Resist the Urge to Merge Purge Data
Surely you’re aware of Jeff Jonas, identity resolution’s poster boy and first real celebrity. Mr. Jonas is the chief scientist behind IBM’s Entity Analytic Solutions (EAS) and the founder of Systems Research & Development (SRD). He’s a media magnet who’s been featured in CNN, Forbes, Newsweek, NPR, Time and Wired. While he hasn’t quite made it to the cover of People Magazine yet, he was recently sought out by an NBC affiliate in Philadelphia to comment on the plot of the new NBC show Chuck.
Mr. Jonas published a great post yesterday comparing identity resolution against match merge, merge purge and list de-duplication systems that is a must-read for the CIOs doing all the hefty lifting in financial services, government and insurance industries. (Note: identity resolution is called “entity resolution” in IBM parlance.)
For example, when two insurance companies get the urge to merge, they always run into data compatibility issues when they begin to look at the databases they have currently have in place. For claims and underwriting purposes, data synchronization is not a luxury — it’s essential to the daily flow of business.
Back in August when Dutch AEGON acquired the life insurance operations of U.S. investment bank Merrill Lynch in a $1.3 billion in cash deal, our own Glenn Hopkins commented
“Instead of implementing a master data management program, I know that AEGON would be better served — and save lots of capital — if they bolted an identity resolution solution onto their existing architecture. And instead of merging or purging all the identities both companies possess, an identity resolution solution can sift through all the information and keep it all in its native formats for future use.”
The always eloquent Mr. Jonas extends this argument further in his post, Entity Resolution Systems vs. Match Merge/Merge Purge/List De-duplication Systems.
If it’s not too late for AEGON, they should consider Mr. Jonas’ reasons below to avoid a “ground up reload”:
- Batch versus real-time
- Snapshot in time versus perpetually current
- Data survivorship versus full attribution
- Data drifting versus self-correcting
- Single version of truth versus every version of truth
- Outlier attribute suppression versus context accumulating
- Binary list processing versus “n” data source ingestion
- Limited scalability versus massive scalability
Please read the post in its entirety for a full explanation of these points and you’ll see why we agree with Mr. Jonas’ conclusion below.
“Entity resolution systems are best suited for real-time missions where processes require access to the most accurate and most current view of that which is knowable [to the enterprise].”
To conclude this post, we’d like to point out to insurance execs that there are other business applications to consider, as mentioned by Glenn in his post on the AEGON merger last month:
“With mergers and acquisitions, there’s always customer overlap issues in any industry. In the insurance industry, with some customers attempting to defraud insurers by using multiple identities, it’s critical to the bottom-line to cross-reference multiple identity records against not just watch lists but also the following business applications:
- Automated fraud detection
- Underwriting risk management
- Enterprise identity management
- Transactional CRM
- Compliance
- Background checks
- Producer risk assessment”
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