Governed data and analytics

Conflicting reports and disconnected data force teams to reconcile numbers before making decisions. Build governed data products with shared meaning, reliable pipelines, and accountable ownership so analytics and AI use information people can trust.

Capabilities

Trace priority business questions to source systems, data owners, and integration constraints.
Design lakehouse, warehouse, and semantic models around agreed analytical needs.
Define quality rules, freshness expectations, lineage, classification, and access boundaries.
Deliver a bounded reporting or analytical pilot with business validation of key measures.
Establish pipeline monitoring, issue resolution, change control, and data-product ownership.

Deliverables

A source-to-consumption blueprint showing integration paths, responsibilities, and dependencies.
A prioritized data-product backlog with shared metric definitions and business acceptance criteria.
A quality and access control plan with exception handling and traceable evidence.
A reporting pilot and operating model covering refresh failures, ownership, and change approval.

Business value

Reduce the uncertainty behind reports and create a reusable basis for decisions. Measure freshness, reconciliation effort, quality exceptions, and consistency of key measures rather than treating data consolidation alone as success.

Start with a business question and the data it needs.

Start with one important business question and the data it needs. Bring current reports, source owners, and known quality issues. Confirm permitted reuse, privacy requirements, and access before integrating sources.

Fabric, OneLake, Power BI, Purview