Talking Shop: Digging in with Data

Keep it simple, make it memorable: This data journalist’s motto for working with analytics

By Meredith Barnett

Mona Chalabi

Mona Chalabi
Data Editor
The Guardian

Data journalist Mona Chalabi is on a mission to, as she puts it, "take the numb out of numbers." In her illustrations, animations, and articles for the Guardian and publications like Fivethirtyeight and The New York Times, she explores data sets from the timely (affirmative action, voting trends, race, politics) to the offbeat (popular dog names in New York City) to the eye-opening (how many Americans eat pizza for breakfast).


Catch Mona Chalabi at CASE's DRIVE/ Conference, March 11-13, 2019, in Baltimore. DRIVE/ explores data science, analytics, and beyond.

How do you decide what data to dig into for your illustrations? What intrigues you?

I often keep a close eye on the news and try to create work that helps people make sense of the information that's coming at them. I also check academic blogs for new studies but very often, the ideas are suggestions from very smart readers who get in touch to ask me something that's been bugging them.

What makes a data visualization good or helpful?

First and foremost, the data behind the visualization should be reliable. Then the visualization itself should clearly communicate those numbers. If it's a memorable image, that's great. If it's beautiful, that's even better. 

What tips do you have for making data digestible?

Keep it as simple as you can. Ask people who don't know anything about data what they understand from the sentences and images you have created. Did they understand it correctly? 

What's a big mistake a lot of people make when writing about/using data?

People often overstate the precision of the numbers. Don't put a decimal place on something like polling numbers: that suggests that you have a high degree of precision in your methodology, which just isn't true.

In your TED Talk, "3 Ways to Spot a Bad Statistic," you say people need to be better at determining good statistics from bad ones. What are a few ways we can do that?

Stay skeptical. Ask yourself who collected the data, when they did it, and if they have any incentives for reaching certain conclusions. 

What makes working with education data—student debt, student diversity, graduation rates—challenging?

I've done a few pieces on educational data. I've looked at the impact of affirmative action and how unemployment rates vary by educational status. I think one of the challenges is that in a country as huge as the United States, national figures really don't necessarily help people understand the state of education where they live. But it can be hard to show statistics for each of the states and break it down by things like race or gender. 

What do you think is the future of data journalism?

I hope there will be much more of an emphasis on creating more accessible data visualizations. This year, I want to think about ways to create work that is fully accessible to people with disabilities, including the blind and visually impaired.