The best part of applying a framework like data product internally at a company is that it helps create a way of thinking about data science investments and their sustainability. The worst part of it is that framework can make it feel like everything that’s valuable is or becomes a data product. But I strongly disagree with that perspective, and I think it is worth talking about why. Just to be clear, I am focusing here on internal data products.
Also consider that the ‘Productizing’ a manual or semi-manual process behind a metric’s assembly and dissemination can remove the experience of categorizing and interpreting data which has inherent value. LSS visual controls are all founded on the idea that the metrics developed (visual controls) are all touched by a human to understand root cause and trends as well as creating the opportunity to throw out ‘wacky’ data. Data literacy also includes the ability to understand what needs to be a finished data product and what needs to be ‘raw’ - ready for humans to sort it before consumption as a metric.
Not Everything Data Related Should Be a Data Product
Also consider that the ‘Productizing’ a manual or semi-manual process behind a metric’s assembly and dissemination can remove the experience of categorizing and interpreting data which has inherent value. LSS visual controls are all founded on the idea that the metrics developed (visual controls) are all touched by a human to understand root cause and trends as well as creating the opportunity to throw out ‘wacky’ data. Data literacy also includes the ability to understand what needs to be a finished data product and what needs to be ‘raw’ - ready for humans to sort it before consumption as a metric.