PRACTICAL GUIDE
Connect the pieces.
Question the assumptions.
A starting framework for a useful data and AI conversation.
01 / FOUNDATION
Start with the workload.
A warehouse supports structured analytical workloads. A lake stores data in flexible formats. A lakehouse combines open storage with features such as transactional tables and analytical processing. These descriptions overlap in real products.
Write down your latency needs, source systems, data volume, expected users and operational constraints. Compare architecture against those requirements rather than choosing a category first.
02 / TRUST
Make responsibility visible.
For each source, identify an owner, the permitted uses, access rules and a way to trace changes. Check freshness and quality against the needs of the workload. Access granted to an assistant should be scoped to what its users are allowed to see.
A catalog can help make these controls discoverable, but its existence does not prove that the controls are complete or enforced.
03 / APPLICATION
Define meaning. Test outcomes.
Business terms need explicit definitions. Retrieval needs relevant, authorized sources. Answers need evaluation against representative examples, including ambiguous questions, missing information and sensitive content.
Set a baseline, choose a small pilot and track accuracy, source support, useful task completion, latency and operating cost. Assign someone to review errors and decide when a system is ready to expand.
Turn this into an action brief →