22. Informational Density

Definition

Informational Density is the relationship between the amount of information presented and the real analytical value that information provides for understanding, decision-making, or action.

A model with high informational density:

• communicates more with less,
• removes noise without losing meaning,
• and maximizes insight per unit of data.

In Power BI, this property ensures that:

every visual, metric, and element exists for a clear analytical reason.

Nature

Synthetic, communicational, and cognitive.

Informational density is not about how much data exists,
but about how much meaning it produces.

A model may:

• contain thousands of columns,
• display dozens of visuals,
• include multiple KPIs,

and still have low density if the information does not translate into value.

Function

Informational density enables:

• faster comprehension,
• reduced cognitive overload,
• focused attention on what matters.

In Power BI, this property:

• improves dashboard effectiveness,
• facilitates executive readability,
• and accelerates decision-making.

Consequence

When informational density is high:

• insights surface quickly,
• users understand with minimal effort,
• analysis feels clean and intentional.

When it is low:

• visual saturation appears,
• users get lost,
• analytical value is diluted.

Excess information does not inform.
It distracts.

Signals of High Informational Density (✔️)

• Visuals with a clear analytical purpose.
• Essential, well-defined metrics.
• Efficient use of visual space.
• Conscious removal of redundancies.
• Fast reading with high impact.

Signals of Low Informational Density (❌)

• Overloaded dashboards.
• Repetitive or irrelevant KPIs.
• Decorative visuals without analytical function.
• Difficulty identifying what matters.
• Phrases like: “there is too much here”.

🔧 Samples — Informational Density in Power BI

🔹 Sample 1 — Saturated dashboard

❌ Low density:
Many visuals showing minor variations of the same data.

✅ High density:
A few visuals summarizing key behavior.

👉 Fewer elements, more meaning.

🔹 Sample 2 — Redundant KPI

❌ Low density:
Multiple KPIs expressing the same metric in different formats.

✅ High density:
A single clear KPI concentrating essential information.

👉 One message, not multiple echoes.

🔹 Sample 3 — Unnecessary detail

❌ Low density:
Showing extreme detail in executive views.

✅ High density:
Detail level adjusted to the user type.

👉 Context defines density.

🔹 Sample 4 — Visual space

❌ Low density:
Space occupied by elements without informational value.

✅ High density:
Every dashboard area communicates something relevant.

👉 Space is also information.

🔹 Sample 5 — Anti-pattern vs Pattern

❌ Anti-pattern — Noisy model

• Too much information.
• Low insight.
• High cognitive fatigue.

✅ Pattern — Dense model

• Concentrated information.
• High value per visual.
• Immediate clarity.

📌 Practical rule:
If removing a visual does not change the decision,
that visual is unnecessary.

Interactions with Other Properties

• Fluency → keeps density readable.
• Resonance → turns density into intuition.
• Informational Scarcity → prevents saturation.
• Informational Density → maximizes analytical value.

Synthesis

Informational density is a signal of analytical maturity.

A dense model:

• does not show more,
• shows better,
• delivers maximum meaning with minimal load.