So you came to work on Monday and realized you are now in charge of a big data cleaning project? Not sure where to begin or what it entails? Here is a simple checklist for you to start your data cleansing initiative:

  1. Check to see if you have duplicates: A fast way to do this is to download a list of emails for contact or lead data or website domains for account records and using highlight conditional formatting in excel determine what percentage of your data is duplicated. If the percentage is greater than 5% then this is the first step.
  2. Field Completeness: Download a sample of 1000 records to see what % of those records you have missing data. A good way to do that is to see use CountBlank formula in excel. If your data completeness for mandatory fields is below 90%, data appending would be required. What are mandatory fields? These are fields that your business needs in order to operate.
  3. Data Validation: Check your bounce rates, if they are higher than average industry. You would need to validate your data. For account data, check how many times did the sales reps change a certain field using history. If you have that data. Check sources that bad data is coming from to figure out if you need to make changes to either your contact forms or data providers.
  4. In Consistent Field Formats: There are fields that should be standardized, in order to be able to run segmentation, have better analytics and for territory planning. Look at the data that you have and see if there is a need to standardize the data. Fields that are typically normalized include: Country, State, Title, Industry, Revenue and Employee Ranges.
  5. Incomplete CRM: You may realize that you have accounts without contacts, contacts with missing company information and so fourth. If that is the case you may wish to acquire data to complete your CRM or remove records that are incomplete as to not create noise for your marketing and sales departments.

Once you are ready to start cleaning your data, you can contact StrategicDB for a free data audit.

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