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XHVERSE

Semantic model tool

Power BI Semantic Model Doctor

Check whether a Power BI semantic model has clear grain, relationships, measures, refresh behavior, and support ownership.

Model clinic

Use this before a semantic model becomes the shared truth layer and ambiguous grain or copied measures become expensive.

Directional planning only. Do not paste confidential platform, customer, credential, or incident details.

Assessment

Diagnose the model before users inherit its debt.

Inspect grain, dimensions, relationships, measures, refresh behavior, and lifecycle so the model can be trusted.

01 · Fact grain

Is the fact grain clear enough that every measure can explain what it counts?

Ambiguous grain is the fastest path to conflicting reports.

Hint

Ambiguous grain is the fastest path to conflicting reports.

Best-result move

Review grain whenever a new fact table or aggregation is introduced.

Metric consequence

Choose a level to see what this decision means for the brief.

02 · Dimensions

Are dimensions conformed, named, and separated from facts in a way report authors can understand?

A model should make the correct path easy for analysts.

Hint

A model should make the correct path easy for analysts.

Best-result move

Review dimension drift when new business domains join the model.

Metric consequence

Choose a level to see what this decision means for the brief.

03 · Relationships

Are relationship directions, many-to-many cases, and bridge tables intentional?

Relationship ambiguity creates subtle measure errors.

Hint

Relationship ambiguity creates subtle measure errors.

Best-result move

Review relationships before every certified model release.

Metric consequence

Choose a level to see what this decision means for the brief.

04 · Measure ownership

Are important measures centralized, reviewed, and owned instead of copied across reports?

The semantic model should reduce metric drift.

Hint

The semantic model should reduce metric drift.

Best-result move

Review measure changes with business owners and report maintainers.

Metric consequence

Choose a level to see what this decision means for the brief.

05 · Refresh and performance

Is refresh mode, Direct Lake or import behavior, aggregation, and performance evidence understood?

Model design needs operational evidence, not only visual success.

Hint

Model design needs operational evidence, not only visual success.

Best-result move

Review refresh and performance health after source or model changes.

Metric consequence

Choose a level to see what this decision means for the brief.

06 · Certification and lifecycle

Can users tell whether the model is experimental, certified, deprecated, or production-supported?

Model status should reduce duplicate report creation and trust disputes.

Hint

Model status should reduce duplicate report creation and trust disputes.

Best-result move

Review certified models on a fixed cadence with usage and incident evidence.

Metric consequence

Choose a level to see what this decision means for the brief.

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