From Data Dump to Data Story: How to Design Dashboards That Speak

Published: June 2025

By Amy Humke, Ph.D.
Founder, Critical Influence

Introduction: Dashboards Aren’t One Thing, They’re Four

Ever built a dashboard that nobody used? Or worse, one that got misused?
That usually happens because we skipped the most important question:
Are you exploring, explaining, monitoring, or reporting?

Because not all dashboards serve the same purpose.
Most confusion and disappointment around dashboards comes from a mismatch between what people think they need and what they actually need.

Here are the four types of data deliverables you should know:

1. Exploratory Dashboards (Discovery Tools)

• Purpose: Help users find the story
• Use Case: When the data is evolving or the goal is open-ended ("What should I be paying attention to?")
• Design Implications: Broad scope, filters, drilldowns, user-led navigation

2. Explanatory Dashboards (Narrative Tools)

• Purpose: Help users follow a story
• Use Case: When there’s a known issue or KPI shift to understand
• Design Implications: Structured layout, visual hierarchy, guided flow, annotations

3. Monitoring Dashboards (Guardrail Tools)

• Purpose: Keep users aware of performance boundaries and trigger action when metrics drift off course
• Use Case: When consistent tracking is needed to ensure processes stay on track or alert when they don’t
• Design Implications: Stable layout, KPI thresholds, alert indicators, minimal distractions, clear status signals

4. Static Reports (Fixed Stories, Not Necessarily Dashboards)

• Purpose: Share a snapshot or outcome
• Use Case: When the story doesn’t change (e.g., A/B testing a specific initiative)
• Design Implications: Printable, non-interactive, clear narrative, final-state emphasis

This article focuses on dashboards: specifically, how to make them live up to their purpose. But knowing when you actually need a report is just as important.

Finding a Purpose

Let’s start with the uncomfortable truth: most dashboards fall flat.
They look good and check the boxes, but don’t get used. Or worse, they get misused.

Why? Because they skip the most critical step: purpose.
If your dashboard doesn't start with a clear question or function, it can't deliver a clear answer.

Here’s how to avoid that: - Start with the decision, not the data. What do you want the user to do after viewing this?
- Define your audience. What do they care about? What level of detail do they need?
- Match your design to your dashboard type. (Exploratory vs. Explanatory vs. Monitoring vs. Report)

Visual Hierarchy and Flow: Make the Important Things Pop

Your dashboard has a job: direct attention.
Visual hierarchy is how you do that. Think of it like stage lighting for your data. Where do you want people to look first?

Use these tools: - Positioning: Top left is prime real estate. Use it for your most important KPIs.
- Size and color: Bigger, bolder elements draw attention. Use color sparingly, but strategically.
- Grouping and whitespace: Chunk related elements. Give them breathing room.

Bonus tip: humans scan in predictable patterns (F-pattern, Z-pattern). Use that to guide the layout.
Whether your user is exploring or being guided, the 5-second rule still applies:
A user should know what story the dashboard tells within 5 seconds. If they don’t? Back to the layout drawing board.

Color Isn’t Decoration, It’s Language

Want to guide users quickly to what matters? Build a consistent color language.

Be mindful that red-green color blindness affects many users. Relying solely on red and green can lead to confusion. Instead, use a color-friendly palette and reinforce meaning with shapes, icons, or text whenever possible.

Quick Color Guide:

• Red: Danger, failure, stop (only when paired with a secondary cue)
• Orange: Caution, at risk, warning
• Blue: Stability, neutral, background, trustworthy baseline
• Teal: Positive trend, progress, improvement
• Purple: Strategic opportunity, potential, insight
• Gray: Context, de-emphasis, not currently active

Use color sparingly. If everything is urgent, nothing is.
Avoid visual noise. Using a limited, purposeful palette that repeats across the dashboard is better than chasing variety.

Also, never rely on color alone. Add icons, directional arrows, or inline text for accessibility.
Color should be doing work in your dashboard, not just making it pretty.

Dashboards as Guided Tours

Exploratory dashboards give users the freedom to roam.
Explanatory dashboards lead them by the hand.
Monitoring dashboards aren’t about stories—they’re about signals.
They exist to flag when something’s off track and prompt immediate attention.

To design well: - Use layout to tell a story, surface patterns, or highlight abnormality
- Use interactivity intentionally. Filters, tooltips, and drilldowns can invite exploration or support context
- Add explanatory text. Short descriptions, notes, or headlines build clarity
- Highlight calls to action:
- For explanatory dashboards: Make "what to do next" unmistakable.
- Use dynamic headlines that summarize the key change (segment, direction, % shift) and suggest a general next step based on business rules (e.g., “Enrollment for adult learners dropped 7.2%; consider increasing outreach”).
- For monitoring dashboards: Make abnormalities stand out as clearly out of place.
- Use control charts with thresholds, alert icons, or color-coded KPI bands.

Whether pointing to the story or helping users find one, you’re designing a journey.

One Dashboard, Many Stories: Designing for Multiple Audiences

One of the biggest mistakes? Building one dashboard for everyone.
The other big mistake? Building a different dashboard for every individual user.

I’ve seen it happen more times than I can count: a dashboard becomes popular, so more teams use it.
Then the requests flood in when people want filters added for their specific use case and need more views and custom charts.
Eventually, it becomes a sprawling, filter-heavy monstrosity with 30 dropdowns and no clear direction.

But duplicating that dashboard into ten or twenty versions isn’t the answer. That creates its own mess.
When a metric definition changes or a business rule shifts, you’re now responsible for tracking down and updating the same element in dozens of places.

So what’s the balance?

• Design for shared structure, not personalized sprawl
• Use modular design. Create core metric tiles and reusable components that can be embedded across different dashboards
• When in doubt, split by audience purpose, not by person

Example:

Instead of a single sprawling dashboard with 12 filters and 40 charts, create: - A 3-card summary for executives
- A detailed funnel breakdown for marketing
- A real-time operations monitor with alerts for call center leads

Each supports the same initiative, but is structured for different minds, workflows, and decisions.

The Art of Dashboard Storytelling (When It’s the Right Fit)

Explanatory dashboards follow a classic story arc: 1. Setup: Where are we?
2. Conflict: What’s changing or needs attention?
3. Resolution: What should we do about it?

Tabs vs. Scroll: Choosing Your Structure

You don’t need tabs to tell a story, but that doesn’t mean they’re wrong either.

• A long vertical layout can be more effective if your dashboard aims to guide users through a single cohesive narrative.
• Tabs might make more sense if the dashboard supports multiple independent questions or caters to very different roles.

Use tabs when: - Each audience needs entirely different metrics
- The dashboard supports operational monitoring rather than narrative flow
- Content is dense enough that separation improves clarity

Use vertical scroll when: - You're walking users through a trend, shift, or insight step-by-step
- You want to minimize clicking and jumping around
- The story is the same for all users, just updated over time

Whatever format you choose, structure matters more than the container. Guide your users with: - Descriptive headlines (“Enrollment is stable, but intent-to-enroll is down 12%”)
- Visual framing (e.g., side-by-side comparisons or arrows indicating flow)
- Icons, annotations, and commentary that reinforce the narrative

You're not scripting one version of the truth; you’re building a dashboard that supports repeatable, evolving stories.

Designing for Change Without Babysitting

What if the story needs to change? That’s not a failure of your dashboard; it’s the whole point.

Plan for the story to change:

• Use dynamic filters and segments. Let users shift focus based on the dimension that matters most this month, without requiring structural updates
• Design in flexible layers. Separate high-level KPIs from diagnostic charts
• Avoid hardcoded interpretations. Instead, use calculated fields to describe trends dynamically

Example:
If a metric increases month-over-month, a calculated field could return the word “increased” along with a teal upward arrow and percentage change.
If it drops, the field returns “decreased” with a down arrow and red text.
This approach makes the dashboard more readable and automatically responsive to new data.

As for AI-driven automation? It’s tempting to chase plug-and-play explanations, but most tools (like Tableau Stories) are rigid and disconnected from real analysis workflows.
Unless you're building a custom solution, it’s better to focus on structures that scale. Prioritize dynamic design over magic buttons.

How Leaders and Doers Work Together to Get This Right

Many dashboards fail at the handoff.
A leader says, "I need a dashboard that shows X."
A doer builds something that technically answers the ask.
But neither side clarified the decision, the audience, or the action.

Here’s a better way:

• Leaders: Don’t ask for charts. Ask for clarity. What decision will this support? What change are we trying to drive?
• Doers: Don’t build in isolation. Ask about the goal. Offer design input. Make the dashboard a conversation, not a transaction.

The most impactful dashboards come from aligned expectations and mutual respect.
Doers bring the data craft. Leaders bring the context.
Together? They create impact.

Common Pitfalls and How to Avoid Them

• Too much data. Solution: Ruthlessly prioritize.
• No context. Solution: Add annotations, comparisons, and benchmarks.
• Bad chart choices. Solution: Use visuals that match the question.
• No clear takeaway. Solution: Write it down. Literally. Add a sentence.
• One-size-fits-none. Solution: Design for flexibility or modular use.
• Trying to tell one story to everyone. Solution: Tailor dashboard versions to specific decisions and roles.
• Using a dashboard when a report is better. Solution: Ask whether the story changes over time.

Dashboards don’t fail because of the tools. They fail because the thinking wasn’t clear.

Conclusion: Know What You’re Building and Why

Your dashboard is not the data. It’s not the analysis. It’s not the answer.
It’s the medium.

And that medium only works when you choose the right format: - Exploratory when the user is looking for a story
- Explanatory when the user needs to follow one
- Monitoring when the user needs to know things are off course
- Report when the story doesn’t change

Dashboards don’t fail because of bad charts—they fail because no one asked the right question upfront: What action should this dashboard support?
When you answer that, everything else gets easier.

So the next time you’re asked to build a dashboard, or you’re the one asking, start here:
Are we exploring, explaining, monitoring, or reporting?
And then build for that purpose.

That’s when dashboards actually inform decisions.
That’s when data work creates real impact.