Matching data about people and organizations can be complicated. In this step-by-step video, Jeff Jonas reduces entity resolution down to its simplest form and highlights specific examples of what can happen when performing fuzzy matching. See if you can guess the correct outcome of each record before Jeff reveals them.

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Learn how records about people are matched, identified as related or determined to not match. Watch as corrections are made as new data reverses earlier assertions. You’ll come away with a much better understanding of the intricacies of entity resolution and the power of entity-centric learning.

After watching, if you want to learn more about how Senzing® entity resolution can help your organization, schedule a call with an entity resolution expert.

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Timestamps
0:00 Intro
0:46 Fuzzy Matching
... Look at these three records... it’s going to become one person... this is a form of fuzzy matching...

1:47 Derived Relationships
... Record four... [is] a derived relationship. If you want to do really good entity resolution, you’ve got to be able to see relationships as well, and this derived relationship is created and persisted...

2:25 Disclosed Relationships
... Record five... You're not guessing that those people are related. This is a disclosed relationship.

2:50 Mismatched Data
Take a look at record six... if you wanted to favor the false negative – meaning only putting things together when you're sure – you would really best off call that a possible match, and so record six becomes entity E4...

3:28 Ambiguous Matches
...This is tricky for you know, all the prior generations of entity resolution would have missed this –it's expensive to do in real time on big data. Record seven has the same email address as record one and four... it's a special form of a possible match, we call it ambiguous.

4:49 Discoverable, Self-Correcting Matches
... This is self-correcting. You see in real time, eight fixes the previous possible match [of record six].... we also would think of this as re-resolve. It re-resolves six upon the arrival of eight.

5:44 Real-Time, Self-Correcting Matches
... Record nine is going to become a new entity out to the right of entity E1 and... it’s going to pull record three out and this is another form of self-correction. It’s unresolving three out of entity E1 and slides it over into entity E6. Doing this in real time at scale is very expensive.

6:46 Entity-Centric Learning
... Most entity resolution engines can’t do this move. That’s because they do record matching... Record 10 becomes part of E1. We call this entity-centric learning.

7:48 Record Deletion for Privacy Compliance (GDPR, CCPA)
... When record eight is deleted, you better unlearn record ten, the entity-centric learning record, and you better unlearn that record six was a match for sure. It can only be a possible match.

8:15 Entity Resolution for Alphabets and Scripts
... To do entity resolution well, you want to be able to do [entity resolution] across script... know the Arabic spelling of Mohammed versus... English versus Mandarin...

8:35 Entity Resolution for Organizations
... [Matching] names, addresses, fax numbers, corporate URLs and so on.

8:44 Entity Resolution for Vessels
... [Matching] could be a vessel name and the I.D.s that come with vessels...

9:16 Bonus Section: Importance of Real-Time Entity Resolution
... Record nine caused record three to pull out... it’s a new entity.

9:49 Accuracy Drift vs. Self-Correcting
... Let’s just say for argument’s sake the database has a one percent error rate. If you can’t fix mistakes like that as new records are arriving, they’re kind of invalidating earlier decisions. Your database is slowly drifting from truth... a large data aggregator [drifts] at one percent a month... [In just four months, if] you just have a million records, you have 40,000 mistakes.

10:51 Periodic Batch Reloads vs. Real-Time Transactional Entity Resolution
... Systems like Senzing, and real-time transactional entity resolution, the new observations reverse earlier assertions. They fix the past in real time, they’re self-correcting. You never need to reload. It’s accurate in every second...

12:00 Channel Separation
12:48 Channel Consolidation
13:16 Entity Resolution with Entity-Centric Learning
13:24 Entity Resolution Explained

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