S🛑RRY but Decision Intelligence is not about optimization!
I've seen people explain decision intelligence with optimizations. At best, this definition is very incomplete and often wrong for many #CDO #CDA #CDAO.
Why?
💡In the field of AI, there are many groups of techniques, one of which is called #optimization. It uses #ConstraintProgramming #CP #MixedIntegerProgramming #MIP and other mathematical and statistical methods to find an answer in a very big space of possibilities.
🚩Suppose you try to explain #DecisionIntelligence to your stockholders #CIO #CDO #CTO with optimization wording. In that case, it will either be mixed up with these special techniques, or it will not be the outcome they are looking for when they like to utilize decision intelligence.
✨Decision Intelligence unifies many techniques from the field of #AI, such as #businessrules #machinelearning #optimization, with a decision-centric approach. #decisioncentric
What does it mean?
It means that the core foundation of #DecisionIntelligence is decision modeling.
Why?
Simply because the purpose of Decision Intelligence is about “business decisions”. For that reason, you need to define business decisions EXPLICITLY.
How?
💣Modeling business decision is not a rocket science, pick business decisions and…
* Decompose it in smaller and more easy-to-understand sub-components (i.e., decision units)
* Identify the metrics for each decision unit
* Determine the dependencies between those decision units
* Specify #data inputs for those decision units
With these☝️ simple steps, you WILL create a decision graph that explicitly defines how a business decision is made and executed.
⚠️Remember, being intentional about business decisions by leveraging decision modeling to explicitly define decisions is the core foundation of #decisionintelligence. This technique shouldn't and can't be replaced by process map, #bpmn, business process, workflow, rules modeling and other #lowcode or declarative approaches.
Posted here.
Published August 23rd, 2024 at 07:30 am
