Business Analysis Technique: Data Dictionary and Glossary

Thanks for the business analysis technique request! Here’s the response, straight from Susan. According to the BABOK® Guide, building a data dictionary and/or glossary during requirements elicitation is a key part of your requirements development efforts.  This technique is included in the 16 essential business analysis techniques found in the standard.  I agree with this recommendation to use this essential technique, particularly during your requirements elicitation activities.

You should definitely take the time to identify and define all of the terminology that is being used as part of your project. If you are lucky, you work in an organization that already maintains a business glossary of terms that will apply to your project efforts.  If not, you need to build one. This work should start when you define the business need and business requirements, and continue across the project life cycle.  The two recommended ways to define and track important terminology across your project life cycle are a glossary and a data dictionary.  Let’s take a quick look at what makes them different from one another. 

Glossaries allow you to document any key business terms along with their definitions. It is a best practice to start your business glossary immediately during that project’s controlled start and to keep it updated throughout the project life cycle.  Much of the information that goes into the glossary will be a result of your business, stakeholder, solution and transition requirements development efforts.

Data dictionaries are a bit more technical in nature.  A data dictionary defines data elements, their meanings and their allowable values. Building a data dictionary for your project may not begin until the project requirements are complete and the technical design effort is underway. There are two types of data elements found in a data dictionary: primitive data elements and composite data elements. Let’s take a closer look at each type:

Primitive data elements: Primitive data elements contain basic information about data elements.  Each data element has a unique name.  The data element may also have aliases in addition to its unique name.  You will see this when different stakeholder groups call the same data element something different. Every data element needs to state the acceptable values for that data element. This could be a range of numbers or limits to the number of characters allowed in a name.  Primitive data elements must also be defined in the data dictionary. A primitive data element might be defined as customer name, customer title, customer age, or a customer phone number.

Composite data elements: Composite data elements are assembled from primitive data elements. Composite data elements allow you to manage multiple pieces of related data as a single composite data element. This can be done in three ways: sequences, repetitions and optional elements. Sequencing primitive data elements specifies that they must always occur in the same order.  Repeating primitive data elements has them occurring more than one time in the composite element.  Optional elements are primitive data elements that may or may not occur as part of a composite data element. A composite data element might consist of an array of values for a unique customer containing the four primitive data elements listed above: customer name, customer title, customer age, and customer phone number.

Well, that is our closer look at yet another successful technique used by business analysts, building a data dictionary and glossary for your project.  I have found it to be of great help on many projects, particularly for IT projects where your glossary keeps folks speaking the same business language throughout your project and your data dictionary can set the basis for the data that gets defined and used by the resulting solution. Give a shout if you have another BABOK® technique you would like to explore in more detail!

Susan Weese

Business analysts are increasingly becoming the critical liaisons between business and solution development (oftentimes IT), so they must communicate and relate with equal effectiveness throughout all levels of an organization. Download this free White Paper to see which five common obstacles business analysts face and how to address them to ensure success.

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