What a data-platform assessment should actually produce

Start with the business consequences

The same technical issue can be minor in one environment and critical in another. A delayed pipeline matters differently when it feeds a monthly exploratory report than when it drives a daily operational review.

Assessment findings should connect architecture and implementation details to consumers, decisions, deadlines, cost, and recovery expectations.

Separate findings from priorities

A platform can have hundreds of imperfections. Treating them as one backlog prevents the team from seeing the few changes that would reduce the most risk or unlock the most value.

• Material operational risk
• Quick improvements with low dependency
• Structural constraints on future delivery
• Governance and ownership gaps
• Work that should be deferred or stopped

Compare repair with replacement

A platform recommendation should explain what the existing system cannot do, what a repair would change, and what a migration would disrupt. Compare options against the actual workloads and responsibilities, not a list of new product features.

Record missing evidence and the decisions it prevents. Deferring a rebuild can be a valid conclusion, especially when source access, ownership, or definitions remain unresolved.

Leave an implementation outline

Name the first scope, prerequisites, responsible owners, dependencies, and acceptance checks. Keep implementation separate from the assessment so the client can commission the next step, use an internal team, or choose not to proceed.

Related services and experience

• Data platform assessment (Assessment scope)
• Power BI health check (Reporting-only review)