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November 01, 2021
Anomalo – Preventing Data PTSD
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Before venture, I spent five years at LinkedIn leading product for the recruiting business. We were the company’s revenue engine, so we closely monitored our performance with a set of dashboards that refreshed nightly. And so, every morning, the first thing I did after waking up was open the dashboard email with a knot in my stomach: if the numbers looked right (up or down a bit), then I was going to have a good day working on product; if the numbers looked wrong (missing data, massive swings), my day would become a fire drill of frantic data forensics to understand whether the dashboard was incorrect or the business had gone off the rails. I still have PTSD!

Fast forward to 2021, when I reconnected with my good friend, Elliot Shmukler, whom I met at LinkedIn during that era of hypergrowth. He had gone on to Head of Product and Growth roles at Wealthfront and Instacart, battling data quality issues every step of the way. At Instacart, Elliot met Jeremy Stanley, Instacart’s Head of Data Science. They decided to channel their frustrations with bad data into starting a company that would help other companies trust the accuracy of their data.

Anomalo was born.

Elliot explained that Anomalo monitors tables in a customer’s data warehouse to identify gaps and unexpected changes in the data. It then alerts relevant stakeholders. The tech combines ML- and rules-based monitoring across a broad set of dimensions, including data freshness, data volume, missing data, variance of key metrics, and adherence to validation rules. And it works! Anomalo already had several paying customers singing its praises, and the company was adding new ARR at a rapid rate.

It only took one call for me to know that I wanted to invest.

I’m thrilled to share that we led Anomalo’s $33 million Series A round. It’s a privilege to partner with Elliot and Jeremy to build a big and important company.

I’m also excited to get Anomalo in the hands of as many companies as possible because my experience at LinkedIn wasn’t unique. Data is only useful if it’s accurate. However, since data warehouses and BI tools don’t provide data quality validation, every company struggles with data quality. Anomalo is a game-changer for organizations that want to stop spending time investigating data issues and start making data-driven decisions with confidence. It’s the solution I wished I had years ago.

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