“All Models are wrong, some are useful”.
In most Machine Learning Algorithms we try to minimize the loss function.
Models are an abstraction of the reality. The word here is abstraction. It is not actual.
If you think about it, the process of building Machine Learning Algorithms itself has a larger ‘Loss Function”. That is we differ from the reality.
So, shouldn’t we build less models to minimize this larger ‘Loss Function’ ?
Hey Data Scientist, Think like a CEO
Often we Data Scientists get pigeon holed into a very technical thinking. We think only in terms of which ML algo can be applied to x, y, z problem. How to do feature selection. How to reduce the number of features. How to improve the accuracy of the models.
What we don’t think is how the ML algorithms will benefit the company. How much money am I gonna save or earn the company through my ML algorithm. Will the ROI be positive ?
The most important question we forget to ask is “Is Machine Learning algorithm really required for this business problem” ?
I know the last statement would have set a cat among the pigeons. Many of you would be alarmed and probably might ask “Are you trying to put us out of our job ?”.
On the contrary, No.
There are many business problems which do require Machine Learning approaches but not all. Most of the business problems can be solved through simple analytics or a base line approach.
What will put us out of our job is Machine Learning Overkill. I have seen implementation of Machine Learning algorithms to very frivolous problems and worse still the companies have invested heavily into the idea. It is a ticking time bomb. The moment the companies realize that the ROI is negative, they will shun the Data Science practice altogether. We all know how difficult it is to win over a chided customer. No Data Science, No Data Scientist.
Cometh The Hour, Cometh The Data Science Auditor
The Industry is both excited and wary about the prospects of Data Science. Many who have implemented the Data Science solution are left disenchanted due to the poor ROI.
Enter the Data Science Auditor
I foresee a new job role being created “THE DATA SCIENCE AUDITOR”, where companies would hire experienced Data Scientist (statisticians / applied mathematicians) to audit the Data Science Projects.
In one of my recent consulting project I felt exactly like an auditor. I was asked to improvise the ML model built by a Data Scientist, but upon analysis found that the ML algorithm applied was not only wrong but for the given business problem no ML algorithm would work !!
The Client was simply taken in for a ride.
The Repercussion — The Client did not have a good opinion about Data Scientists and felt cheated both emotionally and monetarily.
Perhaps, next time ask not a Data Scientist “How many ML Algorithms you have built”
"How Many ML Algorithms You Have NOT Built"