*Regular hygiene practices are key*

When dealing with Big Data, bigger isn't always better. I have seen many orgs only take the time to clean up once the big mess has been done and it's starting to keep them from multiple activities.  You can save your time and efforts by applying data hygiene best practices early in the process. Let's take a look at what that means and what implementation looks like:

1. Develop a Data Quality Plan
2. Get Rid of Useless or Bad Data
3. Consolidate Duplicate Data
4. Standardize Lead Data at the Point of Entry
5. Create a process for enriching and maintaining clean data
6. Put in a regular email verification process and suppress any bounces from mailing.
7. Create a data catalog of fields with definitions of how they are used and updated
8. Remove any unwanted fields from your instance with proper steps taken
9. Validate Your Data Cleansing

Once that is all set up, you can then just review on a quarterly basis and clean as needed!

Have you implemented any processes that have made data hygiene an ongoing or regular practice? Would love to hear it!