Who knew IT was so optimistic? When we surveyed over 300 IT and business leaders about their data quality, 69% of IT professionals rated it as excellent or very good. When business users were asked, only 51% agreed. But here's what was surprising: 74% of IT folks think they collaborate great with business teams, while just 30% of business users feel the same way.

If you're scratching your head wondering why your carefully crafted data initiatives aren't getting the business traction you know they deserve, look to this perception gap as the probable culprit.

You understand the importance of solid data infrastructure and how it aligns with the goals of your company; but how do you convince the business to invest in a data foundation? While forecasts vary, the consensus points to a sustained and substantial growth trajectory for AI spending, with no peak anticipated in the near future — but consider that 67% of CDOs can't get even half their GenAI pilots into production.

You need to shift the thinking within your company that when GenAI pilots fail to deliver value, it's not a business problem — it's a data infrastructure problem.

The data management challenge has never been more complex. According to Informatica's 2024 CDO Insights survey, 38% of organizations are grappling with increasing data volume and variety, with 41% already managing 1,000+ data sources — and 79% expecting that number to grow.

Just recently, I was talking to a data leader at a global pharmaceutical company who told me about their experience five years ago with a $10 million data cleanup project. The moment the consultant walked out the door, he knew it was already falling apart. "There's no amount of manual work that will ever keep our data at the quality we need," he said. And five years ago, AI demands were a fraction of what they are today.

When AI models get fed poor-quality data, they don't just perform badly — they actively damage customer relationships with biased predictions, inaccurate guidance or outright hallucinations.

Let’s look at Air Canada's recent chatbot fiasco: their AI hallucinated a bereavement discount policy that didn't exist, promising a customer a refund. When the customer tried to claim it, Air Canada initially refused, leading to a legal battle that they ultimately lost.¹

That's when AI becomes a liability instead of an asset.

After listening to hundreds of organizations on this exact problem, I've learned that throwing more manual processes at it just makes things worse — slowing things down and riddling the data with errors. You need automation to support a data culture that can scale with AI demands, not against them.

The game-changer is automated enterprise-level data management. I'm talking about:

Foundation:

• A data foundation that has everyone working off the same data, getting the same answers to everyday questions from simple to the most difficult
• Smart duplicate detection and data mastering that creates the unified customer profiles everyone keeps asking for from the back office to the executive floor

Automation:

• AI-powered observability that's monitoring data quality 24/7 without human babysitting
• Data lineage tracking that gives you real-time visibility into where everything comes from and how it's transformed
• Intelligent cataloging that spans multi-cloud and on-premises environments and pulls metadata from your messiest, most complex sources automatically

Access and Governance:

• Governance platforms that actually work across your multi-cloud reality
• Marketplaces that provide insights about the data, where teams can 'shop' for what they need, with visibility to completeness, accuracy and usefulness

The organizations getting this right are seeing pretty compelling returns, too. They're cutting data management costs, eliminating storage waste from redundant data, and — this is the big one — giving business teams the fast access to the trusted data they need to make decisions quickly.

Rodobens, a Brazilian financial services leader, is exactly what I’m talking about. To improve customer personalization and operations across its five business units, Rodobens decided to reimagine its master data strategy. With Informatica’s Intelligent Data Management Cloud (IDMC), they have dramatically improved sales and IT efficiency, saving over 50% of hours in monthly data maintenance and achieving 182% of their Gross Merchandise Value (GMV) target – all within the first six months.

But the technical solution is only half the battle. The other half is speaking business language.

Do you know what business leaders don't really care about? Uptime percentages. System availability. Project delivery dates. Those are expected.

What really gets their attention, using a marketing example, are things like pipeline dollars generated from recently enriched contact records, leads from contacts who just changed companies and campaign bounce rates under 1 percent. Those are metrics impacted by data quality and certainty.

At Informatica, our IT team completely transformed its relationship with marketing by tracking how much pipeline came from their data enrichment work instead of how many servers they kept running. They’ve extended this collaboration to sales, service and support – making the entire company their advocate.

The pattern I see with successful teams is this: they connect every technical metric to a business outcome. Email bounce rates translate to campaign effectiveness. Unified customer views drive cross-sell improvement. Fast data access means 2x faster AI deployment.

When you make those connections visible, everyone suddenly cares about data quality.

Don't try to boil the ocean. Pick one customer journey that matters to the business and nail the end-to-end data foundation. Focus on showing immediate results, not technical perfection.

Join their business review meetings. Build those shared metrics — ones that both you and the business teams actually track and care about. And position yourself as the AI enabler, not the gatekeeper. Poor data quality doesn't just make AI perform badly; it kills confidence in AI initiatives entirely.

The organizations winning at this are the ones who figured out how to make data infrastructure feel like a business accelerator instead of a technical requirement.

Ready to turn your data infrastructure into a business accelerator? We've compiled insights from 300+ IT and business leaders we surveyed into a comprehensive report: "From Data Silos to AI-Enabled Customer Engagement," authored by Kerry Bodine. Get the insights you need to bridge the CX-AI-data gap within your organization.

Get the Report

_{¹https://www.forbes.com/sites/marisagarcia/2024/02/19/what-air-canada-lost-in-remarkable-lying-ai-chatbot-case/}