On the 10th day of CRM Chris Kringle Fritsch gave to me, different ways we can assess our bad data!

Assessing your data is always the first step in any data quality or CRM project. You should sit down and actually look at how much data needs to be cleaned so you can accurately predict the budget and scope of the project.

These assessments can be automated or manual, depending on what you are trying to accomplish. But they will be looking for the same thing, how many duplicate records there are, where all the data lives, and where information is missing. This missing information may be first and last names or GDPR country information.

Other important things that are analyzed during the assessment are invalid emails or bounces. Some of these contacts that bounced may have switched companies and were once a huge referral source. What do you do with these contacts, pull them out, research, and update them?

These are all low-hanging fruit that can easily be updated and provide you with more immediate results and returns.

Other aspects that are looked at maybe contact information in the wrong field, or contacts not associated with a company. Again, these are low-hanging fruit that will allow you to assess the overall scope of your data quality project.

With new developments of GDPR, some assessments may go more in-depth and get country codes, phone numbers, or if they consented to be on the mailing list or not. If a contact asked to be removed from these malign lists, what processes are in place to ensure they get removed from the list and stay off it?

Finally, the assessment may look at orphaned contacts. Contacts that belonged to someone at the firm who was fired, moved jobs, or locations. For orphaned contacts, a process should be put in place to sit down with another professional at the firm and see which contacts should be kept on and switched to another owner, and which ones should be removed.

Watch as Chris Fritsch explains the process of assessing data quality and best practices to handle various types of incorrect data.