A tactical guide for talking to executives about data quality

This year, Openprise partnered with RevOps Co-op and MarketingOps to conduct a survey on data quality. This collaborative effort resulted in over 150 responses from operations professionals, giving us new insights into how people define data quality, what holds businesses back from achieving better data quality, and what patterns differentiate teams that achieve good data quality from the rest. Get the full report here.

Lost in translation? You’re not alone.

RevOps professionals know how much their work matters—but when it comes to communicating that value to leadership, things often get lost in the weeds. You talk about enrichment waterfalls, deduplication logic, or segmentation breakdowns. They hear… noise.

If this sounds familiar, you’re not bad at your job. You’re just speaking a different language.

Executives don’t want to know how the gears turn. They want to know what the machine can do for them.

To make your case—and get buy-in—you need to stop reporting on process and start framing your impact around outcomes. This guide will help you do just that.

Let’s start with the three questions every executive has on their mind.

1. How does data quality improve business operations?

Executives don’t care about list hygiene. They care about pipeline velocity. If you’re solving operational bottlenecks, show how it moves the business forward.

• If you are working on list loading, frame it this way:
  → “Our automated list loading processes mean that the lead records from events are loaded as soon as our marketing team gets them, which means an average follow-up time of four hours instead of two weeks.”
• If you are improving lead routing accuracy, frame it this way:
  → “We used to have 30% of our leads end up in the ‘default’ bucket due to messy data. Now, with automated cleaning, enriching, and routing, our accuracy is 98%, saving hours each day. We’ve cut the routing team from ten people to two, and the other eight people are now working on lead outreach and data analysis.”
• If you are enabling self-service tools for sales and marketing, frame it this way:
  → “Because ABM lists are self-serve, as soon as marketing has an idea for a program, they can query our database themselves using the no-code platform—no more waiting for the ops team to run the list.”
• According to the 2025 State of RevOps Survey, poor data quality stalls progress at every level. 70% of revenue operators said it makes strategic decision-making difficult. Among teams with poor data, 92% reported that it actively undermines their go-to-market execution. When systems aren’t clean or connected, ops becomes reactive, not strategic—and leadership feels the pain in pipeline performance.

2. How does better data quality drive revenue impact?

You may not own the revenue number, but you definitely influence it. When operations saves time or accelerates action, you’re increasing capacity without adding headcount.

• If you are automating manual work, frame it this way:
  → “It used to be a manual process to load lists into our CRM. Now, it’s drag-and-drop. One ops person now supports five more demand gen programs a month.”
• If you’ve improved list segmentation, frame it this way:
  → “By inferring data from the leads we already have, our event invite lists are now 23% larger. That’s more pipeline from the same budget.”
• If you’ve repurposed the team from cleanup to strategy, frame it this way:
  → “Data analysts are no longer just scrubbing spreadsheets. Now they’re uncovering new market segments we can pursue.”

The 2025 State of RevOps Survey shows a clear pattern: teams with better data aren’t just more accurate—they’re more efficient. Respondents with “acceptable” data quality were three times more likely to have automated integrations in place and 60% more likely to use custom tools to manage customer and prospect data. These process improvements free up time, eliminate waste, and let operations focus on driving revenue—not fixing errors.

3. How do automated RevOps processes reduce costs?

Execs love saving money just as much as making it. Many data quality initiatives pay off not just in performance but in operational savings.

• If you are consolidating tools, frame it this way:
  → “We reduced our stack from 12 tools to 3 by consolidating into a single automation platform. That saved 8 to 16 hours per month and $20K a year in vendor costs.”
• If you’re reducing contractor dependency, frame it this way:
  → “We no longer need to hire contractors during event season. Automated list loading now lets our team handle this in-house, saving the cost of outsourcing.”
• If you’re optimizing data vendor costs, frame it this way:
  → “With an enrichment waterfall, we don’t pre-pay for data we don’t use. We estimate a 50% savings in enrichment spend.”

The 2025 data revealed that organizations with better data quality are more likely to consolidate tools and optimize enrichment workflows, and three times less likely to use spreadsheets and manual processes. These changes aren’t just operationally cleaner, they’re also budget-friendly. When ops teams automate enrichment and streamline tech stacks, companies see real cost savings and faster time-to-value.

How to communicate RevOps data quality wins to executives

For execs, it’s not about how the data works. It’s about how the data delivers business value. Here’s how to frame your data wins in a way that resonates:

✅ Use real numbers. Talk in hours saved, programs launched, or costs reduced.
✅ Speak in business outcomes. Think speed-to-lead, campaign lift, or cost per acquisition.
✅ Use metaphors when needed. Not “deduplication logic”—try “removing duplicates so reps don’t step on each other’s toes.”
✅ Tie it to executive priorities. If your CEO is focused on pipeline growth, connect your project to faster conversion or better segmentation.

Data quality messaging mistakes to avoid

Powerful messages and the most well-intentioned explanations can fall flat if they’re loaded with jargon or focused on tools instead of outcomes. Here are a few things to reframe:

“We built an enrichment waterfall that sequences our data providers based on match quality.”
✅ “Our contact data is more complete and accurate, so reps spend less time hunting for the right info and more time selling.”

“We improved dedupe logic to reduce false negatives on our matching rules.”
✅ “Sales no longer wastes time calling the same lead twice.”

“We implemented new lead scoring formulas across GTM platforms.”
✅ “Marketing now knows which leads are worth spending money on—before the campaign even launches.”

Ready to align execs on data quality? Start with this checklist

You’re not just fixing dirty data. You’re enabling growth.

But to get the resources and recognition you deserve, you have to sell the impact. Next time you prep for a leadership meeting, ask yourself:

Am I talking about the problem, or about what it helps the business do better?

Download our 7-step checklist to see how teams like yours turn technical wins into executive alignment—and real momentum.