Business dashboards are everywhere—KPIs, charts, and colourful metrics. But most dashboards fail to improve decisions because they are built without a decision-making model behind them.
In this article, I explain how Herbert Simon’s IDC Model (Intelligence–Design–Choice) can transform a dashboard from a static reporting tool into a real decision-support system.
This is the same framework I use in my BI projects and in my DBA research on decision-making quality in SMEs.

Why Dashboards Need a Cognitive Framework

A dashboard can present information, but it cannot tell a manager how to think about a problem.
Data only becomes useful when it is connected to a process:

• detecting the problem,
• generating options,
• and choosing an action.

The IDC Model gives structure to this process and helps BI tools support real managerial thinking.

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Stage 1: Intelligence — Detecting What Is Going On

The Intelligence stage focuses on understanding the situation.
Dashboards support this by displaying:

• performance deviations,
• early warnings,
• month-to-month changes,
• and trend anomalies.

This is where a manager answers:
“What is happening right now, and what should I pay attention to?”

From a BI perspective, this stage is powered by good data quality, clear visuals, and meaningful KPIs.

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Stage 2: Design — Developing Possible Actions

Once the problem is identified, the next step is designing alternatives.
A dashboard becomes useful when it offers:

• scenario comparisons,
• forecasting outputs,
• cost–benefit views,
• and what-if analysis.

At this stage, the dashboard shifts from describing the problem to exploring potential decisions.

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Stage 3: Choice — Selecting the Best Option

Choice is where managers commit to action.
Dashboards help by:

• presenting the impact of each option,
• showing risks and constraints,
• and visualising the expected outcomes.

In SMEs, choices are often made under resource limitations. This means managers rely heavily on “satisficing”—choosing the option that is good enough based on the data they have.

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Decision-Making Is Iterative, Not Linear

In real organisations, decision-making loops back:

• new data emerges,
• assumptions change,
• environments shift,
• and priorities evolve.

An effective dashboard supports these loops by allowing quick re-evaluation.

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Applying IDC Across Managerial Levels

One insight from my DBA research is that different managerial levels use dashboards differently:

Strategic level:

big-picture trends, long-term risks, scenario simulations

Tactical level:

resource allocation, departmental optimisation, prioritisation

Operational level:

day-to-day issues, performance deviations, process control

Aligning dashboard design with these layers increases decision-making quality.

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Dashboards as Decision Engines

When a dashboard is built around the IDC Model, it evolves into a tool that helps organisations:

• identify issues earlier,
• design solutions faster,
• and choose actions more confidently.

This is what turns BI from visual reporting into true decision intelligence.

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Closing Thoughts

The IDC Model remains one of the most practical frameworks for decision-making in data-rich environments.
Whether you are analysing BI adoption, designing dashboards, or evaluating decision quality, this model provides structure and clarity.
In my work as a Business Development Executive and Insight Analyst, I use this framework to ensure dashboards support real decisions—not just display data.