Coherence — Logical Alignment of the Analytical Model

Definition

Coherence is the ability of a data model to maintain consistent internal logic across its metrics, relationships, rules, and visualizations, so that the model makes sense as a system rather than as a collection of isolated parts.

A coherent model does not contradict itself.

In Power BI, coherence ensures that:

metrics reinforce one another,
relationships follow a clear logic,
and conclusions do not depend on the path taken to reach them.

Nature

Logical, semantic, and systemic.

Coherence is not a technical detail;
it is a structural property of analytical reasoning.

It emerges when:

definitions do not overlap or compete,
rules follow a shared conceptual framework,
and the model can be explained as a single logical whole.

Function

To ensure that analysis reasons correctly.

In practice, coherence allows:

users to trust results,
metrics to avoid generating opposing interpretations,
the model to support complex questions without collapsing.

An incoherent model may be precise in parts,
but false as a system.

Consequence

Clear and aligned interpretations.
Fewer conflicts between metrics.
Greater analytical credibility.
Decisions grounded in logic, not defensive intuition.

Without coherence, the model produces local truths but global falsehoods.

Signals of Coherence

Metrics do not contradict one another.
Aggregations follow clear rules.
Relationships reflect business reality.
A single concept has a single meaning.
Conclusions remain stable across different views.

Signals of Lack of Coherence

Two KPIs tell opposing stories.
Totals do not match details without explanation.
Changing visuals alters the narrative.
The model requires constant exceptions.
Users doubt results even when numbers reconcile.

This is not complexity; it is logical fracture.

Conceptual Example in Power BI

Without coherence (❌)

Total Sales ≠ sum of Sales by Product.
Positive Growth % while Revenue declines.
Isolated KPIs with no shared framework.

Result:
👉 analytical confusion.

With coherence (✅)

Aligned totals and details.
Derived metrics built on base metrics.
Consistent analytical narrative.

Result:
👉 trust and clarity.

Coherence and Model Design

Coherence is reinforced when:

A clear hierarchy of metrics exists (base → derived).
Relationships are well defined and documented.
Duplicated logic across layers is avoided.
Visualizations respect the semantic model.

Coherence is not fixed in the report;
it is designed in the model.

Interactions

Consistency → stable meaning.
Equivalence → same results through different paths.
Semantic Elasticity → adaptation without rupture.
Integrability → coherence across models.
Informational Scarcity → logical focus.
Abstraction → conceptual clarity.
Persistence → coherence over time.
Coherence → system logic.

Without coherence, the other properties lose collective strength.

🔧 Samples — Coherence applied in Power BI

🔹 Sample 1 — Base metrics vs derived metrics

Incoherent (❌)
Each visual calculates its own total.

Coherent (✅)
Clear hierarchy:

Total Sales :=

SUM(FactSales[Amount])


Sales Growth % :=

DIVIDE(
    [Total Sales] - [Total Sales LY],
    [Total Sales LY]
)

📌 Result:
Derived metrics depend on a shared base.

🔹 Sample 2 — Totals that explain detail

Incoherent (❌)
Totals do not match category breakdowns.

Coherent (✅)
Correct context handling:

Total Sales (Coherent) :=

CALCULATE(
    [Total Sales],
    ALL(DimProduct)
)

📌 Result:
Totals explain detail instead of contradicting it.

🔹 Sample 3 — Coherence across financial KPIs

Incoherent (❌)

Revenue ↑
Margin ↓
Profit ↑ with no clear relationship.

Coherent (✅)
Chained KPIs:

Profit :=

[Revenue] - [Cost]

Margin % :=

DIVIDE([Profit], [Revenue])

📌 Result:
Each KPI is explained by the previous one.

🔹 Sample 4 — Coherence across pages

Incoherent (❌)
The same metric changes meaning across pages.

Coherent (✅)
A single global metric definition.

📌 Result:
The narrative remains consistent end to end.

🔹 Sample 5 — Filter and context coherence

Incoherent (❌)
Implicit filters break comparisons.

Coherent (✅)
Explicit, controlled context:

Sales Comparable :=

CALCULATE(
    [Total Sales],
    DimStore[IsComparable] = TRUE()
)

📌 Result:
Comparisons are logical and defensible.

🔹 Sample 6 — Antipattern vs Pattern (Coherence)

❌ Antipattern — Fragmented model

Isolated KPIs.
Duplicated logic.
Contradictory results.

✅ Pattern — Coherent model

Chained metrics.
Semantic hierarchy.
Consistent narrative.

📌 Rule of thumb:
If two metrics tell different stories without explanation, the model is not coherent.

Synthesis

Coherence does not make the model more complex.
It makes it more true.