While this statement is a great starting point for many topics, if you define the alligator as Data and the wrestler as Data Management, you see where this post is going. You either have really good data management or you live with bad data.
We have found that customers that leverage data quality tools generate an average of 260% more leads per month.
Bad Data Hurts You
Like the wrestler who never really perfected his technique, you can get seriously hurt by bad data. And it’s not just the reporting that gets fouled up, it’s the loss of confidence in those reports, and ultimately the loss of confidence in you to provide accurate information that does the real damage. Once trust in data is lost it is extremely difficult to regain it. You typically have to prove yourself over and over again before any confidence returns.
It’s Not Bad Data, Just Bad Processes
Salespeople fill in fields in their CRM with any answer, just because they are required. Form fields are open text fields instead of pick lists, so anything goes.
6 Steps to “Really Good” Data Management
After surveying other colleagues both internally and externally, (thank you Astadia and Eloqua) the following Best Practices were identified:
Step 1: Establish a Standardized Data Set
Collectively establish and agree on a list of standard data fields. What fields do you consistently need to capture? Once you establish your master set of fields, make a Data Dictionary out of it. Convert to pick lists wherever possible – avoid open text fields, since there is no way to consistently score, report or segment on them. Spread the word that this is the Standard Data Set.
Step 2: Enforce the Standardized Data Set at every Acquisition Source
Review every data acquisition source and convert the process to the Standardized Data Set dictionary. Forms, list uploads, you name it.
Step 3: Append Incomplete Data fields
It’s hard to target a marketing campaign to specific Job Functions if the field is only 10% complete. Append those fields that are important for segmentation and/or scoring first. Then focus on filling in the remaining blanks.
Step 4: Build a “Data Washing Machine” to clean existing bad data
Normalize as many fields as possible. Start with all the open text fields you always wanted to leverage for scoring and/or segmentation but couldn’t.
Step 5: Merge and Purge
Clean up your duplicate records and build a process that requires a search for existing records prior to creating new ones.
Step 6: Keep the New Data, Discard the Old Data
Aging plays an important role in overall DB health. According to Eloqua, Lists older than 1 year have a decrease of 25% in open rates and 12% in CTR. Create active and inactive contact groups and treat appropriately.
Updated 5-8-12
Visit my brand new website and learn more about data standardization best practices
Steve Kellogg
-Demand Generation/Marketing Automation Consultant, Astadia
-Eloqua Certified Marketing Best Practices Consultant
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