The Five Requirements Your Data Platform Needs, And Why It Is Probably Failing

Many organizations ship data products, but most do so ad hoc rather than through repeatable programs, creating duplicate work and slowing AI scale.

The key requirements are to embed governance and context at data creation, automatically detect existing assets, and provide explainable trust scoring.

Platforms also need cross-silo discoverability and ongoing lifecycle management so data products stay aligned as they change.

Without these capabilities, data teams waste capacity on one-off work and struggle to build trustworthy AI at scale.