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measure-dashboard-requirements

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Specifies what questions a dashboard must answer and the metrics, visualizations, filters, and data sources it needs, so data teams build something that informs decisions rather than displaying numbers. Use when requesting a dashboard or formalizing ad-hoc reporting. For the event tracking that feeds the dashboard, use measure-instrumentation-spec instead; instrument first, visualize second.

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How to use this skill

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Source SKILL.md: https://github.com/product-on-purpose/pm-skills/blob/HEAD/skills/measure-dashboard-requirements/SKILL.md

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Dashboard Requirements

A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.

When to Use

  • When requesting a new dashboard from data/analytics teams
  • To define KPI tracking for a product, feature, or team
  • When formalizing ad-hoc reporting into a persistent dashboard
  • Before quarterly planning to specify what visibility you need
  • When onboarding stakeholders who need self-serve analytics

When NOT to Use

  • You need the event tracking that feeds dashboards -> use measure-instrumentation-spec; instrument first, visualize second
  • You are designing an experiment readout, not a standing dashboard -> use measure-experiment-design and measure-experiment-results
  • You want OKR progress scored at cycle close -> use measure-okr-grader
  • The questions the dashboard should answer are not yet agreed -> frame outcomes first with foundation-okr-writer or define-problem-statement

Instructions

When asked to specify dashboard requirements, follow these steps:

  1. Define the Purpose Start with the questions this dashboard should answer, not the charts it should show. What decisions will this dashboard inform? A dashboard without clear purpose becomes a vanity metrics display.

  2. Identify the Audience Specify who will use this dashboard, how often, and in what context. An executive weekly review has different needs than a team's daily standup board.

  3. Specify Key Metrics For each metric, document: name, business definition (in plain language), calculation formula, data source, and baseline/target values. Ambiguous metrics lead to misaligned dashboards.

  4. Design Visualizations Recommend chart types based on what the data should communicate. Time trends need line charts; comparisons need bar charts; compositions need pie/treemaps. Include dimension breakdowns.

  5. Define Filters and Segments Specify what drill-downs users need: date ranges, user segments, product areas, geographic regions. Anticipate the "slice and dice" questions users will ask.

  6. Document Data Sources Identify where data comes from and any known data quality issues. Note latency requirements.does the dashboard need real-time data or is daily refresh sufficient?

  7. Set Permissions and Access Determine who can view what. Some metrics may need restricted access. Consider both security requirements and organizational politics.

Output Format

Use the template in references/TEMPLATE.md to structure the output. A complete spec fills every template section: Overview; Purpose and Questions; Audience; Key Metrics; Visualization Specifications; Filters and Segments; Data Sources; Access and Permissions; Alerts and Thresholds; Acceptance Criteria; Open Questions; and Appendix.

Quality Checklist

Before finalizing, verify:

  • Purpose is framed as questions to answer, not charts to build
  • All metrics have clear definitions and calculation formulas
  • Data sources are identified and accessible
  • Visualization choices match the type of insight needed
  • Filters enable the drill-downs users will want
  • Refresh frequency matches decision-making cadence

Examples

See references/EXAMPLE.md for a completed example.