How to model DataGalaxy’s Business Glossary

“Which data are available on our suppliers? What’s the difference between a lead and a customer? Is the formula of this indicator correct?” If you’ve heard these types of questions, chances are you might be considering building your Business Gossary! You might even ask yourself “I’m hearing everywhere people talk about the importance of data… If that’s so, why don’t I still have a referential to understand them all?”

This article is intended for those who wish to start modeling a Business Glossary with DataGalaxy by comparing the advantages and disadvantages of two popular Business Glossary modeling methods. Both Business Glossary organizational approaches explained in this article allow for fairly quick data input and understanding by companies’ users, even non-technical.

The foundations of a user-friendly Business Glossary

A user-friendly Business Glossary should be:

• Easy to understand
• Comprehensive, but not complex
• Autonomous from your Data Dictionary to bring more added value to end users

Depending on your organization’s particular needs, other elements can be taken into consideration when modeling a Business Glossary, including:

• Maintenance: Is it easy to make this model evolve?
• Description: What elements do I need to describe my objects?

Do not underestimate the value of bringing context to your Business Glossary – Business Glossaries must be linked to their environments, both business and technical. This may also include internal notes with other Business Glossary objects so that you can create and manage relations, and transversal links with other objects outside of the glossary. This can include information on where data is implemented, if it’s linked to a business process, used in dashboards, etc.

Vertical Business Glossary modeling

Vertical Business Glossary modeling involves defining the major objects managed by the organization: Employees, suppliers, items, email addresses, phone numbers, names, etc. To these objects, we will link different items hierarchically to create a list of all the elements we have connected to larger objects and describe them in the granularity requested by our use case.

To get a consolidated view, business terms do not necessarily have to be derived from a single system. DataGalaxy can mix information from different sources and even link business terms to different sources.

For example, a supplier may have a large amount of information in different systems including legal forms, type of company in the ERP, the accountant’s name and mail in the treasury system, the delivery point in WMS and TMS, etc. The best way to organize this information in a vertical Business Glossary model is to create clusters under the large object. Data related to the company itself, including the type of company, legal information, sales, and accounting might fit under this category.

The biggest advantage of this approach is that you consider your data without taking into consideration

• Management complexities: Many systems can manage these data points
• Multiple organizational domains can work on these data as producers, consumers, etc.

However, the vertical Business Glossary organizational methods can have its own setbacks, including:

• Related object management: How can we describe the link between different concepts and different objects? Or the link between a lead and a customer?
• Change management: People are used to representing their data in silos. Breaking this habit can be hard!

Urbanization Business Glossary organization

The urbanization-based Business Glossary organizational approach is based on the urbanization guidelines built by data architects to model data. Four main layers can be found in this method, including:

• The business layer: Handles the business process of the organization
• The functional layer: Represents internet security from a functional perspective
• The application layer: Represents the various software bricks making up the IS service layer
• The technical view: The technical elements required to make the application layer work and allow for data exchange.

There are a few main benefits to using this approach, including that data mapping sticks to a functional point of view while highlighting the main data silos. Another is that urbanization allows for easier data mapping and limited change management as compared to traditional data modeling.

Conclusion

The Business Glossary is one of the main entry points to data for many users, therefore its modeling method is crucial for your organization’s data catalog and data governance approaches.

In conclusion, establishing a robust Business Glossary is essential for enhancing data understanding and governance within an organization. By carefully selecting the appropriate modeling method—be it the Vertical or Urbanization approach—companies can ensure that their Business Glossary is not only user-friendly and comprehensive but also adaptable to the specific needs and contexts of their operations.

Each method has its unique advantages and challenges; the Vertical model offers a hierarchical structure ideal for diverse data sources, while the Urbanization approach aligns data with business processes and reduces change management hurdles. Ultimately, the goal is to create a Business Glossary that serves as a foundational tool for data access and comprehension, facilitating better decision-making and operational efficiency.

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About the author: Jessica Sandifer

Tech writer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.