Everything Passed. The Number Was Wrong.
A green pipeline status proves that a process completed. It does not prove that the business number is correct. Most data quality programmes stop at the warehouse, but business users meet data in the semantic model, the dashboard and, increasingly, an AI assistant. Those last layers are where calculations, filters, security and business definitions are applied, and where a clean table becomes a wrong number.
The BI Trust Framework is a field guide to BI data quality from source to decision, written for BI architects, analytics leaders, data quality teams and data engineers.
Inside, you’ll discover:
- The seven-layer BI trust path – a lineage model from source record to business decision.
- The Datagaps BI Data Quality Matrix – eight dimensions across seven layers, showing where each control matters most.
- A 105-check control catalogue organized by layer.
- Deep dives on report checks, semantic models and DAX, AI-powered BI and migration assurance.
- Four industry failure stories, a BI quality operating model and a 90-day implementation roadmap.
- A 20-point BI report readiness release gate and a control scorecard template.
Can your organization explain why a number is correct, which records it includes, how it was calculated, when it was refreshed and who approved its use? This guide gives you the framework, checks and roadmap to answer yes.