In this episode of the Data Show, I spoke with Jeff Jonas, CEO, founder and chief scientist of Senzing, a startup focused on making real-time entity resolution technologies broadly accessible. He was previously a fellow and chief scientist of context computing at IBM. Entity resolution (ER) refers to techniques and tools for identifying and linking manifestations of the same entity/object/individual. Ironically, ER itself has many different names (e.g., record linkage, duplicate detection, object consolidation/reconciliation, etc.).

ER is an essential first step in many domains, including marketing (cleaning up databases), law enforcement (background checks and counterterrorism), and financial services and investing. Knowing exactly who your customers are is an important task for security, fraud detection, marketing, and personalization. The proliferation of data sources and services has made ER very challenging in the internet age. In addition, many applications now increasingly require near real-time entity resolution.

We had a great conversation spanning many topics including:

Why ER is interesting and challenging

How ER technologies have evolved over the years

How Senzing is working to democratize ER by making real-time AI technologies accessible to developers

Some early use cases for Senzing’s technologies

Some items on their research agenda

Real-Time Entity Resolution Made Accessible

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Real-Time Entity Resolution Made Accessible

O'Reilly Radar