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.
Scanned 2/12/2026
Install via CLI
openskills install product-on-purpose/pm-skills<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
---
name: measure-dashboard-requirements
description: Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs.
phase: measure
version: "2.0.0"
updated: 2026-01-26
license: Apache-2.0
metadata:
category: validation
frameworks: [triple-diamond, lean-startup, design-thinking]
author: product-on-purpose
---
# 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
## 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.
## 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.
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