On the fifth day of CRM, Chris Kringle Fritsch gave to me, the five golden A’s of CRM!

Sitting down and cleaning your data can be a very daunting task. So where do you start? Start with the five A’s of data quality – Assess, Analyze, Append, Automate and Allocate.

When starting a data cleaning project, remember that  you won’t get 100% clean data because if you have zero new contact records and clean everything else, in five minutes you’ll have a new record that needs cleaning. That is how quickly data can change.

Don’t let the perfect be the enemy of the good. Just because you can’t get every record perfect does not mean that you shouldn’t do it. To make cleaning your data more manageable, break it down into smaller, easier-to-handle pieces and assess each of them. You don’t need to clean all of your data. Think of which lists you send to the most, where the high-priority clients or contacts live, and start there.

You can then append missing data through a means of outsourcing the work to data quality professionals or hiring in-house data stewards to manually comb through the lists.

Be respectful of your budget and prioritize which data sets need to be cleaned first so you can get the most bang for your ‘data quality buck.’

Next, think of different ways to automate the data clean-up project. There are many pieces of software that can automatically go through data sets and achieve data quality of up to 70%. While it is not 100% clean, you can allocate resources to ensure that the data cleaning process is ongoing so that when new records come in, they are entered correctly and according to your firm’s data standards guide.

Watch as Chris Fritsch walks us through how to start a data cleaning project by following the five A’s of data quality; Assess, Analyze, Append, Automate and Allocate:

1. Assess: Evaluate the current state of your data, identifying areas of inaccuracy or incompleteness.
2. Analyze: Dive deeper into the data to understand the root causes of any issues.
3. Append: Enhance your data by filling in gaps and correcting errors.
4. Automate: Implement automated processes for ongoing data maintenance and quality checks.
5. Allocate: Assign resources and responsibilities for data management to ensure sustained data quality.

Watch Day 1 here.

Watch Day 2 here.

Watch Day 3 here.

Watch Day 4 here.