Is Your Data Catalog Ready for the AI Age?

A Checklist to Challenge Your Vendor

At first glance, data catalogs might seem like straightforward tools for organizing information –an apparently mundane task. However, a closer look reveals that these systems are far more than simple repositories: Data catalogs are at the forefront of bringing AI into your business for at least two reasons.

First, data catalog vendors have been integrating ML algorithms for years to automate tasks such as tagging and data classification, reducing manual effort and improving metadata management. Second, AI governance has become a priority just recently, driven by the sudden ubiquitous surge in (Generative) AI use cases on the one hand. On the other hand, regulatory developments like the EU AI Act and other global efforts like the NIST guidelines require more transparency, accountability and documentation around AI usage.

These regulations require organizations to document and control both traditional and generative AI models, whether they build them or incorporate them into their own applications, thus driving demand for data catalogs that support compliance.

Enterprise data catalogs need to address AI from two perspectives:

1. automating or optimizing data governance, stewardship tasks and user experience
2. supporting the governance of AI models and applications

For organizations evaluating data catalog solutions, understanding the range of available solutions is helpful when defining their own requirements. The following graphic illustrates that there is a clear difference between basic, advanced and leading-edge catalogs in the context of AI readiness. Basic functionalities establish foundational features, advanced capabilities introduce automation and intelligent processing, and leading-edge solutions provide proactive and predictive insights, driving strategic decision-making. This does not necessarily mean that, for a catalog to be a good fit for your organization, it has to be leading-edge in every respect. In most cases, advanced capabilities are sufficient for general requirements. However, for specialized use cases – where your company really needs differentiation, leading-edge capabilities will help you thrive.

For organizations evaluating data catalog solutions, the following checklist can help determine whether a tool meets basic, advanced, or leading-edge requirements in certain areas. This checklist is not just about adding AI features – it focuses on two key goals: automating tasks where possible and ensuring AI governance aligns with your business needs.

1. Data Discovery & Classification

To make a data catalog a great experience for users, automation along the data discovery and classification process is a must.

1. Basic:
   • Does the catalog automatically detect and register new data sources with minimal manual setup?
   • Does it provide a straightforward search and browse interface for end users?
2. Advanced:
   • Does it use ML-based (machine learning) algorithms to infer data relationships?
   • Can it detect and classify sensitive or PII data accurately, integrating with privacy compliance requirements (GDPR, HIPAA, etc.)?
3. Leading-edge:
   • Does it support (semi-)automated domain-specific classification (e.g., finance, healthcare) with relevant taxonomies?

2. Metadata Enrichment & Data Lineage

Metadata enrichment involves attaching more information to data assets, while data lineage analyzes the flow of data throughout the operational and analytical information architecture. Automation can play a major role here. However, lineage information and comprehensive metadata are also crucial to document and assess AI models holistically in the domain of AI governance.

1. Basic:
   • Does the catalog capture further technical, operational, and business metadata with minimal manual effort?
   • Does it provide basic (i.e., table-level) data lineage visualization?
2. Advanced:
   • Does it leverage AI/ML to enrich metadata by automatically linking glossary entries with data assets and performing semantic tagging?
   • Does it offer detailed lineage tracking across systems at the column or attribute level?
3. Leading-edge:
   • Does it provide data quality or anomaly detection features to enrich metadata with quality metrics and insights, proactively identifying potential issues?
   • Does it provide near real-time lineage updates, automatically detecting and documenting changes?
   • Does it offer an adaptive discovery engine that learns from user behavior to improve tagging and classification over time?

3. AI Model Governance

As laid out earlier, the scope of data governance is expanding as AI governance has become an additional requirement.

1. Basic:
   • Does the catalog provide standard data governance features? (e.g., create and execute workflows, stewardship dashboards, data quality features)
2. Advanced:
   • Does the catalog offer robust model governance for ML/AI models, including versioning, model profiling with bias detection, and performance monitoring?
   • Is there a tight link between existing data governance practices and ML/AI model governance, such as through automatic propagation of tags from data to the derived model, alerting the model owner when the data quality of the underlying data changes, or displaying data lineage for AI/ML models?
   • Are there any templates, guidance, or AIsupport to document ML/AI models and applications, such as automatic documentation suggestions when the platform recognizes a model?
3. Leading-edge:
   • Does it allow the implementation of enterprise governance frameworks for end-to-end oversight, enabling continuous compliance monitoring and dynamic risk assessments linked to changing data inputs?

4. Unstructured Data Management

Unstructured data assets – such as text, images, audio, and video – became much more valuable almost overnight when large language models had their moment. It enhances Gen AI model accuracy with richer training datasets and provides more contextual insights for applications using unstructured data at inference time. However, because data, structure, and metadata are intertwined in unstructured data, traditional metadata management is insufficient. Advanced capabilities are needed that bring data catalogs closer to the actual data as a side-effect.

To address this complexity, modern data catalogs are evolving from pure metadata repositories to powerful platforms capable of semantic search and contextual understanding. While for structured data, it might have been sufficient to search for columns, table names and maybe descriptions (all metadata), the increasing usage of structured data in data & analytics (D&A) now makes it necessary to, for example, search for a subheader in a text document.

1. Basic:
   • Does the catalog recognize and register unstructured data sources, such as data lakes or document storage systems?
   • Can users find unstructured files based on basic metadata attributes like file names, author, creation data or storage location?
   • Is there support for manual tagging or categorization of unstructured data assets?
2. Advanced:
   • Does the catalog automatically extract advanced metadata from the content of the unstructured data?
   • Are there capabilities to auto-tag, classify, or annotate unstructured content to improve discoverability?
   • Can it integrate with multiple unstructured data sources, such as cloud storage or content management systems?
3. Leading-edge:
   • Does the catalog support keyword-based or semantic search, enabling users to find relevant unstructured data using natural language queries by leveraging NLP for contextual comprehension and intent detection?
   • Can it analyze and categorize multiple types of unstructured data, including text, images, audio, and video?
   • Does it provide content-based classification to organize unstructured data?
   • Does it offer lineage tracking for unstructured data?

5. Active Metadata

Active metadata goes beyond the static collection and storage of metadata. It continuously updates, integrates with other systems in real time, and drives action through intelligent recommendations and automation – again based at least partially on AI and ML features. In the context of modern data catalogs, active metadata plays a crucial role in both operational efficiency and strategic data governance. It enhances decision-making by delivering timely insights and triggering proactive workflows. This capability is essential not only for supporting AI governance but also for maintaining data quality, improving collaboration, and driving business value through data-driven actions.

1. Basic:
   • Is metadata refreshed on a scheduled basis (e.g., daily or weekly)?
   • Are there reports or dashboards in the tool to visualize selected data-related metrics?
2. Advanced:
   • Is there near real-time synchronization with data pipelines, enabling accurate and up-to-date metadata?
   • Does the platform offer intelligent notifications to platform key users and downstream data consumers, such as schema changes or data quality issues?
   • Are there AI-driven recommendations that suggest actions, like updating documentation or informing stakeholders about data usage changes?
3. Leading-edge:
   • Can it identify potential problems early, such as anomalies that are below traditional alert thresholds but indicate emerging risks?
   • Does the system automatically trigger workflows or notifications based on metadata conditions, such as alerting process owners of data quality issues in their systems?
   • Does the catalog leverage metadata patterns to explain the root causes of data issues?

6. Collaboration & User Experience

Finally, the value of an enterprise data catalog is determined by the business impact. As of today, thisimpact only occurs if business users actively apply the catalog knowledge in their everyday tasks: A data engineer finds and fixes a data quality issue, a business analyst discovers a data product and creates a new report, while a BI specialist explores the lineage to understand why their KPIs have been wrong since the last ERP release.

1. Basic:
   • Does the catalog offer rudimentary possibilities to get in contact with the owner and the consumers of a data asset?
2. Advanced:
   • Are there built-in collaboration features such as discussion threads, rating of data assets, or contextual wikis for each dataset incl. AI models and unstructured data?
   • Does the catalog support standard search & discovery use cases with Natural Language Processing and AI-based search?
3. Leading-edge:
   • Does the platform integrate with popular BI/analytics tools (e.g., Power BI, Tableau) or even Web Browsers and data science notebooks to display and edit metadata within a typical user’s work environment?

An AI Ready Catalog is more than just adding some LLM magic

The capabilities of AI, specifically LLMs, are just as tangible as powerful. However, leading-edge data catalogs in 2025 take a much more differentiated view – ranging from end-to-end AI governance to providing impactful automation capabilities for users.

By using the above checklist and prioritizing which features are most important for your business, you can challenge any data catalog vendor and find out if they deliver on their AI promises. Ultimately, it is all about being aware of the balance your organization needs between enabling data democratization and data / AI governance.

Want to know more about this topic?

At BARC, we have just published the Data Intelligence Platform Score 2025 covering the most important vendors in the enterprise data catalog market. In 2024, we worked with several organizations to select data catalogs that fit their specific use cases. We offer consulting services for software selection in this area – let us know if you need support in finding the right solution.