It is important that dataflows and data harmonization processes are smooth. Also in most cases the data must be made interoperable. But dataflows and harmonization or interoperability aren´t so interesting itself. Data must produce a real value and business should focus on customer value and data value chain. Same goes to data platforms, they should be enabler of the data and customer value creation.
When selling and marketing data platforms to customers, it is often emphasized how easy and fast dataflows are. Perhaps also highlighting lower integration costs than competitors. The customer, in turn, don’t think about the benefits of investing large sums in the platform, not knowing what value it will generate. That is also a significant barrier to sales. So the vendor and customer of the data platform should focus primarily on the data value chain.
But what eventually is the data value chain?
According to Data2X by Open Data Watch report, the data value chain describes the process of creating and using data from identifying the need for information to its end use and possible reuse. There are four main stages in the data value chain: collection, publication, implementation, and impact. These four stages are further divided into twelve more detailed stages: identify, collect, process, analyze, release, spread, connect, encourage, influence, use, change and reuse.
There should be continuous feedback between data producers and users, as well as between different stakeholders. The data value chain can also be as a teaching tool that displays complex steps and enables their development.
In particular, the importance of data reuse has been overshadowed by the development of platforms and their business. Platform vendors should strive to see themselves as part of the data value chain rather than just the supply chain. Instead of merely providing data, one should strive to be an active part of the data value chain.
Open Data Watch report´s great picture sums up nicely, what the data value chain is all about. Data platforms should strive to be involved at all stages, especially when reusing data, so that they can provide their customers with the right value and promote the data economy.