How to deploy data products in an organization?
-
Define what each data product must carry
Think of it as a product label. A bottle of laundry detergent states what it contains, what it is for, and who manufactured it. A data product carries the same information. Those elements let anyone use the data product independently and with trust.
-
Build data products as the foundation for AI
AI agents and models depend on data they can trace, trust, and act on. Unverified data produces confident wrong answers at scale. When meaning is described properly, agents reason from data instead of guessing at it. That is what makes AI reliable enough to put into production.
-
Start with one domain and one champion
Data product work often stalls when the scope is too wide. Starting with one domain, one business owner, and data that is actively used makes it possible to learn what works before scaling. The first product proves the model in practice.
-
Build only what gets used
An unused data product is an investment without ROI. The goal is to build products that get used repeatedly since recurring usage is a real proof that a data product works. When data has a clear owner and description, 40-50 % of analyst time that typically goes to data preparation shifts to actual analysis. Data built once gets discovered and reused by the next team, and cycle times drop from weeks to days.
What organizations should know before setting up data products?
-
Most of your available data is noise
In a real procurement environment spanning 24 sites 26,679 source columns were available. The business used less than 1 % of them*. 12 data products covered everything that actually mattered. The challenge is deciding what needs to be a product and letting the rest go.
*Measured at full production start, models have evolved after that to increasingly reflect the business needs. Data products are not meant to be static, but evolve as the business and world around as evolves.
-
Data products need to be owned
Every data product has an owner. A named person whose job is to keep the data correct and up to date. Without one, a product goes stale, nobody knows who to ask about it, and it quietly stops being used. Governance keeps that ownership clear and keeps products maintained as requirements change. Match governance to the product's maturity and its use case, and compliant data becomes the easier path as the organisation grows.
-
A modern data product frames how information is used across the organization
A modern data product serves any user, whether it's a human, a system, or an algorithm. That shift matters because when AI agents and automations consume data alongside people, the product must carry enough context to be used without human interpretation.
