CRM data deduplication is like an equation waiting to be solved. Just like a high school math, the more variables you know the values for, the easier the problem is to solve.

Data Markers

CRMs include is a vast number of data markers.  These markers are a literal road map to filling in missing data. For example, if 100% of the emails for a particular company have the email format of, then you can fill in missing emails for other contacts with confidence. If you have the email domains for contacts, but the account record is lacking a website, that can be filled in too.

Unique Identifiers

A company’s website address is a unique identifier. It is more important than the company name.  Look at Peoplesoft as an example. Long after the company was acquired by Oracle, you could navigate to  A few years later, it was absorbed, but the website did outlive the company.

Done properly, leveraging data markers within a CRM allows the you to pre-fill data for a more complete picture before deduping. In addition the data should be standardized (sometimes called normalized) before the dedupe process is done.

If you take the correct measures of: (1) data normalization and (2) data fill before deduping, your dedupe process will be greatly improved.

Preventing Duplicates

The One Thing You Must Do Before Data DeduplicationA Data Plan is the protection against duplicates, and the protection against bad data. Bad data comes in via many routes including technology, the users entering the data, lists, etc. No matter what avenue the data comes in, a lack of process and planning will result in bad data.

It’s important to note that the Data Plan goes beyond CRM. Marketing automation, CRM, SFA, ERP, accounting systems, etc. all benefit from a Data Plan. Have you ever not received payment because you sent a bill to the wrong division of a big company? That’s due to bad data.

The Data Plan encompasses a strategic, corrective, and preventative set of processes around what’s going to happen to data.

No matter what system or platform process, make data validation, data compilation and data standardization a priority.

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