AI Begins With Data Quality

The Data Exchange Podcast: Jeremy Stanley on key dimensions and features of modern data quality and data monitoring solutions.

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This week’s guest is Jeremy Stanley, co-founder and CTO of Anomalo, a startup building SaaS tools to help companies with data quality (DQ). Prior to Anomalo, Jeremy was VP of Data Science at Instacart. Anomalo just announced a $33M Series A Round on the same day this podcast went live. Based on our upcoming data engineering survey, DQ is one of the most important challenges facing data teams today.

Download the 2022 Data Engineering Survey Report and learn how companies are designing and building data platforms.

Jeremy Stanley:

I’ve been a data person for a long time. I’ve worked on data in everything from insurance applications, to advertising technology, to personalization, to all the logistics and fun challenges at Instacart. If you work with data long enough, you begin to appreciate that all of the fancy things that you can do with machine learning and analytics, they’re all only as good as the quality of the data going into those processes and into those models. I often found myself either personally or with the teams that I was a part of, at these different companies building solutions in-house, to be able to monitor the quality of the data that we were depending on.

At Instacart, there was a table we were using to launch new markets from. That table was based upon people coming to our website, and trying to sign up and putting their zip code in. This table got switched and stopped being updated, and yet, we were still making decisions on it. So for six or nine months, we were using stale data about zip codes. … and it cost us a lot of growth. Fixing that data quality issue was one of the biggest things that we did for growth in my tenure at Instacart.

Highlights in the video version:

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[Photo: Matrix from Piqsels.]