The Chart Building Process That Changed How I Present Data
How to think like a storyteller when building your next chart
Most charts fail because we skip the thinking part and jump straight to the clicking part.
We open Excel or Tableau, dump in our data, pick the first chart type that looks decent, and call it done.
But your chart is trying to communicate something important. If you don't know what that something is, how can you expect your audience to figure it out?
That's where this process comes in. It forces you to think before you build. And trust me, your future self will thank you for it.
Step 1: Analyze Requirements & Choose Your Chart Type
This is where most people mess up. They start with the data instead of starting with the question.
Here's what you need to ask yourself:
What story am I trying to tell?
Are you comparing things? (Bar charts, column charts)
Are you showing change over time? (Line charts, area charts)
Are you displaying parts of a whole? (Pie charts, donut charts)
Are you showing relationships? (Scatter plots, bubble charts)
Who is my audience?
Are they data people or regular folks?
How much time do they have to look at this?
What level of detail do they need?
What action do I want them to take?
Should they investigate something?
Do they need to make a decision?
Are you just informing them?
Once you answer these questions, the right chart type becomes obvious.
Step 2: Check Your Initial Format (This Changes Everything)
Now here's something most people never think about: the format of your data matters way more than you think.
Look at your spreadsheet. Really look at it. Is it set up in a way that makes building your chart easy, or are you about to fight with your tool for the next hour?
Good data format looks like this:
One row per observation
Clear column headers
No merged cells or fancy formatting
Missing data handled properly
Consistent data types in each column
Bad data format looks like this:
Summary tables with totals mixed in
Multiple variables crammed into one column
Inconsistent naming (Jan, January, 01)
Colors and formatting doing the work that data should do
If your data isn't chart-ready, fix it now. Seriously. Don't try to work around bad formatting. It always comes back to bite you later.
Pro tip: If you find yourself doing weird tricks to make your chart work, your data probably isn't formatted right.
Step 3: Create Calculated Fields & Test Your Logic
This step separates the good analysts from the great ones.
Most people take their raw data and chart it directly. But raw data rarely tells the story you want to tell. You usually need to calculate something first.
Common calculated fields you might need:
Percentages (what portion of total sales came from each region?)
Growth rates (how much did we grow month over month?)
Running totals (what's our cumulative progress toward the goal?)
Averages or medians (what's typical performance?)
Ratios (how efficient are we getting?)
But here's the key part: test your calculations before you build the chart.
Make sure the numbers make sense. Check your math. Verify that your percentages add up to 100% when they should.
Step 4: Build Your Chart
Okay, now you get to do the fun part. But even here, there's a right way and a wrong way.
Start simple. Don't add every bell and whistle your tool offers. Build the most basic version of your chart first, then add complexity only if it helps tell your story better.
Focus on the data, not the decorations. Your chart should highlight the important stuff and fade everything else into the background. That means:
Make your data stand out with color and size
Make your axes and gridlines light and subtle
Only label the things that matter
Remove any elements that don't add value
Make it readable. Use fonts big enough to read easily. Pick colors that work for colorblind folks. Make sure there's enough contrast.
Step 5: Format Like a Pro
This is where good charts become great charts. The formatting is what makes people want to keep looking and actually understand what you're showing them.
Clean up your axes. Round your numbers to sensible values. If you're showing millions of dollars, don't display 2,847,293. Show 2.8M instead. Your audience will thank you.
Add context. A line that goes up is nice. A line that goes up 23% while the industry average went down 5% is a story. Add reference lines, benchmarks, or targets that help people understand if your numbers are good or bad.
Use color with purpose. Don't make everything rainbow colored just because you can. Use color to highlight what's important. Make the good stuff green and the bad stuff red only if that makes sense. Sometimes gray is your best friend.
Why This Process Actually Works
Look, I know this seems like a lot of steps for "just making a chart." But here's why it works:
You avoid the back-and-forth nightmare. You know how you build a chart, show it to someone, and they immediately ask for something completely different? This process stops that. When you think through requirements first, you build what people actually need.
Your charts tell clear stories. When you start with the story instead of the data, your charts have a point. People look at them and immediately get what you're trying to say.
You save time in the long run. Yes, this takes longer upfront. But you'll spend way less time rebuilding charts and explaining what they mean.
You look like you know what you're doing. When you present a well-thought-out chart that clearly communicates an insight, people notice. This is how you build a reputation as someone who really gets data.
Here's what I want you to do: take your next chart project and try this process. All of it. Don't skip steps because you're in a hurry.
Start with the requirements. Figure out what story you're telling before you touch any tools. Clean up your data format. Test your calculations. Then build and format with purpose.
I promise you'll be surprised at how much better your charts get.