute)

Before any data sharing can commence, the value proposition of the data sharing relationship – exactly why data is being shared and who with – requires defining. We discussed some of the benefits of data sharing on page 7. The first step toward successful data sharing is to clearly define its purpose. There should be a document outlining how all parties will be engaged. Questions that this should address are:

  1. What is the benefit of getting involved for each of the intended organisations or individuals? 
  2. How is it ensured that these benefits are realised?
  3. What does each of the stakeholders have to commit to realise them?
  4. Will any external stakeholders receive benefit/need to be involved?
  5. What terms and licence will be needed to ensure these benefits accrue to each party?

This does not mean that the exact desired result has to be known upfront – this will rarely be possible – but a general understanding of the intention of the parties involved is necessary. While it might be only one party that defines this initially, it is important to agree what success looks like; this will flow through to other parts of the process, such as the definition of KPIs or other measures of success.

Aside from benefits to the involved organisations, it is also helpful to consider what the individuals in charge within these organisations may want to get out of the process. For example, what motivates a technology manager in a large corporation to engage in data sharing may differ from what motivates the owner of a small enterprise. Data sharing is more likely to be successful if the decision-makers across the involved organisations agree what success looks like. 

There are different ways how data holders and users can find one another. For example, a data holder could publish a challenge or a call for tender for data users to apply to, or directly approach a data user of their choice.

Key resources:

A framework for data sharing for open innovation (Walker et al., 2019): Outlines a framework that identifies the conditions which enable value to be created through a data sharing format. (p. 112)

Data collaboratives (Verhust et al., n.d.): Comprehensive overview of data sharing institutions for public good, spearheaded by the US-based GovLab.

Data trusts: lessons from three pilots (Hardinges et al., 2019): Report by the Open Data Institute on their work to establish what Data Trusts are and how they can function.

Creating the energy data commons (Webb, 2018): Overview of a data commons model in the energy sector.