Checklist · Data and ML
Analytics Dashboard Checklist
Opens with creditsA checklist to run before you launch a business or product analytics dashboard. It covers purpose and audience, metric definitions, data freshness, correctness checks, performance, access control, accessibility, ownership and adoption, with a severity and a concrete way to verify each check.
Written by Tauseef Fayyaz9 chapters, 32 checksAbout 18 minReviewed 13 October 2026What you will learn
9 chapters · about 18 min
1Purpose and audienceA dashboard without a clear audience and question tries to serve everyone and helps no one. These checks pin down who it is for and what they will decide with it.3 checks · 2 min
2Metric definitionsTwo dashboards that disagree on the same number destroy trust in both. These checks make every metric defined once, in writing, and visible to readers.4 checks · 2 min
- 1Write a definition for every metric: formula, filters, grain and time zone
- 2Define each metric once in a shared layer, not separately in every chart
- 3Show definitions, data period and methodology on the dashboard itself
- 4Give each headline number a comparison: a target, a previous period or a benchmark
4 in this chapterOpen this chapter- Data freshnessA dashboard showing last month's data as if it were today's is worse than no dashboard. These checks make staleness visible and loud.4 checks · 2 minOpens with credits
- Correctness checksOne wrong number found by a senior reader can end a dashboard's credibility. These checks catch errors before your audience does.4 checks · 2 minOpens with credits
- PerformancePeople stop opening dashboards that are slow. These checks keep load times short, mostly by showing less data at once.3 checks · 2 minOpens with credits
- Access and privacyDashboards spread data widely by design. These checks make sure each reader sees only what they should.3 checks · 2 minOpens with credits
- AccessibilityA dashboard that some readers cannot use is not finished. These checks follow the Analysis Function's testing guidance and WCAG 2.2.3 checks · 2 minOpens with credits
- Ownership and maintenanceDashboards outlive the projects that created them. These checks make sure someone keeps this one correct, and retires it when it is no longer useful.4 checks · 2 minOpens with credits
- AdoptionA dashboard nobody opens has no impact. These checks make sure the audience knows about it, can use it, and that you can tell whether they do.4 checks · 2 minOpens with credits
About this checklist
Building a dashboard takes a few days. Getting people to trust it, use it and keep using it is the harder part. Most dashboards that fail do so for reasons that have nothing to do with the charts: nobody agreed what "active user" means, the data stopped refreshing two weeks ago and nobody noticed, the page takes forty seconds to load, a regional manager can see every other region's numbers, or the person who built it moved teams and nobody owns it now. This analytics dashboard checklist covers those problems before launch.
The checks draw on the UK Office for Statistics Regulation's guidance on dashboards, the UK Government Analysis Function's guidance on testing dashboards for design and accessibility, the dbt documentation on metrics and freshness, and Microsoft's Power BI guidance on optimization, row-level security and usage metrics. Tool names appear where they make a check concrete, but the checks apply equally to Looker, Tableau, Metabase, Superset or a dashboard you build yourself.
One question from the Office for Statistics Regulation is worth asking before any of the others: do you need a dashboard at all? Its guidance notes that after thinking through user needs, you may find a simpler product, such as a weekly email with three numbers or a single well-made chart, meets those needs better.
Each check has a severity (must, should or nice) and a way to verify it. Clear every must before you share the link widely, and plan the rest for the weeks after launch. For chart-level advice on color, axes and titles, see the data visualization best practices guide.
Common questions
What should I check before launching a dashboard?
Confirm who it is for and which decisions it supports, that every metric has one written definition, that data freshness is visible and alerted on, that headline totals reconcile with a source of truth, that it loads quickly, that each reader sees only the rows they should, that it is usable by keyboard and screen reader, and that it has a named owner.
How do I stop two dashboards showing different numbers for the same metric?
Define each metric once in a shared layer, such as the dbt Semantic Layer or the measures in a Power BI semantic model, and have every chart reference that definition. Write down the formula, filters, grain and time zone, and link the definition from the dashboard.
How do I show data freshness on a dashboard?
Display a "data as of" timestamp from the latest load time of the underlying data, plus when the next update is due. Behind it, run freshness checks on each source with warning and error thresholds so stale data raises an alert instead of quietly appearing as current.
Why is my dashboard slow?
Usually because visuals load more data than users need or there are too many visuals on a page. Apply restrictive default filters such as a recent date range or a Top N limit, cut visuals that do not answer the dashboard's questions, and use a tool such as Power BI's Performance Analyzer to find the slowest ones.
Do I need a dashboard, or would a report do?
A dashboard suits people who return often to answer the same questions themselves. If readers want a conclusion once, a written report is better; if they want to know when something crosses a threshold, an alert is better. The UK Office for Statistics Regulation suggests asking this question before building anything.
Sources (8)
Checked against these, last on 13 October 2026.
- Regulatory guidance: Dashboards, Office for Statistics Regulationosr.statisticsauthority.gov.uk/publication/regulatory-guidance-dashboards/
- Data visualisation: testing dashboards for design and accessibility, UK Government Analysis Functionanalysisfunction.civilservice.gov.uk/policy-store/data-visualisation-testing-dashboards-for-design-and-accessibility
- Creating metrics, dbt docsdocs.getdbt.com/docs/build/metrics-overview
- freshness, dbt docsdocs.getdbt.com/reference/resource-properties/freshness
- Optimization guide for Power BI, Microsoft Learnlearn.microsoft.com/en-us/power-bi/guidance/power-bi-optimization
- Row-level security (RLS) guidance in Power BI Desktop, Microsoft Learnlearn.microsoft.com/en-us/power-bi/guidance/rls-guidance
- Monitor usage metrics in Power BI workspaces, Microsoft Learnlearn.microsoft.com/en-us/power-bi/collaborate-share/service-modern-usage-metrics
- Data visualisation: charts, UK Government Analysis Functionanalysisfunction.civilservice.gov.uk/policy-store/data-visualisation-charts/
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