Is Your Data Catalog Actually Hurting Your AI Efforts?

“A new data scientist in our company saw a revenue field in a table and assumed it was the source of truth for company revenue data. He then spent several months building out some complex models for revenue forecasting assuming that was the right one. A day before presenting the project to the CFO, he realized that in fact that table should have been retired a long time ago, and was completely wrong. The DS had to cancel the CFO meeting at the last moment and redo a mountain of work”

When we talk about data catalogs, the conversation typically revolves around their value—improving discoverability, ensuring compliance, driving data democratization. But here’s the reality: if your data catalog isn’t implemented right, it could actually be hurting your AI efforts. And no one’s talking about it.

Let’s start with what a poorly implemented data catalog looks like. It’s often outdated, bloated with irrelevant entries, and lacks proper context. You end up with a list of datasets that are misclassified, incomplete, or poorly documented. Imagine an AI model trying to pull insights from this mess—it’s like asking a chef to create a five-star meal with a pantry full of unlabeled, expired ingredients. The AI may go through the motions, but you’re definitely not getting a Michelin-worthy result.

Obscuring Data Relationships: The Silent Killer of AI

Data relationships are crucial to building robust AI models. You need to know how datasets connect, where they overlap, and which sources are trustworthy. A poorly structured catalog can obscure these relationships, leaving your AI flying blind. Instead of a clear, well-mapped path between data points, you’re left with a murky web where the connections are unclear—or worse, misleading.

Take an AI model designed to predict customer churn. If the relationships between datasets—such as customer interaction history and service usage—are hidden or incorrectly categorized, the model can draw the wrong conclusions. What should be predictive insights turn into noise, and your AI is effectively spinning its wheels, wasting time and resources.

Data Quality: Garbage In, Garbage Out

Let’s talk about data quality for a second. A key role of the data catalog is ensuring that your AI models are using clean, high-quality data. But if your catalog isn’t maintained, it can actually feed your AI poor-quality data—resulting in flawed outputs. You know the old saying: garbage in, garbage out. If your AI is ingesting stale or incorrect data, don’t expect cutting-edge insights to emerge.

The problem gets worse when users start to distrust the data catalog. Once it’s seen as unreliable, they’ll bypass it entirely—making governance a nightmare and potentially reintroducing data silos. Your AI projects stall, and you’re back to square one.

The False Sense of Security

A poorly implemented data catalog can also create a dangerous false sense of security. Just because you have a catalog doesn’t mean it’s adding value. In fact, it could be leading your teams to rely on incorrect, outdated, or irrelevant datasets for AI projects. The result? Misguided decisions and, ultimately, AI failures. You may have the best AI model on paper, but if it’s trained on bad data, the outcomes are going to be disastrous.

How to Avoid the Trap

So how do you prevent your data catalog from becoming a liability? It starts with a shift in thinking. A data catalog isn’t just a repository—it’s a living, breathing system that needs regular care and feeding. You need to actively manage data quality, ensure real-time updates, and provide context-rich metadata that surfaces the right relationships between datasets.

At Metaphor, we believe in data catalog agility—moving beyond static, monolithic catalogs to dynamic, context-driven platforms that continuously learn and evolve. These catalogs don’t just store data; they intelligently suggest connections, highlight potential biases, and ensure that AI models are built on the strongest possible foundation.

Ultimately, if your data catalog is hurting your AI efforts, it’s time for a rethink. Don’t settle for a glorified index. Your catalog should be the backbone of your AI initiatives, not a roadblock. By taking a more active, agile approach to cataloging, you’re not just building better AI—you’re building a future where data empowers every decision, without compromise.

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About Metaphor

The Metaphor Metadata Platform represents the next evolution of the Data Catalog - it combines best in class Technical Metadata (learnt from building DataHub at LinkedIn) with Behavioral and Social Metadata. It supercharges an organization’s ability to democratize data with state of the art capabilities for Data Governance, Data Literacy and Data Enablement, and provides an extremely intuitive user interface that turns even the most non-technical user into a fan of the catalog. See Metaphor in action today!