Business Glossary vs. Data Catalog

A business glossary (sometimes called a data glossary) is a repository of business terms that define important concepts within an organization.

A business term is a word or phrase that uses business language to define relevant concepts for business users in an organization. A business term contains properties such as name, description and usage.

Metadata is data about data. It provides the context you need to use your data effectively and unleash its full value. Specifically, metadata helps you discover and track data. It helps you understand the relationships between different pieces of data.

Generally, there are four different types of metadata:

1. Technical metadata, including database schemas, mappings and code, transformations and quality checks
2. Business metadata, including glossary terms, data governance processes, and application and business context metadata
3. Operational and infrastructure metadata, including run-time stats, time stamps, volume metrics, log information and system and location
4. Usage metadata, including that for user rating, comment and access-pattern

A business glossary contains business metadata, as it contains glossary terms.

A data catalog is a tool that helps data users assess which data assets are available and provides relevant information about that data. Data catalogs help you identify and organize information about your data including:

• The source and origins of the data (data provenance)
• Data lineage
• The data's classification
• The location of the data

A data catalog and business glossary are related but different: A data catalog is a tool that scans and indexes data across an organization to provide a searchable inventory of all data assets. Data catalogs play a critical role in simplifying data discovery and understanding data.

A business glossary contains the definitions of commonly used business terms in an organization. The business glossary typically serves as the single authoritative source for concepts and definitions of an organization’s business terms.

Modern data catalogs typically include a data glossary and allow users to associate business glossary terms and their definitions to physical data assets which helps add valuable business context to the data.

Although some practitioners use the terms data catalog, business glossary and data dictionary interchangeably, they are not the same. Here are some of the main ways that they are different:

Primary Audience

• Business Glossary: Business audience
• Data Dictionary: Technical/IT stakeholders
• Data Catalog: Business and technical/IT stakeholders

Purpose

• Business Glossary: Define commonly used business terms across the organization
• Data Dictionary: Provide technical information about data assets and data elements
• Data Catalog: Help data users assess which data assets are available and provide relevant information about that data

Examples of Typical Content

• Business Glossary: Descriptions of commonly used business terms within an organization
• Data Dictionary: Technical information about data assets, such as data sources, data models, columns, fields and data types
• Data Catalog: Business and technical information, including data sources, data classifications, business terms, data lineage, data quality and relevant policies

Here is a real-world example of how users interact with a modern data catalog that includes an integrated business glossary and data dictionary:

The business staff in the sales department of an organization use business language in marketing collateral and customer interactions. These employees need to use consistent, accurate and traceable business terms that are easy to locate. Some of these employees are business analysts who need to understand the relationship between business and technical metadata for sales reporting.

You assign a data steward to create and maintain a sales business glossary in the organization’s data catalog. The data steward interacts with subject matter experts in the sales department to define business terms. The data steward categorizes the business terms to make it easy for other business users to find content in the data catalog. The data catalog allows the glossary modifications to go through an approval process before the data steward is able to publish the changes.

Once published, business users, such as business analysts, can browse the categories or search for business terms in the data catalog. The users can view related terms and see linked data assets to understand the technical metadata related to the term.

In addition to a data glossary, data dictionary and the ability to automatically associate glossary terms to technical data assets, some of the key capabilities and features of a modern data catalog include:

• Automated metadata extraction
• Automated data discovery
• Semantic search
• Data recommendations
• Domain and entity recognition
• Automated data tagging and classification
• Data profiling
• Inferred lineage and relationships

There are many benefits of using a data catalog with an integrated data glossary. Using a data catalog powered by artificial intelligence (AI) and machine learning (ML) can further enhance those benefits. For example, an AI/ML-powered data catalog can help with:

• Enabling data analysts and data scientists to find, assess and use relevant data for value-creating analytics and AI initiatives
• Quickly identifying and classifying sensitive data to help mitigate risk exposure
• Advancing data literacy across the organization by providing business context for data at scale
• Improving the productivity of data stewards, allowing them to focus on more valuable work

To learn more about how an AI-powered intelligent data catalog lets you quickly discover, inventory, organize and understand your data assets, check out these resources:

• Start with the fundamentals: What is a data catalog?
• Learn more about how machine learning enhances enterprise data catalogs: Machine learning data catalogs
• Watch our Back to Basics: Data Catalog webinar series to learn more about the essentials of data cataloging.
• Read about the fundamental features of an enterprise-scale data catalog: Which data catalog features do I really need?
• Experience Cloud Data Governance and Catalog in action.