What Is Data Cleaning?

Data cleaning is the process of fixing errors, inconsistencies, and gaps in your data. It's not glamorous. Nobody gets excited about removing duplicates or standardizing date formats. But it's the difference between data you can trust and data that leads you astray.

Why Data Gets Dirty

Data doesn't start dirty - it gets dirty over time through normal operations:

Manual entry errors. "Jonh" instead of "John." "NYC" vs "New York City" vs "New York, NY." Phone numbers with and without dashes. Every human touch introduces variation.

System migrations. When you switch CRMs, some data doesn't map cleanly. Fields get truncated. Relationships get lost. The migration "works" but leaves debris.

Integration issues. When systems sync, they can create duplicates. A contact gets updated in Salesforce and HubSpot separately, and now you have two slightly different versions of the truth.

Changing business rules. What counted as a "qualified lead" three years ago is different from today. Historical data doesn't update itself.

Time and decay. People change jobs, companies get acquired, email addresses bounce. Data that was accurate becomes stale.

Types of Data Quality Problems

Duplicates. The same customer exists three times with slightly different names. Every duplicate skews your counts and wastes your sales team's time.

Missing values. Fields that should have data but don't. Sometimes it's okay (not everyone has a middle name). Sometimes it breaks your analysis (revenue without a date).

Invalid values. Negative quantities. Dates in the future. Email addresses that aren't email addresses. Data that doesn't make sense.

Inconsistent formats. "2024-01-15" vs "01/15/2024" vs "January 15, 2024." "United States" vs "US" vs "USA." The same information represented differently.

Outdated information. A contact who left the company two years ago. A product that's been discontinued. A price that's been updated.

Referential integrity issues. An order that references a customer who doesn't exist. A product category with no products in it.

The Impact of Dirty Data

Bad data isn't just an annoyance - it has real costs:

• Sales reps waste time on dead leads
• Marketing sends emails to invalid addresses (hurting deliverability)
• Reports show different numbers depending on who runs them
• Executives lose trust in analytics
• Decisions get made on faulty assumptions

How to Clean Data

Deduplication. Identify records that represent the same entity. This usually involves fuzzy matching - "Jon Smith" and "John Smith" at the same company are probably the same person. Tools like Dedupe.io or custom matching rules help.

Standardization. Pick a format and enforce it. All dates in ISO format. All phone numbers with country codes. All states as two-letter abbreviations. Apply rules consistently.

Validation. Check that data meets expected criteria. Emails should have @ symbols. Prices should be positive. Dates should be in the past (for historical data) or future (for scheduled events).

Enrichment. Fill in gaps from external sources. Append company size from Clearbit. Add geographic coordinates from addresses. Fill missing data from more authoritative sources.

Archival. Some data isn't worth cleaning - it's worth removing. Contacts who haven't engaged in five years. Test records from development. Historical data that's no longer relevant.

Prevention vs Cure

Cleaning data is necessary, but preventing dirty data is better:

Validation at entry. Don't let invalid emails get saved. Require mandatory fields. Use dropdowns instead of free text where possible.

Integration standards. When systems sync, have clear rules for conflict resolution. Which system wins? How are duplicates detected?

Regular audits. Don't wait until data is a mess. Schedule quarterly reviews of data quality metrics. Catch problems early.

Clear ownership. Someone should be responsible for data quality. When everyone owns it, no one owns it.

Getting Started

Pick your highest-value data and clean that first. For most companies, this is customer data - the contacts and accounts that drive revenue. Get your CRM clean before tackling less critical systems.

Set up a dashboard that tracks data quality metrics: duplicate rate, completeness of key fields, email bounce rate. What gets measured gets managed.

Clean data enables everything else. Learn about data pipelines that move data and data warehouses that store it.