Zaloni is introducing a machine learning data matching engine that leverages the company’s data lake solution, enabling enriched data views for multiple use cases across business sectors.
“A lot of our customers want to create a 360-degree view of x, their customer product or whatever it may be, so since we have all this great meta data about this information in the data lake we felt it would be really valuable to build out an engine that could leverage the Spark machine learning capabilities to identify similar data or duplicate data across multiple sources,” said Scott Gidley, vice president of product marketing.
Zaloni’s data matching engine provides a new approach for creating an integrated, consistent view of data that is updated, efficiently maintained, and can drive customer-facing applications.
With Zaloni’s Data Master extension, companies can leverage their data lake environment to achieve an enriched view of customer or product data for applications such as intelligent pricing, personalized marketing, smart alerts, customized recommendations, and more.
Because it works directly in the data lake, organizations can capture and combine any data type, including unstructured data, which allows the engine to create a more robust single version of truth.
Zaloni’s data matching engine is built on top of the Zaloni Data Lake Management Platform and uses Spark machine-learning libraries and analytic approaches to integrate data silos.
In addition, Zaloni’s data matching extension uses reinforced learning techniques that enable customers to train the matching models based on live sample data. This approach provides maximum accuracy that may be adjusted as the data changes.
Zaloni’s data matching engine also leverages the Zaloni Data Lake Management Platform for metadata, data quality, scalability, user interface, and operational data pipelines for creating master records.
For more information about this platform, visit www.zaloni.com.