What is Data Hygiene?

When you think of the term “data hygiene,” what comes to mind? If it’s ‘data scrubbing’ or ‘data cleansing,’ the definition has evolved right in front of you! Today’s definition of data hygiene means more.

Data hygiene is defined by the International Data Sanitization Consortium as the process of ensuring all incorrect, duplicate or unused data is properly classified and migrated into the appropriate lifecycle stage for storage, archival or destruction on an ongoing basis through automated policy enforcement. By following data hygiene best practices, organizations can effectively manage ‘where’ their data is throughout the lifecycle and reduce the amount of data they store by successfully destroying it (when applicable) to reduce risk.

TechTarget, for its part, defines data hygiene as “[t]he collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free. Dirty data can be caused by several factors, including duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems. Errors can be introduced at any stage as data is entered, stored and managed.”

This definition, and many like it across the web, stem from the original meaning of “data hygiene,” which evolved as computers began to store database information. To ensure that sales, marketing and customer materials were going to the right prospects and clients, organizations needed to ensure that their databases were “clean” and free of errors.

But data hygiene is more than just ensuring you have the right information when you need it—and that you aren’t keeping the old data you no longer need. It’s about creating a bigger process around how you manage that data.

Data Hygiene Best Practices Action Plan

1. Locate all the data you have—from old hard disks to current laptops, servers, cloud storage, removable media, mobile devices and beyond.
2. Classify your current data into business-critical (need it now), necessary for compliance (need it later) or unnecessary (redundant, trivial or obsolete).
3. Build a program to encourage ongoing data classification across the organization. Track and classify data throughout its lifecycle (from creation, storage and use to sharing, archiving and destruction).
4. Securely erase the data you don’t need throughout its lifecycle—not only when assets are being retired.

By following data hygiene best practices, you can ensure your organization has taken the first step to holistic data management—meaning you’ll know what you have, where you have it and when it should be properly disposed of. Such an approach will save you time, money and hassle in the long run.