Want better data quality? Start with leadership, not technology

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.

Imagine you’ve just moved to a small village in a foreign country. You’ve studied the language for years and can read technical manuals fluently. However, the locals speak a different dialect filled with idioms and cultural references you’ve never encountered.

One day, your car breaks down. You confidently explain to the local mechanic that the “transaxle differential has excessive backlash, causing drivetrain oscillation.” He looks at you blankly. But when your neighbor steps in and says, “It makes a clunking noise when he turns left,” the mechanic immediately nods in understanding.

The problem isn’t your technical knowledge. You correctly diagnosed the issue. The problem is that you and the mechanic, despite technically speaking the same language, aren’t communicating in a way that builds shared understanding.

This is the challenge RevOps professionals face when discussing data quality with executives. The technical vocabulary that accurately describes data problems – normalization, deduplication, field validation, integration orchestration – can sound like a foreign dialect to C-suite leaders who think in terms of business outcomes, not technical processes.

According to the 2025 State of RevOps Survey, this communication gap is the primary reason that data quality initiatives fail:

• 79% of respondents said their company doesn’t have a standard definition of “data quality”
• 55% said executives aren’t enforcing usage of critical systems like CRMs
• Nearly half (48%) report that leadership doesn’t understand what’s technically possible

The question isn’t whether your data needs improvement – the survey found 99% of companies face technical data challenges. The real question is: How do you translate your technical expertise into everyday language that resonates with the people who control the resources?

Expert insights on speaking the language of the C-suite

One of our customers, Ali Rastiello, VP of RevOps at Health Catalyst, has mastered this communication challenge. She doesn’t expect executives to learn technical jargon. Instead, she transforms complex concepts into stories.

“You have to simplify what you need down to ‘here is the problem and here are solutions,'” Ali explains. “And you’ve got to be able to explain the impact to the business.”

When talking to leadership, Ali focuses on outcomes that executives value:

• Will this data initiative boost revenue?
• Improve team productivity?
• Provide clarity for better decisions?

Answering these fundamental questions is more important than the mechanics of the problem.

Sometimes, this means using creative metaphors that bridge the knowledge gap. When Ali needed to explain how marketing automation programs capture form submissions to a colleague, she didn’t dive into technical specifics. Instead, she used a simple faucet analogy:

“You can turn a faucet on and just let it flow, or you can get a cup and pour the water into this cup so I can drink it, or put a pot so I can boil it for pasta, or use a mop bucket because I’m going to clean something,” Ali explains. “You have different reasons that you’re trying to get that water out of that spigot, and you need to put it in a place to go get that next action.”

This approach transformed an abstract technical concept into something intuitive: water flowing toward different purposes. The metaphor created an immediate understanding that technical language couldn’t achieve.

Ali’s approach works because it meets executives where they are. She doesn’t expect them to become technical experts overnight. Instead, she builds bridges using language they already understand, connecting technical requirements to business outcomes they already value.

The data maturity roadmap: creating a shared vision

Beyond day-to-day communications, Ali has found that executives need a bigger-picture framework to understand how data quality evolves.

At Health Catalyst, she implemented a three-stage data maturity model that helps leadership visualize the journey:

“You can’t flip a switch and say ‘We’re data mature!'” Ali emphasizes. This roadmap helps executives understand that advanced capabilities like AI-powered insights require the foundation of clean, standardized data – just as you can’t build a house without laying the foundation first.

The model serves as a translation tool between technical requirements and business aspirations. When leadership wants sophisticated revenue forecasting (Stage 3), Ali can show them why they first need consistent field completion and data standardization (Stage 1).

This approach aligns perfectly with our survey findings. Organizations with acceptable data quality are 26% more likely to enforce system adoption – a key leadership responsibility – than those with poor data quality. The best leaders don’t just approve budgets; they actively champion the practices that make good data possible.

One frustrated survey respondent put it bluntly: 

“No matter how much I stress the importance, leadership believes they can sprinkle some money, and a fairy will just clean it all up. They shy away from having to make big boy/girl decisions.”

This disconnect is even more critical as organizations invest in AI initiatives. If leadership wants to leverage AI as a competitive differentiator, they must understand that the underlying data needs to be clean, standardized, and enriched—something that requires recognition and support from the top.

Checklist: 7 steps to align with execs on data quality

Based on insights from our survey and experts like Ali, here’s how to engage leadership in your data quality initiatives:

1. Ask executives what they mean by “data quality” – Establish a shared definition across departments to ensure everyone is working toward the same goal.
2. Get buy-in on how data should be categorized and prioritized – Not all data is equal. Determine which data points directly impact revenue and customer experience.
3. Educate executives about the reality of data quality – Help them understand what’s technically possible and the resources required.
4. Put a dollar amount on the cost of bad data – Quantify the impact of poor data quality on revenue, productivity, and decision-making.
5. Build to scale – Develop processes and systems that can grow with your organization.
6. Develop KPIs for data quality – Create measurable benchmarks to track progress and demonstrate value.
7. Accept that perfection is impossible but continuous improvement is essential – Position data quality as an ongoing business practice, not a one-time project.

The most successful organizations don’t view data quality as a technical problem to be solved but as a fundamental business practice that requires continuous attention, cross-functional alignment, and leadership commitment.

Want to implement these strategies in your organization?

Download the Data quality action plan: your 7-step project checklist to learn how to communicate with and engage leadership in your data quality initiatives.

Moving from data quality to business quality

Go-to-market teams and ops practitioners speak the language of data. Management is not always a native speaker. However, they do hold the checkbook. So if you want to get your initiative funded and work better with the C-suite, learn to speak executive.

By effectively communicating with leadership and establishing alignment, GTM ops professionals can shift from order-takers to strategic advisors. Take your role and career to the next level by helping leaders connect the dots between data and growth.

For more insights into the state of data quality in operations, check out the full 2025 State of RevOps Survey: Data quality’s impact on GTM execution.