What Is Dirty Data? Definition, Common Types, and How to Fix It

Using dirty data to fuel your business is like putting the wrong kind of fuel in your car: the engine might start, but you could be doing serious damage. If you want a well-oiled revenue engine, you've got to fuel it with clean data.

In this post, we tackle some foundational dirty data questions:

• What is dirty data?

• What are examples of dirty data?

• What are the consequences of dirty data?

• How do you clean and prevent dirty data?

What is Dirty Data?

Dirty data is inaccurate, incomplete, inconsistent, or duplicate information in your CRM database that hinders analysis, causes operational inefficiencies, and leads to poor decision-making. Also called messy data or bad data, it represents faulty bits of information that can present problems in a business's database.

Clean data is the opposite: verified, complete, standardized records that sales and marketing teams can trust to drive pipeline. Dirty databases accumulate when raw data sources flow in without validation, when records decay over time, or when improperly formatted entries pile up across systems.

The hallmarks of dirty data include:

• Inaccurate: Wrong phone numbers, outdated job titles, incorrect company names

• Incomplete: Missing email addresses, empty fields, partial contact records

• Duplicate: The same contact appearing multiple times with slight variations

• Outdated: Records that were accurate when entered but have since become stale

• Inconsistent: Non-standardized formatting across similar data elements

How Dirty Data Enters Your CRM and Marketing Systems

Most contamination happens at five predictable entry points:

• Manual data entry errors: Typos, misspellings, and formatting mistakes when reps log information by hand

• Web form submissions without validation: Prospects enter fake emails, incomplete phone numbers, or nonsense data to access gated content

• System migrations: Moving data between platforms introduces duplicates, drops fields, or corrupts formatting

• Integrations from multiple non-standardized sources: Each tool uses different naming conventions, creating inconsistency across your stack

• Natural data decay: Job changes, company moves, mergers, and contact detail shifts make once-accurate records obsolete

Common Types of Dirty Data in B2B Databases

Six types of dirty data negatively impact sales and marketing teams:

Insecure or Non-Compliant Data

Data security and privacy laws are being established left and right, imposing financial penalties on businesses that don't follow these laws to the letter. With steep fines for non-compliance, insecure data is quickly becoming one of the most dangerous types of dirty data.

Industry giants know the real costs of ignoring privacy regulations. Amazon announced an $888 million EU fine in its 2021 earnings report due to data violations. WhatsApp, an application owned by Meta, also received a €225 million fine for alleged GDPR infringements.

The consequences go beyond a price tag, as non-compliance negatively impacts company productivity, brand reputation, and disrupts business operations. Insecure data includes records processed without proper consent, data retained beyond regulatory limits, or information that violates GDPR, CCPA, and other privacy laws.

Inconsistent Formatting

Inconsistent or non-standardized data looks different, but represents the same thing. Just like duplicate records exist in various places within your database, multiple versions of the same data elements can exist across different records in your system.

Common examples of inconsistent formatting include:

Without standardized naming conventions and field formats, your segmentation breaks down and your reporting becomes unreliable.

Duplicate Records

Duplicates are the doubling of information in your CRM. A single employee might show up twice under different companies or with different job titles across your prospect lists, contact data, and sales accounts.

Duplicates typically enter during data migrations and manual inputs. Ridding your database of duplicates should be a top priority in any data hygiene campaign.

Outdated or Stale Data

Records that were accurate when entered but have since become incorrect due to job changes, company moves, mergers, or contact detail shifts represent one of the most pervasive forms of dirty data. This is data decay in action.

Common causes of stale data include:

• Job changes: Contacts move to new companies, get promoted, or leave the workforce entirely

• Company relocations: Headquarters move, offices close, or new locations open

• Mergers and acquisitions: Companies get acquired, rebrand, or consolidate operations

• Contact detail shifts: Phone numbers change, email addresses get deactivated, or personal information updates

Data decay happens naturally over time. The question isn't whether your data will become stale, but how quickly you can refresh it.

Incomplete Records

Do you have data gaps? Any incomplete data will certainly poke holes in your outreach efforts.

Without attributes like industry type, job title, or last name, you risk excluding valuable leads in your campaigns. Additionally, incomplete data hurts your sales team's call-to-connection rate.

Commonly missing fields in B2B databases include:

• Direct dial numbers: Mobile or desk phone numbers for direct contact

• Job titles: Seniority level, department, or functional role

• Industry classification: Vertical, sector, or market segment

• Company size: Employee count or revenue range

Inaccurate Data

If your data is plain wrong, you run into all sorts of problems, from missteps on cold calls to inaccurate reporting and decision-making:

• 43% of sales and marketing teams say inaccurate data remains a challenge for them.

• 54% of B2B businesses say poor data quality is their biggest challenge.

It's far cheaper to verify and cleanse data regularly than to do nothing at all.

The Business Impact of Dirty Data

Dirty data doesn't just slow you down. It actively costs you deals, wastes budget, and fractures team alignment. The impact shows up in two places: your revenue line and your team's productivity.

Revenue and Pipeline Losses

Dirty data skews your understanding of your target audience, throwing off your ability to target the right accounts and personas. This domino effect impacts every campaign you run. Here's how bad data drains revenue:

• Wasted marketing spend: Campaigns target the wrong accounts, wrong personas, or wrong contacts entirely. Budget goes to prospects who will never convert.

• Unreliable forecasts: When bad data contaminates your sales and marketing metrics and reporting, it can hurt your business on a massive scale. Executives and key stakeholders can't make informed long-term business decisions when the underlying data is wrong.

• Misattributed pipeline: Having access to a constant stream of new data that can provide a comprehensive view of your customers is critical for meeting your sales and marketing goals. Without it, you can't accurately track which campaigns, channels, or tactics actually drive revenue.

Real-world impact: Tradeshift, a B2B payments platform, used ZoomInfo to cleanse, deduplicate, and enrich their data assets. The result: cleaner pipeline reporting and more accurate attribution across their GTM motion.

Reduced Sales Productivity

When bad data results in poor customer experience, you'll lose out on valuable prospects and fail to retain current customers. The modern customer has more control over their buying journey than ever before. When they're interested in buying from your company, they want seamless interactions. These interactions' success depends on clean data.

Productivity losses compound across your GTM team:

• Wasted rep time: Sellers spend hours researching accounts, only to find phone numbers disconnected or contacts no longer at the company. Time that should go to selling goes to data cleanup instead.

• Failed outreach: Dirty data can hurt your company's reputation in more ways than simply encouraging negative customer feedback. In today's hyper-connected world, customers don't just abandon your business when they have a poor experience. They tell their friends, family, and colleagues about it.

• Sales-marketing friction: Your marketing team will end up sending low-quality leads to sales. Over time, the relationship between the two departments fractures, leading to a decreased lead flow and fewer conversions. To ensure that marketing sends the most qualified, ready-to-close leads to sales, your teams need data they both trust.

• Stalled deals: This slows down leads moving through the sales process. And as a result, good leads go bad and miss opportunities.

How to Clean Dirty Data

Cleaning dirty data isn't a one-time project. It's an ongoing discipline. But you have to start somewhere. Here's the sequence that works:

Audit and Profile Your Data

Cleaning starts with understanding what you have. Before you fix anything, assess data quality across your database to identify patterns of errors and prioritize which records and fields to fix first.

What to audit:

• Completeness rates: What percentage of records have empty fields for critical attributes like email, phone, or job title?

• Duplicate counts: How many contacts, leads, or accounts appear more than once in your system?

• Age of records: When was each record last updated? Which segments of your database are most likely stale?

• Format consistency: Are phone numbers, dates, and addresses standardized across your database?

Deduplicate and Standardize Records

Once you know what's broken, fix the structural problems first. Duplicates and inconsistent formatting create the most immediate friction for your teams.

Steps to deduplicate and standardize:

• Define matching criteria: Decide what constitutes a duplicate. Same email? Same name and company? Establish clear rules.

• Merge duplicates: Automated solutions for detecting and merging duplicates now exist. External solutions to de-duplicate data, like ZoomInfo, allow users to match leads, contacts, and accounts based on customizable criteria. This prevents duplicates at all points of entry into your database.

• Establish naming conventions: Create standard naming conventions and ensure your organization follows them closely. Tools like ZoomInfo can normalize records in batches for more unified field names and more accurate segmentation.

• Normalize field formats: Incorporating a data management tool that can standardize data from multiple sources helps create a centralized approach to data management. This enables data to be processed, analyzed, and leveraged across each department.

Enrich with Verified B2B Intelligence

Manual research to append missing fields is neither realistic nor scalable. Enriching your data with a service like ZoomInfo before the lead gets handed to sales is the best way to automate the filling of empty fields and gain a more complete profile of targets and customers.

Data enrichment software like ZoomInfo corrects mistakes and overrides dirty data with clean data sourced from the most reliable sources. By augmenting existing data with purchased third-party information, organizations can attain more accurate data that may not have been possible before.

What enrichment adds:

• Direct dial numbers: Verified mobile and desk phone numbers for higher connect rates

• Job titles and seniority: Accurate role information for better targeting and personalization

• Firmographic data: Company size, revenue, industry, and location details

• Technographic intelligence: Tech stack data showing which tools and platforms your prospects use

How to Prevent Dirty Data from Recurring

Cleaning your database once isn't enough. Without prevention systems in place, dirty data creeps back in. The goal is to stop contamination at the source and automate ongoing maintenance.

Establish Data Entry Standards

Most dirty data enters through unvalidated forms or inconsistent manual entry. Set standards that make it harder for bad data to get in:

• Validation rules on forms: Require proper email formats, phone number structures, and complete fields before submission

• Required fields: Make critical attributes mandatory. Don't let records enter your system with empty job titles or company names.

• Dropdown menus vs free text: Use dropdowns for standardized fields like industry, company size, or state. Free text invites inconsistency.

• Training for manual entry: Train reps on naming conventions and data entry best practices. Make data quality part of onboarding.

• Clear ownership of data quality: Assign data stewardship responsibilities. Someone needs to own data hygiene as a function, not a side project.

Prevention checklist:

Automate Ongoing Enrichment and Refresh

Begin with regular CRM health assessments. You can do this manually or partner with your data provider.

Automation opportunities include:

• Scheduled data refreshes: Set cadences for updating records. Quarterly refreshes catch job changes and company updates before they impact outreach.

• Automated enrichment triggers: Enrich records automatically when they enter your CRM or when key fields are empty.

• Continuous verification: Use a good mix of data sources, first-party and third-party, including intent data. Cleanse your data regularly and fill in any gaps by enriching each field with the most reliable source possible.

• Real-time sync: Practice ongoing data management. The key is to identify the types of bad data in your CRM, clear them out, and replenish them with a stream of high-quality, actionable data.

Real-world impact:Capital One eliminated manual research and saved hours of data entry time by automating enrichment and refresh workflows with ZoomInfo.

With ongoing data hygiene, your teams develop faster, more efficient sales processes to ensure every lead touchpoint is phenomenal.

Dirty Data FAQs

What percentage of CRM data is typically dirty?

Industry benchmarks suggest that B2B databases experience natural decay rates of 20-30% annually due to job changes, company moves, and contact updates.

How often should you clean your CRM data?

Run quarterly data audits at minimum, with automated enrichment and verification running continuously in the background.

What's the difference between data cleansing and data enrichment?

Data cleansing removes errors and duplicates from existing records. Data enrichment adds missing information to incomplete records.

Can dirty data affect email deliverability?

Yes. Bounced emails from bad addresses hurt your sender reputation, which can cause future emails to land in spam folders.

What's the ROI of cleaning dirty data?

Clean data increases connect rates, reduces wasted outreach, and improves forecast accuracy. Most organizations see measurable productivity gains within the first quarter.

Talk to our team to learn how ZoomInfo can help automate your data quality workflows.