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Most dashboards get built, looked at once, then forgotten. They show too much, answer no questions, and require too much interpretation to be useful.
Good dashboards answer specific questions at a glance. They're designed for their audience, updated automatically, and make the right action obvious. Let's talk about building dashboards people actually use.
Start With Questions, Not Metrics
Bad approach: "Let's put all our metrics on a dashboard."
Good approach: "What questions do we need to answer daily? What decisions does this inform?"
Examples of good questions:
One dashboard shouldn't answer all questions. Build focused dashboards for specific purposes.
Dashboard Types and Audiences
Executive dashboard
Audience: Leadership, investors, board Questions: High-level health, growth trajectory, key risks Metrics: Revenue, user growth, retention, burn rate Update frequency: Weekly or monthly Design: Simple, big numbers, clear trends
Product dashboard
Audience: Product team, designers Questions: What are users doing? What's working? What's broken? Metrics: Feature usage, user flows, drop-off points, feedback themes Update frequency: Daily Design: Detailed, actionable, drill-down capability
Marketing dashboard
Audience: Marketing team, growth Questions: What channels work? What's our CAC? What content performs? Metrics: Traffic sources, conversion rates, CAC by channel, content engagement Update frequency: Daily or weekly Design: Channel comparisons, trend lines, ROI focus
Engineering dashboard
Audience: Developers, DevOps Questions: Is the system healthy? Where are errors? What's slow? Metrics: Error rates, response times, uptime, deployment frequency Update frequency: Real-time Design: Technical detail, alerts, historical context
Dashboard Design Principles
Most important metrics first
Top of dashboard = most critical information. If someone only looks for 5 seconds, what should they see?
Use size, color, and position to establish hierarchy. Big numbers for key metrics, supporting details smaller.
Show trends, not just numbers
"1,250 users" means nothing without context. "1,250 users (+18% this week)" tells a story.
Small sparklines next to numbers show trajectory at a glance.
Use color meaningfully
Green for good, red for bad, yellow for warning. Consistent across all dashboards.
Highlight exceptions and problems. Don't make people hunt for issues.
Minimize cognitive load
Every additional metric increases mental processing time. Ruthlessly cut non-essential information.
Group related metrics. Use white space. Make scanning easy.
Make it actionable
Dashboard should make next actions obvious. If metric is red, what do you do about it?
Link to deeper analysis or relevant tools. Dashboard is starting point, not end point.
Dashboard Design Patterns - Examples and anti-patterns
Tools for Building Dashboards
No-code/low-code:
Google Looker Studio - Free, connects to many data sources, good templates Tableau - Powerful, enterprise-grade, learning curve Metabase - Open source, great for SQL users, self-hosted or cloud Geckoboard - TV-friendly dashboards, beautiful design Klipfolio - Custom dashboards, lots of integrations
Developer-focused:
Grafana - Open source, excellent for time-series data, technical metrics Redash - Open source, SQL-based dashboards Apache Superset - Open source BI platform Cube.dev - Headless BI platform, embed in your app
Integrated in analytics tools:
Mixpanel - Product analytics with built-in dashboards Amplitude - Product analytics and dashboards PostHog - Open source product analytics Plausible - Simple web analytics with dashboard
Key Metrics by Dashboard Type
SaaS Product Dashboard:
E-commerce Dashboard:
Content/Media Dashboard:
Mobile App Dashboard:
Data Refresh and Reliability
Stale data kills dashboard credibility. If users can't trust the numbers, they won't use it.
Automate data updates: Manual data entry doesn't scale. Connect directly to data sources.
Show last update time: Users need to know if data is current.
Handle failures gracefully: If data pipeline breaks, show error clearly. Better than showing wrong data silently.
Monitor data quality: Set up alerts for anomalies. Sudden 10x spike or drop usually means broken pipeline, not real change.
dbt (data build tool) - Transform and test data reliability Great Expectations - Data quality testing
Dashboard Implementation Process
1. Define purpose and audience
Who will use this? What decisions does it inform? How often will they check it?
2. Identify key questions
What do users need to know? List 5-10 specific questions.
3. Choose metrics
What data answers those questions? Be specific about calculation methods.
4. Design layout
Sketch on paper first. Most important info top-left. Group related metrics.
5. Build and iterate
Start simple. Get feedback. Add complexity only if needed.
6. Share and maintain
Make dashboard accessible. Check data quality regularly. Update as needs evolve.
Common Dashboard Mistakes
Too many metrics
More is not better. Every metric dilutes focus. Aim for 5-7 key metrics per dashboard.
Vanity metrics
Page views, registered users, social media followers—impressive but not actionable. Focus on metrics that drive decisions.
No context
"500 signups" means nothing without knowing if that's good, bad, or expected. Always show trends, targets, or comparisons.
Poor data hygiene
Inconsistent definitions, broken tracking, uncleaned data. Dashboard is only as good as underlying data quality.
Not mobile-friendly
Leadership checks dashboards on phones. If it doesn't work mobile, it doesn't work.
Static and unchanging
Business evolves, dashboards should too. Review quarterly and update metrics that matter.
Real-Time vs. Batch Updates
Real-time dashboards:
Pros: Always current, catch issues immediately Cons: More complex infrastructure, higher costs Best for: System monitoring, customer support, operations
Batch updates (hourly/daily):
Pros: Simpler, more reliable, cheaper Cons: Slight lag in data Best for: Business metrics, trends, strategic decisions
Most dashboards don't need real-time. Daily updates suffice for 90% of use cases.
Sharing and Access
Public dashboards
Some companies share metrics publicly. Builds trust, creates accountability.
Baremetrics Open Startups - SaaS companies sharing metrics publicly Buffer's Transparency Dashboard - Revenue and user metrics
Internal dashboards
Control access by role. Sales sees sales metrics, engineering sees technical metrics.
Make dashboards easily discoverable. Central directory or regular sharing in team meetings.
Embedded dashboards
Put key metrics where people already work. Slack bots, email digests, in-app displays.
Statsbot - Analytics in Slack Databox - Dashboard mobile app and notifications
Dashboard Maintenance
Regular reviews: Monthly check if metrics still matter and data is accurate
Update for new features: When product changes, dashboards should reflect new realities
Deprecate unused dashboards: If nobody's looking at it, delete it. Reduces maintenance burden.
Document definitions: What exactly is "active user"? Write it down. Prevents confusion.
Measuring Dashboard Success
Good dashboard is used regularly and influences decisions. Track:
If dashboard isn't being used, either fix it or **** it. Unused dashboards waste time and server resources.
The best dashboard is the one that gets checked daily and drives better decisions. Everything else is just pretty charts nobody looks at. Build for your specific audience, answer their specific questions, and keep it simple enough to understand at a glance.
Good dashboards answer specific questions at a glance. They're designed for their audience, updated automatically, and make the right action obvious. Let's talk about building dashboards people actually use.
Start With Questions, Not Metrics
Bad approach: "Let's put all our metrics on a dashboard."
Good approach: "What questions do we need to answer daily? What decisions does this inform?"
Examples of good questions:
- Are we growing? (User acquisition trends)
- Are users engaged? (Active users, retention, feature usage)
- Are we making money? (Revenue, MRR growth, churn)
- Is the product working? (Error rates, performance, uptime)
- What should we focus on? (Top user requests, biggest pain points)
One dashboard shouldn't answer all questions. Build focused dashboards for specific purposes.
Dashboard Types and Audiences
Executive dashboard
Audience: Leadership, investors, board Questions: High-level health, growth trajectory, key risks Metrics: Revenue, user growth, retention, burn rate Update frequency: Weekly or monthly Design: Simple, big numbers, clear trends
Product dashboard
Audience: Product team, designers Questions: What are users doing? What's working? What's broken? Metrics: Feature usage, user flows, drop-off points, feedback themes Update frequency: Daily Design: Detailed, actionable, drill-down capability
Marketing dashboard
Audience: Marketing team, growth Questions: What channels work? What's our CAC? What content performs? Metrics: Traffic sources, conversion rates, CAC by channel, content engagement Update frequency: Daily or weekly Design: Channel comparisons, trend lines, ROI focus
Engineering dashboard
Audience: Developers, DevOps Questions: Is the system healthy? Where are errors? What's slow? Metrics: Error rates, response times, uptime, deployment frequency Update frequency: Real-time Design: Technical detail, alerts, historical context
Dashboard Design Principles
Most important metrics first
Top of dashboard = most critical information. If someone only looks for 5 seconds, what should they see?
Use size, color, and position to establish hierarchy. Big numbers for key metrics, supporting details smaller.
Show trends, not just numbers
"1,250 users" means nothing without context. "1,250 users (+18% this week)" tells a story.
Small sparklines next to numbers show trajectory at a glance.
Use color meaningfully
Green for good, red for bad, yellow for warning. Consistent across all dashboards.
Highlight exceptions and problems. Don't make people hunt for issues.
Minimize cognitive load
Every additional metric increases mental processing time. Ruthlessly cut non-essential information.
Group related metrics. Use white space. Make scanning easy.
Make it actionable
Dashboard should make next actions obvious. If metric is red, what do you do about it?
Link to deeper analysis or relevant tools. Dashboard is starting point, not end point.
Dashboard Design Patterns - Examples and anti-patterns
Tools for Building Dashboards
No-code/low-code:
Google Looker Studio - Free, connects to many data sources, good templates Tableau - Powerful, enterprise-grade, learning curve Metabase - Open source, great for SQL users, self-hosted or cloud Geckoboard - TV-friendly dashboards, beautiful design Klipfolio - Custom dashboards, lots of integrations
Developer-focused:
Grafana - Open source, excellent for time-series data, technical metrics Redash - Open source, SQL-based dashboards Apache Superset - Open source BI platform Cube.dev - Headless BI platform, embed in your app
Integrated in analytics tools:
Mixpanel - Product analytics with built-in dashboards Amplitude - Product analytics and dashboards PostHog - Open source product analytics Plausible - Simple web analytics with dashboard
Key Metrics by Dashboard Type
SaaS Product Dashboard:
- Monthly Recurring Revenue (MRR) and growth rate
- Customer acquisition and churn
- Net Revenue Retention
- Active users (DAU/MAU ratio)
- Feature adoption rates
- Customer health score
E-commerce Dashboard:
- Revenue and orders
- Conversion rate (visitor to purchase)
- Average order value
- Cart abandonment rate
- Top products by revenue and margin
- Customer lifetime value
Content/Media Dashboard:
- Pageviews and unique visitors
- Engagement time per visit
- Top performing content
- Traffic sources
- Email subscribers and growth
- Social engagement metrics
Mobile App Dashboard:
- Daily/Monthly Active Users
- Session length and frequency
- Screen flow analysis
- Crash rate and stability
- App store ratings and reviews
- User acquisition by channel
Data Refresh and Reliability
Stale data kills dashboard credibility. If users can't trust the numbers, they won't use it.
Automate data updates: Manual data entry doesn't scale. Connect directly to data sources.
Show last update time: Users need to know if data is current.
Handle failures gracefully: If data pipeline breaks, show error clearly. Better than showing wrong data silently.
Monitor data quality: Set up alerts for anomalies. Sudden 10x spike or drop usually means broken pipeline, not real change.
dbt (data build tool) - Transform and test data reliability Great Expectations - Data quality testing
Dashboard Implementation Process
1. Define purpose and audience
Who will use this? What decisions does it inform? How often will they check it?
2. Identify key questions
What do users need to know? List 5-10 specific questions.
3. Choose metrics
What data answers those questions? Be specific about calculation methods.
4. Design layout
Sketch on paper first. Most important info top-left. Group related metrics.
5. Build and iterate
Start simple. Get feedback. Add complexity only if needed.
6. Share and maintain
Make dashboard accessible. Check data quality regularly. Update as needs evolve.
Common Dashboard Mistakes
Too many metrics
More is not better. Every metric dilutes focus. Aim for 5-7 key metrics per dashboard.
Vanity metrics
Page views, registered users, social media followers—impressive but not actionable. Focus on metrics that drive decisions.
No context
"500 signups" means nothing without knowing if that's good, bad, or expected. Always show trends, targets, or comparisons.
Poor data hygiene
Inconsistent definitions, broken tracking, uncleaned data. Dashboard is only as good as underlying data quality.
Not mobile-friendly
Leadership checks dashboards on phones. If it doesn't work mobile, it doesn't work.
Static and unchanging
Business evolves, dashboards should too. Review quarterly and update metrics that matter.
Real-Time vs. Batch Updates
Real-time dashboards:
Pros: Always current, catch issues immediately Cons: More complex infrastructure, higher costs Best for: System monitoring, customer support, operations
Batch updates (hourly/daily):
Pros: Simpler, more reliable, cheaper Cons: Slight lag in data Best for: Business metrics, trends, strategic decisions
Most dashboards don't need real-time. Daily updates suffice for 90% of use cases.
Sharing and Access
Public dashboards
Some companies share metrics publicly. Builds trust, creates accountability.
Baremetrics Open Startups - SaaS companies sharing metrics publicly Buffer's Transparency Dashboard - Revenue and user metrics
Internal dashboards
Control access by role. Sales sees sales metrics, engineering sees technical metrics.
Make dashboards easily discoverable. Central directory or regular sharing in team meetings.
Embedded dashboards
Put key metrics where people already work. Slack bots, email digests, in-app displays.
Statsbot - Analytics in Slack Databox - Dashboard mobile app and notifications
Dashboard Maintenance
Regular reviews: Monthly check if metrics still matter and data is accurate
Update for new features: When product changes, dashboards should reflect new realities
Deprecate unused dashboards: If nobody's looking at it, delete it. Reduces maintenance burden.
Document definitions: What exactly is "active user"? Write it down. Prevents confusion.
Measuring Dashboard Success
Good dashboard is used regularly and influences decisions. Track:
- How often is it viewed?
- Who's viewing it?
- Does it spark conversations and actions?
- Do teams reference it in meetings?
If dashboard isn't being used, either fix it or **** it. Unused dashboards waste time and server resources.
The best dashboard is the one that gets checked daily and drives better decisions. Everything else is just pretty charts nobody looks at. Build for your specific audience, answer their specific questions, and keep it simple enough to understand at a glance.