You need more than a data catalog to get quality AI data

80% of AI projects are failing to meet expectations because the data feeding them isn’t up to the task. For businesses, this a leadership risk. Without quality data, promising AI initiatives stall, trust erodes, and strategic advantage slips away.

Many organizations turn to data catalogs to help teams discover existing data more easily. Catalogs bring a semblance of transparency, making it possible to identify what datasets exist, where they reside, and sometimes providing useful lineage or governance metadata.

To be sure, the right data catalog is essential. But you need more than a data catalog to solve for the real challenge: data quality.

Data catalogs are a starting point but not the solution

Data catalogs are essential because they help businesses manage the sheer scale of modern enterprise data. Knowledge workers spend up to 30% of their time simply trying to find and prepare data rather than analyzing it for decisions. Catalogs help reduce that time sink by centralizing visibility across data repositories and creating a single source of reference. They also support collaboration across teams by giving business users, analysts, and engineers a shared view of the data landscape.

Yet despite these benefits, catalogs, whether built internally or offered by external providers, can create a false sense of progress. They make data discoverable but not necessarily usable. They catalog the existence of data but do little to guarantee its quality. In practice, this leads to several impediments that undermine AI initiatives:

• Discovery ≠ readiness: A catalog may help you locate a dataset, but it doesn’t tell you whether that data is clean, complete, or structured for AI use. Executives can end up investing in AI projects only to learn that the data needs months of remediation before it can be useful.

• Quality is invisible in metadata: Metadata fields can describe a dataset’s size, location, or owner, but they can’t reveal whether the underlying content is biased, outdated, or riddled with gaps. Teams may check a dataset out of the catalog only to discover too late that it introduces errors into models.

• Governance ≠ integrity: Some catalogs label datasets as “approved” or “compliant,” but that approval often reflects governance processes, not an actual quality review. A dataset may tick the box for lineage and access rights while still harboring inconsistencies or hidden compliance risks.

Data catalogs often stop at the surface. They tell you what exists, not whether it’s usable. Executives investing in AI need more than visibility into assets. They need assurance that the data fueling their models is complete, current, and reliable enough to support real business outcomes. Without that assurance, catalogs risk becoming expensive indexes of unusable data, leaving businesses no closer to solving the quality bottleneck at the heart of AI failure.

Centific’s approach delivers quality

Centific tackles the data-quality conundrum head-on. Our Data Marketplace serves as a discovery hub, but the real power doesn’t end there. Centific offers a three-part foundation turning fragmented, untrusted data into trusted, deployable intelligence:

With the Centific Data Marketplace, discovery meets readiness

The Centific Data Marketplace is a well-stocked storefront showcasing 400+ proprietary datasets, thousands of partner and third-party assets, and the ability to enhance your own datasets. Each dataset is deploy-ready, enriched, compliant, and designed to integrate seamlessly into AI workflows.

The Centific Data Marketplace is designed to give enterprises immediate access to data you can trust. Beyond its breadth of datasets and assets, it offers tailored enrichment options that let businesses enhance their own data with the same rigor—and also provides the flexibility for customers to request custom datasets built specifically to meet their unique AI requirements.

The Marketplace also supports dynamic scaling: as new AI use cases emerge, organizations can draw from a continuously expanding library that stays current with evolving domain needs.

For businesses, this means discovery is an ongoing capability that keeps pace with business growth and AI innovation. 

With seamless integration into Centific’s Data-as-a-Service and AI Data Foundry AI model platform, customers can easily move from dataset selection to deployment in a few clicks. This makes the entire pipeline from data to insight frictionless and value-driven.

Centific’s Data-as-a-Service (DaaS) curates data for quality

Our Data Marketplace goes beyond listing datasets. Through our Data-as-a-Service, each dataset is curated by our global network of 1.8 million experts spanning 1,000+ domains. This helps ensure that every data element is vetted, annotated, and contextually enriched, for relevance, accuracy, and consistency at the quality standards AI deserves.

The AI Data Foundry bakes quality into the workflow

The AI Data Foundry is the environment where curated data is continuously validated, governed, and prepared for real-world deployment. In this setting, quality is built into every process:

• Automated QA pipelines check datasets against rigorous standards.

• Responsible AI safeguards catch potential bias and ethical risks.

• Governance and auditability provide end-to-end traceability, so leaders know exactly how their data has been shaped.

The Centific AI Data Foundry is a live pipeline that keeps data clean and fit for purpose.

Your business need more than a data catalog; you need a partner that elevates data from raw to reliable, from listing to launch. Centific delivers that partner through our trio of capabilities: Data Marketplace, Data-as-a-Service, and AI Data Foundry. We turn fragmented data into a strategic AI advantage.

Explore the Centific Data Marketplace.