Anomalo Raises $33M Series A to Revolutionize Data Quality

Data Quality Assurance with Machine Learning: Anomalo Secures $33 Million

With the continuous expansion of data volumes within organizations, identifying data quality issues that could compromise the performance of machine learning models is becoming critically important. Anomalo is leveraging machine learning to provide an automated solution to this data viability challenge.

Series A Funding

The company recently announced a $33 million Series A investment. This funding round was spearheaded by Norwest Venture Partners, with additional participation from Two Sigma Ventures, Foundation Capital, First Round Capital, and Village Global.

Origins and Core Problem

Anomalo was established by two former Instacart employees who encountered similar data challenges in their previous roles. According to Elliot Shmukler, co-founder and CEO of Anomalo, data integrity is paramount for businesses reliant on data-driven operations.

“Anomalo’s function is to connect to enterprise data warehouses, such as Snowflake, where companies accumulate vast amounts of collected data. It then continuously monitors these datasets for anomalies and undesirable alterations that could disrupt business processes dependent on that data,” Shmukler clarified.

How Anomalo Works

The system operates by establishing a connection to data warehouses and training a machine learning model to recognize normal data patterns. It subsequently flags any deviations from this established baseline. This approach differs from conventional methods.

“Alternative solutions typically require data teams to manually define what constitutes ‘good’ data, which becomes increasingly complex and time-consuming as data volumes and diversity increase,” Shmukler explained.

Automating Data Expectations

The founders experienced the burden of constantly updating data definitions during their time at Instacart. A key objective in launching Anomalo was to automate this process, relieving data teams from this repetitive manual effort.

Developing this automated system presented significant hurdles. Shmukler and CTO Jeremy Stanley founded the company in 2018, and it took several years to refine the machine learning model to achieve the desired accuracy, minimizing false positives and reducing the need for extensive historical data.

Growth and Diversity

While the precise number of employees remains undisclosed, Anomalo intends to expand its team by 40 to 50 individuals in the coming year. Shmukler emphasized the company’s commitment to diversity as a core value.

“We established a set of organizational values, with diversity being a central tenet. This was a priority at Instacart, and we are dedicated to continuing this focus. We actively strive to ensure a diverse pool of candidates for each position… and we are seeing positive results, with women currently comprising 25% of our engineering team – a relatively high proportion for a company at this stage. We aim to maintain and improve upon this,” he stated.

Current Traction and Customers

Despite its formal launch occurring today, Anomalo already has paying customers and has generated at least $1 million in revenue. The company’s pricing model is based on the number of datasets monitored, rather than user count or data volume.

Initial customers include prominent organizations such as BuzzFeed, Discover Financial Services, and Substack.

• Key Technology: Machine Learning
• Target Platforms: Snowflake and other enterprise data warehouses
• Pricing Model: Per-dataset monitoring