The Data Quality Checklist Template for GTM Teams

What Is a Data Quality Checklist?

A data quality checklist is a structured framework that evaluates contact and company data against accuracy, completeness, and consistency standards. GTM teams use it to audit CRM records, catch data entry errors, and maintain clean prospecting data. Without one, bad data breaks email deliverability, routes leads to wrong reps, and kills forecast accuracy.

Why Data Quality Matters for Revenue Teams

Bad data doesn't just slow you down. It breaks your revenue engine. Here's what happens when data quality fails:

• Email bounce rates: Inaccurate contact data tanks deliverability and damages sender reputation, making it harder for legitimate emails to reach inboxes.

• Misrouted leads: Wrong territory assignments or incomplete routing fields send leads to the wrong reps, wasting SDR time and killing response speed.

• Broken attribution: Duplicate records and inconsistent data make it impossible to measure marketing spend accurately or understand what's actually driving pipeline.

• Lost productivity: Sales reps waste hours chasing dead ends, calling disconnected numbers, and researching contacts that should already be qualified.

The Cost of Bad Data in Sales and Marketing

Poor data quality creates operational drag across the entire go-to-market motion:

• SDRs calling wrong numbers: Reps burn through call blocks reaching no one, crushing activity metrics and morale.

• AEs working dead accounts: Stale company data wastes time on accounts that have been acquired, shut down, or moved out of ICP.

• Marketing sending campaigns to invalid emails: High bounce rates damage sender reputation and waste budget.

• RevOps unable to trust pipeline reports: Duplicate opportunities inflate pipeline and turn forecasting into guesswork.

How Data Quality Impacts Pipeline and Forecasting

Data quality problems compound as they move up the funnel, directly impacting forecast accuracy and pipeline reliability:

• Deals in wrong stages: You can't predict close dates or identify where deals are stuck.

• Duplicate opportunities inflating pipeline: The same deal entered multiple times creates false confidence in coverage.

• Stale contacts showing false engagement: Engagement metrics attribute activity to people who left the company.

• Territory misalignment skewing rep performance: Wrong assignments make fair quota measurement impossible.

The Six Core Dimensions of Data Quality

Before building your checklist, understand the six dimensions that define data quality:

Data Quality Checklist Template

Use this template to systematically check data quality across the six dimensions. The checklist is organized into basic checks you perform every time data is reviewed, and advanced checks for submission and approval processes.

Basic Data Checks

Run these checks every time new data enters your system or during regular review cycles:

• Verify formatting is correct: Confirm phone numbers, addresses, and dates follow your CRM's standard format. Non-standard formats break automation and cause sync failures.

• Confirm the data is what you expect: Check that field values match the type of information they should contain. Industry codes should be industry codes, not free text descriptions.

• Compare data to previous values: Look for unexpected changes that might signal data entry errors or failed updates from integrations.

• Observe changes in information where change might not be expected: Flag unusual shifts in fields like industry classification, company size, or account tier that could indicate errors.

• Identify values that are not valid: Catch entries that fall outside acceptable ranges or don't match your defined picklist values.

• Perform spot checks at random: Sample a subset of records to catch systematic issues that automated checks might miss.

Advanced Data Checks

These checks verify data quality across all six dimensions before records are approved or uploaded in bulk:

• Accuracy verification: Check that the data reflects reality. Cross-reference contact information against authoritative sources or validate through outreach attempts.

• Error resolution before submission: Confirm that all red flags and errors were identified and fixed before data is submitted or approved.

• Document matching: Confirm that information in reports matches the original documents to catch transcription errors.

• Source verification: Determine if the information was recorded by someone with direct knowledge or came from a reliable secondary source.

• Completeness check: The data must be checked to ensure that all of the records required are there. Any blank entries in the data should be intentional and not omissions of information.

• Full information validation: All fields with text should include complete information, not abbreviations or nicknames. For example, if a person's name is "David," it should not be shortened to "Dave" in the text field.

• Hard copy reconciliation: There should be an inquiry as to whether any hard copies of additional data have been submitted since the report was completed. If so, the additional information will have to be entered correctly.

• Uniqueness verification: It is important to ensure the information is unique. Alphabetical sorting may be necessary to spot duplicate entries. There should also be a check to compare the current information to last year's or last quarter's information.

• Timeliness check: The data checklist should include the timeliness of the data reported. The timestamp should be checked to confirm the version of data received is the most current.

• Consistency validation: Finally, the data should be checked for consistency. The information that is being reviewed and submitted should be the same in all of the departments within the organization.

How to Build a Data Quality Checklist for Your Team

Your data quality standards need to match your specific go-to-market motion. Start by identifying your primary use cases, then prioritize the dimensions that matter most for each one.

Follow these steps to build a checklist tailored to your organization:

• Identify your primary GTM use cases: outbound prospecting, account-based marketing, demand generation, or a combination.

• Map which data quality dimensions matter most for each use case.

• Define quality thresholds for critical fields: what's the acceptable error rate for email accuracy? How fresh does job title data need to be?

• Assign clear ownership for data quality across different record types and fields.

• Set audit frequency based on data decay rates and business impact.

Define Quality Standards by Use Case

Different GTM motions require different data quality standards. Use this table to prioritize which dimensions and fields matter most for your primary use case:

For outbound prospecting, email validity and direct dial accuracy matter most. Bad contact data kills connect rates and damages sender reputation.

For ABM, firmographic accuracy and account hierarchy matter most. You need complete company data to build target account lists and route opportunities correctly.

For demand gen and lead routing, completeness of routing fields matters most. Missing industry, company size, or geography data breaks assignment rules and sends leads to the wrong teams.

Assign Data Ownership and Stewardship

Data quality fails when no one owns it. Define clear roles for data stewardship and ownership:

• Data steward: Maintains standards, documents processes, and monitors quality metrics across all data assets.

• Data owner: Accountable for specific record types or fields. Resolves quality issues and approves changes to their domain.

• Escalation path: Clear process for resolving conflicts when quality issues span multiple systems or teams.

Common ownership assignments include:

• Contact data: Sales Operations or RevOps owns contact records, validation rules, and deduplication processes.

• Account/company data: RevOps or Sales Ops owns account hierarchy, firmographic fields, and territory assignments.

• Lead routing fields: Marketing Operations owns the fields that drive lead assignment and scoring.

• Integration/sync issues: RevOps with IT support owns data flow between systems and resolves sync failures.

Address common ownership gaps: who owns the handoff between marketing and sales data? Who resolves conflicts when CRM and marketing automation have different values for the same field?

Capital One reduced manual data entry by bringing data directly into their CRM, allowing their relationship managers to spend more time on prospecting work. This shows how clear data ownership combined with automated enrichment reduces operational drag.

Data Governance and Compliance for B2B

Data quality and compliance are connected. You can't maintain accurate opt-out records, honor deletion requests, or prove consent without clean data.

B2B teams need to understand these regulatory requirements:

• GDPR: Consent documentation, right to erasure, and lawful basis for processing EU contact data. Bad data makes it impossible to honor deletion requests or prove consent.

• CCPA: Opt-out handling and disclosure requirements for California residents. Duplicate records mean you might keep contacting people who opted out.

• DNC/TCPA: Scrubbing against do-not-call lists before phone outreach and consent for automated calls. Outdated phone data increases the risk of calling numbers on DNC lists.

Compliance failures happen when data quality breaks down:

• Calling numbers on DNC lists

• Emailing contacts who opted out

• Failing to delete records when requested

These aren't just data quality problems. They're legal risks.

How to Maintain Data Quality Over Time

Data quality is not a one-time project. Data decays constantly: people change jobs, companies get acquired, phone numbers disconnect.

Without continuous monitoring, your data quality will degrade no matter how good your initial cleanup was. Set up ongoing maintenance with regular audit cadences, automated monitoring, and clear thresholds for action.

Setting Audit Cadences

Different checks need different frequencies based on data decay rates and business impact:

Automating Quality Monitoring

Manual checks don't scale. Move from periodic reviews to continuous monitoring by automating quality checks:

• Threshold alerts: Get notified when bounce rate or duplicate rate crosses acceptable thresholds.

• Automated duplicate detection: Flag potential duplicates as soon as they're created, not weeks later.

• Integration monitoring: Track sync failures between CRM and marketing automation to catch problems early.

• Dashboard tracking: Show key quality metrics over time so you can spot degradation trends.

Technology to Support Data Quality at Scale

At a certain scale, manual data quality processes break. You need technology to maintain quality across thousands or millions of records.

Consider these tool categories:

• CRM enrichment tools: Automatically append missing fields, update stale data, and validate contact information as records are created or updated.

• Data validation services: Real-time email verification, phone validation, and address standardization at the point of entry.

• Integration platforms: Manage data flow between systems, apply transformation rules, and catch sync errors before they create duplicate records.

• B2B intelligence platforms: ZoomInfo and similar tools provide continuously refreshed contact and company data, reducing manual enrichment work.

The build vs. buy decision depends on your data volume, technical resources, and how critical data quality is to your revenue motion. Building in-house makes sense when you have unique requirements and engineering capacity. Buying makes sense when data quality is mission-critical and you need proven solutions that scale.

Frequently Asked Questions

What should be included in a data quality checklist?

Include checks for accuracy (valid contact info), completeness (required fields populated), consistency (standardized formats), timeliness (current data), validity (follows defined rules), and uniqueness (no duplicates).

How often should you run data quality checks?

Run daily checks for bounce rates and sync failures, weekly spot checks for new records, monthly deduplication, and quarterly full database assessments.

What is the difference between data quality and data governance?

Data quality measures how accurate and usable your data is, while data governance defines who owns it, how it's managed, and how compliance is maintained.

How do you measure data quality in a CRM?

Track metrics like email bounce rate, duplicate rate, field completeness percentage, data decay rate, and the percentage of records with validated contact information.

What causes poor data quality in B2B sales?

Manual data entry errors, lack of validation rules, outdated contact information, duplicate records from multiple sources, and poor integration between systems.

A data quality checklist template turns reactive firefighting into proactive maintenance. The difference between teams that hit their numbers and teams that miss often comes down to whether they can trust their data.

Talk to our team to learn how ZoomInfo maintains data quality at scale.