Data cleansing
In short
Data cleansing, also called data cleaning, is the process of detecting and correcting errors in a dataset. It covers fixing typos, removing duplicates, standardizing formats, filling or flagging gaps, and deleting records that are no longer usable.
In GTM it is the repair step. It cleans up what is already wrong, while related practices such as validation prevent new errors from entering.
What cleansing fixes
- Duplicates: The same person or company stored more than once.
- Formatting: Inconsistent phone formats, country names, and capitalization.
- Invalid values: Emails that bounce, impossible dates, and placeholder entries.
- Stale records: Contacts who changed jobs and companies that no longer exist.
- Missing fields: Blanks that block routing, scoring, or segmentation.
A step-by-step approach
1. Back up
Export or snapshot the data first so changes can be reversed.
2. Profile
Count blanks, duplicates, and format variations per field to see where the problems are.
3. Standardize
Agree on one format per field, such as country names or job-level values, and convert everything to it.
4. Deduplicate and merge
Match on email, domain, or other keys, and merge with clear rules for which value survives.
5. Validate and enrich
Verify emails and fill key gaps from trusted sources.
6. Prevent recurrence
Add required fields, picklists, and entry validation so the same errors do not return.
An example
Say a team finds the same company stored as three variants of its name across 40 contacts. They standardize company names against the domain, merge the accounts, and reassign the contacts. Reps now see one account with the full history.
Common mistakes
- Cleaning without a backup: A bad merge rule can destroy good data permanently.
- One-time projects: Data decays continuously, so schedule recurring cleanups.
- Deleting too eagerly: Archive suppressed and do-not-contact records so they are not re-imported.
- No ownership: Name who defines formats and who approves merges.
Cleansing versus hygiene
Cleansing is the corrective task. Data hygiene is the ongoing habit of rules and monitoring that keeps data clean afterwards.
Data cleansing FAQs
What is data cleansing?
Data cleansing is the process of finding and fixing errors in a dataset, including duplicates, typos, inconsistent formats, invalid values, and outdated records, so the data is accurate and usable for outreach, routing, and reporting.
What is the difference between data cleansing and data enrichment?
Data cleansing corrects or removes bad data that already exists. Data enrichment adds new information from outside sources. Teams usually cleanse and deduplicate first, then enrich, so they are not enriching bad records.
How often should you do data cleansing?
Run a deeper cleanup on a recurring schedule, such as quarterly, and use automated validation and duplicate rules continuously. Contact data decays steadily, so a single cleanup quickly becomes outdated.
What are the steps of a data cleansing process?
Back up the data, profile its problems, standardize formats, deduplicate and merge, validate and enrich key fields, then add entry rules and monitoring so the same errors do not return.
