For go-to-market teams, data is critical to staying competitive, but not just any data. The quality of data matters greatly because bad data negatively impacts every step along the customer's journey with your brand.
To ensure the quality of their data, some organizations hire data quality managers. Also called quality assurance or quality control managers, this person is responsible for coordinating activities to attain the organization's standards of quality for their database. Below we define what a data quality manager is, their roles and responsibilities, and why the job is so important.
What Is a Data Quality Manager?
A data quality manager owns the accuracy, completeness, and consistency of customer and prospect data across CRM and marketing systems. They set standards, enforce validation rules, and coordinate enrichment workflows within RevOps, Sales Operations, or Marketing Operations teams to prevent bad data from breaking forecasts, reports, and marketing automation campaigns.
The title varies by organization. Common variations include:
CRM Data Manager: Focuses on Salesforce, HubSpot, or Dynamics hygiene
Data Steward: Owns specific data domains or business units
Marketing Operations Manager: Handles marketing automation data quality
RevOps Analyst: Manages data across the full revenue stack
Why Data Quality Managers Matter for Revenue Teams
Bad data breaks everything. Forecasts miss. Reports lie. Reps chase dead leads.
Without someone owning data quality, revenue teams face predictable problems:
Duplicate records: Reps waste time on accounts already worked
Incomplete contacts: Missing phone numbers and emails stall outreach
Stale data: Job changes and company updates go untracked
Inconsistent formatting: Reports break, segments misfire
Data quality managers prevent these failures. They establish standards, enforce validation rules, and keep CRM and marketing automation systems clean so sales and marketing teams can hit their numbers.
Core Responsibilities of a Data Quality Manager
The role and responsibilities of a data quality manager may vary depending on the industry in which they work. However, some common responsibilities include resolving issues in the data quality management process, maintaining company standards for data quality, and being an effective communicator about the importance of quality data.
Responsibility Area | Primary Focus |
|---|---|
Data Profiling | Audit databases for completeness, accuracy, consistency |
Validation Rules | Enforce quality controls at point of data entry |
Deduplication | Merge duplicates and standardize field values |
Enrichment | Fill gaps in contact and account records |
Monitoring | Build dashboards and track quality metrics |
Remediation | Investigate failures and train teams on prevention |
Data Profiling and Assessment
Data quality managers audit existing databases to understand completeness, accuracy, and consistency. They review CRM fields, contact records, and account hierarchies to identify gaps and errors.
This assessment work is foundational. You can't fix what you don't measure. Data profiling reveals which fields are populated, which records are duplicated, and where data decay is happening fastest.
Validation Rules and Quality Controls
Data quality managers create and enforce rules for data entry. This prevents garbage-in-garbage-out at the source.
Common validation rules include:
Required fields before record creation
Email format validation
Phone number standardization
Picklist enforcement for industry and job title fields
Deduplication and Standardization
Data quality managers merge duplicate records and normalize field values. Company name variations like "Acme Inc." vs "Acme, Inc." vs "ACME" all refer to the same account, but inconsistent formatting breaks reporting and territory assignments.
Standardization extends beyond company names to job titles, industry classifications, and address formatting. Clean, consistent data makes segmentation and targeting possible.
Enrichment Workflows
Data quality managers coordinate data enrichment to fill gaps in contact and account records. They work with data providers and internal systems to append missing fields like direct dials, verified emails, firmographics, and technographics.
Enrichment typically covers:
Contact details (email, phone, title)
Company firmographics (size, revenue, industry)
Technographics (installed technologies)
They may work closely with business development teams to ensure data growth, enrichment, and accuracy. They may also be responsible for partnering with product and engineering teams to prioritize the company's data roadmap.
Dashboards, Scorecards, and SLAs
Data quality managers build visibility into data health. They create dashboards that track completeness rates, duplicate counts, and enrichment coverage. They set SLAs with stakeholders for data quality targets.
Metrics tracked typically include:
Record completeness rate by field
Duplicate record count
Email deliverability rate
Data decay rate over time
Communication is important because data quality managers meet with other employees and managers in their organization, to ensure that the system for quality management is operating correctly.
Root-Cause Analysis and Remediation
When data quality fails, data quality managers investigate. Where did bad data enter? What process broke? They trace issues back to their source.
The data quality manager also provides insight into how much work can effectively be completed within one work shift. When needed, they will recommend changes and explain how things can be implemented into the system, provide necessary training, utilize ideal data quality tools, and recommend techniques to regularly manage data quality.
Training is critical. Data quality managers coach teams on proper data entry, run workshops on CRM hygiene, and update documentation to prevent recurrence of quality issues.
What Skills Does a Data Quality Manager Need?
Data quality managers need a mix of technical proficiency and business acumen. The role sits at the intersection of systems, process, and people.
Technical Skills | Business Skills |
|---|---|
CRM proficiency: Salesforce, Microsoft Dynamics, HubSpot administration | Stakeholder management: Aligning Sales, Marketing, and RevOps on data standards |
Marketing automation: Marketo, Pardot, Eloqua configuration | Communication: Translating technical issues into business impact |
SQL and data querying: Write queries for data analysis | Project management: Leading data cleanup and enrichment initiatives |
Spreadsheet skills: Advanced Excel or Google Sheets for reporting | Change management: Training teams and driving adoption of new processes |
Data visualization: Building dashboards in Tableau, Looker, or native CRM tools | Analytical thinking: Identifying patterns in data quality issues |
Process management: Maintain a data quality checklist, set objectives, test and modify systems |
What to Look for in Data Quality Management Software
The popularity of CRM systems like Salesforce and Microsoft Dynamics, and marketing automation systems like Marketo and Pardot, has increased the need for data quality managers. They make sure that these databases are clean, standardized, and enriched so that sales and marketing departments can realize greater ROI.
When CRM and marketing automation systems are deduped and enriched, companies see better engagement, conversion, and deliverability rates. This translates directly to higher revenue and faster growth.
Data orchestration tools can make cleaning, organizing, and enriching data across any platform a simple, automated process that saves valuable time. When evaluating data quality management software, look for these capabilities:
Capability | What It Does | Why It Matters |
|---|---|---|
Automated validation | Catches errors at point of entry | Prevents bad data from entering the system |
Enrichment workflows | Appends missing contact and company data | Fills gaps without manual research |
Deduplication | Identifies and merges duplicate records | Eliminates wasted effort on duplicate outreach |
Native integrations | Connects to Salesforce, HubSpot, Marketo, ZoomInfo | Syncs data across your revenue stack |
Routing and remediation | Assigns bad records for cleanup | Creates accountability for data quality |
Auditability | Tracks what changed, when, and why | Provides transparency for compliance and troubleshooting |
Compliance controls | Enforces GDPR, CCPA, and data privacy rules | Reduces legal risk and builds customer trust |
Building a Data Quality Practice That Scales
Data quality is not a one-time project. It's ongoing maintenance. Records decay. Contacts change jobs. Companies get acquired.
Building a data quality practice that scales starts with three things: clear ownership, measurable standards, and the right tooling. Assign someone to own data quality. Set SLAs for completeness and accuracy. Automate validation and enrichment wherever possible.
The data quality manager role exists because revenue depends on it. Clean data drives better targeting, faster outreach, and more accurate forecasting. Talk to our team to see how ZoomInfo helps revenue teams maintain data quality at scale.