Informational Scarcity

Definition

Informational Scarcity is the ability of a data model to display only the necessary, meaningful, and actionable information, avoiding excess metrics, dimensions, and details that dilute understanding and erode decision-making.

A model that respects informational scarcity does not hide information,
but it does not expose it without purpose.

In Power BI, this property ensures that users receive the minimum amount of data required to understand a situation, without noise or redundancy.

Nature

Cognitive, semantic, and methodological.

Informational scarcity is not a technical limitation,
but a conscious design decision oriented toward the user’s mental clarity.

It emerges when:

• the objective of the analysis is clearly understood,
• signal is prioritized over volume,
• and the model is designed around human cognitive processes.

Function

To protect users from analytical noise and cognitive fatigue.

In practice, this property enables:

• dashboards that are understandable in seconds,
• metrics that stand out by meaning rather than quantity,
• analysis that leads to decisions, not endless exploration.

Consequence

• Greater clarity when reading data.
• Faster and more reliable decisions.
• Reduced misinterpretations.
• Increased real-world analytical impact.

A model without informational scarcity informs a lot,
but explains very little.

Signals of Informational Scarcity

• Every metric has a clear purpose.
• No duplicated or redundant KPIs exist.
• Dimensions are visible only when they add context.
• Users can explain the dashboard without reading labels.
• Fewer visuals generate greater understanding.

Signals of Informational Excess

• All metrics are shown “just in case”.
• Each page contains more than needed to decide.
• Users need guides to understand the dashboard.
• Exploration does not converge into action.
• Depth is confused with complexity.

This is not informational richness: it is structured noise.

Conceptual Example in Power BI

Informational excess (❌)

• 20 KPIs on a single page.
• Minimal variations with no real impact.
• Irrelevant slicers visible by default.

Result:
👉 the user does not know where to look.

Informational scarcity applied (✅)

• 3–5 key KPIs.
• Variations that imply action.
• Detail accessible only on demand (drill, tooltip).

Result:
👉 the user understands and decides.

Informational Scarcity and Model Design

Informational scarcity is reinforced when:

• Measures are hierarchized (core vs derived).
• The model separates:
• operational metrics,
• strategic metrics,
• exploratory metrics.
• The visual design follows the semantic model logic.

It is not just a visual issue:
it is a property of the entire model.

Interactions

• Consistency → prevents contradictions.
• Equivalence → reduces semantic duplication.
• Semantic Elasticity → adapts without inflating.
• Integrability → connects without multiplying noise.
• Informational Scarcity → protects meaning.

Without informational scarcity, the other properties lose impact.

Samples

🔹 Sample 1 — KPI Hierarchy (Core vs Derived)

Problem (excess ❌)
A dashboard shows:

• Sales
• Sales YTD
• Sales MTD
• Sales LY
• Sales YoY
• Sales Growth %
• Sales Variance
• Sales Target
• Sales vs Target

👉 The user does not know which one matters.

Scarcity applied (✅)

Core KPIs (visible):

• Total Sales
• Sales Growth %

Derived KPIs (on demand):

• Sales YTD
• Sales vs Target (tooltip / drill)

📌 Result:
The page communicates status and trend in seconds.

🔹 Sample 2 — Action threshold metrics

Informational excess (❌)
Irrelevant variations are shown (±0.3%, ±0.5%).

Scarcity applied (✅)
Metrics are shown only when they exceed an action threshold.

📌 Result:
The dashboard speaks only when action is required.

🔹 Sample 3 — Dimensions visible only when they add context

Excess (❌)
Default visible slicers:

• Country
• Region
• City
• Channel
• Subchannel
• Salesperson

Scarcity (✅)

• Only Region visible.
• Others activated via:
• drill-down
• drill-through
• secondary pages

📌 Result:
Fewer visible options → less cognitive friction.

🔹 Sample 4 — Metric separation by intent

Strategic metrics

• Revenue
• Growth
• Margin

Operational metrics

• Volume
• Units
• Orders

Exploratory metrics

• Variants
• Exceptions
• Outliers

👉 Only strategic metrics live on the main page.

📌 Result:
Each page responds to a distinct cognitive intent.

🔹 Sample 5 — Drill as a scarcity mechanism

Excess (❌)
All details visible on the same page.

Scarcity applied (✅)

• Main page → synthesis.
• Drill-through → detail.
• Tooltip → quick context.

📌 Result:
The user chooses to go deeper; they are not forced.

🔹 Sample 6 — Antipattern vs Pattern (Informational Scarcity)

❌ Antipattern — Exhaustive dashboard

• “Show everything”
• 15+ visuals
• All metrics visible
• Slow or nonexistent decisions

✅ Pattern — Scarce dashboard

• “Show what matters”
• 3–6 visuals
• Hierarchized metrics
• Fast, clear decisions

📌 Rule of thumb:
If the user needs more than 10 seconds to understand the page, informational scarcity is missing.

Synthesis

More data does not generate more knowledge.
More clarity does.