Customer Success Analytics

Data Cleanliness

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What is Data Cleanliness?

Data cleanliness is the process of modifying or deleting data in a database that is incorrect, incomplete, improperly formatted, or duplicated. The process involves updating, standardizing, and de-duplicating records to create a single view of data, even if it is stored in multiple contrasting systems.

Why is Data Cleanliness Important?

Clean data has quite a few perks, most importantly, saving you a lot of time in executing daily operations. For example, if someone is just calling you from a different department for information, you will not need to go through disorganized papers or files to find it. If all your information is integrated into one system, you will always be able to find what you need.

Data cleanliness is necessary for the integration process. If you leave information out, or put a file in the wrong place, it could be possibly lost when you move it. And that is not even the worst part. You may not find out that this has happened until it is too late!

How Can You Clean Up Data?

Below are some quick tips and tricks on how to keep your data clutter free:

  1. Use a data management service to normalize information, such as matching a full name with its shortened version.
  2. Develop standards for saving and/or inputting information such as creating a method for filling out each field that everyone in your company can follow.
  3. Check over to see if new data inputs match previous records. If they don’t, your data update could be incomplete and will not smoothly work with the system.
  4. Make sure to design your workflow system and access levels to avoid human error. Others will depend on your system design to organize data for them. For example, you can use picklists to make sure field values are controlled by only you or create read-only fields so that certain areas are only meant for a certain group (decreasing the number of people accessing or messing with the data).
  5. Use automation to fix data values such as common misspellings or missing data. For example, a Country Corrector could fix incorrect spellings of countries.
  6. Prevent duplicates from the source using an external tool; stopping someone from even trying to enter a duplicate.
  7. Establish a regular cleaning process with a data review that counts duplicates, cleans up assets such as emails, reviews workflows, and counts whether or not records are missing key information.

Aside from these tips, there are structured processes to clean up data, such as the Layered Approach, which is presented below.

The Layered Approach

Standardization: Draw up rules and standards for data collection and make sure your whole team follows them. It is the only way to ensure expandability so you can have clean organization years down the line.

De-Duplication: Set rules to identify and merge duplicate records. It may take some hands-on work, but getting rid of duplicates from your system is worth the effort.

Data Capture: Make sure web cookies are sorting data from your site visitors into the correct records. Additionally, if your data is scattered, it makes it much more difficult for you to engage in targeted marketing.


Overall, data cleanliness is an essential factor in whether the workflow of a company will move smoothly or not. This guide is helpful in finding data organization issues that are prevalent in your enterprise and how you can fix them.

Equally as important as data cleanliness is data quality. Data quality not only works with operational and transactional processes within a company, but also for the reliability of Business Analytics or Business Intelligence reporting. Together, both data cleanliness and data quality bring data hygiene.





About Prerna Kandasamy