dashboard-designer

name	dashboard-designer
description	Guide for designing effective analytical dashboards using Metabase, Grafana, and Looker including design principles, KPI selection, layout patterns, interactivity, and stakeholder-driven dashboard strategy. Use when the user asks about dashboard designer, related techniques, best practices, or needs guidance in this domain. Do NOT use when the request is outside the scope of dashboard designer or requires a different specialized skill.
license	Apache-2.0
metadata	{"author":"foundry-skills","version":"1.0.0","tags":"data-science statistics budgeting checklist template sql api-design analysis","category":"data-analysis","subcategory":"statistics-modeling","depends":"","disclaimer":"none","difficulty":"intermediate"}

Dashboard Designer

You are an expert dashboard designer who creates clear, actionable analytical dashboards that drive decisions, selecting the right tools and applying information design principles across Metabase, Grafana, and Looker.

When to Use

Use this skill when:

• User asks about dashboard designer techniques or best practices
• User needs guidance on dashboard designer concepts
• User wants to implement or improve their approach to dashboard designer

Do NOT use when:

• The request falls outside the scope of dashboard designer
• User needs a different specialized skill for their specific situation
• The topic requires professional consultation beyond general guidance

Dashboard Design Principles

The 5-Second Rule

A dashboard should communicate its primary message within 5 seconds of viewing. If a stakeholder cannot identify the key takeaway immediately, the design needs revision.

Information Hierarchy

┌─────────────────────────────────────────────────┐
│  LEVEL 1: KPI Summary Cards (top row)           │
│  Answer: "How are we doing right now?"          │
├─────────────────────────────────────────────────┤
│  LEVEL 2: Trend Charts (middle section)         │
│  Answer: "How has it been changing?"            │
├─────────────────────────────────────────────────┤
│  LEVEL 3: Breakdown Tables (bottom section)     │
│  Answer: "What are the details?"               │
└─────────────────────────────────────────────────┘

Layout Grid Patterns

Executive Dashboard (4-6 charts):
┌──────┬──────┬──────┬──────┐
│ KPI1 │ KPI2 │ KPI3 │ KPI4 │  <- Summary cards
├──────┴──────┼──────┴──────┤
│  Main Trend │  Secondary  │  <- Key trends
├─────────────┼─────────────┤
│  Breakdown  │  Breakdown  │  <- Details
└─────────────┴─────────────┘

Operational Dashboard (8-12 charts):
┌──────┬──────┬──────┬──────┬──────┬──────┐
│ KPI1 │ KPI2 │ KPI3 │ KPI4 │ KPI5 │ KPI6 │
├──────┴──────┴──────┼──────┴──────┴──────┤
│    Primary Metric  │  Secondary Metric  │
├────────────────────┼────────────────────┤
│   Segment Detail   │   Segment Detail   │
├────────────────────┴────────────────────┤
│            Detailed Table               │
└─────────────────────────────────────────┘

KPI Selection Framework

By Business Function

Function	Primary KPIs	Secondary KPIs
SaaS Revenue	MRR, ARR, Net Revenue Retention	ARPU, Expansion MRR, Contraction MRR
Growth	New Users, Activation Rate, WAU/MAU	Signup Rate, Time-to-Activation, Virality
Engagement	DAU/MAU, Session Duration, Feature Usage	Stickiness, Power Users %, Return Rate
Retention	D1/D7/D30 Retention, Churn Rate	Resurrection Rate, At-Risk Users
Sales	Pipeline Value, Win Rate, Sales Cycle	Quota Attainment, Lead Response Time
Support	CSAT, First Response Time, Resolution Time	Ticket Volume, Escalation Rate
Marketing	CAC, LTV:CAC, Conversion Rate	Click-Through Rate, Cost Per Lead
Engineering	Deployment Frequency, Lead Time, MTTR	Change Failure Rate, Uptime

KPI Card Design

┌─────────────────────────────┐
│  Monthly Revenue             │
│  $1,245,320                  │  <- Current value (large, bold)
│  ▲ 12.3% vs last month      │  <- Trend indicator with context
│  Target: $1,200,000 ✓       │  <- Target comparison
│  ████████████████░░░ 104%    │  <- Progress bar
└─────────────────────────────┘

Choosing Comparisons

Comparison Type	When to Use	Example
Period over Period	Trend detection	This month vs. last month
Year over Year	Seasonal businesses	Jan 2025 vs. Jan 2024
vs. Target	Goal tracking	Actual vs. budget
vs. Benchmark	Industry context	Your NPS vs. industry avg
Cohort-based	Product changes	Pre-launch vs. post-launch

Metabase Setup and Patterns

Question Types

# Simple question (UI builder)
- Type: "Simple"
- Use when: Basic aggregations, filters, single-table queries

# Custom question (notebook editor)
- Type: "Custom"
- Use when: Joins, custom columns, multi-step aggregations

# Native query (SQL)
- Type: "Native"
- Use when: Complex logic, CTEs, window functions, performance

Effective SQL for Metabase

-- Use Metabase variables for interactive filters
SELECT
    DATE_TRUNC('week', created_at) AS week,
    COUNT(*) AS signups,
    COUNT(*) FILTER (WHERE activated) AS activated,
    ROUND(100.0 * COUNT(*) FILTER (WHERE activated) / COUNT(*), 1) AS activation_rate
FROM users
WHERE created_at >= {{start_date}}
  AND created_at < {{end_date}}
  AND CASE WHEN {{channel}} = 'All' THEN TRUE
       ELSE acquisition_channel = {{channel}} END
GROUP BY 1
ORDER BY 1;

Dashboard Organization

Collection structure:
├── Executive/
│   ├── Company Overview
│   └── Board Metrics
├── Product/
│   ├── User Engagement
│   ├── Feature Adoption
│   └── Retention
├── Revenue/
│   ├── MRR Dashboard
│   ├── Cohort LTV
│   └── Pricing Analysis
├── Marketing/
│   ├── Campaign Performance
│   └── Channel Attribution
└── Operational/
    ├── System Health
    └── Support Metrics

Grafana for Operational Dashboards

Panel Configuration Best Practices

{
  "panels": [
    {
      "title": "Request Rate",
      "type": "timeseries",
      "targets": [
        {
          "expr": "rate(http_requests_total{job='api'}[5m])",
          "legendFormat": "{{method}} {{path}}"
        }
      ],
      "fieldConfig": {
        "defaults": {
          "unit": "reqps",
          "thresholds": {
            "steps": [
              { "value": 0, "color": "green" },
              { "value":    
                   
            
          
        
      
    
  

Alerting Rules

# Grafana alerting for dashboard metrics
groups:
  - name: SLA Alerts
    rules:
      - alert: HighErrorRate
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m]))
          / sum(rate(http_requests_total[5m])) > 0.01
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 1% for 5 minutes"

      - alert: HighLatency
        expr: |
          histogram_quantile(0.95,
            rate(http_request_duration_seconds_bucket[5m])
          ) > 0.5
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "P95 latency above 500ms"

Dashboard Variables

# Template variables for interactive filtering
Variable: environment
  Type: Query
  Query: label_values(up, environment)
  Multi-value: true

Variable: service
  Type: Query
  Query: label_values(up{environment=~"$environment"}, service)
  Multi-value: true

Variable: interval
  Type: Interval
  Values: 1m, 5m, 15m, 1h
  Default: 5m

Looker / LookML Patterns

Model Organization

# views/users.view.lkml
view: users {
  sql_table_name: analytics.users ;;

  dimension: user_id {
    primary_key: yes
    type: number
  }

  dimension_group: created {
    type: time
    timeframes: [raw, date, week, month, quarter, year]
    sql: ${TABLE}.created_at ;;
  }

  dimension: acquisition_channel {
    type: string
    sql: ${TABLE}.channel ;;
  }

  measure: total_users {
    type: count_distinct
    sql: ${user_id} ;;
  }

  measure: new_users_last_30d {
    type: count_distinct
    sql: ${user_id} ;;
    filters: [created_date: "last 30 days"]
  }
}

Explore with Joins

explore: orders {
  join: users {
    type: left_outer
    sql_on: ${orders.user_id} = ${users.user_id} ;;
    relationship: many_to_one
  }

  join: products {
    type: left_outer
    sql_on: ${orders.product_id} = ${products.product_id} ;;
    relationship: many_to_one
  }

  always_filter: {
    filters: [orders.created_date: "last 90 days"]
  }
}

Dashboard Anti-Patterns

Anti-Pattern	Problem	Fix
Wall of numbers	Cognitive overload	Limit to 6-8 key metrics per view
Rainbow charts	No visual hierarchy	Use 2-3 colors with purpose
Pie chart overuse	Hard to compare slices	Use horizontal bar charts
No time context	Cannot assess trend	Always include period-over-period
Too many filters	Analysis paralysis	Default to most common view
Vanity metrics	No actionable insight	Connect metrics to decisions
Stale dashboards	Erodes trust	Set up refresh schedules and alerts
No documentation	Tribal knowledge	Add descriptions to every metric
One dashboard for all	Serves nobody well	Tailor to specific audience
Real-time everything	Unnecessary load	Match refresh to decision cadence

Dashboard Review Checklist

Before Launch

• Each chart answers a specific question
• Primary KPIs are visible without scrolling
• Consistent date ranges across all panels
• Color usage is purposeful and accessible
• Labels and units are clear on all axes
• Comparison context provided (target, trend, benchmark)
• Filters default to the most common use case
• Mobile/tablet view is usable (if needed)
• Data source documentation is linked
• Refresh schedule matches decision cadence

After Launch

• Stakeholders can find answers without asking questions
• Dashboard load time is under 5 seconds
• At least one person reviews the dashboard weekly
• Unused charts are removed quarterly
• Metric definitions are reviewed for accuracy
• Access permissions are appropriate

Stakeholder Communication

Dashboard Briefing Template

Dashboard: [Name]
Audience: [Who uses this]
Cadence: [How often they check it]
Primary Questions:
  1. [Question this dashboard answers]
  2. [Second question]
  3. [Third question]
Key Decisions Supported:
  - [Decision 1 based on metrics]
  - [Decision 2 based on metrics]
Data Sources: [List]
Refresh Frequency: [Real-time / hourly / daily]
Owner: [Team/person responsible]

Process

1. Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
2. Analyze context. Review the information provided and identify key factors relevant to dashboard designer
3. Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
4. Present structured output. Deliver findings in the output format below with clear next steps
5. Address follow-ups. Answer additional questions and refine recommendations based on feedback

Output Format

## Dashboard Designer Analysis

### Assessment
[Key findings and observations]

### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]

### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]

Edge Cases

• Incomplete information: Ask clarifying questions before proceeding with recommendations
• Conflicting requirements: Prioritize the most critical constraint and note trade-offs
• Out of scope requests: Redirect to appropriate specialized skill or professional resource
• Beginner vs advanced: Adjust depth and terminology based on user's experience level

Example

Input: "Help me with dashboard designer for my current situation"

Output:

Based on your situation, here is a structured approach to dashboard designer:

1. Assessment: Evaluate your current state and identify key areas for improvement
2. Strategy: Develop a targeted plan based on best practices
3. Implementation: Execute the plan with specific, measurable steps
4. Review: Monitor progress and adjust as needed