An organization’s database is a critical resource. It’s used for outbound marketing, identifying types of customers, and pinpointing titles. But many of these databases are full of duplicate data fields that don’t accurately reflect the customer base. For example, if a business has a list of 100,000 customers, it’s highly likely that number is inflated because during the intake process customers often get entered multiple times, in slightly different ways. For example:

  • 21st Century Insurance Company of the Southwest
  • 21st Century Software
  • 21st Century North America Insurance Company
  • 21st Century Cooperative
  • 21st Century Fox America, Inc.
  • 21st Century Oncology, Inc.
  • 21st Century Equipment
  • 21st Century Life and Health Company, Inc.
  • 21st Mortgage Corporation

For both humans and your database, it’s easy to see that these are all different customers. However, when leads are entered into Salesforce, the results often look like this:


Salesforce counts this as 5 records, when in reality it could be only two. Or it could include any of the eight “21st Century” accounts listed above.

To accurately determine how many unique customers your company actually has, you’ll need to de-dupe, or identify and eliminate the duplicate entries, to create a new master list. Using Monarch Complete, 20 rows of data representing the same customer, for example, can quickly and easily be turned into one unique customer.

The alternative is to do all of this work manually, however not only will the accuracy decrease because of human error, but the hours of labor required could be staggering. For instance, if it takes three seconds for a human to de-dupe one row, that equates to 80 work hours, or 10 work days to de-dupe 100,000 rows. Instead, Datawatch lets you cut this time by 99% and save significant resources, since you’re no longer sending out multiple pieces to the same audience.

Seeing is believing- try it yourself with a FREE TRIAL of Datawatch Monarch.


Already a Monarch user? Check out this step-by-step guide for how to de-dupe in

  1. Original customer name list with repeated values
  2. Determine unique customer names (e.g. 75% fewer extra rows)
  3. Cases and spaces fixing
  4. Unique customer names post cases and space (77% fewer extra rows)
  5. Fuzzy match, and pick a winner (83% fewer extra rows)
  6. Second fuzzy match and pick a winner (85% fewer extra rows)
  7. Repeat if necessary removing up to 90 to 95% reduction in extra rows in practice
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