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To Improve Data Quality, Start at the Source

Source | hbr.org | Thomas C. Redman

You can’t do anything important in your company without high-quality data, and most people suspect, deep down, that their data is not up-to-snuff. They do their best to clean up their data, install software to find errors automatically, and seek confirmation from external sources — efforts I call “the hidden data factory.” It is time-consuming, expensive work, and most of the time, it doesn’t go well.

Even worse, cleanup never goes away! Imagine that you had cleaned all your existing data perfectly, but not addressed the problem of poor quality at the source. As you acquire new data, you will also acquire new errors that impact your work. You and your team will once again waste time dealing with errors. Cleanup as the primary means of data quality is long past its sell-by date.

Rather than fixing data quality by finding and correcting errors, managers and teams must adopt a new mentality — one that focuses on creating data correctly the first time to ensure quality throughout the process. This new approach — and the changes needed to make it happen — must be step one for any leader that is serious about cultivating a data-driven mindset across the company, implementing data science, monetizing its data, or even simply striving to become more efficient. It requires seeing yourself and the role you play in data in a new way, all the while identifying and ruthlessly attacking the root causes of errors, making them disappear once and for all.

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