As data is associated with numerous value creation opportunities, protecting them and making sure others cannot access them seems a smart move to control the value capture. Though counter-intuitive at first, there is a clear case for value capture while sharing data. However, sharing doesn’t always mean the same and several options are possible. Why sharing data? How to frame a data sharing opportunity?
The case for data sharing
Reports after reports, the value associated with data increases. In particular, Accenture estimated in 2018 at 3 000 B$ the value generated by Internet of Things data marketplaces in 2030. The value of data can be extracted in three ways:
- commodity: when data is bought or sold, either by data brokers or corporations (for example banks or retailers)
- lever: when data is used to improve the performance of an existing business model (reducing costs or increasing revenue)
- asset: when companies use the data they collect as one side of their two-sided business model (like social networks or search engines) or when they use them to increase their bargaining power within an industry or a value chain
Sharing can be related with each of these three form of value extraction: trading data as a commodity is a form of sharing, as are two-sided data sharing agreements, at last, benefitting from some other company data can serve as a lever for improving the performance of a business model. Let’s point 4 reasons why data-sharing market is booming:
- As a non-rivalrous and non-depletable good, data is particularly attractive for sharing from the data owner perspective. Sharing data with other companies doesn’t prevent from using the very same data for internal benefit.
- The owner of the data is not always in the perfect position to extract the whole value. Examples are numerous of value created from a data source in industries of business ecosystems not directly connected. For example, the AIS system which locates the tankers in the sea proved to be a very useful data source for financial markets operators which have then a real-time picture of oil movements. AIS mission is to ensure safety and sharing data with other operators way a way to extract more value from this data source. From the offer side of the market: sharing data is a way to capture more value without investing in exploring or extracting it directly.
- The value creation opportunities associated with a dataset are bigger when combined with other data. Retailers loyalty program data is a very valuable source for improving their marketing and promotion actions. It gets more valuable when combined with weather or traffic data. From the demand side of the market: other datasets can prove to be very valuable for one’s business.
- The business environment favours or forces data sharing. Regulations in Europe are more and more in favor directly or indirectly to industrial data sharing practices (see in particular the European Strategy for Data published by the European Commission in 2020). Intermediaries and platforms have positioned specifically to make easier data sharing.
Defining data sharing opportunities
Very different realities and practices are grouped under the term “sharing”, which comforts the idea of several data sharing configurations. Deciding to open its data to public in an open data format (i.e. without getting a direct revenue) is not the same as selling access to the real-time data of its infrastructure to a digital platform that would sell insights (e.g. when telecommunication companies sell geolocation data of their users to Thasos or when car makers sell the geolocation data of the cars of their clients to Otonomo). And it’s another story when retailers share their inventory data with their suppliers in exchange of the logistics risk.
5 questions can help to define data-sharing opportunities:
- What to share: raw data vs cleaned data, realtime data vs past data, aggregated data vs calculated data, …
- Who to share with: public, clients, selected partners, governments, …
- Which rights and terms for the users: financial terms (free, flat fee, subscription, commission, …), reuse terms (use, distribute, modify, monetize, …)
- What expected benefit for the data owner: additional revenue, access to a scarce resource, brand image, …
- What expected benefit for the data user: new revenue source, current product revenue increase, operations cost reduction, differentiation against the competition, …