CRM Data Quality Guide
How to Clean Your CRM Data (Without Spending a Week on It)
Your CRM has thousands of contacts. Some have moved. Some are duplicated. Some are missing critical fields. Here's how to find and fix the problems systematically, not one record at a time.
The four CRM data problems nobody talks about
1. Duplicates: the silent pipeline inflator
The same person, uploaded by three different reps, in three different batches. Your pipeline looks 20% bigger than it actually is. Territory conflicts are inevitable. And when it hits the forecast, your board sees a number that doesn't exist.
Harvard Business Review found that 47% of newly created records contain at least one critical error (Nagle, Redman and Sammon, 2017). Duplicates are among the most common.
2. Stale contacts: the credibility killer
Roughly a quarter of the workforce changes employer every year, and median job tenure is under four years (US Bureau of Labor Statistics). A large share of your CRM contacts have outdated information right now: wrong titles, wrong organisations, wrong phone numbers. Your team discovers this when they call.
3. Role and organisation changes: the missed relationship
Median job tenure is 3.9 years (Bureau of Labor Statistics) and falling. Your past customer who championed your product just started at a new organisation. That's a warm introduction waiting to happen, if you know about it.
4. Missing fields: the productivity blocker
Your team is working from incomplete records. Half your contacts are missing LinkedIn URLs, a third have no phone number, and some don't even have a verified email. Your sales team can't call a number that isn't there, and AI tools can't automate what they can't verify. Gaps in the data slow everyone down.
Why manual cleanup doesn't scale
Manually researching and verifying a single contact takes roughly 12 minutes on a conservative working assumption. At the UK National Living Wage (£12.21/hour, April 2025), that's about £2.44 per contact.
A CRM with 5,000 contacts would take one person over 1,000 hours of manual research. That's 6 months of full-time work, by which time the first contacts you cleaned are already going stale again.
Manual cleanup is a treadmill. You need a systematic approach that processes contacts in bulk and keeps them current over time.
The systematic approach: Score, Identify, Fix, Verify
Step 1: Score every contact
Before you can fix anything, you need to know where the problems are. A Trust Score (0 to 100) for every contact gives you a data quality baseline. You can see immediately which contacts have complete, reliable data and which don't.
Step 2: Identify the gaps
Scoring reveals patterns. Maybe 40% of contacts are missing LinkedIn URLs. Maybe 15% have no phone number. An AI Blockers field per contact tells you exactly what's missing, whether that's blocking your team from picking up the phone or preventing AI tools from running sequences.
Step 3: Fix in bulk
Instead of researching contacts one at a time, process them in batches. Upload a spreadsheet to the portal. Enrich the ones that need it. Merge the duplicates. Flag the role and organisation changes. Separate the protected accounts. All in one pass.
Step 4: Verify and maintain
Data cleanup is not a one-time project. Contact data decays continuously. Set a re-enrichment cadence (every 60 to 90 days for active contacts) and score each new batch as it enters the CRM. Prevention costs less than correction.
How Datuma automates CRM data cleanup
Datuma runs all four steps automatically. Upload a spreadsheet to the portal. In minutes, every contact has a Trust Score (0 to 100) showing whether your team can act on it, plus Verified Readiness and AI Blockers listing exactly what's missing for both human outreach and automated sequences.
Duplicates are caught across your current batch and every previous batch, at £0.00. Protected accounts (existing customers, partners, competitors) are enriched like everyone else but separated into a dedicated file, so nobody contacts them by accident. Contacts who've changed role or organisation are flagged with their previous organisation and title.
The output is a spreadsheet with 50+ verified fields per contact, ready to import into your CRM. At about £0.10 per enriched contact, a 500-contact cleanup costs around £50.
Compare that to the £2.44 per contact for manual research. The maths speaks for itself.
How fast CRM data decays
Contact data does not go stale all at once. It erodes as people change jobs. Roughly a quarter of the UK workforce changes employer each year (ONS and CIPD analysis), so the share of your contacts carrying an out-of-date title, employer or email compounds the longer a list sits untouched.
| Time since last check | Contacts that may have moved on |
|---|---|
| After 1 year | around a quarter |
| After 2 years | close to half |
| After 3 years | around six in ten |
Directional, compounding a roughly 25% annual employer-change rate (ONS and CIPD). It excludes leavers, mergers and rebrands, so the real erosion is usually higher. Decay is faster in high-turnover sectors like technology, retail and hospitality, and slower in the public sector.
How long can you keep contact data under UK GDPR
UK GDPR sets no fixed retention period. The storage-limitation principle says you keep personal data only as long as it is necessary for the purpose you collected it for. That makes stale contacts a compliance question as well as a productivity one. A person who has left the organisation, with no live relationship to you, is a candidate to review, refresh or erase.
A practical retention routine is to set a documented schedule, re-check active contacts on a regular cadence, and remove records you no longer have a lawful basis to hold. See the security and data-handling page for how Datuma stores, isolates and erases contact data on request.
Cleaning CRM data, common questions
How often does CRM data go out of date?
How long can you keep contact data under UK GDPR?
Which industries have the fastest CRM data decay?
Score 100 contacts free and see what needs fixing
Upload a sample from your CRM. See your Trust Score distribution, duplicate rate, and a field-by-field breakdown of what's missing. It takes 5 minutes.