Why Explainability is Critical for Entity Resolution

Why is explainability critical for entity resolution? It’s important that the results entity resolution systems provide are easily explainable. Business users, big data scientists and auditors all want to know why records matched or didn’t, as well as the details of how decisions were made. Explainability helps you and others understand and trust how your system works when it comes to big data.

If you don’t have full confidence in the results your entity resolution system delivers, it’s hard to feel comfortable making important decisions based on those results. Plus, there are times when you will need to explain why and how you made a business decision.

Explainability is also extremely important for meeting compliance and audit requirements. It enables you to show regulators or other compliance auditors why specific matches were made, or not made, and exactly how the records involved came together.

Significant problems can result when entity resolution systems can’t provide the details that clearly explain and demonstrate why and how decisions were made. However, for many of today’s entity resolution systems based on artificial intelligence (AI) and machine learning (ML), explaining why and how decisions were made can be incredibly difficult or impossible.

With the explainability tools Senzing^® entity resolution provides, in just a few clicks or keystrokes you can clearly see why a match was made or not, as well as the details of how an entity evolved.