Dashboard design: principles and KPIs that drive decisions

A great dashboard doesn't just display data. It drives decisions.

Most dashboards fail at this. They're data dumps, pages of charts that show everything and communicate nothing. Users glance at them, feel informed, and make the same decisions they would have made without looking.

The discipline of dashboard design is about making the right information obvious to the right person at the right moment.

The hierarchy of dashboard purpose

Before designing a dashboard, answer: what decision does this dashboard enable?

Not "what information does it show" but "what decision." A CEO dashboard should enable resource allocation decisions. A marketing dashboard should enable campaign optimization decisions. An operations dashboard should enable staffing and inventory decisions.

If you can't name the decision, you don't have a dashboard, you have a report.

Three types of dashboards:

Strategic (C-suite): KPIs against targets, 4-week trend, exceptions that need attention. Should be readable in 30 seconds. One page. High-level metrics only.

Operational (managers): Daily/weekly operational metrics. Drill-down capability. Performance vs. target by team, product, region. Used in weekly reviews.

Analytical (analysts): Exploration tool. Multiple dimensions, filter capability, raw data access. Used for deep-dive analysis, not regular monitoring.

Most teams try to build one dashboard that serves all three purposes and end up with one that serves none.

The 5 dashboard design principles

1. One dashboard, one audience, Design for a specific user, not for everyone. A dashboard for the CFO and a dashboard for the regional sales manager are different documents.

2. The most important metric gets the most prominent position, Top left, largest element. Users scan dashboards top-left to bottom-right. Don't make them search for the number that matters most.

3. Context beats raw numbers, A number without context is meaningless. €2.3M revenue is good or bad depending on target, prior period, and trend. Every KPIKPIKey Performance Indicator, a measurable value that shows how effectively you're achieving a specific objective, tracked over time against a target.View full definition → should show: current value, target, variance, trend.

4. Fewer charts, more insight, The temptation is to add more charts. Resist it. Each additional chart divides attention. If you have 12 charts, users look at none carefully. If you have 4, they understand all of them.

5. Alert, don't report, The best dashboards highlight anomalies. Use conditional formatting, color coding (red/green/amber), and threshold alerts to direct attention to what needs action, not what's normal.

The KPI selection framework

Not all metrics deserve dashboard real estate. A good KPI has four properties:

Actionable: The viewer can do something based on this number. If no action follows from seeing it, it's a data point, not a KPI.

Leading, not lagging: Lagging indicators (last month's revenue) tell you what happened. Leading indicators (pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.View full definition → coverage, trial sign-ups) tell you what will happen. Both matter, but leading indicators enable earlier intervention.

Comparable: Meaningful only with context, prior period, budget, benchmark. Standalone numbers without comparisons are decorative.

Trusted: If users doubt the data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.View full definition →, they won't use the dashboard. A trusted dashboard with fewer metrics beats an untrusted one with more.

A/B testingA/B testingA/B testing is a controlled experiment that compares two versions of something (A and B) by splitting traffic randomly to learn which performs better on a chosen metric.View full definition → your dashboards

Dashboards are products. Products get iterated based on user feedback.

Run dashboard usage analytics: which charts are viewed? Which are ignored? Which filter combinations are most used? This tells you what's valuable and what's clutter.

Ask users directly: "In the last week, what decision did this dashboard help you make?" If they can't answer, the dashboard isn't working.

Quiz Questions

1. Quelle est la question fondamentale à répondre avant de concevoir un dashboard ?

A) Quelles données sont disponibles ?

B) Quelle décision ce dashboard doit-il permettre de prendre ?

C) Combien d'utilisateurs auront accès ?

D) Quel outil BIBITechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.View full definition → utiliser ?

Réponse: B

1. Parmi les 5 principes de design de dashboard, lequel concerne la priorisation visuelle ?

A) Un dashboard, une audience

B) Alerter plutôt que rapporter

C) La métrique la plus importante occupe la position la plus proéminente (haut gauche, plus grand élément)

D) Moins de graphiques, plus d'insights

Réponse: C

1. Qu'est-ce qu'un indicateur "leading" par opposition à un indicateur "lagging" ?

A) Un indicateur calculé plus rapidement

B) Un indicateur qui prédit ce qui va se passer (ex: pipeline) par opposition à ce qui s'est passé (ex: revenus passés)

C) Un indicateur affiché en haut du dashboard

D) Un indicateur plus précis

Réponse: B