What is a business glossary?
A business glossary is a collection of unique business terms and definitions that helps understand the data assets' key characteristics. It makes data discoverability easier for businesses because it consists of popular business terms rather than IT keywords. A business glossary is also referred to as a data glossary.
Unlike traditional glossaries, business users own the responsibility of building and maintaining a business glossary. Since it helps create a common data language across an organization, building a business glossary is a crucial step toward data democratization.
Creating a business glossary is not an easy task. Maintaining and regularly enriching it is even more difficult. However, crowdsourcing the work of linking terms to data can make a business glossary more alive and easier to maintain.
What does a business glossary include: An example
The key objective of a business glossary is to build an open knowledge base of business terms, concepts, and metrics.
The prevalence and the use of inconsistent terminology adversely affect data discovery, accessibility, and the correct usage of data assets.
For example, if we run a search for a business term like “customer acquisition cost", a business glossary would give you information about:
- The short definition of the term, concept, and metric
- A README section that goes in-depth into describing the term. In this case, it tells us the way the metric is calculated, the data assets involved in the calculation, what are the other places where this term is used, and what are the other terms that are related to it.
- It also gives us information about the owners, subject matter experts, classification, and verification/validity status of the term.
Five common challenges with Business Glossary
Everybody hailing from the data world understands the significance of a business glossary and has even invested in building one. Who doesn’t want a clean list of unique business terms defining the contents of each data table?!
Here are five challenges that prevent organizations from realizing the full value of a business glossary:
- Labor-intensive to build
- Challenging to standardize
- Difficult to update
- Far away from the actual data
- Missing domain expertise
Labor-intensive to build
Building a business glossary takes time and effort. Whether it is created on Excel or fed into a glossary management system, it takes time to develop unique business glossary terms and define them for each data table.
Here is a list of tools by DBMS Tools to help you build, maintain, and share business glossaries inside your organization.
Challenging to standardize
A business glossary must follow a standard structure. It should have a consistent hierarchy based on the general nature of data in the organization. The challenge is to keep the structure generic enough to incorporate glossary terms from multiple domains like finance, HR, sales, etc.
Difficult to update
It is essential to keep enriching and updating a business glossary. Unless it is up to date with new glossary terms and definitions, the users will not trust it and hence not use it. A dedicated group of people should maintain the business glossary, add new terms, and enrich the existing ones.
Read this article by the open-source community about how to iterate on your business glossary collaboratively.
Far away from the actual data
Usually, the business glossary system is built and stored away from the data. This makes the business glossary a non-interactive, static repository. Ideally, business glossary terms should be attached to each data set — this makes search faster and data easier to understand.
Missing domain expertise
The people responsible for building the business glossary cannot know everything about every data set. They may lack domain expertise or contextual information, which leads to incomplete glossary terms and inaccurate linking of glossary terms to data.
The Ultimate Guide to Evaluating a Data Catalog
How to use crowdsourcing to improve your business glossary
The challenges above clearly show two things. First, the business glossary and the data have to be linked together. Second, the people who know the most about data should be able to recommend glossary terms.
A business glossary must act, in part, like a shared data workspace that enables:
- Creating, updating, and maintaining the definitions and descriptions of business and functional terms.
- Attaching appropriate business glossary terms to the respective data assets.
- Validating and approving the integrity/quality of the definitions
- Identifying the owner/subject matter expert and initiating conversations with them
- Building a governance model by proving user roles and by protecting sensitive information
- Auditing all the changes — who, what, when — made to a glossary term
Pro Tip: Auto-glossary suggestions by an AI powered bot is also proving to be useful in reducing the manual work of linking glossary terms to each data table.
However, the crowdsourcing process should be centrally managed to maintain the business glossary’s hygiene. The data users should create requests to link existing glossary terms to the data, and the stewards or admins should approve or reject the request. This will keep the glossary creation process iterative and robust.
A business glossary is vital to lay the grounds for data governance in an organization.
Crowdsourcing business glossary terms, along with a centrally administered approval mechanism, keeps your business glossary useful and data democratized. It allows human tribal knowledge to flow within the data system without messing up its centralized structure and hygiene.
If you evaluating a business glossary tool for your team, do take Atlan for a spin - Atlan is more than a business glossary solution, it is a collaborative metadata management and data catalog tool that enables shared understanding of data.
Business Glossary: Related resources
- Business glossary vs. data catalog: What are the key differences?
- What is a business glossary template & how to create one
- Data inventory vs. data catalog: Why understanding the difference is important?
- What is a data glossary? and how can a data glossary help your data teams?