Data Products Done Right: Delivering Value Now
This article was originally published on dataops.live.
At Big Data London, I participated in a keynote panel exploring how organizations can implement data products effectively. The discussion surfaced a pattern I see constantly: the gap between expectations in software delivery versus data delivery.
In software, if someone asks for a feature on Monday, they expect to see progress by Wednesday. In data, telling stakeholders they'll have their answer in six months is considered normal. That gap is exactly what the data products model — combined with DataOps practices — is designed to close.
The panel converged on a practical definition: a data product should be discoverable, trustworthy, composable, interoperable, backward compatible, tested, and built with agile methodology and product management principles. That's a high bar, but each of those properties directly addresses a failure mode that data teams encounter regularly.
The most compelling evidence came from real-world implementations. Roche increased their release cadence from one deployment per quarter to over a thousand quarterly, while managing hundreds of data products in production. That kind of velocity — with reliability — is only possible when you treat data with the same engineering discipline as software.
We also discussed three categories of value that data products can deliver: wrapping existing data assets in better interfaces, selling data as a commercial offering, and improving existing products with embedded data intelligence. Organizations that think about all three categories tend to build stronger business cases for investment.