You're about to present your quarterly business review showing that mid-market pipeline is up 45% with win rates improving from 32% to 41%. Your CEO is impressed. Then someone asks, "Wait, how do we define mid-market?" and you discover that sales reps classify companies inconsistently—some use employee count, others revenue, others gut feel. Your impressive metrics are meaningless because the underlying data is garbage.
This isn't an analytics problem. It's a data quality problem.
Product marketing and revenue operations both rely on clean, consistent, accurate data to make strategic decisions. PMM needs reliable customer segment data to optimize positioning. RevOps needs accurate pipeline data to forecast and measure GTM effectiveness. When data quality is poor, both teams waste time analyzing noise instead of signal.
Data quality isn't an IT problem or a RevOps problem. It's a cross-functional discipline that requires PMM and RevOps to collaborate on governance, standards, and enforcement.
Why Data Quality Matters for PMM
Bad data doesn't just create reporting errors—it leads to bad strategy decisions.
Unreliable segmentation analysis. If customer segment classification is inconsistent or missing in 40% of records, your analysis of which segments convert best is built on incomplete data. You might deprioritize a high-converting segment because half the wins aren't properly categorized.
Misleading competitive intelligence. When competitive data isn't captured consistently—some reps log every competitor mention, others only log the winner—competitive win/loss analysis shows incomplete patterns. You might think you're strong against a competitor when you're actually only capturing your wins, not your losses.
Inaccurate content effectiveness measurement. If campaign tagging is inconsistent, attribution analysis can't connect content to pipeline. You can't identify your highest-performing assets or sunset underperforming content because you don't have reliable usage data.
False product-market fit signals. When use case or industry data is missing or wrong, PMM can't identify where product-market fit is strongest. You might chase segments that look promising in noisy data but don't actually convert.
Broken feedback loops. Win/loss analysis depends on accurate deal data. If loss reasons are generic ("price," "competitor," "timing"), you can't identify actionable patterns. Bad data prevents learning from wins and losses.
Core Data Quality Dimensions
Data quality isn't binary—it has multiple dimensions that matter differently for PMM and RevOps.
Accuracy. Is the data correct? Is "ABC Corp" actually in the financial services industry, or did someone select the wrong dropdown? Are deal values accurate or inflated?
Completeness. Are critical fields populated? If 50% of opportunities are missing customer segment data, your segment analysis is incomplete.
Consistency. Is data entered the same way across records? Do all team members use the same criteria to classify company size categories? Are product names spelled identically?
Timeliness. Is data current? Are opportunity stages updated promptly or stale? Are competitor fields updated when competitive dynamics change mid-deal?
Validity. Does data follow established rules and formats? Are email addresses properly formatted? Do dates make logical sense (created date before closed date)?
Uniqueness. Are there duplicate records creating confusion? Multiple entries for the same company or contact?
For PMM, accuracy and completeness of segment, competitive, and use case data matter most. For RevOps, consistency and timeliness of pipeline data drive forecast accuracy.
Building Data Quality Governance
Data quality requires proactive governance, not reactive cleanup.
Define field definitions explicitly. Every field used for strategic analysis needs crystal-clear definitions. "Mid-market" means what exactly? 100-1,000 employees? $10M-$100M revenue? Document it. When fields have dropdown values, define what each value means.
Establish data ownership. Who is responsible for ensuring each field is accurate? Usually: sales owns opportunity data, marketing owns lead and campaign data, customer success owns post-sale data. Clear ownership prevents "someone else will fix it" abdication.
Create data entry standards. How should company names be formatted? When should opportunities be created? At what stage is competitor data required? Document these standards and train teams on them.
Implement validation rules. Configure your CRM to enforce basic data quality: required fields at certain stages, format validation for emails and phone numbers, logical checks (close date must be after create date).
Build quality monitoring. Create dashboards showing data completeness and quality metrics: percentage of opportunities missing segment data, incomplete records by sales rep, stale opportunities not updated in 30+ days. Make these visible to leadership.
Assign accountability. Include data quality metrics in sales manager dashboards and reviews. If a rep's opportunities are consistently missing critical data, their manager should address it in 1-on-1s.
How PMM and RevOps Collaborate on Data Quality
PMM and RevOps have complementary responsibilities in data quality governance.
PMM defines what data matters. Through customer research and market analysis, PMM identifies which attributes predict success: industry, company size, technology stack, use case, growth stage. These attributes should be captured as CRM fields with high data quality standards.
RevOps operationalizes data capture. Once PMM defines critical data, RevOps configures CRM fields, validation rules, and required field logic. They also monitor compliance and flag quality issues.
PMM trains on why data matters. Sales teams are more likely to maintain data quality when they understand the "why." PMM can explain: "We need accurate segment data because it helps us identify which prospects to prioritize and which messaging resonates."
RevOps trains on how to enter data. RevOps provides tactical training on field definitions, dropdown values, and when data should be entered or updated.
PMM analyzes patterns to identify gaps. When PMM runs analysis and finds data gaps or inconsistencies, they report back to RevOps: "30% of opportunities are missing use case data, concentrated in the West region team."
RevOps investigates and addresses root causes. Is missing data due to unclear definitions? Lack of training? Fields being too hard to find? Insufficient validation? RevOps diagnoses and fixes systematic issues.
Both teams review quality metrics. In regular PMM-RevOps syncs, review data quality dashboards. Celebrate improvements. Identify new problem areas. Adjust governance as needed.
Common Data Quality Failures
Treating data quality as a one-time cleanup project. You can spend months cleaning your database, but without ongoing governance, it degrades immediately. Data quality is continuous, not a project.
Making too many fields required. If reps must complete 30 fields to progress a deal, they'll enter garbage to move forward. Limit requirements to truly essential fields and build a culture of quality rather than relying solely on enforcement.
Inconsistent field definitions across teams. If marketing defines "enterprise" as 5,000+ employees but sales uses 1,000+ employees, your data will be inconsistent. Establish company-wide definitions for critical classification fields.
No consequences for poor data quality. If reps face no downside from leaving fields blank or entering inaccurate data, quality will suffer. Data completeness metrics should be part of sales manager scorecards.
Ignoring data quality until analysis time. Discovering that 50% of your data is unreliable when you're trying to prepare a board presentation is too late. Build quality monitoring into ongoing operations.
Practical Implementation
Build data quality governance incrementally.
Phase 1: Define critical fields. PMM and RevOps jointly identify the 10-15 CRM fields most critical for strategic analysis and operational forecasting. Document clear definitions for each field and establish target quality standards (e.g., 95% completion rate).
Phase 2: Implement validation and requirements. Configure CRM validation rules, required field logic, and data quality checks. Start with the most critical field, ensure it reaches quality targets, then expand to others.
Phase 3: Build monitoring dashboards. Create data quality dashboards showing completion rates, accuracy metrics, and trends over time. Make these visible to sales leadership and GTM teams.
Phase 4: Establish governance meetings. Monthly or quarterly data quality reviews where PMM and RevOps analyze quality metrics, discuss issues, and refine standards. This prevents quality from degrading over time.
Phase 5: Train and reinforce. Ongoing training for new hires and refresher training for existing teams. When quality slips, don't just fix data—address root causes through better definitions, easier data entry, or clearer understanding of why it matters.
Making It Sustainable
Data quality governance isn't exciting work, but it's foundational to both product marketing and revenue operations effectiveness.
Make data quality a standing agenda item in PMM-RevOps meetings. Even five minutes reviewing quality dashboards and addressing emerging issues prevents small problems from becoming crises.
Celebrate improvements. When a team improves completion rates from 60% to 95%, recognize that publicly. Data quality is usually thankless work—make it visible and valued.
Connect data quality to outcomes. Show how better segment data led to more focused campaigns that generated higher-quality pipeline. Demonstrate how accurate competitive data enabled battlecard improvements that increased win rates. When teams see data quality driving results, they maintain it.
Periodically audit and refresh field definitions. As your business evolves, some fields become obsolete and new ones become critical. Annual reviews ensure your data governance stays aligned with business priorities.
The best product marketing insights and revenue operations analytics are built on clean, reliable data. Garbage in, garbage out isn't just a cliché—it's the reality that makes data quality governance essential. When PMM and RevOps collaborate to define, capture, and maintain high-quality data, you build a foundation for confident strategic decisions that actually drive revenue.