A CRM data quality assessment tells you how accurate, complete, and consistent your records are before you invest in repairs fixing them. You can't fix CRM data until you know what's broken, and most teams have not looked closely.
The usual reason is mundane: nobody was ever made accountable for checking, so the data quietly drifted.
Go through each item, note your findings, and use the benchmarks to see where you stand. By the end you'll have a clear picture of your data health and a defensible score you can take to your team. If you'd rather have it done for you, skip to the free data assessment at the bottom.
The Six Dimensions of CRM Data Quality
Each question on this checklist maps to one of six things data teams measure. Knowing the categories helps you read your own results.
| Dimension | What it answers |
|---|---|
| correctness | Does the record match the real person or company? |
| Completeness | Are the fields you rely on filled in? |
| Consistency | Is formatting standardized across records? |
| Validity | Do the emails and phones still work? |
| Uniqueness | How many duplicate records exist? |
| Timeliness | How stale are the records? |
How to use this checklist: Run the queries or reports needed to answer each question. Record the percentage or count. Compare against the benchmark to see if you're in good shape, need improvement, or have a critical problem.
📧 Email Quality
Filter contacts where Email is blank. Calculate as percentage of total contacts.
Benchmark: 95%+ should have email for B2B databases (per DAMA data quality standards)
Check your marketing platform for the hard-bounce percentage during the campaign window you are reviewing.
Benchmark: Under 2% is good, 2-5% needs attention, over 5% is critical (according to Mailchimp's email benchmarks)
Count emails ending in gmail.com, yahoo.com, hotmail.com, etc. (for B2B databases)
Higher suggests low-quality lead sources.
Search for patterns like test@, fake@, asdf@, noemail@, or repeating characters.
Any significant number indicates form abuse or lazy data entry.
📱 Phone Data
Count contacts where Phone is populated. Include mobile if tracked separately.
Lower limits outbound calling effectiveness.
Check if numbers follow a single format or are mixed (with/without country codes, dashes vs. dots, etc.)
Benchmark: numbers should follow one standard format for click-to-dial and deduplication.
Look for numbers with wrong digit counts, fake patterns (555-555-5555), or non-numeric characters.
Higher suggests data entry problems or bad list imports.
👤 Contact Completeness
Count contacts where Title/Job Title field is populated.
Check for variations: VP Sales, VP of Sales, Vice President Sales, Sales VP. How many unique values exist?
Benchmark: Use a controlled vocabulary or mapping. Dozens of variations for the same role breaks segmentation.
Count contacts with no Account association (orphaned contacts).
Orphaned contacts break ABM, routing, and reporting.
Check City, State/Province, Country fields. What percentage are complete?
Benchmark: Depends on use case. Critical if you do territory-based routing or regional marketing.
🏢 Account/Company Data
Count accounts where Industry is blank or "Unknown".
Count accounts missing employee count or company size indicator.
Find accounts with zero contacts linked. These are often junk or incomplete records.
High numbers suggest account creation without follow-through.
Search for variations: Acme, Acme Inc, Acme Inc., Acme Corporation, ACME. How fragmented is your data?
Benchmark: Each company should appear once with a consistent name format.
👥 Duplicates
Run duplicate detection on Email (exact match) and Name + Company (fuzzy match).
Benchmark: Under 5% duplicate rate. Over 15% indicates serious problems (per Gartner data quality research).
Run duplicate detection on Company Name (with fuzzy matching for variations) and Website domain.
Duplicate accounts cause rep conflicts and broken reporting.
Compare Lead emails against Contact emails. How many leads exist as contacts?
Benchmark: Leads should convert to Contacts rather than coexist. Any matches are process failures.
📅 Data Freshness
Filter by Last Modified Date or Last Activity Date using the inactivity cutoff your team has adopted.
Higher suggests stale data that may be decayed.
Group records by last-activity date, then validate a representative sample in each cohort. Use the measured invalid share for that cohort rather than assigning a general decay rate.
These should be flagged for verification or archiving.
Check if you track enrichment dates. If not, assume firmographic data is decaying.
Benchmark: Company data should be refreshed at least annually (according to DAMA data freshness guidelines).
⚙️ Process Health
Check if your CRM enforces email format, needed fields, picklist values.
Benchmark: At minimum, validate email format and require key fields on record creation.
Check if your CRM warns or blocks when creating potential duplicates.
Benchmark: Duplicate rules should be active for Contacts, Leads, and Accounts.
Is there a person or team accountable for data quality metrics and maintenance?
Benchmark: Someone should own data quality as part of their defined responsibilities.
Count how many items fall outside the benchmarks. Focus on Critical items first, then Warning, then Info.
What To Do Next
Now that you know where your problems are, prioritize based on business impact:
Fix first: Issues affecting revenue operations, lead routing failures, duplicate accounts causing rep conflicts, bounce rates damaging sender reputation.
Fix second: Completeness issues, missing data that limits segmentation, scoring, or personalization.
Fix third: Standardization and formatting, important for long-term maintenance but less urgent than correctness issues.
For most companies with significant issues, a one-time cleanup project makes sense before establishing ongoing CRM hygiene. Trying to maintain a broken database is like mopping while the faucet's running.
Want a professional assessment of your data quality?
Get a Free Data AssessmentCRM Data Quality Assessment FAQ
What is a CRM data quality assessment?
It's a structured review of how accurate, complete, and consistent your CRM records are. You measure duplicate rate, email bounce rate, field completeness, and data freshness against benchmarks, then score the database overall. The point is to find where your data is failing, and how badly, before you invest fixing it. This 25-point checklist is the self-serve version you can run in an afternoon.
How do you measure CRM data quality?
Across six dimensions: correctness, completeness, consistency, validity, uniqueness, and timeliness. Run a report for each, record the percentage, and compare to the benchmark.
What is a good CRM data quality score?
Interpret the score against a dated baseline and the failures behind it. Weight Critical items more heavily than Warning or Info, and investigate when the trend declines or a workflow-specific threshold is missed.
How often should I run a CRM data quality assessment?
Quarterly for most teams, monthly if you import lists or run heavy outbound. Measure change against a dated baseline instead of applying a general decay benchmark, then set the review cadence from the changes observed in your own system. A fast monthly check on the Critical items (bounces, duplicates, missing emails) catches problems early.
Can I run a data quality assessment in Salesforce or HubSpot natively?
Native list views and reports can expose blanks, duplicate candidates, bounce outcomes, and inactive records. Use each platform's current field documentation to build those checks; mailbox validation remains a separate test from CRM status.
How long does a CRM data quality assessment take?
Audit time depends on record count, saved reports, object complexity, and the sample needed for fuzzy matches and title review. Time the first pass by stage and use that observed effort to plan repeat runs.
Related: How to Build a Data Hygiene Strategy | The Cost of Bad CRM Data | Data Validation Services | Data Cleaning Services
Need help with your data?
Run the checklist on your own data, then send us the sample and we'll grade it against what we usually see.
See What We'll FindAbout the Author
Rome Thorndike is the founder of Verum. He led sales at Datajoy (acquired by Databricks) and Snapdocs and built ML algorithms at Microsoft, so most of his career has been spent either relying on CRM data or cleaning up after it.