Spotting and fixing data issues: how we help improve data quality on and off our publication

In today’s Data Insight, we’re sharing a behind-the-scenes look at a part of our work we rarely talk about, but that is crucial in contributing to a more accurate understanding of the world.

We work with hundreds of datasets from many different sources. To check their quality, we’ve built in-house tools that flag unusual patterns, helping us spot when something seems off. Even in high-quality datasets, occasional errors can slip through.

The chart shows a recent example: after we updated a dataset, we noticed an unexpected spike in one of its time series. Investigating further, we traced the issue back to the data provider and let them know. They reviewed it, confirmed the problem, and corrected the error. Thanks to exchanges like this, several datasets have been improved this year.

Improving data quality is always a collaborative effort. We deeply appreciate the work of statisticians and data providers worldwide, who play a critical role in creating and maintaining these datasets. Our role is to help flag issues when we spot them and provide constructive feedback to make the data better for everyone.

Where this page came from

This page was imported from Our World in Data. “Spotting and fixing data issues: how we help improve data quality on and off our publication” by Pablo Rosado, Edouard Mathieu, Esteban Ortiz-Ospina, published by Our World in Data under CC BY 4.0. Changed here: set as a page, its interactive charts shown as pictures. Data from third parties keeps its own licence.

Nobody has written it yet — it is the source material at a new address, which is why search engines are asked to skip it and why no one earns from it. It is up for grabs: take it on, and it is yours to rewrite and to earn from.