23. Polarity

Definition

Polarity is the property of an analytical model that allows it to identify, represent, and analyze meaningful contrasts between opposite states, without losing coherence or meaning.

A model with well-defined polarity:

• clearly distinguishes between high and low,
• separates success from failure,
• and reveals relevant tensions within the data.

In Power BI, this property ensures that:

extremes and contrasts are not diluted into irrelevant averages.

Nature

Comparative, relational, and analytical.

Polarity does not exist in isolated data.
It emerges from relationships between values, comparison, and contextual interpretation.

A model can:

• be consistent,
• be semantically aligned,
• have high informational density,

and still lack polarity if everything is presented as neutral values.

Function

Polarity enables:

• detection of critical deviations,
• prioritization of actions,
• understanding of contrasting dynamics.

In Power BI, this property:

• strengthens gap analysis,
• facilitates clear comparisons,
• guides rapid decision-making.

Consequence

When polarity is high:

• contrasts are evident,
• decisions become clearer,
• analysis becomes actionable.

When it is low:

• extremes are hidden,
• differences are overly smoothed,
• analysis loses decision power.

An analysis without polarity may inform,
but it does not guide action.

Signals of High Polarity (✔️)

• Clear use of ranges, thresholds, and comparisons.
• Visuals that display evident differences.
• Metrics that highlight gaps.
• Contrasts understandable without extra explanation.
• Ability to identify “best” and “worst”.

Signals of Low Polarity (❌)

• Excessive reliance on averages.
• Flat visuals with little contrast.
• KPIs that fail to differentiate states.
• Difficulty prioritizing.
• Phrases like: “everything is more or less the same”.

🔧 Samples — Polarity in Power BI

🔹 Sample 1 — Average vs extremes

❌ Low polarity:
An average that hides critical values.

✅ High polarity:
Use of percentiles, minimums, and maximums alongside the average.

👉 The average does not tell the whole story.

🔹 Sample 2 — KPI without thresholds

❌ Low polarity:
A KPI showing a number without reference.

✅ High polarity:
KPI with clear thresholds (good / acceptable / critical).

👉 Numbers need polar context.

🔹 Sample 3 — Neutral visual

❌ Low polarity:
A chart without visual contrast encoding.

✅ High polarity:
Intentional use of color, size, or direction to highlight differences.

👉 Contrast guides attention.

🔹 Sample 4 — Temporal comparison

❌ Low polarity:
Time series without highlighting peaks or drops.

✅ High polarity:
Explicit marking of highs, lows, and breaks.

👉 Time has extremes too.

🔹 Sample 5 — Anti-pattern vs Pattern

❌ Anti-pattern — Flat model

• Neutral metrics.
• No references.
• Diffuse decisions.

✅ Pattern — Polar model

• Contrasted metrics.
• Clear thresholds.
• Evident action paths.

📌 Practical rule:
If you cannot quickly distinguish what is good from what is bad,
the model lacks polarity.

Interactions with Other Properties

• Informational Density → prevents contrast saturation.
• Semantic Alignment → ensures poles mean the same thing.
• Opportunity → turns polarity into timely action.
• Polarity → enables clear decisions.

Synthesis

Polarity is the decision engine of analytics.

A model with polarity:

• does not soften reality,
• does not hide extremes,
• clearly shows where to act and where not to.