Illustrative process diagram. It contains no live customer data or automatically executed action.
01
Define quality through the work it supports
A missing phone number can matter in one process while a clear owner or company relationship is more important in another. Identify information that affects daily decisions. Requiring every field does not automatically improve quality; users may fill unclear fields with guesses. Explain why information is collected and how it will be used. Quality checks should build dependable context rather than reward form completion. Review an actual working question and ask whether the record helps answer it. A complete-looking form can still fail that practical test.
02
Review possible duplicates carefully
Two people with the same name can be different individuals. Two records sharing an email address may still belong to different people or business relationships. Similarity is a reason to inspect, not conclusive evidence for merging. Examine source information and linked opportunities or tasks. Combining or removing a record can affect historic context. Confirm the actual tool behavior and recovery approach before making a change. The person reviewing data should see relationships as well as individual fields. Make the reason for a correction understandable to another team member.
03
Check relationships as well as field accuracy
A contact’s details can be correct while the person is linked to the wrong company. Field validation alone will not reveal every relationship problem. Similar company names, changed responsibilities or old habits can create misleading context. Open an illustrative record and review the company, opportunity and follow-up together. Do not draw conclusions about a customer before verifying the relationship. When CRM information supports daily decisions, a correct value in the wrong context can still mislead. Review the connection rather than treating each field as an isolated answer.
04
Make recurring reviews small and owned
Instead of indefinitely postponing a large cleanup, select a manageable group of records to review regularly. State who checks them and how findings are recorded. New records, imported information and reassigned work may need different checks. If the same error keeps appearing, investigate how the record was created. A mapping rule or daily convention may need correction too. Quality tools do not replace ownership; they can help make issues visible. Keep the process understandable so a colleague can continue it when responsibilities change.
05
Check access before importing or changing records
Hubz presents data quality and import/export tools alongside the relevant available modules and team roles. Do not assume every user can perform identical operations on all records. Preserve source information and test the mapping with a small sample before a broader transfer. After importing, review relationships and useful fields as well as counts. Check the current package, module and permission scope in the detailed guides. Lasting quality depends on a maintained record standard, not only a technically successful first upload. Give that ongoing work an identifiable owner.
06
Try it with your team
Review a small group of records from the same source. Inspect the represented person and company relationship before counting filled fields. Where entries appear similar, compare the source and linked work before deciding they are duplicates. If one error recurs, inspect the import mapping or daily record habit that created it. Share the reason for the correction with the responsible owner. Leave a clear note that makes the next review comparable. The exercise should help prevent repeated errors, rather than reward a one-time cleanup that nobody can maintain.
PUT IT INTO PRACTICE
Check before you begin.
- Identify fields that matter to the work.
- Distinguish similarity from confirmed duplication.
- Check company and opportunity relationships.
- Record the reason for a correction.
- Address the source of repeated errors.
Use one of your team’s current customer records to work through this checklist. Assign a person to each step and agree what a completed step looks like. For example, a follow-up task needs an owner and a date; a quote line needs the right item, quantity and currency. If information is missing, check the original record or ask the colleague responsible. Try the process with a small group, then adjust it based on the questions they raise.
Frequently asked questions
Is data high quality when every field is filled? +
Not necessarily. Accuracy, freshness, relationship context and usefulness for actual work also matter. A complete-looking record can still contain misleading information.
Should records with similar names be merged automatically? +
Similarity alone is not sufficient evidence. Review the source and related work before making a change that may be difficult to reverse.
By Hubz Editorial Team. Prepared with AI assistance and aligned with source-backed Hubz product descriptions. Examples are illustrative, not customer outcomes, independent research or personalized advice. Read our editorial policy to understand how product scope and explanatory examples are distinguished.
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