In August of 2016, my team and I spun the G2 technology out of IBM. Into stealth mode we went, again. We are now back out of stealth mode and set to democratize Entity Resolution (yes, I am starting to blog mainly on Medium, not here).

Are you ready for a free taste of our G2v2 technology via our Senzing ER Workbench? For a limited time, I’d like to give you a FREE license for one month which supports up to 1M records. We send you . No private flows to us. Registration details are below. It’s so easy to use, you should experience value in under 10 minutes.

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Here are some examples of how you can use our Entity :

Personal Examples

  • Find all the duplicates in your address book (e.g., Gmail, MS Outlook, IBM Notes)
  • Compare your address book with your family and friend’s address books to find surprising intersections (“Wow, we both know Mr. Bigglesworth!”)
  • Deduplicate your holiday mailing list
  • Help your favorite local nonprofits find duplicates in their donor lists

Customer-Centric Examples

  • Find duplicates in your customer, loyalty club or CRM databases. (e.g., SalesForce, SugarCRM, Zoho)
  • Discover customer relationships beyond householding (e.g., those sharing phones and emails)
  • Compare your customer lists against your prospect lists to make sure you are not to people who are already customers
  • Compare your current customers with previously terminated customers (e.g., for money laundering) to see if they are sneaking back in
  • Improve your ability to comply with data requests. (e.g., GDPR, subpoenas, internal investigations)

Vendor-Centric Examples

  • Find all the duplicates in your vendor files (e.g., Quickbooks, Zoho)
  • Compare current vendors to previously terminated vendors (e.g., for counterfeits) to see if they are sneaking back in
  • Compare your vendor to the U.S. Department of Treasury’s OFAC SDN list to get instant peace of mind that you are probably not transacting with anyone on OFAC

Employee-Centric Examples

  • Compare your employees to vendors to see if there is any potential for collusion (e.g., your accounts payable manager is roommates with a well-paid vendor)
  • Improve your ability to comply with data access requests (e.g., GDPR, internal investigations)
  • Compare your employees to internal watch lists or investigation data to flush out potential insider threats
  • Compare your authorized system access lists against terminated employees to ensure they no longer have system access

Data Scientist-Centric Examples

  • Clean up the duplicates in your identity-rich data sources before using machine learning and predictive analytics
  • Auto-label your data by entity resolving data sources (e.g., combining your drug prescription data with deceased persons data) to allow machine learning to discover such things as Vioxx, the drug that killed thousands before being noticed
  • Fill in missing fields by combining data sets (e.g., improve the coverage of city and state indicators in a data set)



Watch my IBM Think 2018 presentation – it explains everything and it contains the super-secret password. (haha)

Go to and click on the red Workbench Challenge banner button. Read this Getting Started post.

And please do tell us what you think.


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