Databricks backs $33M round for data quality monitoring startup Anomalo
Anomalo Inc., a startup helping enterprises ensure their business data is accurate, today announced that it has closed a $33 million funding round.
The Series B investment was led by SignalFire with participation from Databricks Inc.’s venture capital arm. Returning Anomalo backers Norwest Venture Partners, Two Sigma Ventures and Foundation Capital chipped in as well. The company will use the capital to grow its engineering and go-to-market teams.
“The world increasingly runs on data with companies large and small investing in the technologies they need to become more data informed,” said Chief Executive Elliot Shmukler, who co-founded the startup in 2021 with Chief Technology Officer Jeremy Stanley. “As Jeremy and I suspected when we founded Anomalo, data quality has become an urgent issue in this new world.”
Palo Alto, California-based Anomalo provides a platform that can connect to the systems in which a company keeps its business data and scan that data for quality issues. According to the software maker, its platform spots out-of-date records that should be replaced with fresh information. Anomalo also detects duplicate database rows, missing fields and a range of other issues.
The company’s platform uses unsupervised machine learning to detect data quality problems. Artificial intelligence models that implement unsupervised learning can generally be trained on larger datasets than other neural networks, which makes them more accurate in some cases. According to Anomalo, its platform’s AI models are supported by a set of automated controls designed to reduce the risk of false positives.
False positives, or cases where accurate information is flagged as erroneous, are often caused by seasonal changes in a company’s datasets.
During the holiday shopping season, a sudden spike in the number of e-commerce purchase logs added to a database is likely to stem from increased customer demand. But in January, such a data spike may more likely be the result of a processing error. Anomalo says that its platform can automatically take into account factors such as seasonal business fluctuations when determining whether a data error might be a false positive.
Besides detecting erroneous records, the company’s platform can also help developers fix them. It includes a tool that automates some of the steps involved in finding the root cause of data quality issues. The tool can, for example, identify which parts of a dataset contain the most out-of-date records or missing fields.
Anomalo says that its platform helps support analytics teams at multiple Fortune 500 companies as well as a range of other organizations. According to the software maker, its annual recurring revenue jumped 177% year-over-year in the third quarter thanks to strong demand. Anomalo claims that its recurring revenue has increased by a factor of 15 since its previous funding round in 2021. It hasn’t revealed more detailed numbers.
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