Creating information solutions for a business requires an accurate analysis of the required domain. Regardless of the nature of the project analysts shouldn’t have to worry about technical implementation requirements. Similarly, people on the business side should be able to verify the relevance of the analysts’ proposed solutions.

Among the couple of hundred countries on this planet, there are way more languages and dialects. Communication over language domains is hard. We need translators or interpretors to cross those boundaries. Even though tech is trying to chip away at this hard problem by automating that, we still have a long way to go. And language is not static, it changes constantly in space and time. As soon as time passes or communities become isolated from others, people change their language for the purpose of their own communication.

As enterprises continue to grow, so does the complexity of their data environment, including data terminology both internal and external. Consider a firm that builds computer chips for new devices: Each project may have its own Database System and Data Dictionary. Yet engineers, management, accountants, and customers need to speak the same language to effectively understand one another. A Data or Business Glossary solves this problem by referencing the vocabulary needed to run the company, covering multiple Data Dictionaries and business segments.

IT stands for Information Technology. At one time, the abbreviation “ICT” still contained the “C”, or communication. However, since it was related only to the telecommunication sector and IT took over, we are now left with a two-letter acronym. Almost prophetically, we now see the process of IT lacking in communication.

Data modeling is often described as a craft, and, once completed, the results may even seem artful. Yet outsiders may seem data modeling as abstract, time consuming, or even unnecessary. In many cases, the modeler interviews business experts; studies large amounts of requirements; talks some more, and then: hocus pocus! The modeler presents an elaborate diagram, and so begins the slow process of maintaining the diagram. The modeler makes sure that the diagrams stay up-to-date and explain what the diagrams hold. Sometimes, through this complex process, even data modelers get lost while maintaining a growing set of data models and requirements.