data-dashboard-design

name	data-dashboard-design
description	Plans data dashboard layouts with metric selection, visualization types, refresh frequency, and user-focused design. Use when building dashboards that drive decisions.
allowed-tools	Read Write Glob
metadata	{"author":"matthewhitcham","version":"1.0"}

Data Dashboard Design

When to Use This Skill

Use this skill when you need to:

• Design a business metrics dashboard layout
• Select the right visualizations for different metric types
• Plan a dashboard hierarchy (executive, operational, team-level)
• Specify requirements for a developer or BI tool configuration

DO NOT use this skill for building dashboards in code, creating actual charts, or configuring specific BI tools. This is for dashboard planning and design.

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Core Principle

A DASHBOARD THAT REQUIRES EXPLANATION HAS FAILED — EVERY METRIC SHOULD ANSWER A QUESTION THE VIEWER ALREADY HAS IN 5 SECONDS OR LESS.

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Phase 1: Brief

Required Inputs

Input	What to Ask	Default
Audience	"Who will view this dashboard? (founder, team leads, investors, whole team)"	Founder / CEO
Purpose	"What decisions should this dashboard inform?"	Weekly business health check
Key questions	"What 3-5 questions should someone answer by looking at this?"	Must be provided
Data sources	"Where does your data live? (Stripe, GA4, CRM, spreadsheets)"	Mixed sources
Tool	"What will you build this in? (Google Sheets, Looker, Tableau, Databox, Notion)"	Google Sheets or Looker Studio
Refresh frequency	"How often should data update? (real-time, daily, weekly)"	Daily

GATE: Confirm brief before proceeding.

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Phase 2: Design

Dashboard Architecture

1. Top-line KPIs — 3-5 big numbers at the top answering "How are we doing overall?"
2. Trend charts — time-series showing direction over the last 30/90 days
3. Breakdowns — dimensions that explain the top-line numbers (by channel, product, segment)
4. Comparison context — vs. last period, vs. target, vs. benchmark
5. Action triggers — thresholds that signal when something needs attention

Visualization Selection Rules

Data Type	Best Visualization	Avoid
Single KPI	Big number with trend arrow	Pie chart
Trend over time	Line chart	Bar chart for 30+ data points
Part of whole	Stacked bar or donut	3D pie charts (always)
Comparison across categories	Horizontal bar chart	Vertical bars with 10+ categories
Correlation	Scatter plot	Line chart
Geographic	Map / heatmap	Table with location names

GATE: Present the dashboard wireframe and wait for approval.

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Phase 3: Build

Deliverables

1. Dashboard Specification Document

• Complete layout with sections, metrics, and visualization types
• Data source mapping for each metric
• Filter and drill-down requirements
• Refresh schedule and data latency notes

2. Metric Definitions

• How each metric is calculated (formula)
• Data source and table/field references
• Expected range and alert thresholds

3. Wireframe Layout

• Text-based or sketch layout showing placement of each element
• Reading order: top-left to bottom-right, most important first
• Mobile considerations if viewed on phones

4. Implementation Checklist

• Data sources connected and verified
• Metric calculations validated against source
• Date filters working correctly
• Comparison periods accurate
• Loading time under 5 seconds
• Mobile layout reviewed

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Phase 4: Polish

Review Cadence

• Week 1: Verify all numbers match source data
• Week 4: Survey users — is anything missing or confusing?
• Month 3: Remove metrics nobody looks at, add any new questions

Dashboard Hygiene Rules

• No more than 10 metrics on a single view — if you need more, create sub-dashboards
• Every metric has a definition tooltip or footnote
• Color coding is consistent (green = good, red = attention needed)

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Example 1: SaaS Founder Dashboard

Top-line: MRR, Active Users, Churn Rate, NPS Trends: MRR growth (12 months), signup trend (30 days) Breakdowns: Revenue by plan tier, signups by acquisition channel

Example 2: E-commerce Weekly Dashboard

Top-line: Revenue, Orders, AOV, Conversion Rate Trends: Daily revenue (30 days), traffic (30 days) Breakdowns: Revenue by product category, traffic by source, conversion by device

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Anti-Patterns

• Dashboard overload — 30 metrics on one screen means nobody reads any of them. Ruthlessly prioritize.
• Vanity metrics — total pageviews without context is meaningless. Always show metrics that connect to revenue or retention.
• No comparison context — a number without a benchmark is just a number. Show vs. last period, vs. target, or vs. industry.
• Rainbow color coding — 8 colors for 8 categories creates visual noise. Use 2-3 colors max with gray for secondary elements.
• Stale data — a dashboard that updates monthly when decisions happen weekly is useless.

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Recovery

• User cannot define key questions: Ask "What do you check first thing Monday morning? What number keeps you up at night?" Extract dashboard requirements from the answers.
• Too many data sources: Start with 1-2 sources for the MVP dashboard. Add sources incrementally.
• No BI tool: Google Sheets with charts and a refresh button is a valid V1 dashboard for small businesses.
• Data quality issues: Flag metrics with known quality problems. A metric with an asterisk is better than a missing metric.