Data Quality Checklist: A GTM Leader's Guide

Data drives every GTM decision, from lead scoring to pipeline forecasting, yet poor quality costs organizations millions. Gartner finds bad data costs an average of $12.9 million per year. MIT Sloan reports companies lose 15–25% of revenue annually, and a 2025 IBM study shows over a quarter of organizations lose more than $5 million yearly, with 7% losing over $25 million.

For revenue teams, dirty data means wasted outreach, missed opportunities, and unreliable forecasts. SDRs chase outdated contacts, campaigns hit the wrong audience, and AI produces garbage outputs. A data quality checklist provides a repeatable framework to assess, monitor, and improve the datasets that drive revenue. 

This guide breaks down core dimensions, offers a template, and shows how to scale quality checks across your GTM operations.

What Is a Data Quality Checklist?

A data quality checklist is a structured framework for evaluating whether your datasets meet the standards required for reliable decision-making. It defines specific criteria, metrics, and validation rules that data must pass before being used for data analysis, outreach, or automation.

For GTM teams, this covers CRM records, contact databases, intent data, and engagement data. The checklist functions as a quality gate, catching issues like missing required fields, duplicate entries, and inconsistent formatting before they contaminate downstream processes. An effective data quality checklist addresses seven dimensions: accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity.

Why Data Quality Matters for GTM Teams

Data quality issues compound at every stage of the sales funnel. A Fivetran and Vanson Bourne study of 550 data professionals found that 76% of organizations struggle to use AI to its full potential due to siloed, low-quality, and stale data.

For revenue operations, poor data quality creates three critical problems:

Unreliable forecasting. When CRM data contains outdated opportunities or missing fields, pipeline projections become meaningless.

Wasted sales activity. Reps burn hours pursuing contacts who left the company or duplicating effort on leads already touched by another team member.

Failed automation. Workflows that depend on accurate triggers break silently. A lead scoring model trained on dirty data produces unreliable rankings.

High-quality data accelerates every GTM function: clean records improve connect rates, accurate firmographics enable precision targeting, and complete engagement histories inform personalization at scale.

The 7 Data Quality Dimensions Every Checklist Should Cover

Most data quality frameworks converge on seven core dimensions. Each addresses a specific aspect of data fitness your checklist should validate.

1. Data Accuracy

Accuracy measures whether data values correctly represent real-world entities. An accurate record contains the right email address, correct job title, and true firmographic details.

Quality checks:

• Cross-reference contact data against verified sources
• Validate email addresses through verification services
• Confirm phone numbers are active and correctly formatted

Accuracy decays over time as job changes and company rebrands introduce inaccuracies. Regular validation cycles maintain accuracy standards.

2. Data Completeness

Completeness assesses whether records contain all required fields. Incomplete records create blind spots in segmentation and scoring.

Quality checks:

• Define required fields for each data type (contacts, accounts, opportunities)
• Measure fill rates across critical data attributes
• Flag accounts lacking firmographic data needed for segmentation

Lead intelligence applications demand richer profiles than basic contact management. Define completeness thresholds based on how the data will be used.

3. Data Consistency

Consistency ensures the same entity is represented identically across all systems. Is "IBM" the same as "International Business Machines"?

Quality checks:

• Standardize naming conventions across data sources
• Enforce consistent formatting for addresses, phone numbers, and dates
• Align status and stage definitions across CRM objects

Without standardization, deduplication fails, and reports produce conflicting numbers.

4. Data Timeliness

Timeliness measures whether data is current enough for its intended use. Contact data accurate six months ago may be worthless today.

Quality checks:

• Track data age and last-verified timestamps
• Set refresh cadences based on decay rates for different data types
• Monitor real-time indicators like job changes and funding events

B2B contact data decays at 22-70% annually, depending on industry and data type. Your checklist should account for timeliness relative to the data source.

5. Data Validity

Validity confirms that the data conforms to defined business rules and acceptable value ranges. A valid email follows proper formatting; a valid revenue figure falls within plausible bounds.

Quality checks:

• Apply format validation to structured fields (emails, URLs, phone numbers)
• Check numerical fields against acceptable ranges
• Validate categorical fields against defined picklist values

Validity checks catch data entry errors before they propagate through your systems.

6. Data Uniqueness

Uniqueness ensures each entity is represented only once. Duplicate records waste outreach effort and inflate metrics.

Quality checks:

• Implement duplicate detection using fuzzy matching algorithms
• Define matching rules based on email, company, and name combinations
• Monitor duplicate creation rates to identify root causes

According to IndustrySelect research, 70.8% of business contacts experience changes within 12 months, with duplicate creation a common byproduct.

7. Data Integrity

Integrity captures the overall trustworthiness of data across its lifecycle, maintaining accuracy through collection, transformation, and storage.

Quality checks:

• Audit data lineage and transformation history
• Validate that integration processes preserve data accuracy
• Monitor for unauthorized modifications or access

Integrity issues often hide in ETL processes where data transforms between formats and schemas.

Building Your Data Quality Assessment Framework

A data quality checklist becomes operational when paired with clear methodology and defined metrics.

Define Critical Data Elements

Identify which datasets directly impact revenue outcomes:

• Contact records: Email, phone, job title, seniority
• Account data: Company name, industry, employee size, technology stack
• Engagement data: Email opens, website visits, meeting history
• Opportunity data: Deal stage, close date, value, decision-makers

Prioritize quality efforts on datasets that feed high-impact processes like automated lead generation and outreach sequences.

Establish Quality Metrics and Thresholds

Each dimension needs quantifiable metrics that translate abstract quality concepts into measurable outcomes. Without clear thresholds, data quality becomes subjective and inconsistent across teams. The table below provides baseline targets for each core dimension, though your specific requirements may vary based on data usage and business impact.

Use these metrics as your starting point for building accountability into your data quality program:

Thresholds should reflect business requirements, not arbitrary ideals. A prospecting database may tolerate higher incompleteness than a customer master file.

Implement Quality Control Processes

Data entry validation. Apply validation rules at data collection to prevent invalid entries.

Automated quality checks. Schedule regular scans that flag records failing criteria.

Integration monitoring. CRM data enrichment processes should preserve or improve quality, not degrade it.

Dashboards. Build dashboards tracking data quality metrics over time. Share visibility with stakeholders.

Data Quality Checklist Template

Use this data quality checklist template to assess your GTM data systematically. Whether you need a data quality audit framework, a data quality assessment tool for regular reviews, or a foundation for a data quality assessment report, this template provides actionable quality checks. Score each item on a scale of 1-5 (1 = critical issues, 5 = excellent) or mark as Pass/Fail for binary checks.

Contact Data Checklist

Contact records drive every outbound motion and inbound follow-up. Missing or inaccurate email addresses waste SDR time and damage sender's reputation. The checks below focus on the fields that directly impact connect rates and deliverability.

Apply these validation rules to your contact database:

Account Data Checklist

Account-level data enables segmentation, territory planning, and account-based marketing strategies. Without reliable firmographic details, your targeting becomes guesswork. Clean account records ensure marketing and sales align on which companies to pursue and why.

Use these criteria to evaluate your account database quality:

Opportunity Data Checklist

Opportunity records form the foundation of accurate forecasting and pipeline reporting. Incomplete or invalid deal data creates false confidence or unwarranted panic in revenue projections. These checks ensure your pipeline data supports reliable decision-making at every stage.

Validate your opportunity records against these quality standards:

Data Governance Checklist

Technical checks catch data errors, but governance ensures those checks actually run and someone acts on the results. Without clear ownership and automated processes, quality standards erode over time. These governance checks verify that your data quality system operates consistently.

Monitor these governance indicators to maintain long-term data health:

Download this data quality checklist template and customize thresholds based on your requirements. Adapt these tables into a data quality checklist Excel template, data quality checklist pdf, or data quality checklist template Word document to match your team's workflow and data quality assessment needs.

Common Data Quality Issues and How to Fix Them

Even with a solid data quality framework, certain problems recur across GTM organizations. Data scientists and RevOps teams encounter these issues whether using enterprise platforms or open-source data quality tools. Here's how to address the most common failures.

• Stale contact information. People change jobs, get promoted, and move companies. Solution: Implement continuous enrichment that monitors job change data and updates records in real time. Tools that track LinkedIn activity and company announcements catch changes faster than periodic batch updates.
• Duplicate records from multiple sources. When leads enter through forms, imports, and integrations simultaneously, duplicates multiply. Solution: Deploy duplicate detection at ingestion, not just during periodic cleanups. Use fuzzy matching that catches near-duplicates like "Bob Smith" and "Robert Smith."
• Inconsistent data from different systems. Marketing automation and CRM often contain conflicting versions of the same record. Solution: Establish a system of record for each data type. Implement bidirectional sync with conflict resolution rules.
• Missing firmographic data. Contact records without company context can't be properly scored or routed. Solution: Enrich records at capture using AI data enrichment that appends firmographic data from multiple providers automatically.
• Invalid email addresses degrade deliverability. Hard bounces damage the sender's reputation and reduce inbox placement. Solution: Validate emails at entry and re-verify before outreach campaigns. Remove chronic bounces from active lists.

By proactively addressing these recurring failures at the source, GTM teams transform data quality from a reactive cleanup exercise into a scalable competitive advantage.

Unlock Next-Level Data Accuracy Automatically

Traditional data quality management requires substantial manual effort: building reports, investigating issues, cleaning records, and monitoring dashboards. This operational burden grows with data volume.

11x approaches data quality differently. Instead of reactive cleanup, 11x's AI agents maintain data quality through continuous, autonomous operation.

Alice, the AI SDR, connects to 21+ elite data providers to research prospects before outreach. This research-first approach means Alice works with fresh, enriched data rather than relying on static lists that decay over time. When contact information changes, Alice's research agents detect updated records and adjust targeting automatically.

Julian, the AI phone agent, validates leads through live conversations. Rather than trusting form data at face value, Julian qualifies prospects in real time, confirming decision-maker status and buying intent through natural dialogue. This conversational validation catches data issues that automated checks miss.

Both agents sync activities bidirectionally with Salesforce, HubSpot, and Pipedrive, maintaining consistent data across your GTM stack. Every interaction is logged, every record is updated, and every data point flows through clean pipelines.

The result: teams using 11x report 50% decrease in cost per lead and 80% increase in meeting-to-qualified opportunity conversion. Gupshup saw a 50% increase in SQLs per SDR after adopting Alice, proving that autonomous data validation and enrichment directly improve pipeline quality. Clean data feeds every interaction, and autonomous execution eliminates the operational burden of manual data management.

Streamline Your Pipeline with Smarter Data

Data quality isn't a one-time project. Your data quality checklist provides the framework, but execution determines whether standards actually hold.

11x eliminates the operational burden of manual data management by maintaining quality through continuous, autonomous operation. Alice and Julian validate, enrich, and update every contact record automatically while executing outreach and qualification conversations at scale. Instead of building reports and cleaning records manually, your GTM data stays accurate through every interaction.

Ready to see how autonomous execution maintains data quality while generating a pipeline? Book a demo with 11x to see how digital workers keep your GTM data clean and actionable.