21. Contextual Compatibility

Definition

Contextual Compatibility is the ability of an analytical model to preserve the validity, coherence, and meaning of its metrics when the analytical context changes, whether due to filters, hierarchies, visuals, users, or business scenarios.

A contextually compatible model:

• does not collapse under context changes,
• does not produce misleading results,
• and does not require “tricks” to be interpreted.

In Power BI, this property ensures that a metric:

remains correct when the context in which it is evaluated changes.

Nature

Contextual, logical, and relational.

Contextual compatibility does not depend on a specific visual.
It depends on how the model understands and responds to context.

A model may:

• be well designed,
• be semantically aligned,
• be consistent,

and still fail contextually if it was not prepared for multiple forms of exploration.

Function

Contextual compatibility enables:

• data exploration without fear of misinterpretation,
• metric reuse across different scenarios,
• confidence in results under any view.

In Power BI, this property:

• enables ad-hoc analysis,
• protects against out-of-context readings,
• reduces dependency on report designers.

Consequence

When contextual compatibility is high:

• metrics behave predictably,
• users explore freely,
• analysis remains stable.

When it is low:

• metrics “break” when filtered,
• confusing numbers appear,
• users lose trust.

Lack of contextual compatibility is not always visible.
It appears when someone explores beyond the intended path.

Signals of High Contextual Compatibility (✔️)

• Measures robust across different filters.
• Consistent behavior in tables, charts, and KPIs.
• Correct use of row and filter context.
• Reusable metrics across multiple reports.
• Users explore without fear of “breaking something”.

Signals of Low Contextual Compatibility (❌)

• Measures that only work in a single visual.
• Incorrect results when filters change.
• Dependence on specific configurations.
• Constant need for warnings.
• Phrases like: “that metric only works there”.

🔧 Samples — Contextual Compatibility in Power BI

🔹 Sample 1 — Visual-dependent metric

❌ Low compatibility:
A measure that only produces correct results in a specific table.

✅ High compatibility:
The same measure works correctly in tables, charts, and cards.

👉 Logic does not depend on the visual.

🔹 Sample 2 — Unexpected filters

❌ Low compatibility:
An additional filter alters the KPI’s meaning.

✅ High compatibility:
The measure responds correctly to the new context.

👉 Context is part of the design.

🔹 Sample 3 — Granularity change

❌ Low compatibility:
The metric works at a monthly level but fails at a daily level.

✅ High compatibility:
The metric remains coherent at all levels.

👉 Granularity does not break meaning.

🔹 Sample 4 — Different users

❌ Low compatibility:
The same report produces different interpretations for different users.

✅ High compatibility:
The model ensures stable interpretation for everyone.

👉 Usage context is anticipated.

🔹 Sample 5 — Anti-pattern vs Pattern

❌ Anti-pattern — Fragile metric

• Visual-dependent.
• Context-sensitive.
• Difficult to reuse.

✅ Pattern — Context-compatible metric

• Context-aware.
• Reusable.
• Reliable.

📌 Practical rule:
If a metric only works “in that visual”,
it is not a mature metric.

Interactions with Other Properties

• Semantic Alignment → ensures stable meaning.
• Consistency → reinforces predictable behavior.
• Fluency → facilitates contextual exploration.
• Contextual Compatibility → enables free analysis.

Synthesis

Contextual compatibility is a test of analytical maturity.

A compatible model:

• does not guide users through restrictions,
• does not break during exploration,
• supports any valid analytical context.