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Daily Data InsightsSpotting and fixing data issues: how we help improve data quality on and off our publication

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

Line chart showing an example of a data error that was detected and flagged for correction. The old data has a large spike in the middle of the timeline, while the new data shows a consistent line without the anomaly.

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.

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