Data validation is the process of verifying that business data is accurate, complete, and usable before it enters your CRM or other systems. This includes email deliverability verification, phone number validation, business status checks, address verification, and duplicate detection. Catching a bad record before import costs a fraction of fixing the downstream damage it causes: bounced emails, wasted sales calls, polluted reporting, and degraded sender reputation. Pre-import validation is the most cost-effective step in any data quality program.
The Problem: Bad Data Is Already in Your CRM
Most companies discover data quality problems after the damage is done. Emails bounce. Calls go to disconnected numbers. Sales reps waste hours on leads that don't exist. Marketing sends the same message three times to the same person.
By then, the bad data has already:
- Damaged your email sender reputation
- Wasted your sales team's time
- Polluted your reporting and forecasts
- Created duplicate records that take forever to untangle
The fix is simple: validate data before it enters your systems. Not after.
What We Validate
We check your data against multiple verification sources to catch problems before they become expensive:
- Email deliverability verification. Not just format checking. We verify the email address will actually receive mail. Catch bounces before they hurt your sender reputation.
- Phone number validation. Verify numbers are dialable, identify line type (mobile, landline, VoIP), and flag disconnected numbers.
- Business status checks. Is the company still operating? Has it been acquired, merged, or closed? We verify against public records and business databases.
- Address verification. Standardize and verify mailing addresses. Catch typos, incomplete addresses, and non-existent locations.
- Duplicate detection. Before you add records, we check them against your existing database. No more creating duplicates of contacts you already have.
- Data completeness scoring. Which records have enough information to be useful? Which need enrichment before they're worth pursuing?
Industry-Specific Validation
Generic validation catches generic problems. Industry-specific validation catches the issues that actually matter to your business.
Healthcare Validation
We've validated 150K+ healthcare practices with rules built specifically for medical and life sciences companies:
- NPI Registry verification. Cross-reference against the National Provider Identifier database to confirm provider credentials.
- Practice status validation. Verify practices are open and operating, not closed or relocated.
- Hospital system detection. Flag records that belong to hospital systems vs. independent practices.
- Specialty classification. Validate and standardize medical specialties against recognized taxonomies.
- Practice type categorization. Distinguish private practices from regional health systems, academic medical centers, and other facility types.
We're building similar validation rule sets for other industries. Contact us if you have industry-specific validation needs.
What You Get
Every validation project delivers:
- Validated file. Your original data with validation status flags on every record.
- Validation report. Summary showing percentage valid, percentage fixable, percentage to exclude, and recommended actions.
- Issue breakdown. Specific counts by problem type so you know exactly what's wrong with your data.
- Recommendations. Which records to proceed with, which need enrichment, and which should be excluded.
Pre-CRM vs. Post-CRM Validation
There are two ways to think about data validation:
Pre-CRM validation checks data before it enters your systems. This is what we specialize in. You're importing a list from a trade show, onboarding data from an acquisition, or loading leads from a partner. Validate first, import clean.
Post-CRM validation audits data that's already in your systems. We do this too, but it's more expensive to fix. By the time data is in your CRM, it's already created duplicates, triggered automation, and polluted reports.
The economics are simple: catching a bad record before import costs pennies. Fixing the downstream damage costs dollars.
How It Works
Step 1: Send us your data. The list you're about to import, the vendor file you just received, the spreadsheet your partner sent over.
Step 2: We validate against multiple sources. Email verification services, phone databases, business registries, and our industry-specific rule sets.
Step 3: You get a validation report. Clear breakdown of what's valid, what's fixable, and what should be excluded. No guessing.
Step 4: Import with confidence. You know exactly what you're putting into your CRM. No surprises later.
"We validated 50,000 contacts before our CRM migration. Caught 12,000 invalid emails that would have destroyed our sender reputation."— Director of Marketing Operations
Pricing
Data validation starts at $0.01-0.03 per record, depending on validation depth. Minimum project is $500.
We'll give you a fixed quote after reviewing your data. You know the cost before we start.
Common Questions
How is validation different from cleaning?
Validation checks if data is accurate and usable. Cleaning fixes formatting and removes duplicates. Many projects need both, but validation comes first. No point cleaning data that shouldn't be in your system at all.
How long does validation take?
Most projects complete in 24-48 hours. Large datasets (100K+ records) may take 3-5 days depending on validation depth.
What if I need to validate data regularly?
Check out our Ongoing Maintenance service. We can set up recurring validation on whatever schedule works for you.
Can you validate data already in my CRM?
Yes. Export your data, we validate it, and you get a report showing which records need attention. This is a good first step before a larger data cleaning project.
Stop Importing Problems
Every bad record you import creates work downstream. Bounced emails, wasted calls, polluted reports, frustrated sales reps.
Validate before you import. It's cheaper to catch problems at the door than to fix them after they're inside.
Related: Data Cleaning | Data Enrichment | Ongoing Maintenance