A recent blog post takes a look at what constitutes a good data model: “Designing data models is fun – at least if you are a geek like me. But as much as I like the academic thrill of building something that is complex – I am aware that it is often humans that eventually must see and maintain my beautiful (data) model. How can we design something that humans can understand? Humans are buggy! In general, they don’t deal well with complexity. You can blame modern education, you can scream and shout, or languish on the fact that the IT industry is riddled with incompetence, you may even throw Kimball or Inmon books at the wall in anger. But the empirical tests all show the same: the wetware is the final test of the model.”

The writer goes on to define the four criteria of a good data model: “ (1) Data in a good model can be easily consumed. (2) Large data changes in a good model are scalable. (3) A good model provides predictable performance. (4)A good model can adapt to changes in requirements, but not at the expense of 1-3.”

Commenting on the fourth point, the writer remarks, “I foresee that this will turn out to be one of the central contention point in the discussions to follow. Changes are a fact of life. If you have not yet experienced the intense pain of changing a beautifully designed data model half way through a project, I recommend you go work a few years for any organisation led by a guy with an MBA degree who believes 10% growth is maintainable forever, and that the organisation must react quarterly to ‘market changes’. I am sure you will find plenty such employers that will teach you the nature of change for good mammon. For now, I will assume that you believe that ‘change happens’ – though we shall later look more closely at what form such change typically takes. 1-3 must often be balanced with 4. The data model must be flexible in some way; it must remain agile.”