One number. A chain of evidence.
Connecting operational data to analytics and AI without rebuilding a different truth for every team.
Databricks on AWS · Amazon S3 · data contracts · lineage
The question
Why does the service dashboard disagree with finance, and which answer should an AI assistant use? When definitions and ownership are unresolved, a new dashboard or vector index merely distributes the disagreement faster.
The hard decision
Choose a business data product with a named owner and a clear reconciliation test. For an order-to-fulfilment journey, agree what an order, cancellation and completed delivery mean before connecting more sources.
Inside the system
Ingest operational changes with explicit handling for late and duplicate events. Preserve source records on Amazon S3 and build validated tables in Databricks on AWS. Publish governed data products with documented definitions, lineage, access and freshness expectations. Analytics and retrieval consume those products through appropriate interfaces.
Where the engineering earns its place
A failed pipeline is visible. A pipeline that succeeds with the wrong data is more dangerous. Reconciliation, schema-change checks and freshness monitoring belong in the delivery path. Ownership determines who resolves an exception and whether consumers should keep using the data.
The operating measures
Measure reconciliation breaks, time to detect and resolve quality failures, data freshness and the effort required to onboard a new consumer. Track compute and storage costs against the products that create them.
What the client owns
Data contracts, pipeline code, quality checks, lineage, a catalogued product and runbooks. The goal is a foundation the business can reuse, with a clear explanation of where its numbers come from.
What would this look like
in your business?
A working conversation about the opportunity, the constraints and a useful first step.