CRM Data Quality Checklist: Fields, Tags & Ownership

How do you improve CRM data quality?

Define the fields your workflows rely on, record who maintains them and check a representative sample against the source. Standardize tags and field meaning, preserve source and communication-permission records, and test imports before expanding them. In Chirply, custom fields store structured facts, tags support grouping and triggers, and lists hold explicit contact groups.

Start with the fields used in real decisions

Create a dictionary for fields that affect assignment, messages, reporting or service delivery. Include a plain-language definition, allowed values, source, owner and how a missing value should be handled. A field used only occasionally can still be important if an automation relies on it.

For an illustrative service business, Service requested describes the inquiry, Assigned coordinator names responsibility, and Preferred contact channel records the person's stated preference. Keep these meanings distinct so a reporting filter does not quietly become a messaging-permission rule.

Check a small sample from different sources: manual entries, forms and prior imports. Compare the values with the source evidence. Record findings before making bulk changes so you can understand what was corrected and why.

Choose fields, tags and lists deliberately

Each structure has a different job. Review automations before changing a tag used as an event.

1. Open Contacts and inspect representative records, including their source details and the fields used by your workflow.
2. Use the custom field manager for facts the built-in record does not hold. Document the meaning and expected values before creating similar fields with different names.
3. Review tag names and their purpose. Tags can filter contacts, target audiences and fire Tag added workflows, so inspect those dependencies before applying changes.
4. Use Lists for an explicit fixed group, such as a pilot cohort. Document who belongs and why rather than assuming a saved group changes itself.
5. For an import, prepare a small CSV sample and inspect the mapping, matching and historical-date options. Check the resulting background job and records before the full file.
6. Record the correction, source evidence and responsible owner in the worksheet. Verify any affected messages, filters or workflow cases with controlled records.

Treat matching and correction as separate operations

An import can match an existing contact by email or phone rather than create a new record. That does not make it a general-purpose overwrite operation. Review the importer's explicit date behavior separately and use the supported editing path for other corrections.

A shared phone or email can create ambiguity in the real business process. Review the specific people and source evidence before deciding how to represent them. This guide does not prescribe bulk merging or deleting records, and no cleanup is performed by downloading the worksheet.

Keep an original export and a dated mapping record for migrations. Use made-up or controlled examples when testing message fields. A data-quality review should improve the records while preserving the ability to explain how they were obtained.

Build a small recurring review

Choose a manageable sample and inspect required workflow fields, source accuracy and owner assignment. Record the number reviewed and the number with a defined issue. Use the same definition next time so the result is comparable.

When a pattern appears, fix its source: a confusing form question, inconsistent import mapping or an unclear ownership rule. Then check new records from that source. Improving the entry process usually gives the team a more maintainable result than repeatedly correcting the same field by hand.

Common questions