Every business leader knows the frustration of asking for a report and getting three different answers. You launch an AI initiative and watch it stall because nobody agrees on what the underlying data actually means. You spend millions on analytics tools, yet your best people are still burning hours hunting for the right data or arguing about whose numbers are correct.

This isn't a fault of your analytics and AI tools. It's a data governance problem. And it's silently undermining some of your most important business investments.

What happens when your organization decides to deploy an AI agent to support customer service, accelerate credit approvals, or speed up claims processing? The technology itself might be ready, but is the data feeding it?

In most enterprises, data is still fragmented across dozens of systems — CRMs, data warehouses, cloud platforms, legacy databases — with no shared understanding of what each field means, where it came from or whether it can be trusted, resulting in:

• AI agents hallucinating or delivering inconsistent answers, because they're drawing on unverified, incomplete data.
• Analytics dashboards contradicting each other, because "Net Revenue" means something different in Finance than it does in Sales.
• Data science teams spending the majority of their time on data preparation rather than on generating insights — with one industry estimate suggesting analysts waste up to 80% of their time just cleaning and finding data before they can analyze it.
• Regulatory and audit requests becoming ordeals as a regulator's request to show data lineage takes multiple days and multiple teams to pull together.

This is the day-to-day reality for organizations trying to scale AI and analytics in environments where data has never been fully inventoried, documented or governed.

If your CDO says, "My best people are wasting time on manual data prep. By the time we have answers, the business has moved on," — that's a data governance problem.

If your CIO says, "We want to stand up autonomous agents, but our data foundation is broken," — that's a data governance problem.

If a line-of-business leader says, "I want to automate workflows with AI, but I'm afraid it will hallucinate because of our data," — that’s a data governance problem too.

The common thread is trust. Leaders don't just need more data — they need reliable data that comes with context, and explainable origins.

A data catalog is, at its core, a system that helps your organization find, understand and trust its data. But a modern, AI-powered catalog will go much further. Informatica Cloud Data Governance and Catalog (CDGC) automates the discovery, classification, profiling, and enrichment of data assets across your entire enterprise — to every on-premises and cloud system you operate.

Here's what that means in practice:

For AI and Agentic Initiatives

AI models and agents are only as good as the data they consume. CDGC helps teams discover, describe and validate data before it feeds an AI pipeline, ensuring that RAG pipelines, ML models and Agentforce agents draw on data that is complete, consistent and contextually rich. When an AI makes a decision, teams can trace exactly which data informed it, making AI explainable.

For Self-Service Analytics and Reporting

When business users can browse a catalog of trusted, well-described data assets with clear definitions, ownership and quality scores, they stop waiting on IT and start analyzing. CDGC eliminates the bottlenecks forcing every data request through an overloaded data engineering team, empowering analysts and business users to understand and trust the quality and suitability of data. They get faster time to insight and less time arguing about whose numbers are right.

For the Questions That Matter Most

One of the most powerful capabilities in any modern catalog is data lineage — the ability to trace a piece of data from its original source through every transformation, system and report it has touched. Lineage answers the questions that keep compliance officers and regulators awake at night: Where did this data come from? Who has touched it? How was it transformed? But it also answers strategic questions: Which data pipelines are critical to our AI models? What happens downstream if this source changes?

With automated lineage, what once took many weeks can be completed in hours.

The business case for governed data is concrete:

• Banco ABC accelerated credit approvals by 70% by making trusted data more accessible to its decision-making systems.
• Paycor saved an estimated 36,500 work hours annually in data preparation by cataloging and governing its data assets.
• Helia unlocked 3x faster claim processing by making information discoverable across its organization.
• Genesis Energy reduced monthly operating costs significantly by automating compliance and data protection workflows.

By investing in Informatica’s data governance and catalog capabilities, these organizations laid the data foundation that makes their AI and analytics tools more effective.

The gap between raw data and a trusted business decision is wider than most organizations realize and filled with manual processes, tribal knowledge, duplicated effort, and avoidable risk. Closing that gap requires more than the latest AI model. It requires an intelligent system that continuously discovers, documents, classifies, and governs your data estate — one that gives every team, from AI developers to business analysts and compliance officers, a single, authoritative place to understand what data they have and how to use it responsibly.

The organizations that win with AI won't necessarily be the ones with the most data. They'll be the ones who know and trust the data they have deeply.That's why Informatica CDGC delivers an AI-powered data foundation that turns raw data into the trusted, contextual and compliant asset that modern AI and analytics programs demand.

Learn more about Informatica Cloud Data Governance and Catalog.