Many AI problems eventually reveal themselves as data problems.

Organizations often begin with the model. I usually look underneath it.

Where does the data come from? Who owns it? Is it trustworthy? How quickly can it move? What does it mean? Who is permitted to use it?

A modern data platform needs more than storage. It needs an operating architecture that connects:

  • Sources
  • Ingestion
  • Storage
  • Processing and transformation
  • Semantic models and serving
  • Governance and ownership
  • Security and privacy
  • Quality, lineage and observability

Only then do analytics, machine learning and AI have a dependable foundation.

A lake is not a platform

One common mistake is building a data lake and assuming that the organization has built a data platform.

A lake can store information. A platform must make that information discoverable, understandable, governed and usable. It needs clear ownership, reliable data products and access patterns that serve consumers without creating uncontrolled duplication.

Poor quality, unclear ownership and platform sprawl do not disappear when better models are introduced. They become more visible—and often more expensive.

Storage is infrastructure. Trusted, governed and consumable data is the product.