Data governance leaders know the tension well. On one side: strategic mandates to enable trusted data for AI, analytics, and regulatory confidence. On the other: constrained budgets, limited headcount, and executives who want proof that another investment will move the needle.
That tension was at the center of a recent Qualytics webinar on Making the Business Case for Data Quality. Qualytics Founder & CEO Gorkem Sevinc, was joined by Matt Robuck, VP of Data & Analytics at Georgia-Pacific, and Renee Colwell, Global Data Quality Lead at Revantage, to talk about how to justify investment in data quality in a language executives actually respond to.
What emerged was a clear framework for reframing data quality from a cost center into a business enabler that supports AI readiness, reduces risk, and improves operational efficiency. Here’s what we learned:
1. Start With Business Outcomes, Not “Data Quality”
One of the most consistent themes from the session was deceptively simple: don’t lead with data quality as the goal.
As Matt put it, “The first rule of data quality is you do not say data quality.” He shared a moment that many data leaders will recognize. His team deployed an AI agent on top of a sales dataset to help sales teams win deals faster. The technology worked quickly. The data did not.
“It took us two weeks to get the large language model up on top of the data set returning answers. It took another six months for it to actually be useful. And that was because the data was in such a poor shape that we coded around it. We spent six months coding around bad quality data.”
That contrast—two weeks versus six months to see value from an AI project—became a story that unlocked executive understanding. When you have a story that hits on the business impact of poor quality data, that clicks with business executives.
The lesson for governance leaders is clear: executives don’t fund data quality in the abstract. They fund the acceleration of outcomes they already care about. Data quality matters when it’s framed as the thing standing between intent and impact.
2. Make the Cost of Inaction Explicit with Examples
Several attendees echoed this sentiment in the live chat: AI without trusted data doesn’t just fail quietly. It scales mistakes.
Renee illustrated this with a concrete risk example from her work supporting AI-driven insurance decisions. A mis-coded attribute (marking a 30-story urban building as having a wooden frame) triggered a massive spike in insurance cost when processed by an automated model.
“This little teeny, on a tech level, very small problem turned into X amount of dollars that you would have to pay because you didn’t catch it in time.”
For governance leaders, this reframes ROI conversations. The question is no longer what does this cost? but what does it cost if we don’t catch this problem earlier?
This is where data quality shifts from being seen as “operational hygiene” to being seen as financial exposure management. Real examples like this resonate strongly with executives as evidence for how investment in data quality mitigates:
- Liability risk
- Regulatory exposure
- Data breaches tied to poor-quality data
- Automated decisions made at scale without explainability
3. Translate Quality Into Business Metrics Executives Recognize
Another strong theme from both the panel and the audience: internal data quality scores don’t move budgets. Business impact does.
Matt reinforced this by explaining that while Georgia-Pacific tracks internal data quality metrics, leadership conversations focus on outcomes like missed revenue, margin leakage, rework, delayed decisions, and inventory errors.
This translation layer is critical for governance leaders. Instead of reporting quality in isolation, successful teams connect it directly to:
- Manual reconciliation effort and cycle time
- Inventory accuracy and buying leverage
- Delayed decisions caused by low trust
- Rework created when errors are discovered downstream
Data quality metrics matter most when they’re tied to business outcomes.
4. Use AI Readiness as a Forcing Function
AI has raised the stakes for data quality exponentially. Derivative users of data have exploded, and now issues that once stayed isolated now move faster, spread further, and carry real consequences.
As Matt explained, the old mindset of “if we don’t know about it, it doesn’t exist” no longer holds when AI systems are operating at scale. “That’s how you end up with a $100,000 problem,” he said. AI doesn’t apply judgment or context—it simply amplifies whatever data it’s given.
This also forces a change in ownership. AI collapses the distance between source systems, data pipelines, and business consumption. Data quality can no longer be treated as something the data team fixes downstream. It has to be embedded into value streams and co-owned by the teams responsible for outcomes, not just infrastructure.
For governance leaders, AI readiness becomes the clearest forcing function. It reframes data quality as the mechanism that keeps speed from turning into risk—and makes end-to-end accountability possible as automation scales.
5. Acknowledge the Human and Organizational Reality
The live chat surfaced something governance leaders rarely say out loud: sometimes “data quality problems” sit at the intersection of data, process, and culture. Attendees asked how to handle ambiguous cases where it’s unclear whether the root cause is a process issue or a data issue—and how to avoid becoming the default scapegoat.
The panel’s implicit answer was consistent: transparency and explainability matter more than blame. When issues are visible end to end, teams stop arguing about ownership and start fixing root causes together.
That’s why Renee works closely with different groups across her organization to connect directly with their needs. “I don’t just go, here’s a spreadsheet, I’m throwing it over the wall. I spend some time listening to their pain.”
“There’s a lot of fear that people have with data. Frankly, they’re terrified. So soothe some of that by saying we’re going to start small, focused, and on something that’s important.”
This also ties directly to data literacy. Multiple attendees called it out as the “gorilla in the room.” Governance leaders aren’t just enforcing controls; they’re translating data behavior into business meaning so leaders can act with confidence.
Turning the Framework Into Action
For governance leaders preparing to justify investment to a CDO, CFO, or executive sponsor, the framework is practical:
- Anchor the conversation in a real business initiative (AI, reporting, operations)
- Highlight the gap between intent and impact caused by poor data quality
- Quantify delay, risk, or rework in business terms
- Use concrete stories, not abstract scores
- Position data quality as the fastest path to outcomes leadership already wants
Data quality doesn’t earn investment because it’s “important.” It earns investment because it makes everything else work.
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