Purisma Data Hub

Self Learning Matching System

Can your data hub solution continuously improve match quality based on the knowledge learned from each data source, in order to maximize immediate and long term accuracy? 

Purisma’s unique self-learning matching system progressively learns from contributing data sources, external reference data and data steward actions to ensure continuous improvement in match quality.  While some MDM solutions throw away key knowledge about data correlations and hierarchical relationships, Purisma maintains complex relationships between elements by retaining associations with data entries such as alternative spellings, abbreviations, misspellings or data entry mistakes. As a result, the Purisma Data Hub™ allows you to:

• Gain the highest match accuracy
• Continuously improve accuracy over time
• Improve match quality by learning from data stewards
• Improve match rates based on both internal and external reference data

This key capability is ideal for organizations where high accuracy of data matching must be achieved from many different data sources, and where new data sources are often incrementally added for matching and association with exiting master records