DQLabs today announced at Snowflake's annual user conference, Snowflake Summit 2023, the launch of DQLabs' Modern Data Quality Platform, Powered by Snowflake. The new solution will enable customers to deliver reliable and accurate data to customers for better business outcomes by automating business quality checks and resolution using an AI/ML-powered semantic layer to deliver "fit-for-purpose" data for consumption across reporting and analytics.

By building DQLabs' Modern Data Quality Platform on Snowflake, DQLABS can provide visibility into data reliability and "fit-for-purpose" accuracy issues, scalability of business quality checks across data, automated deep profiling for analysis leveraging decades of best practices, and quality stewardship. DQLABS is uniquely positioned with the convergence of three capabilities and the combined power of Data Observability, Data Quality, and Data Discovery, to automate business quality checks and resolution using an AI/ML-powered semantic layer to deliver reliable and business-fit data. Additionally, DQLabs is able to leverage Snowflake Metadata, User Defined Functions for deep profiling, Semantic categories and Object Tagging for Governance, SnowPark for ML-powered semantic classification to deliver out-of-the-box ability to automate business quality checks and resolution and deliver reliable and "fit-for-purpose" data to customers.

"We are delighted to partner with DQLabs for their modern data quality and observability capabilities," said Scott Schilling, Senior Director of Global Partner Development at Snowflake. "With DQLabs, Snowflake customers can automate business quality checks and resolution using an AI/ML-powered semantic layer to deliver "fit-for-purpose" data for consumption across reporting and analytics"

Joint customers will be able to leverage out-of-the-box automation of business quality checks using DQLabs' Modern Data Quality Platform, Powered by Snowflake. This automation has helped customers drastically reduce inefficiencies across business processes, decreased 85% of the hours spent on manual upkeep of their data quality checks and gain 10x improvement in operational efficiency.