Business Glossary vs. Data Dictionary

Business Glossary

The terms business glossary and data dictionary refer to two different artifacts used by data-driven companies to make more effective use of data and information. This post will look at the differences between these two entities and tools to synchronize them to improve their effectiveness. 

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What is a Business Glossary?

A business glossary is a collection of business terms and definitions, documented to ensure business terms are used consistently. They provide a standardized internal vocabulary regarding an organization’s data resources and inform the wider data governance initiative.

When everyone in the company is using the same data definitions, organizations can use information resources more efficiently.

Developing a business glossary helps organizations in:

  • Identifying how terminology regarding data assets varies across different departments and areas of the business;
  • Supplying the framework for understanding company business concepts and terminology;
  • Improving data governance initiatives by aligning data assets with business objectives;
  • Increasing trust in the accuracy of enterprise data so it can be used effectively throughout an organization;
  • Reducing the misuse of data related to inconsistencies and multiple definitions for the same term;
  • Enabling the effective use of data resources by all employees to address business objectives.

A business glossary is meant to be used by everyone in an organization. Technical and non-technical personnel can consult the glossary to ensure they are using data terms correctly. 

What is a Data Dictionary?

A data dictionary is a compilation of data and its metadata including detailed information and descriptions of the data itself such as its format. It also defines the relationships between the data elements as well as their source and how they are used. It’s a more technically-oriented document than a business glossary. However, data dictionaries reference the business glossary, and should be built in line with vocabulary established within the glossary.  

Data dictionaries can be defined as being either active or passive.

An active data dictionary is built into most database management systems. It is accessible to database users through system tables or views and is updated automatically whenever a database schema is updated. No manual maintenance is required with an active data dictionary.

A passive data dictionary is separate from a database and changes in database structure need to be applied manually or with dedicated software tools.

Data dictionaries benefit data-driven organizations, supporting them in:

  • Fostering a consistent use of the vocabulary of data sources;
  • Facilitating database upgrades;
  • Identifying errors in systems descriptions;
  • Enabling the creation of more useful reports;
  • Simplifying data document management.

Differences Between Business Glossaries and Data Dictionaries

Business glossaries and data dictionaries are both important entities that help organizations use data efficiently. The terms are sometimes used interchangeably but they have substantial differences that make them appropriate for serving different needs.

Business Glossary

Data Dictionary

Focus

Business terms and concepts from across the organization

Physical data assets from a specific data source

Objective

Defining a common enterprise data vocabulary and understanding basic business concepts

Gaining an understanding of data resources and databases

Key artifact

A list of business terms and definitions

A list of datasets, tables, fields, and columns

Scope

One business glossary exists for each organization

A separate data dictionary is created for every data source

Application

Data governance

Data modeling, database design

Owner

The business as a whole

The business’s IT department and database team

Business Glossary + Data Dictionary = Data Catalog

A data catalog is a centralized inventory of enterprise data. It utilizes a complete business glossary and data dictionary to help users locate and access the data they need, and understand its data lineage, metadata and use. 

A functioning and effective data catalog must be dynamic, meaning that changes to one element of the data catalog are automatically reflected elsewhere.

For example, as an asset is created in the data dictionary it must be analyzed and each field mapped back to a business term in the business glossary. Changes to the metadata of an asset should be reflected in changes to the business glossary also. 

Creating and managing a data catalog can be a time consuming manual task. 

Fortunately, organizations can now benefit from automations and synchronizations that help ease the burden. 

ER/Studio enables organizations to efficiently create and manage business glossaries by harvesting business terms directly from logical data models. Now, the capabilities of ER/Studio are enhanced with a purpose-built integration between ER/Studio and Collibra.

Data catalogs help organizations enable and/or improve data democratization throughout the enterprise. 

Create a Unified Data Ecosystem

ER/Studio’s data modeling and collaboration features integrated with the Collibra data intelligence cloud help create a truly dynamic data catalog and a unified data ecosystem. By integrating ER/Studio’s business glossary with Collibra, organizations can:

  • Automatically synchronize business terms with Collibra’s glossary;
  • Send business terms created in ER/Studio to Collibra where they can be included in Collibra approval workflows. The ER/Studio Glossary is then updated via synchronization;
  • Use business terms in ER/Studio to classify logical and physical modeling artifacts within Team Server and Data Architect;
  • Publish physical data models to Collibra Data Dictionaries with mappings to business terms to expand the Collibra Data Catalog;
  • Publish logical data models to Collibra Data Dictionaries with or without mappings to business terms or Physical Data Models to drive Guided StewardshipBusiness Glossary Automation

ER/Studio and Collibra working together forms a collaborative, unified data ecosystem between data architects and data stewards. This has the following benefits:

  • Satisfy the needs of managing information and data across large organizations;
  • Accelerate the journey to building a business glossary as an ontology arranged into taxonomies by harvesting from logical data models
  • Take advantage of the knowledge of data architects in building the data catalog
  • Allow development teams to build data assets within a governance framework

More on the ER/Studio Collibra integration can be found in this solution brief. 

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