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Storytelling with data — From numbers to compelling narrative

Learn how to turn data into a compelling story. Discover the three ingredients of data storytelling, the SCQA framework, and practical tips for impactful presentations.

Last updated: 2026-03-08

What is data storytelling?

Data storytelling is the art of combining data, narrative, and visualization into a message your audience understands, remembers, and acts upon. It's not about pretty charts — it's about the story behind the numbers.

Imagine you've discovered that customer churn increased by 23% in Q3. You could put that number in a table. Or you could explain why it increased, what it costs the organization, and how to fix it. That difference — between presenting data and telling a story — is the difference between being ignored and getting action.

Research from Stanford shows that stories are 22 times more memorable than isolated facts. Data storytelling is therefore a core skill for anyone working with data.

The three ingredients

Effective data storytelling rests on three pillars:

  • Data — The facts and analyses that support your message. Without reliable data, there's no foundation.
  • Narrative — The story that wraps around the data, providing context and leading your audience to a conclusion.
  • Visualization — Charts and visual elements that make complex patterns visible at a glance.

The magic lies in the overlap: data + narrative without visuals = convincing report but less impact. Data + visuals without narrative = nice dashboard but no action. All three together = a story that convinces, sticks, and drives action.

Building your story: the SCQA framework

One of the most effective methods for structuring a data story is the SCQA framework, developed by Barbara Minto at McKinsey:

  • Situation — Set the context. What does your audience already know?
  • Complication — What's the problem or change? Why is action needed?
  • Question — What question does this raise?
  • Answer — Your recommendation, backed by data.

This works because it builds natural tension — from recognition (situation) through concern (complication) to curiosity (question) and resolution (answer). Start your Power BI presentation with a summary slide using SCQA in 4 bullets, then use detail pages to support each point.

Adding emotion and context

Numbers alone rarely convince. To give your story power, you need context and emotion:

  • Comparisons and benchmarks — "Our absence rate is 6.8%" says little. "Our absence rate is 6.8% — nearly double the industry average of 3.5%" tells a story.
  • Human scale — "€2.3M in unused licenses" becomes "the salary of 30 employees."
  • Anecdotes — Start with a concrete example, then zoom out to the data.
  • Visual emphasis — Use color, size, and position to direct attention. Make the most important number the largest.

Pitfalls in data storytelling

Data storytelling is powerful but can mislead. Avoid these common pitfalls:

  • Cherry-picking — Only showing data that supports your story. Show nuance and limitations too.
  • Misleading axes — Y-axes not starting at 0, dual axes with different scales, irregular time intervals.
  • Correlation ≠ causation — Two lines following the same pattern don't prove a causal link. Be precise in your language.
  • Overcomplicating — Too much data, too many charts. Effective storytelling is about removing everything that doesn't support your core message.
  • No call to action — A story without a conclusion or recommendation is incomplete. Always end with: what needs to happen now?

Tools and techniques

Key Power BI features for storytelling:

  • Bookmarks — Save different report views and navigate between them like slides
  • Drillthrough — Let your audience navigate from overview to detail
  • Smart Narratives — AI-generated text summaries of your data
  • Conditional formatting — Color-code cells and bars for instant good/bad indicators
  • Tooltip pages — Add extra context that appears on hover

Recommended books: Storytelling with Data by Cole Nussbaumer Knaflic, The Big Picture by Steve Wexler, and Data Story by Nancy Duarte.

Frequently asked questions

What's the difference between data storytelling and data visualization?
Data visualization is displaying data in charts. Data storytelling goes further: it combines visualization with narrative and context to deliver a message that drives action.
How do I start with data storytelling without presentation experience?
Start small: take your next report and add one summary that explains the key message in plain language. Use the SCQA framework as structure. Practice by presenting to a colleague and asking if the message comes across.
How do I prevent my data story from being misleading?
Be transparent about sources and limitations, start axes at 0, show nuancing data, and have a colleague review your work. Ask yourself: would I draw the same conclusion seeing this data for the first time?
Which tools are best for data storytelling?
Power BI with bookmarks and drillthrough is excellent. PowerPoint with embedded Power BI works great for presentations. The book "Storytelling with Data" by Cole Nussbaumer Knaflic is the standard reference.
How long should a data story be?
As short as possible, as long as necessary. An executive summary of 3-5 slides with a clear SCQA structure is often more effective than 30 slides full of detail. Offer details as appendix or drillthrough.

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About the author — Peter Heijnen is a data and process specialist with 20 years of experience at multinationals. He runs business-intelligence.info and helps companies with planning, reporting and automation through BPA.