Business AI and Analytics Maturity Model

Analytics maturity model (thoroughly explored at an IBM blog) makes a lot of sense. Like any model or theory it puts order into chaos. At the same time it has the same limitations as all theories have in practice. Pros and cons as always.

Analytics maturity model

There are four levels in the maturity model: 1) descriptive, 2) diagnostic, 3) predictive and 4) prescriptive. Descriptive is the “lowest” and prescriptive the “highest” level. Levels are like steps, to get into one you have to pass the previous ones. Different maturity levels are easily understood by looking at what kind of questions they aim to answer.

  1. Descriptive: “What happened?”
  2. Diagnostic: “Why did it happen?”
  3. Predictive (our favourite): “What will happen?”
  4. Prescriptive: “What should I do?”

I personally find the model really helpful because it helps to understand new companies. It gives you one more way to understand the analytical side of a company. Instead of hunches and feelings you have something more solid. Who wouldn’t like to lean on theories and frameworks?


At what level is you company? How do you get to a higher level? Is higher maturity better in the first place? Should you “just” focus on getting your descriptive analytics in line?

There are more questions arising if you want to get to a higher level. (Supposedly you are not peaking at the moment. :D)

How does predictive analytics and business AI create value for you? How is predictive analytics integrated to business processes? What kind of capabilities does predictive analytics require?

I hope you learned something new. Have a nice day!