PALO ALTO — Anomalo, a complete data quality platform company, formally launched with its product that helps teams trust the data they use to make decisions and build products. Anomalo’s customers include BuzzFeed, Discover Financial Services and Substack. The company says it has exceeded 7-figures of annualized recurring revenue, tripling its revenue over the last quarter.
The company also has raised $33 million in Series A funding, bringing the total raised to $38.95 million. The round was led by Norwest Venture Partners with Two Sigma Ventures, Foundation Capital, First Round Capital and Village Global participating.
Much like software before it, data is the next competitive battleground for modern enterprises. Inspired by the successes of Amazon, Google and Netflix, companies are rushing to become data-powered organizations. They are standing up data technology stacks, ingesting data from internal and external sources and using it for everything from business decision-making to predictive analytics and machine learning.
But every data-driven company quickly encounters one unfortunate fact: much of their data is missing, stale, corrupt or prone to unexpected and unwelcome changes. As a result, companies spend more time dealing with issues in their data rather than unlocking that data’s value.
Anomalo addresses this problem by monitoring enterprise data and automatically detecting and root-causing data issues, allowing teams to resolve any hiccups with their data before making decisions, running operations or powering models.
Anomalo co-founders Elliot Shmukler, CEO, and Jeremy Stanley, CTO, worked closely together at Instacart and bonded over their shared love of using data. They applied data to everything from optimizing marketing spend through machine learning to improving the efficiency of the grocery delivery process by mapping out the best way to shop for items in stores.
They witnessed many situations where Instacart’s data quality broke down. At one point, a geographic expansion strategy stalled by using data that was six months stale. The difficulty of ensuring their data was of high quality led them to found Anomalo.
“When you’re working with data, an old computer concept often applies: garbage in, garbage out. Trying to get good results while using inaccurate or corrupted data is simply an exercise in futility. Anomalo’s goal is to make sure that you never have to worry about the quality of the data you are using,“ said Elliot Shmukler, co-founder and CEO of Anomalo.
Legacy approaches to monitoring data quality require extensive work writing data validation rules or setting limits and thresholds. In contrast, Anomalo leverages machine learning to rapidly assess a wide range of data sets with minimal human input. If desired, enterprises can fine-tune Anomalo’s monitoring through the low-code configuration of metrics and validation rules.
The result is a complete data quality platform that is particularly suited to the work of large data teams or enterprises with broad and complex data sets such as those in the financial services, e-commerce and media verticals.
“Everyone wants high quality data but it rarely gets the attention it deserves because existing approaches are tedious and prone to sending lots of false-positive alerts. We use robust machine learning methods to automate as much of the setup and maintenance as possible. When data breaks, we generate rich and insightful visualizations that quickly convey to our users exactly what and where their data went astray. We make monitoring data quality so easy and powerful, it’s fun!” said Jeremy Stanley, co-founder and CTO of Anomalo.