5 simple steps to writing an attention-grabbing headline for your next data analytics project:
Headlines are the most prominent part of a data analyst report or dashboard.
It’s right at the top, and it’s the thing that people see first.
Unfortunately, many data analysts skip the headline altogether.
But that’s a big mistake because it’s “prime real estate.”
Imagine you own a luxury storefront in New York City.
Would you:
- Cram garbage into every nook and cranny?
- Put cheap items next to expensive items?
- Distract shoppers from buying?
Of course not! But that’s what data analysts do every time they neglect these 5 steps:
Here are the 5 steps:
1) Create an early rough draft as soon as possible.
2) Put your conclusions right in the headline.
3) Use a sub-headline to provide extra context.
4) Add some formatting to drive your point home.
5) Create the final headline.
Let's break each one down...
1) Create an early rough draft as soon as possible.
After you’ve analyzed the data a bit, you’ll start noticing a theme, trend, or something interesting.
Jot down your initial headline ideas in a physical notebook or right in the analysis (Excel, Tableau, Jupyter, whatever)
2) Put your conclusions right in the headline.
Always choose a clear headline for your analysis instead of something clever.
Your coworkers and your boss are busy. Don’t make them think about what your analysis is about.
Just tell them straight up.
3) Use a sub-headline to provide extra context.
Sub-headlines are a great way to put additional background information into the headline without being too long or wordy.
4) Add some formatting to drive your point home.
Use color, bold, or italics to highlight key points.
💡Protip: match the colors of your report as a quick legend or reference for the end user.
5) Create the final headline.
Your final headline should be tight:
– no extra words.
– straight to the point.
That's it! Here's a before-and-after example from my online data analytics portfolio.
Brian Graves
@NewPrediction
Data professional w/ 15+ years exp. I write about SQL, Tableau, Excel, & Python to help data analysts earn more in their careers, side hustles, and freelancing.