Metadata Management

Business Glossary

Blindata's Business Glossary is more than just a list of definitions. It can also be used to model the semantic meaning of your data assets, creating a shared ontology that fosters a deeper understanding of your data



In order to spread the culture of data, it is necessary to have knowledge of the data itself. And therefore having a shared and unambiguous definition, which can highlight the links with business processes. We are answering the question “What is data?” and we do it thanks to the element of Data Governance identified as Business Glossary.

The Business Glossary contains the semantic definition of the data and defines a common corporate lexicon. It is the access point to the world of data, the first element that brings us closer to the goal of a shared data culture.


It captures the knowledge of the organization and makes it easily accessible to everyone.


It bridges the communication gap between business and IT teams by providing a common language.


It ensures that business terms and concepts are defined consistently, which is essential for compliance and risk management.


It defines key business terms and concepts, and ensures that everyone in the organization is using the same definitions, which reduces confusion and improves communication.


Your Business Glossary can be a powerful tool not just for defining terms, but also for modeling the semantic meaning of your data assets. By capturing the relationships between terms and concepts, you can create a shared ontology that fosters a deeper understanding of your data. This ontology acts as a foundation for ensuring the composability of your data assets and products, even if they are distributed across different systems.

The Business Glossary, in addition to these elements, can contain a series of other information that better defines data, such as:

  • Aliases, synonyms or acronyms: Capture alternative ways to express the same concept, important in complex environments where different departments develop specific terms.
  • Calculation method: Specify how data is derived from other information within the organization.
  • Pattern: Define validity patterns for specific data types (e.g., product code format).
  • Naming Convention: Establish consistent naming conventions for data structures to ensure clarity and organization.
  • Taxonomies: Include relevant classification systems or tags used within the organization for better searchability and organization.
  • Relationships: Define connections between business terms to navigate the knowledge graph of your data.