The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

Here’s a good analogy to Descriptive, Predictive and Prescriptive Analytics..
Descriptive tells you that you’re fat. The weighing machine, your gut and the mirror only confirms this.
Predictive Analytics offer a few options, going to the gym, eating less carbs, sleeping earlier etc.
Now Prescriptive tells you exactly which works best, given your (own) constraints. There also global constraints, such as social norms, perceptions and peer pressure.
For example if time is a constraint and money is less so, then hit the gym more often and sign up a personal instructor. Time is a proxy constraint, for spending time in a gym (and recuperating after) takes you away from work i.e. theres an opportunity cost attached. These constraint sets are the basis for scenarios and makes Optimization the branch of Analytics most strategic for balance builds and maximising outcomes (or minimising impact).
The need for Optimization is really pronounced in sectors where risk adjusted returns are prevalent, or when there’s need to assess and balance the impact of economic trade-offs. The best may not be optimal, and quantity may not trump quality, depending on the problem statement and what we are solving for.
In more ways than one, Optimization mimics the decisions we make in our daily lives. We trade time (away from loved ones) for money, for example. We only have a set of finite resources to make the best out of things, and we have to decide what we want and what’s important.