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More data makes CRM data management harder, but still doable

According to Salesforce.com, about 70% of CRM data “goes bad,” or becomes obsolete, annually. Find out the truth behind this statistic, and strategies companies can use to keep data up to date.

We were reading some sales data from Salesforce.com, and the company says about 70% of CRM data “goes bad” annually. Do you agree with that? What are the newer CRM data management strategies you’ve been hearing about to keep CRM data up to date?

The problem with “bad” data has been magnified by the easy availability of unstructured customer data such as Twitter conversations and blog comments, and by the increasing velocity of data creation on scales that were unimaginable about five years ago. We now speak about data creation in zettabytes, which is 1021 bytes.  Terabytes of storage have become very inexpensive.

The other issue with these vast volumes of data is that the data changes constantly and these changes become difficult to keep up with. So the issues are with data quality and data changes, all on a scale we’ve ever seen before.

A number of data strategies are available to “keep CRM data up to date,” though of course, they vary depending on the companies and the amounts and types of data that are required.

The two strategies below have proven track records for handling CRM data effectively, even as it scales to the heights we now see:  

  1. Keep CRM data current. Integrate unstructured social data with traditional transactional data. In addition, use social media monitoring tools that can tie the data captured from the social Web to a customer record, such as with an email address or Twitter handle.
  2. Apply master data management (MDM) strategies to CRM data. MDM’s purpose is to handle the collection, aggregation, cleansing, organization and distribution of data gathered from multiple sources. Some features that are part of an MDM strategy include the identification of sources and the collection of the data.

There are other reasons, however, why MDM shines as a strategy. It can act on the rules that have been created for normalizing data, it can find errors in data and correct them, it maps out the kind of schemas you might want to use and apply, and it works with whatever data governance you’ve created for the system or are required to apply.

If applied, these two strategies should help to keep your organization’s data manageable and fresh.

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