Businesses can gain from sharing data – but it can be difficult to demonstrate this effectively. We are exploring this as part of our R&D project – The value of data sharing in the private sector.
We think that a great way to illustrate how data sharing creates value – both in terms of boosting reputation and positively impacting the bottom line – is to share case studies of businesses which have done this successfully.
This would also help to address business concerns uncovered in our previous user research. This found: that data sharing can be seen as a commercial and legal risk; that there are not enough established business models; and that data sharing may feel like a “leap of faith”.
Case studies showing responsible, profitable and ethical data sharing can demonstrate how others have built a strong business case, effectively managed the risks, and had confidence in the data-sharing process.
Exploring data sharing
We started the project with desk research that mapped over 80 examples of private sector organisations that share data. From this, we established 10 categories of types of ‘business value’ that businesses can gain by sharing data externally. We focused on value that directly or indirectly affect the bottom line by increasing revenue or reducing costs.
Types of data-sharing benefits
- Brand trust
- Market analytics
- Increasing serviceable available market
- Open innovation
- Platform model
- Proactive compliance
- Regulated data sharing
- Supply chain optimisation
- Speed to market
- Sustainable business model
We validated and refined these initial values through user research interviews with seven companies across different sectors.
The goal of the interviews was to:
- get feedback on the categories above
- understand more about companies' experience in building the internal business case for sharing data externally
- ascertain what incentives they think would lead to a decision to share data externally
Alongside this, we identified specific examples of companies that are gaining business value from sharing data with external organisations in finance, retail, agriculture, sport, transport, aerospace and maritime. Each example demonstrates a different type of value created through sharing data. We used a cross-sector view to see what data sharing initiatives looked liked across different industries.
Definitions and conditions for data sharing
Overall, the interviewees verified that the types of value we had suggested aligned with their experiences. The answers also helped us to refine the business value categories. The interviews showed that awareness about how data is currently used and shared varies considerably across industries.
In particular, there was a lot of confusion about the definition of data sharing, and, for example, the differences between open and shared data. This is not surprising given the range of access models available (see our Data Access Map), and that data sits on a spectrum, from closed to shared to open.
The conversations also highlighted when companies are most likely to share data. For example, in a sector or industry where businesses treat data as infrastructure, they are more likely to share data because it is relatively easy and cost effective. In mature data ecosystems companies perceive that there is a lower risk of losing competitive advantage, and are motivated by the potential network effect (when increased numbers of participants improve the value of a good or service).
Another example which is clear from both the desk research and interviews is the role that data intermediaries provide. (Data intermediaries sit between data providers, data users and other stakeholders in the sharing and use of data). Where present in a market they provide useful services across data ecosystems which data providers often lack.
For example, the presence of aggregators in a sector can accelerate the data sharing across competitors. This is valuable to data providers as it increases their serviceable available market, and can provide useful insights about market and competition.
We also researched where data sharing occurs along Porter’s Value Chain, a classic firm behaviour concept. However, we came to the conclusion that the traditional value-chain theory was not applicable to all business models, particularly in the case of digital products and services. Other versions of value chains may provide greater insights into data sharing activities in a firm, such as network dynamics.
This led us to identify two specific clusters of business value:
- Business As Usual (BAU) data sharing, which encompases standard behaviour of private sector organisations. This is either one-to-one data sharing, or data sharing with typical partners in a value chain, such as along a supply chain. BAU data sharing requires one business to share data with partners, suppliers and customers. These activities are aimed at a short-term return, and relatively direct value, economic or otherwise.
- Ecosystem data sharing, where benefits are created through more complicated relationships. This will often involve multiparty data-sharing arrangements, and can include data sharing with atypical partners, including competitors. By sharing data across a sector there are chances for additional returns that would be otherwise unattainable, such as through industry benchmarking, platforming, and research sharing.
Based on our discovery phase research, we believe that there is a relationship between BAU direct data sharing and the value of data sharing for an ecosystem.
Our hypothesis is: in the right conditions, if there are enough businesses sharing consistently, it is possible to achieve ecosystem dynamics which release larger, network values.
We will continue to test this hypothesis through our alpha plan, by doing further research into selected case studies through interviews and desk research, and learning more about ecosystem dynamics.
Get in touch
If you work in the private sector and are interested in the work we are doing, or have good examples of how your business is currently getting value from sharing data, please get in contact.