How To Do Data Storytelling

Data alone isn’t enough. The charts you get from analytics tools are designed for exploration, not explanation. While they help you analyze data, they often fall short when it comes to conveying a clear message to your team, investors, customers, stakeholders etc.

Data storytelling bridges this gap by turning raw data into a compelling narrative that drives understanding and action.

At its core, Data Storytelling consists of three key elements:

• Data – The foundation of your story

• Narrative – The storyline that gives data meaning

• Visuals – The design that makes insights clear

In this post, we’ll focus on how to transform your data into powerful stories that drive change. The emphasis will be on visuals — turning data into clear, impactful graphs.

The 3 Types of Graphs

Before diving into the practical steps, let's look at the three levels of chart quality:

📉 Bad Graphs → Cluttered, inconsistent, and confusing for the audience

📊 Good Graphs → Clean, consistent, and easy to read

📈 Great Graphs → Tell a story, highlight insights, and drive action

I covered this concept at Turing Fest, but let’s dive deeper into a practical guide on how to transform bad graphs into great ones.

From Bad Graph to Good Graph

Let’s go step by step.

1. Start with a Question

Every data story starts with a clear question — what do you want to learn?

In our case: Why are founder signups dropping? Is it a market shift, an acquisition issue, or something else? Finding the right insight will help us take action.

The right insights lead to the right graphs.

For this example, we’re analyzing product signups over time, but real datasets are often bigger and messier — the process stays the same.

Spend enough time here. If you skip this step, you risk creating graphs that look ‘good’ but don’t drive action.

Step 2: Default Visualization (Bad Graph)

Using Google Sheets, we generate a default chart, in this example we’ll only focus on 3 Job Titles from the spreadsheet.

At this stage, the chart is cluttered and hard to read.

Step 3: Clean Up the Chart

To improve readability, we need to remove visual noise:

❌ Borders are too thick – Remove or simplify them.

❌ Colors are too strong – Use a softer, more intentional color scheme.

❌ Axis labels take up too much space – Adjust font size and spacing.

❌ Grid lines are too prominent – Reduce emphasis on them.

After these refinements, the chart becomes much clearer. While these may seem like small tweaks, they make a big impact—people will feel less overwhelmed and focus on the insights, not the design clutter.

Step 4: Use a Tool Designed for Clarity

A faster way to get a clean and readable chart is to use Graphy. The default settings ensure clarity from the start, making it easy to interpret and interact with the data.

At this point, we have a good graph—it's clear and easy to understand. But to make it great, we need to add an insight.

From Good Graph to Great Graph

Every dataset holds multiple stories. The best storytelling only focuses on one insight at a time and emphasizes it.

Let’s explore a few potential insights:

1️⃣ Highlighting a Missed Goal

One way to frame the story is by showing how we missed our target. This can be a strong discussion point to drive action.

• A stacked bar chart works best for this case.

• Adding a goal line clearly shows that targets were significantly missed for the past two months.

• This visualization makes it easy to spark a conversation around why performance dropped and what actions can be taken to improve it.

2️⃣ Analyzing User Proportions

Another approach is to look at the composition of users over time.

• A 100% stacked bar chart helps visualize the relative proportions instead of absolute numbers.

• This shift in perspective can reveal hidden trends — for example, an increase in marketers compared to other user types.

• This insight could lead to a strategic decision: Should we optimize our messaging for this growing segment, or find ways to attract other user types?

  

3️⃣ Tracking the Decline of Founders

A third and perhaps most critical insight is the declining number of founders, a key audience we care about.

• Instead of showing all data equally, we focus on the founder trend, making it the center of our story.

• This insight raises an important question: Why are fewer founders signing up?

• The goal of this visualization is to drive action—whether that’s adjusting acquisition strategies, running targeted campaigns, or improving retention for this segment.

  

  For this example, let’s focus on the Insight #3 decline in new founders. Let’s see step by step on how we can get there.

Step 1: Add a Meaningful Title

Instead of a generic title like "New Users by Job Title", use a conclusion-driven title:

✅ "The Number of New Founders Has Dropped by 72%"

Step 2: Highlight the Key Trend

• Emphasize the most important data line (e.g., new founders).

• Keep other lines in the background for context.

• Adjust the legend placement to make comparisons easier.

Step 3: Visualize the Insight Clearly

• Annotate the chart to highlight key points.

• Use a difference arrow to show the decline.

• Add a short narrative to make the insight even clearer.

Step 4: Add a narrative

Depending on where are you presenting:

• Use bullet points in Google Slides.

• Add a short paragraph in Notion.

• Drop a caption in Graphy (AI can help).

• Make it as actionable as possible, focusing on the next steps.

This approach ensures that your audience immediately understands the key takeaway — without needing to interpret the data themselves.

Final Thoughts: Why Data Storytelling Matters

Turning data into a story isn’t just about making charts look good—it’s about making insights clear and actionable.

By following this simple process, you can transform cluttered, confusing graphs into compelling visuals that drive decisions.

So next time you're creating a chart, ask yourself:

📌 Is my chart just showing data, or is it telling a story?

If it’s not telling a story yet, use the steps above to refine it!