Anomalo was founded in 2018 by Jeremy Stanley and Elliot Shmukler, who’d previously led data teams at Instacart and LinkedIn. They’d seen firsthand how bad data silently corrupts business decisions — dashboards showing wrong numbers, ML models making predictions on stale inputs, analysts wasting hours debugging data issues that should’ve been caught automatically.
The company built an automated data quality monitoring platform that uses machine learning to detect anomalies without requiring users to write rules for every table and column. Connect Anomalo to your data warehouse (Snowflake, BigQuery, Databricks, Redshift), and it learns the normal patterns in your data — volume, distributions, freshness, schema changes, and relationships between tables.
When something breaks — a pipeline delivers half the expected rows, a column’s distribution shifts dramatically, or values that should never be null suddenly are — Anomalo catches it and alerts the right people before bad data reaches dashboards or production models. The key insight is that machine learning can detect anomalies that rule-based systems miss, because it adapts to changing patterns without manual threshold updates.
The platform provides root cause analysis alongside alerts. When an anomaly fires, Anomalo traces the issue through table lineage to identify where the problem originated. This saves data engineers from manually investigating which upstream pipeline broke.
Anomalo targets data teams at mid-size and enterprise companies running modern data stacks. The platform serves customers in financial services, technology, and e-commerce who process enough data that manual quality checks simply don’t scale. By catching data issues hours or days before they’d otherwise be discovered, Anomalo prevents the costly downstream effects of decisions made on faulty information.
