19. Connectivity

Definition

Connectivity is the ability of an analytical model to integrate coherently and stably with multiple data sources, systems, models, and analytical layers, without losing meaning or generating structural friction.

A connected model does not live in isolation.
It is part of a broader analytical ecosystem.

In Power BI, connectivity is expressed when the model can:

• consume data from multiple sources,
• relate to other models or datasets,
• and coexist with other systems without breaking its semantics.

Nature

Systemic, relational, and architectural.

Connectivity is not only technical (connectors).
It is the ability for analytical coexistence.

A model can be:

• correct,
• resonant,
• transmissible,

and still not be connectable if it was not designed to coexist with other models and flows.

Function

Connectivity enables:

• expanding analytical reach,
• reusing information,
• and building progressive analytical layers.

In Power BI, strong connectivity:

• facilitates distributed architectures,
• enables shared semantic models,
• and reduces information silos.

Consequence

When connectivity is high:

• models integrate without friction,
• data flows across systems,
• analytics scales organically.

When connectivity is low:

• models become isolated,
• data and logic are duplicated,
• analytics fragments.

Lack of connectivity does not break the model.
It isolates it.

Signals of High Connectivity (✔️)

• Well-defined star schemas.
• Shared dimensions across datasets.
• Compatible semantic definitions.
• Integration with other reports or services.
• Ability to extend analysis without redesign.

Signals of Low Connectivity (❌)

• Closed, self-contained models.
• Duplicated dimensions.
• Semantic incompatibilities.
• Manual or fragile integrations.
• Phrases like: “this model cannot be joined”.

🔧 Samples — Connectivity in Power BI

🔹 Sample 1 — Multiple sources

❌ Low connectivity:
Each source is analyzed in a separate report without integration.

✅ High connectivity:
Sources integrated through shared dimensions.

👉 Analysis crosses systems without friction.

🔹 Sample 2 — Shared models

❌ Low connectivity:
Each team builds its own isolated model.

✅ High connectivity:
A central semantic model reused across multiple reports.

👉 Meaning is shared.

🔹 Sample 3 — Analytical scaling

❌ Low connectivity:
Adding a new source requires redesigning the model.

✅ High connectivity:
The new source plugs into the existing framework.

👉 The ecosystem grows without disruption.

🔹 Sample 4 — Semantic connectivity

❌ Low connectivity:
Same metrics with different definitions.

✅ High connectivity:
Metrics semantically aligned across models.

👉 Consistency spans systems.

🔹 Sample 5 — Anti-pattern vs Pattern

❌ Anti-pattern — Isolated model

• Self-sufficient but closed.
• Difficult to integrate.
• Low scalability.

✅ Pattern — Connected model

• Open to the ecosystem.
• Integrable.
• Scalable.

📌 Practical rule:
If the model cannot coexist with others,
it is not truly analytical.

Interactions with Other Properties

• Transmissibility → enables model sharing.
• Resonance → preserves cognitive coherence when integrating.
• Consistency → ensures semantic compatibility.
• Connectivity → enables analytical ecosystems.

Synthesis

Connectivity is not only about joining data.
It is about building systems that can dialogue.

A connected model:

• does not live alone,
• does not isolate itself,
• evolves as part of a whole.