Power BI Modeling

Use this guidance-only skill to design and audit Power BI modeling practices. It does not execute scripts or external tools.

When to use

• You need a scalable semantic model for decision-making.
• You want cleaner measures and consistent KPI definitions.
• You need model quality checks before dashboard rollout.

Core workflows

1) Model architecture

• Use star schema patterns with clear fact and dimension boundaries.
• Standardize grain definitions and surrogate key usage.
• Minimize many-to-many joins unless there is a documented exception.

2) DAX and KPI governance

• Separate base measures from presentation measures.
• Keep KPI definitions versioned and approved by business owners.
• Enforce naming conventions and consistent time-intelligence logic.

3) Performance and maintainability

• Reduce high-cardinality dimensions where possible.
• Audit relationship directions and inactive relationships regularly.
• Track report load and query latency after major model changes.

4) Documentation and ownership

• Assign owners per table, measure set, and dashboard domain.
• Maintain a short data dictionary for key fields and KPIs.
• Review stale or duplicate measures on a fixed cadence.

Risk controls

• Avoid KPI drift by preventing ad-hoc measure rewrites.
• Prevent hidden data quality issues with source-to-report reconciliations.
• Keep report security and access policies auditable.

Output format

When asked for help, provide:

• A semantic model review checklist.
• A KPI definition and DAX governance template.
• A performance and quality audit plan.