Your Data Is Boring Everyone to Death (Here's How to Fix It with AI)
Why your spreadsheets aren't convincing anyone, and the AI prompts that fix it
Why your spreadsheets aren’t convincing anyone, and the AI prompts that fix it
Hey there!
Ever stare at a spreadsheet full of numbers and think “there’s a story in here somewhere” but can’t quite figure out how to tell it? You’re not alone. Most of us have been handed data, be it sales figures, user analytics, survey results, and been told to “make it compelling.” Yeah, easier said than done.
But data on its own is about as exciting as watching paint dry. But the insights hiding inside that data? Those can be absolute goldmines. The problem is most people don’t know how to dig them out and turn them into stories that actually land with an audience.
In this article, you’ll learn:
Why your brain naturally craves narrative structure (and how to use that to your advantage)
A simple 3-act framework that works for any data story
How AI can help you find the narrative thread buried in your numbers
Examples of turning boring data into engaging content
The Data Storytelling Problem Nobody Talks About
So your boss wants a presentation, your client needs a report, or you’re trying to create content that demonstrates your expertise. You’ve got the data but when you try to write about it, everything comes out sounding like a technical manual.
That’s because most of us were taught to present data, not to tell stories with it. We learned charts and graphs and bullet points. We mastered the art of the executive summary. But somewhere along the way we forgot that humans are hardwired for stories, not statistics.
The real issue is that data lives in one part of your brain (the analytical side) while storytelling lives in another (the creative side). Trying to bridge that gap manually is exhausting. It’s like trying to speak two languages at once.
The Basic Framework: Data Meets Drama
Actually, you don’t need to be Hemingway to turn data into a compelling narrative. You just need a framework. And lucky for you, stories have been following the same basic structure for thousands of years.
Every great data story has three acts, just like every great movie:
Act 1 - The Setup: What’s the current situation? What question are we trying to answer? This is where you establish context and make your audience care. Don’t throw numbers at people hoping some will stick, tell them why these numbers matter.
Act 2 - The Conflict: What does the data reveal? What’s surprising, concerning, or exciting about what you found? This is where tension builds. Maybe your conversion rate dropped 23% last quarter (yikes), or customer satisfaction jumped after you changed your onboarding process.
Act 3 - The Resolution: What does this mean going forward? What action should we take based on these insights? Every good story has a conclusion, and every good data story should end with clear implications or recommendations.
Getting Started: Your First Data Story
Let’s say you’re looking at website traffic data and you need to present it to your team.
Step 1: Identify your main insight. Don’t try to tell every story in your data at once. Pick one clear finding. For example: “Our blog traffic increased 40% but our conversion rate dropped.”
Step 2: Ask “so what?” Keep asking until you get to something that matters. Traffic up but conversions down means... we’re attracting the wrong audience? Our content isn’t aligned with our offer? Our landing pages need work? Find the human element.
Step 3: Use this basic prompt structure with any AI tool:
“I have the following data: [paste your data]. Help me identify the most compelling narrative thread. What’s the ‘before and after’ story here? What changed and why does it matter?”
This gives you a starting point. The AI won’t write your story for you but it’ll help you see patterns you might have missed.
Step 3 Example: Let’s say you have quarterly sales data showing a 15% decline in Product A but 60% growth in Product B. You might input:
Input: “I have quarterly sales data. Product A declined 15% while Product B grew 60%. Product A has been our flagship for 5 years. Product B is newer and targets a different demographic.”
What you might get back: The AI might identify that you’re witnessing a market shift, not just a product performance issue. It might suggest framing this as “The Changing of the Guard: How Our Customer Base Is Evolving.”
Common Mistakes That Kill Your Data Story
Mistake #1: Starting with the numbers. Nobody cares about your methodology in the first paragraph. Lead with the insight, the surprise or the implication. You can explain how you got there later.
Mistake #2: Forgetting your audience. Your CEO doesn’t need the same story as your marketing team. The data might be identical but the narrative angle should shift based on who’s listening and what they care about.
Mistake #3: No stakes, no story. If nothing hangs in the balance, your data presentation is just trivia. Always connect your findings to something that matters—revenue, customer satisfaction, competitive advantage, whatever makes sense for your context.
Want the advanced frameworks, detailed prompts, and real-world examples?
Next up is:
The 7-step Data Narrative System I use for client presentations
5 battle-tested AI prompts that transform raw data into compelling stories
Complete examples with actual data inputs and story outputs
How to adapt your narrative for different audiences (executives, clients, social media)
Advanced techniques for finding hidden stories in complex datasets
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