Trust is the product
A warehouse full of tables that nobody believes is not a data platform — it is expensive storage. Leaders need metrics they can explain in a meeting. Operators need pipelines that do not break every Monday. Start with the decisions that matter, then design the smallest architecture that answers them reliably.
Define metrics with stakeholders
Conflicting numbers usually come from conflicting definitions, not from missing BI tools. In discovery, align on metric definitions, grain, and owners before dashboards proliferate. Document what “active customer,” “revenue,” or “cycle time” means in your business language.
- Shared metric dictionary with named owners
- Source-to-consumption lineage for critical numbers
- Quality checks at ingest and transform boundaries
- Clear escalation when a pipeline or metric fails
Pipelines and ownership over tool catalogs
ETL/ELT tooling changes; ownership does not. Assign who fixes broken feeds, who approves schema changes, and how consumers are notified. Prefer durable contracts between producers and consumers over ad-hoc extracts and spreadsheet bridges.
Foundations for later AI
Practical AI depends on context quality, permissions, and clean operational data. If leadership cannot trust today’s reports, embedding AI on top of the same sources will amplify the problem. Fix trust and access first; model bets second.