Incremental Flow For Identity Resolution

Zingg maintains the identity graph with resolved entities as records are added, updated and deleted

Zingg Merges Previously Resolved Clusters With New Signals In The Data

Zingg Matches Records Based On Existing Signals

Poorly populated or third party systems generally have less information to identify entities

AI-powered golden records creation with Zingg ID for data accuracy and compliance.

New Infor-mation Gets Added To The Record

Over time, records get updated due to impro-vements in systems and pre-processing and trans-formation logic

Zingg updates the resolved identities

Updated records present new opportunities to match with existing entities

Zingg Breaks Previously Resolved Clusters When New Data Has Different Signals

Zingg Matches Records Based On Existing Information

Poorly or erroneously populated records and third party get resolved based on existing attributes

Records Get Updated And Corrected

New feeds, improved transformation and data processing logic cleans up errors

Open source entity matching pipeline built natively on Databricks

Zingg Breaks Such Clusters And Assigns New Zingg IDs

The identity graph is updated and maintained with every available bit of information