Stop Building Exploratory Dashboards and Start Building Decision Dashboards

Levels 1–3 consist of information. Levels 4 and 5 represent worth. Prior to designing any chart, the critical question is: What decision at Level 5 are we aiming to facilitate? All that follows is support structure.

The ADA Model: Action-Driven Analytics: To operationalize this, I developed a three-step diagnostic model I call ADA, which I apply to any dashboard, metric, or report. It’s how you turn the top two levels of the Pyramid into a daily habit:

• A—Alert: What requires immediate attention?

• D—Diagnose: Why is it happening?

• A—Act: What should the organization do next?

Take a program experiencing a cohort drop—a common scenario in workforce development or student success contexts. A research dashboard displays a decline in enrollment over a period of 6 months and halts at that point. An ADA-structured perspective indicates that Program X is 18 points short of its completion goal, identifies that the decline is linked to onboarding delays at a particular site, and suggests shifting advising staff to that site within 2 weeks. This is the same underlying data with a completely different outcome.

Five Principles for Building Decision Dashboards

1. Every dashboard needs one decision. Stop asking, “What metrics should we show?” and start asking, “What decision should this support?” A financial aid dashboard isn’t there to display disbursement totals—it’s there to answer, “Which students are at risk of losing eligibility this term, and who needs to intervene?”

2. Every KPI must have a threshold. A number without context is trivia. “74%” tells you nothing on its own. “Above target” versus “Intervention required” tells you everything in half a second. If a metric doesn’t have a defined threshold, it isn’t ready for a dashboard—it’s still in the research phase.

3. Highlight exceptions, not everything. Don’t show all 40 programs performing at various levels. Isolate the six sitting below 60% completion. The goal is drawing the eye to what needs attention today.

4. Connect metrics to actions. Before: “Program completion: 58%.” After: “Program completion: 58%, down 6 points—recommend adding a second cohort orientation session before Month 2, based on peer programs that recovered similarly.” The second version moves someone toward doing something.

5. Assess choices, not page visits. Stop tracking dashboard views and login counts. True engagement isn’t about screen time—it’s about business impact. Instead, track decisions accelerated, hours saved, projects unblocked, and revenue recovered.

Proof: A Grant Dashboard, Before and After

Here’s what applying these principles looks like in practice. Consider a composite example modeled on U.S. Department of Labor grant programs, a familiar case type in workforce and education analytics. The traditional version lists raw counts: total participants enrolled, gender breakdown, average age, and completion percentage by quarter. It’s accurate. It’s also inert—nobody reads it and immediately knows what to do.

The decision-driven version restructures the same underlying data around targets and gaps. Instead of “412 participants enrolled,” it shows “412 of 500 targets enrolled—88 short with 6 weeks remaining.” Instead of a demographic breakdown for its own sake, it flags which regions are underperforming recruitment goals and recommends specific outreach markets based on where comparable programs found success. Instead of a static completion percentage, it projects the expected shortfall date and the staffing adjustment needed to close it.

This is the same data source during the same reporting period. One version informs a compliance file. The other prevents a missed grant target.

The Close

Organizations don’t suffer from a lack of data—most have more than they’ll ever fully use. What they are missing is the ability to transform data into a subsequent step that someone can take responsibility for. This week, choose your top-viewed dashboard and evaluate it with one question: Does it indicate a decision, or merely outline a scenario? If it merely describes, you already know how to proceed. Exploration is where analytics begins. A decision is the only place it’s allowed to end.