We have always believed that when the world of business collides with the world of math, magic unfolds. As these 2 worlds collide, it also presents a set of unique challenges - bridging the semantic language gap between business and math. Modelling complex business outcomes using math requires an interdisciplinary team consisting of business folks, data folks and math folks. While doing so business folks are always at a loss because a language chasm exists. Math folks love their ”geek speak” ( Tanimoto coefficient, chi square, odds ratio) and business folks are focussed on impactful outcomes (Mean time between failure, Next best action etc.).
Having been caught in between, we at Flutura we have been obsessed with the question - How do we bridge the world of business to the world of math?
So here they come in no particular order …
- Which business outcome are we attempting to model and predict? For ex : Mean time between failure of asset - MTBF, Next best action- NBA
- What surgical actions can we drive once we are able to predict the outcome? For ex : preventive replacement of asset, stock up on spares
- What is the impact of these actions? For ex: Reduced down time, minimized risk
- What is the economic impact of a correct prediction? For ex : cost of reduced downtime translated into $