Analytics is one of the few industries or may be the only industry where usage is more than awareness.
Let me try to explain this by citing few examples from our day-to-day life.

Example 1: The IPL Phenomenon

In the recently concluded IPL cricket match, team “A” needed 14 runs to score from the last over. Bowler “X” who bowled superbly in the first spell was the obvious choice for many cricket pundits. But the captain tossed the ball to a bowler “Y” who had moderate success with the ball on that day. Everyone thought the captain made a horrible decision. From the body language of the vice-captain, it was evident that even he was not happy with the decision. He had a brief chat with the captain. But the captain was firm. Contrary to everyone’s expectation, the bowler bowled an excellent over. The team won by four runs. The gamble paid off. In the post-match presentation, the captain revealed his reason for tossing the ball to the bowler. Bowler “Y” had a reputation of restricting the opposition during the death over while bowler “X” was quite expensive when he was asked to bowl for the death overs.

Later the data revealed that bowler “X” was particularly expensive during the death over against the same team, especially when the matches were played away from the home. It was indeed a risk, but a calculated one. Analytics is all about how much confidence you have in your decisions and how much risk you are willing to take.

By the way, was the decision to ask Messi to take a penalty kick against Iceland based on sound analytical decisions? Let’s dive deep into the data and wear our analytical hats.

Example 2: The Butterfly Effect

Once I went to a temple. By the time I reached the temple, I was surrounded by garland sellers. We (family) had already made up our mind to buy coconuts and some tulasi (holy basil) leaves only. No garlands. But somehow, one of the garland sellers could persuade me to buy a garland. I had to shell out Rs. 40.

Garland seller: Sir, shall I give you 4 garlands?

Me: No, just one is enough.

Garland seller: Sir, there are four deities of Ram, Laxman, Sitaji, Hanuman inside the temple. So if you are buying, you might as well buy four.

Finally I decided to buy 4 garlands (@ Rs. 150, I got a Rs. 10 discount).
Then we all stood up in a queue for ‘darshan’ (seeking blessings). It was a long queue. Suddenly, we saw a beggar enter the queue. I ignored him. But he was quite persistent with his plea and was after me for almost 2-3 minutes. I lost my cool.

Me: Boss, there are so many people here. Why are you targeting me? You are wasting your time and opportunity to get money.

But beggar tha ki maanta hi nahi (didn’t listen to me). I got frustrated. As an analyst I was trying to analyse why the beggar was targeting me. I got the answer after a few days when I purchased an item from Amazon. The moment I completed the transaction, I saw a message.

‘Those who have bought item X have also bought item Y’.

Maybe, the beggar must have done a similar analysis. He must have thought – if this customer can be persuaded to buy four garlands, he can be my potential customer too. The probability of me giving him money was more than others. He was 90-95% confident and was willing to take a 5-10% risk. So he may not be aware of the field of analytics, but he was implementing it.

Example 3: Lasting Impressions

We were at a restaurant. A server came to take the order. My wife says, ‘One tea without sugar and one with sugar’.

The server promptly served the tea without sugar to me.

My son then asked me, ‘How does almost every server know that you are the one that drinks sugarless tea and not mom?’

Me: According to him, maybe people with white hair are older and older people have a higher chance of getting lifestyle issues like high sugar problems.

Wife: Why don’t you colour your hair? You will look at least 10 years younger after colouring your hair. I am dying to see you younger.

Me: That will not solve the problem. Even then, the server will serve the tea without sugar to me thinking males have a higher probability of getting high sugar problems than females.

The Conclusion:

A spiritual guru explains the basis of Karma (action). ‘Your karma depends on your state of mind. It could be either satvik (goodness), rajasik (passion) or tamsik (ignorance)’.

Cluster analysis is a grouping technique in which people are grouped according to their interests, desires and opinions. Our ancient masters grouped people depending on their state of mind.

In all of the above cases, I am sure that neither the captain, the beggar, the server or the spiritual guru knew what analytics was all about. Yet they were analysing. In fact, many people I know find it difficult to pronounce the word ‘analytics’.

Five years from now, analytics as an industry will be as popular as FMCG. But till such time we will have to live with people pronouncing ‘Analtitics, Analgesics, Analseptic, Antiseptic’ etc.

The day is not too far when a bank customer’s loan will get rejected citing a reason that ‘according to the logistic regression model, you are very likely to default on the loan’.

ABOUT THE AUTHOR:
I always believe that life is beautiful.
But sometimes life gives me a shock, just to check whether I really mean what I believe.

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