Data Modelling — Breaking down terminology

I’ve been hearing some new terminology from my team that is related to data modelling. In particular different types of data models, such as conceptual, logical or physical. So I thought I’d do some research and read around on what these actually mean. It turns out it’s not as complicated as it sounds.

To keep it vey simple

• Conceptual (Big picture view).
• Logical (Provides more detail).
• Physical (How the data will be stored in the database).

It’s simply refining the detail as you go along from high level down to the technical detail. I find this interesting as initially it sounded complicated, however, when you break it down, it’s not that difficult to understand.

A data model’s goal is to visual the system at whatever level (big picture > fine detail) to help you understand types of data, relationships between the data and figure out how it can be all organised.

There were a few things that were interesting to me as I was reading around the topic.

The first is, you need to understand the business needs. What is the business need of collecting the data? What data are you collecting and why?

Secondly data models do not remain static. You need to refine them as you go along with changing business needs.

Overall though, the reason why I like writing about these things, is that as testers we can break things down for anyone to understand — then look for assumptions, gaps, misunderstandings and so on.