The Data Trust Playbook for RevOps Leaders
Nobody wakes up excited to talk about data quality. But every RevOps leader has had the meeting where someone questions a number in the dashboard, and suddenly the whole room is debating whether the data is even trustworthy instead of making the decision they came to make.
That's the real cost of bad data. Not the duplicates or the formatting errors themselves, but the erosion of confidence. When people don't trust the data, they either make decisions based on gut feel or they don't make decisions at all. Either way, you've lost the point of having data in the first place.
Data trust isn't a technical problem with a technical solution. It's an organizational problem that requires organizational discipline. This playbook covers how to build that discipline: who owns what, what rituals keep things on track, and how to measure whether it's working.
Why Data Trust Matters Now
RevOps exists to create a single source of truth across marketing, sales, and customer success. That only works if people believe the source.
The stakes have gotten higher. More decisions are automated or semi-automated based on data—lead scoring, territory assignment, renewal predictions, compensation calculations. A small error doesn't just mislead a report; it triggers wrong actions at scale.
Meanwhile, data sources have multiplied. The average company has dozens of tools feeding into their systems. Each integration is a potential source of inconsistency. Each migration is a chance for data to get mangled. The surface area for problems has expanded faster than most teams' ability to manage it.
And leadership expects more. Boards want data-driven narratives. Investors want metrics they can trust. Executives want dashboards that actually reflect reality. "We're not sure if this number is right" isn't an acceptable answer anymore.
Data trust isn't a nice-to-have. It's the foundation that makes everything else in RevOps possible.
Roles and Responsibilities: Who Owns What
Data quality fails when everyone assumes someone else is handling it. You need explicit ownership.
Here's a RACI framework for data trust:
Data Steward (Responsible)
This is the person who actually does the work—monitoring quality metrics, investigating issues, running cleanup projects, maintaining documentation. In smaller orgs, this might be a RevOps analyst. In larger ones, it could be a dedicated data quality role.
Responsibilities
- Monitor data quality dashboards daily/weekly
- Investigate and resolve data issues
- Document data definitions and business rules
- Run periodic cleanup and validation
- Train teams on data entry standards
RevOps Leader (Accountable)
The person who's on the hook if data trust erodes. They don't do the day-to-day work, but they're responsible for ensuring it gets done and escalating when it doesn't.
Responsibilities
- Set data quality standards and targets
- Allocate resources for data initiatives
- Escalate systemic issues to leadership
- Report on data trust metrics
- Make trade-off decisions when priorities conflict
Department Heads (Consulted)
Sales, marketing, and CS leaders need input on what "good data" means for their functions. They define requirements and flag when data isn't meeting their needs.
Responsibilities
- Define data requirements for their function
- Flag data issues affecting their teams
- Enforce data entry standards within their teams
- Provide context on business rules
End Users (Informed)
Reps, marketers, CSMs—the people who create and consume data daily. They need to know what's expected and what's changing.
Responsibilities
- Follow data entry standards
- Report suspected data issues
- Participate in training
The specific names don't matter as much as having clear answers to: "Who notices when something's wrong?" and "Who fixes it?"
Quarterly Roadmap: Building Trust Over Time
Data trust isn't a one-time project. It's an ongoing practice. Here's a four-quarter roadmap for getting started.
Q1: Foundation
Goal: Establish baseline and visibility.
- Audit current data quality across key objects (accounts, contacts, opportunities)
- Define your core metrics: duplicate rate, completeness rate, accuracy rate
- Build or configure a data quality dashboard
- Document your most critical data definitions (What counts as an MQL? When does an opportunity close?)
- Identify your top three data pain points based on stakeholder input
Exit criteria: You can answer "How good is our data?" with numbers, not guesses.
Q2: Quick Wins
Goal: Demonstrate value and build momentum.
- Fix the top three pain points from Q1
- Implement validation rules to prevent the most common errors
- Run a deduplication project on your highest-impact object
- Establish a weekly data quality review ritual
- Train one team on improved data entry practices
Exit criteria: Stakeholders notice improvement. At least one pain point is resolved.
Q3: Systematic Improvement
Goal: Move from reactive to proactive.
- Implement automated data quality monitoring with alerts
- Expand validation rules to cover more fields and objects
- Create a data issue intake process (how do people report problems?)
- Document and share data quality wins internally
- Begin tracking data trust score trends over time
Exit criteria: You're catching issues before users report them. Trends are improving.
Q4: Sustainability
Goal: Make data trust self-sustaining.
- Integrate data quality into regular business reviews
- Establish SLAs for data issue resolution
- Create onboarding materials for new hires
- Plan the next year's data initiatives based on what you've learned
- Celebrate progress and recognize contributors
Exit criteria: Data trust is part of how the organization operates, not a special project.
Rituals That Keep Things on Track
Roadmaps are nice, but rituals are what actually make change stick.
Weekly: Data Quality Standup (15 min)
Who: Data steward + RevOps leader
When: Same time each week, non-negotiable
Agenda
- Review data quality metrics vs. targets (5 min)
- Triage new issues from the past week (5 min)
- Update on in-progress fixes (5 min)
This is a forcing function. Even if nothing's wrong, the meeting happens. It keeps data quality visible and ensures small issues don't pile up.
Monthly: Data Trust Review (30 min)
Who: RevOps leader + department heads
When: Aligned with your monthly business review cadence
Agenda
- Data trust scorecard review (10 min)
- Feedback from each department (10 min)
- Prioritization decisions for next month (10 min)
This is where you get cross-functional input and make trade-offs. It also keeps leadership aware of data quality as an ongoing concern, not something that only comes up when there's a crisis.
Quarterly: Data Trust Retrospective (60 min)
Who: Full RevOps team + key stakeholders
When: End of each quarter
Agenda
- Review quarterly metrics and progress against roadmap (15 min)
- What worked well? (15 min)
- What didn't? What surprised us? (15 min)
- Adjustments for next quarter (15 min)
This is where you learn and adapt. Data trust isn't a static target—the business changes, tools change, and your approach needs to evolve.
Templates You Can Steal
Data Trust Scorecard
| Metric | Target | Current | Trend |
|---|---|---|---|
| Duplicate rate (Contacts) | <2% | ||
| Duplicate rate (Accounts) | <1% | ||
| Completeness (Critical fields) | >95% | ||
| Stale records (No activity 12mo) | <10% | ||
| Data issues resolved <48hr | >80% | ||
| Stakeholder satisfaction (quarterly survey) | 4/5 out of 5 |
Adjust metrics for what matters in your business. The point is having a consistent way to measure and communicate progress.
Weekly Standup Template
Date:
Metrics check
- Duplicates: (target: )
- Completeness: (target: )
- Open issues:
New issues this week
In progress
- (owner: , ETA: ________)
- (owner: , ETA: ________)
Decisions needed
-
Pitfalls to Avoid
- Boiling the ocean. You can't fix everything at once. Pick the highest-impact problems first and show progress before expanding scope.
- Making it purely technical. Data quality tools help, but they don't solve organizational problems. If sales leadership doesn't care about data entry, no tool will fix your CRM hygiene.
- Perfectionism. 100% data quality is impossible and pursuing it is a waste of resources. Define "good enough" for each use case and focus on maintaining that bar.
- Invisible progress. If you're improving data quality but nobody knows, you're not building trust—you're just doing maintenance. Communicate wins, share metrics, make the work visible.
- No consequences. If bad data entry has no consequences, it won't change. That doesn't mean punishing people, but it does mean making quality part of how performance is measured and discussed.
Quick Wins to Start This Week
You don't need a full program to start building trust. Here are five things you can do immediately:
- Run a duplicate report on your contact or account object. Just knowing the number is a start.
- Ask three stakeholders: "What's your biggest data frustration right now?" The answers will tell you where to focus.
- Add one validation rule to prevent the most common data entry error you see.
- Schedule the weekly standup. Put it on the calendar. Make it recurring. Protect the time.
- Document one critical definition that people argue about. Get agreement and publish it.
Data trust is built one decision at a time. Start making those decisions this week.
How do I get leadership to care about data quality?
Connect it to outcomes they already care about. "Our duplicate rate is 8%" is abstract. "We're emailing the same leads multiple times and annoying them" or "Sales is wasting 3 hours a week dealing with bad contact data" is concrete. Find the pain points that affect pipeline, revenue, or customer experience, and frame data quality as the solution. Metrics and dashboards help, but stories about real impact land better.
What's a reasonable data quality target to start with?
For most B2B companies, aim for under 3% duplicate rate on contacts, under 2% on accounts, and over 90% completeness on fields you've defined as critical. These aren't perfect, but they're achievable baselines that represent meaningful improvement for most organizations. Once you're consistently hitting those, tighten the targets. The right goal is one that's challenging but not demoralizing—something the team can actually achieve within a quarter.
Should I hire a dedicated data quality person?
It depends on your scale and pain level. Under 50,000 records, a RevOps generalist can probably handle data quality as part of their role. Between 50,000-500,000 records with multiple data sources, consider a part-time or dedicated data steward. Above that, or if data quality is a constant fire drill, a dedicated role pays for itself quickly in time saved and errors prevented. The question isn't really headcount—it's whether someone's job description explicitly includes data quality accountability.
William Flaiz is a digital transformation executive and former Novartis Executive Director who has led consolidation initiatives saving enterprises over $200M in operational costs. He holds MIT's Applied Generative AI certification and specializes in helping pharmaceutical and healthcare companies align MarTech with customer-centric objectives. Connect with him on LinkedIn or at williamflaiz.com.
