Data as a Product is gaining ground everywhere in most domains ( more so in Lifesciences). Some of the firms have got it right, making great strides and few of them have got it wrong. In my interaction with my Data leads, there are some common patterns that I have seen in successful implementations of Data product which I want to mention here.
- They have a dedicated Product Manager for Data products within a domain who owns the creation and evolution of Data Product
- Data is treated as a first-class citizen - When the applications and systems undergo evolution, there is a corresponding update to the data product and not as a lazy delayed update.
- Data Product usage is monitored and proactively metrics are used to course correct both from tech and business perspective.
- Similar to how the platforms have the dichotomy of creating value & providing space for consumers to create new value, the data products' usage( source/target) & the pattern of usage are monitored proactively to update the source DP periodically.
- Monetisation costs: Legacy systems may not earn money directly, but the data they own can be monetised internally within the organisations.
- Team and costs strategy: A clear cost based accounting for the DPs- cloud, development costs and drive the ROI with the usage.
- There is an overall branding, organisation change around Data Products. Usage of DPs is incentivised, celebrated as how APIs are used. Value Lineage is measured and measured.
I'm sure there are few more that I have overlooked (or) hasn't come in my interactions. Data Products is an exciting space and a great opportunity to develop decentralisation & faster response to capture value from data.
