What is a data catalog?

A data catalog is a detailed inventory of data assets within an organization. It helps users easily discover, understand, manage, curate and access data.

Using metadata and data management tools, data catalogs organize data assets so that users—such as data analysts, data scientists and data stewards—can quickly find the right data for their analytical or business use cases. Many data catalogs support natural language search, allowing users to discover data without writing code or SQL queries.

Data catalogs typically include a wide range of data assets, including:

• Structured data (databases, data files)
• Unstructured data (documents, emails, images and videos)
• Reports and query results
• Data visualizations (charts, graphs, infographics and dashboards)
• Machine learning (ML) models
• Connections between databases (data relationships and lineage)

A robust data catalog also includes metadata management capabilities for collecting and curating the metadata of each data asset. These features can make it easier to identify, evaluate and use data effectively. The catalog should also provide data governance tools to help safeguard data quality, data integrity and data security.

Metadata is "data about data." It’s information about data separate from the content of the data itself—such as author, creation date or file size. Metadata makes it easier to search for, organize and use data.

A classic example of metadata is the card catalog or online catalog at a library. In these, each card or listing contains information about a book: title, author, subject, publication date, edition, location within the library or synopsis.

This information makes it easier for readers to find and evaluate the book: Is it current or outdated? Does it have the information I’m looking for? Is the author someone I trust or whose work I enjoy? In the same way, metadata makes it easier for data users to find and evaluate their organization’s data.

Different types of metadata serve different functions. Data catalogs typically deal with several classes of metadata, including:

Typically, a data catalog has metadata management tools to curate and enrich metadata with tags, associations, ratings and annotations.

Modern organizations house increasingly complex data environments. Assets may originate from various cloud environments and on-premises systems, and from siloed teams, geographies and platforms. A data catalog makes it easy for any user to find, evaluate and use all of this data with little technical prowess or effort.

Consider this analogy: Digital library systems spare readers the time and effort of wandering through shelves in search of a specific book. A data catalog serves a similar purpose, helping users quickly find data they need rather than having them navigate vast, unorganized datasets. Better data access significantly improves the efficiency of insight generation initiatives across the organization—just as a digital library catalog gets readers to the first page faster.

Data catalogs also play a crucial role in data governance, risk mitigation and regulatory compliance, particularly in avoiding violations. Capabilities in this arena range from automated data classification for sensitive data to notifications when data anomalies are detected.

Through data catalogs, data professionals can access data independently—without relying on IT teams, data engineers or risking compliance and governance issues. These factors create an agile, self-sufficient data environment that benefits the entire organization.

Data catalogs and data dictionaries serve different purposes but work together to make data more usable.

A data catalog offers a broad overview of all data assets within an organization. It provides business context to help users discover and evaluate datasets.

In contrast, a data dictionary defines the structure and content of individual datasets. It includes details like field names, data types, allowed values, ranges and formats. It also ensures that data fields are standardized across different data projects, files and programs.

Data catalogs provide a variety of benefits that support data discovery, governance and usage across an organization, including:

• Accelerated data analysis
• Operational efficiency
• Enhanced data-driven decision-making
• Reduced regulatory risk
• Improved data silos

Data catalogs enable self-service analytics, making it easier for data analysts to find, access, prepare and trust data—accelerating the overall data analysis process.

By creating an optimal division of labor between users and IT, data catalogs reduce bottlenecks. Data citizens can access and analyze data independently, allowing IT teams to focus on strategic, high-priority tasks.

With centralized, contextual and trusted data at their fingertips, data professionals can respond faster and make better-informed decisions—helping them meet business intelligence (BI) and big data metrics.

By promoting, simplifying and automating governance, data catalogs give analysts confidence that they’re working with the data they’re authorized to use, in compliance with industry and data privacy regulations.

Data catalogs can unify large amounts of siloed data from across an organization’s data sources (such as data warehouses, data lakes and data lakehouses). Breaking down these silos promotes broader data accessibility and collaboration among stakeholders.

Modern data catalogs offer a broad set of tools and capabilities that help data consumers responsibly find, understand and use enterprise data. Key features include:

• Data discovery: A data catalog should enable all data users to quickly and easily find relevant data. The search experience should be intuitive and user-friendly, mirroring that of Netflix, Amazon or other consumer platforms.
  
  
• Metadata management: Effective metadata management helps improve the searchability and discoverability of data catalogs. A strong data catalog keeps metadata well-structured, accessible and actionable.
  
  
• Data lineage: Through metadata, a data catalog should visualize the lifecycle of data—offering a transparent, end-to-end view of its origin, how it has changed and its destination within the data pipeline.
  
  
• Data governance: A robust data catalog integrates seamlessly with governance policies and tools, including data quality rules, business glossaries and workflows.
  
  
• Data profiling: Modern data catalogs include profiling tools that can review data and then help maintain data quality standards through cleansing and validation processes.
  
  
• Data privacy: To help ensure the data security and privacy of sensitive data, a catalog should enforce access controls and user permissions, supporting compliance with regulations such as the General Data Protection Regulation, or GDPR.
  
  
• Data integration: A data catalog should connect with an organization’s data ecosystem—including data warehouses, BI tools and other platforms—via crawlers, connectors or application programming interfaces (APIs).
  
  
• Support for artificial intelligence (AI): Data is key to the success of AI models. Modern enterprise data catalogs help tag and prepare datasets for optimal model performance and transparency.

An AI data catalog uses advanced technologies such as automation, artificial intelligence and machine learning to enhance and optimize traditional data catalog functionalities. Key features of an AI data catalog may include: