How CRM data updates lead to data corruption

By David Taber, CIO |  Enterprise Software, CRM

So our recommendation is that you purposefully create a duplicate record, with pointers to the record you believe is already there. When the record is created, an alert should be sent to your sales operations or accounting department for subsequent evaluation and data reconciliation. Typically, the new record will be made a child of your existing record in a master-detail relationship, so that the outside system can continue to update the "copy" of the data that it likes&and your accounting system can get the roll-up data it needs to balance the books.

This is a fairly straightforward decision when the Account name is nearly identical. It's a bit more of a judgment call when the new Account is ESPN and the existing Account is ABC Networks or even Disney. They're all part of the same corporation, but do they need to be a single account, or an account hierarchy? It all depends on your business rules.

OK, but what about other CRM tables? It's not at all unusual to have noisy data updates coming in for both Leads and Contacts. The data are noisy in three key ways:

  • You're not sure whether Joe Bigshot is the same as Joe Biggs-Haute
  • You're not sure whether the data quality of the new record is better or worse than the old one (particularly when you are merging data from an external "industry standard" database).

Given these issues, simply updating your existing records can be an act of data corruption. The faulty update can case receipts and other official communication goes to the wrong place, or can interrupt the sales cycle. Neither of these symptoms will be particularly endearing to the CRM user community.

So the intentional creation of duplicate records is again not a bad strategy - as long as you have (and live up to) an SLA around the reconciliation of data updates.

The Urge to Merge

When merging records that have been intentionally created as dupes, you'll need to use some subtlety and guile. Conventional merges collapse records together using static rules (such as "most recently updated" or "best data quality"). Since the merges are typically done at the record level - rather than cell-by-cell - the losing record might have some values in it that get wiped out by the winning record.


Originally published on CIO |  Click here to read the original story.
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