Overview
A financial services firm had grown Power BI organically. Every department built their own reports. There was no single source of truth, and refresh times were exceeding 30 minutes for critical datasets.
The Challenge
Over 500 reports existed across workspaces. Data quality and definitions varied. No governance meant sensitive data could be exposed. Large datasets were overwhelming the shared capacity.
The Approach
We implemented a governance framework: centralized workspaces, standardized data models, and a shared lakehouse (Databricks) as the source for enterprise datasets. Power BI Premium provided dedicated capacity for critical workloads.
Implementation
Key components included:
- Data Lakehouse: Databricks Delta Lake as the single source of truth for enterprise data
- Governance: Workspace structure, approval workflows, and sensitivity labels
- Optimization: Incremental refresh, aggregation tables, and DAX tuning
- Dataflows: Reusable data preparation with parameters for environment switching
Results
- 50% reduction in report count through consolidation and deduplication
- 90% faster refresh times through incremental refresh and model optimization
- Centralized governance with approval workflows and sensitivity labels
- Self-service analytics maintained with guardrails and training
Key Lessons
- Governance doesn’t mean “no self-service”—it means guardrails and standards
- Incremental refresh is a game-changer for large datasets
- Work with business stakeholders to identify redundant reports
- Invest in training and documentation for adoption