What Is Data Cleaning?
B2B data cleaning is the process of identifying and correcting errors in business databases, including duplicate records, invalid email addresses, inconsistent formatting, outdated contact information, and corrupted fields. 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.
Data Cleaning vs. Data Cleansing: Is There a Difference?
No. Data cleaning, data cleansing, and data scrubbing describe the same work: finding and fixing errors in a database. "Cleansing" shows up more often in UK usage and in direct-mail circles, "cleaning" dominates in US B2B and analytics, and "scrubbing" is the older IT term. Vendors sometimes draw acceptable distinctions between them to sell tiered packages. The distinctions don't survive contact with an RFP.
One thing this page is unrelated to: data center cleaning, which is a physical facilities service involving raised floors and anti-static vacuums. Different search results, same keyword collision.
Why Data Cleaning Is Important
Bad records compound. A duplicate account splits pipeline history, a stale email is rejected, and a misformatted phone field can break a dialer integration. Measure each failure by source and workflow so the cleanup is based on observed records rather than a generic damage estimate.
There's an analysis spend too. Surveys of data scientists have put data preparation at more than half of their working time, which is why "data cleaning" and "data analysis" show up in the same job description so often. Each dashboard built on a dirty table inherits its errors. Clean the data once, and each report, model, and campaign downstream of it gets more accurate for free.
What a Data Cleaning Project Looks Like
Check current official vendor materials and a dated proposal, then test each option with the same representative sample, field list, provenance rules, and export requirements.
The problem was everything else.
A multi-source contact file can use several column names for the same field: "BUSINESS NAME," "Business Name," and "business name," plus misspelled vendor labels. The first task is inventorying those headers and agreeing on the target schema.
Phone numbers were losing their first digit during formatting. Thousands of records with numbers that just wouldn't dial. LinkedIn URLs existed in a dozen different formats, pointing to the same people but looking like separate contacts to their CRM.
And they couldn't just merge everything together. They'd paid each vendor for their data. Each email, each phone number needed its attribution preserved. You don't spend serious money on five data sources just to throw away the paper trail.
What we built
Column mapping groups case variations and misspellings under approved target fields, with ambiguous headers held for review.
A multi-phase matching algorithm. Exact matching for the straightforward stuff. Fuzzy matching for company name variations. LinkedIn URL normalization so "www.linkedin.com/in/johndoe" and "linkedin.com/in/johndoe" stopped looking like different people.
Cell-level validation that caught the phone corruption issue before it shipped. Vendor attribution preserved in separate columns so they could track which data came from where.
The results
Report labor saved only from measured correction time, affected records, and the team's loaded rate. Treat missed-opportunity estimates separately because a cleaned file alone cannot prove revenue that would have been lost.
We caught it before it shipped.
What We Clean
Each database accumulates problems differently. Some have obvious duplicates. Some have subtle formatting issues that break downstream systems. Here's what we look for:
- Duplicate detection and merge. Exact matching is table stakes. We use fuzzy matching to catch "Acme Corp" versus "Acme Corporation" versus "ACME Co." and merge them while preserving the best data from each record.
- Email validation. We check deliverability on top of format. A properly formatted email that bounces is worse than useless because it damages your sender reputation.
- Phone standardization. Convert values to the approved international or domestic format, preserve the original, and flag records that do not match the expected pattern.
- Address normalization. Consistent formatting, abbreviation standardization, postal code validation. Your mail reaches the people you sent it to.
- Job title mapping. "VP of Sales" and "Vice President, Sales" and "Sales VP" become the same standardized category. Segmentation and targeting depend on this one field.
- Company name standardization. registered entity names validated. Parent-subsidiary relationships mapped. Your account hierarchy makes sense.
- LinkedIn URL normalization. the different ways people format LinkedIn profiles resolved to consistent, deduplicated records.
- Multi-value field protection. Some fields contain pipe-separated values that can't be processed like regular text. We identify and protect them so nothing gets corrupted.
Data Cleaning by Industry
Each industry has unique data challenges. Healthcare deals with NPI validation and provider credentialing. Financial services requires compliance-grade audit trails. SaaS companies struggle with freemium-to-paid tracking across product-led funnels.
We've cleaned data for companies across these industries:
Technology
Commerce & Logistics
Other Industries
Don't see your industry? Contact us. We've likely worked with similar data challenges.
How It Works
Step 1: Send us your data. Export from Salesforce, HubSpot, Outreach, Marketo, or send us Excel/CSV/Google Sheets. We work with whatever format you have.
Step 2: We analyze and provide a cleaning plan. Before we touch anything, you'll know exactly what we're going to do. What fields we'll standardize. What duplicates we've identified. What issues we found.
Step 3: AI processing + human QA. Automation handles the scale. Humans handle the judgment calls. Each project gets both. We don't ship "pretty good" data.
Step 4: You get clean data back. Same format you sent, or we can push directly to your CRM. Your choice.
How Much Do Data Cleaning Services Cost?
Verum provides a written, scope-specific charge and schedule after reviewing the records, requested checks, exception rules, and delivery format.
After reviewing a representative sample, we document the eligible records, included checks, exception rules, delivery format, schedule, and written quote before work begins.
Common Questions
How long does data cleaning take?
The written scope sets the schedule after the eligible records, requested transformations, exception rules, review requirements, and delivery format are known.
What file formats do you accept?
We work with exports from Salesforce, HubSpot, Outreach, Marketo, and any spreadsheet format (Excel, CSV, Google Sheets). If your system can export data, we can clean it.
How is this different from a self-serve platform or other data platforms?
A sales-intelligence subscription and a managed cleanup solve different jobs. Compare current vendor documentation and a dated proposal against the records you already own, the output you need, and the internal work each option leaves with your team.
What about data security?
Encrypted transfers, no data sharing with third parties, deletion after project completion unless you request otherwise. Happy to sign NDAs or discuss specific compliance requirements.
What is the difference between data cleaning and data cleansing?
Nothing. They're two names for the same service. "Cleansing" is more common in UK and direct-mail usage, "cleaning" in US B2B. Any vendor charging extra for one over the other is charging for a synonym.
Do you offer data cleansing services in the UK, India, or Australia?
We're a remote service, so geography doesn't limit who we work with. Most of our verification sources are deepest for US and Canadian business records, so for international datasets we'll tell you before the project which checks run at full depth. File in, clean file out, wherever you are.
What does a data cleaning service cost compared to doing it in-house?
Compare an internal workflow and managed service on the same eligible records, rules, review labor, delivery requirements, schedule, and written total charge.
Ready to Stop Fighting Your Data?
Send a sample file and we will return a scoped assessment of duplicates, formats, and records that need review.
Two options:
Not sure yet? Tell us about your data challenges. We'll give you an honest assessment of where your biggest gaps are and whether we can help.
Ready to fix this? Send us a sample file. We'll show you what clean data looks like.
Related: Data Enrichment | Data Analysis | pricing