Dashboard Design Principles: Stop Building Report Graveyards

Episode Overview

I’ve seen hundreds of dashboards in my career, and I can tell you that 80% of them are never looked at after the first two weeks. Today, I’m sharing the design principles that separate useful dashboards from expensive report graveyards.

The Dashboard Graveyard Problem

You know you’ve built a dashboard graveyard when:

• Dashboards are built but never opened
• Users ask for the data in different formats
• Teams screenshot dashboards and rebuild them in slides
• Nobody can explain what action to take based on the dashboard
• “Can you pull this for me?” requests keep coming despite the dashboard

Sound familiar? You’re not alone.

The Three-Second Rule

If a user can’t understand the key insight in three seconds, your dashboard has failed.

Here’s the test:

1. Open the dashboard
2. Count to three
3. Close it
4. What was the main message?

If the answer is “I don’t know” or “There’s a lot of data,” redesign it.

The Five Principles of Effective Dashboard Design

Principle #1: Design for Audience, Not Data

Most dashboards fail because they’re organized around data structure instead of user needs.

Wrong Approach:

• “Here’s all our marketing data”
• “Here’s everything about revenue”
• “Here’s the complete customer view”

Right Approach:

• “CMO: Are campaigns driving pipeline?”
• “Sales Leader: Are reps hitting quota?”
• “RevOps: Where are deals stalling?”

Action Step: Before building, complete this sentence: “This dashboard helps [WHO] understand [WHAT] so they can [ACTION].”

Principle #2: Hierarchy of Information

Information on a dashboard should follow visual hierarchy:

Level 1: The Headline (Top 20% of screen)

• Primary KPI
• Trend indicator (up/down)
• Context (vs. goal, vs. last period)

Level 2: Supporting Metrics (Next 30%)

• 2-4 secondary KPIs
• High-level breakdown
• Key segments

Level 3: Diagnostic Details (Bottom 50%)

• Detailed breakdowns
• Historical trends
• Drill-down capabilities

Never lead with detail. Always lead with insight.

Principle #3: Action Orientation

Every dashboard should answer three questions:

1. What’s happening? (Insight)
2. Why is it happening? (Context)
3. What should I do about it? (Action)

Example: Sales Dashboard

Bad:

• “Total revenue: $1.2M”
• “Deals closed: 45”
• “Average deal size: $26,667”

Good:

• “Revenue 8% behind goal” ← Insight
• “Deal size down 12% vs. last quarter” ← Context
• “Focus: Upsell existing customers” ← Action

Add action prompts directly to dashboards:

• “⚠️ Pipeline coverage below 3x—time to ramp prospecting”
• ”✓ On track—maintain current activity levels”
• ”🚀 Ahead of goal—expand into new segment”

Principle #4: Smart Defaults with Flexibility

Balance is key:

• Default view: Answers 80% of questions for 80% of users
• Filters: Let power users customize for their 20% questions

Common Filter Categories:

• Time period (always include)
• Team/region/product (for multi-segment orgs)
• Customer segment (for detailed analysis)
• Comparison period (month/quarter/year)

Don’t Include:

• Filters that would confuse the primary metric
• So many filters users don’t know where to start
• Filters that should actually be separate dashboards

Principle #5: Visual Best Practices

The right chart for the job:

Use Line Charts For:

• Trends over time
• Multiple series comparison
• Identifying patterns

Use Bar Charts For:

• Comparing categories
• Rankings
• Period-over-period comparisons

Use Pie Charts For:

• Almost never (seriously, bars are usually better)
• Only if showing 2-3 segments of a whole

Use Tables For:

• Detailed reference data
• When exact numbers matter
• Supporting drill-downs

Use Gauges/Scorecards For:

• Single KPI headline
• Progress to goal
• At-a-glance status

Never Use:

• 3D charts (they’re harder to read)
• More than 7 colors (cognitive overload)
• Chartjunk (unnecessary decoration)

The Dashboard Design Process

Step 1: Define Success

• Who will use this?
• What decision do they need to make?
• How often will they look at it?

Step 2: Choose Primary Metric

• What’s the ONE number that matters most?
• How should it be contextualized?

Step 3: Add Supporting Context

• What 2-4 metrics explain performance of #1?
• What breakdowns are most useful?

Step 4: Build Action Triggers

• What thresholds trigger action?
• What should users do when metrics hit those thresholds?

Step 5: Design Layout

• Sketch on paper first
• Headline at top
• Progressive detail moving down
• Most important info in top-left

Step 6: Test with Users

• Can they understand it in 3 seconds?
• Do they know what action to take?
• Is anything confusing or unnecessary?

Step 7: Iterate Based on Usage

• Track which filters are used
• See what questions still come up
• Refine based on actual behavior

Common Dashboard Design Mistakes

Mistake #1: Too Much Data

More data ≠ better dashboard. Start minimal and add only what’s needed.

Mistake #2: Wrong Visualization Type

Line chart showing categories? Pie chart with 12 slices? Use the right chart for the job.

Mistake #3: No Context

“Revenue: $500K” means nothing without context. vs. goal? vs. last period? trending up or down?

Mistake #4: Inconsistent Formatting

Different date formats, number formats, or calculation methods across metrics kills credibility.

Mistake #5: Buried Insights

Don’t make users do math. Calculate the insight and display it prominently.

Real-World Example: Marketing Dashboard Redesign

Before:

• 47 metrics on one page
• Users had no idea what to look at first
• Nobody used it

After:

• Headline: Pipeline generated vs. goal (↑ 12%)
• 3 supporting metrics: MQL volume, conversion rate, cost per opportunity
• Detail section: Channel breakdown, campaign performance
• Action triggers: “⚠️ MQL volume down 8%—review top-of-funnel campaigns”

Result:

• Daily usage by CMO and team
• Decisions made faster
• Questions reduced by 60%

Building Your First Action-Oriented Dashboard

Week 1: Pick ONE audience and ONE primary question Week 2: Design the headline and 3 supporting metrics Week 3: Add diagnostic details and filters Week 4: Test with 3 users and iterate

Don’t try to build the perfect dashboard. Build the minimum useful dashboard and improve it based on actual usage.

The Bottom Line

Great dashboards aren’t about having all the data—they’re about having the right data organized for decision-making. Follow these principles:

1. Design for audience, not data
2. Follow visual hierarchy
3. Make action obvious
4. Smart defaults with flexibility
5. Right chart for the job

Next week: Building forecast models that actually work.

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What dashboard design challenges are you facing? Comment below or reach out on LinkedIn.