Anomalo, the leading data quality platform company, has announced a major expansion of its deep data quality monitoring platform. With this expansion, Anomalo can now monitor an entire data warehouse in just minutes, providing enterprises with the ability to trust the contents of their data and ensure it arrives on time and complete.
The new capability allows enterprises to perform basic monitoring of their entire data warehouse quickly and cost-effectively. This serves as a pathway into deep data quality monitoring, enabling businesses to identify any issues with the contents of their data. Anomalo will be showcasing this groundbreaking feature at the Data + AI Summit by Databricks and Snowflake Summit.
According to Elliot Shmukler, co-founder and CEO of Anomalo, customers have expressed the need to monitor every single table in their data warehouse or data lake for data issues. However, applying Anomalo’s full deep monitoring capabilities to every table is neither necessary nor cost-efficient. With the new table observability checks, enterprises can now apply basic monitoring to the entire data warehouse at a low cost. They can then use Anomalo’s unsupervised data monitoring, metric checks, and validation rules specifically on the most critical tables. This flexibility allows enterprises to cover all their data observability and data quality needs.
Anomalo, which launched in 2021, offers the industry’s most robust deep data quality monitoring platform. Its customer base includes renowned brands such as Block, BuzzFeed, Discover Financial Services, Notion, and Substack. The platform utilizes machine learning to automatically detect and root-cause data issues within enterprise data. This enables teams to resolve any data hiccups before making critical decisions, running operations, or powering models. Anomalo’s monitoring can be fine-tuned through low-code configuration of metrics and validation rules, providing further customization for enterprises.
The addition of metadata-based monitoring for the entire data warehouse brings peace of mind to customers, knowing that their data warehouse is broadly covered without incurring additional costs. This two-tiered approach to data observability allows enterprises to focus on critical data, such as financial or compliance-sensitive records, while avoiding unnecessary processing of lower-tier data. Kevin Petrie, the vice president of research at Eckerson Group, highlights the importance of reducing expensive compute workloads while ensuring accurate data.
Tim Ng, engineering lead of data products at Square, expressed his enthusiasm for the new functionality of table observability. He believes that data observability fills a need for the future of their data strategy and gives their data teams another tool to ensure data quality at Block, establishing trust in their data among users and customers.
Anomalo’s mission is to help enterprises build confidence in the data they use to make decisions and build products. By connecting Anomalo’s complete data quality platform to their data warehouse, enterprises can start monitoring their data in less than five minutes, with minimal configuration and no coding required. The platform’s advanced capabilities enable automatic detection and understanding of the root causes of data issues. Anomalo is backed by reputable venture capital firms, including Norwest Venture Partners, Foundation Capital, Two Sigma Ventures, Village Global, and First Round Capital.