Tag: analytics

  • Options, analysis and decisions







    I have been analyzing buying a house vs living on rent and buying a four wheeler vs riding a cab. This is not a new comparison and analysis for me. Most of the time, answers to these two were – buy a house and rent a car or use a cab service.

    How did I arrive at these conclusions? Of course after some calculations and considering options. The question is – what was the measure on which I measured these things to come up with some conclusion? The measure was “utility”. In economics utility means resultant benefit achieved from something (usage or consumption of product or service). When you eat Idly on road side @20 INR and when the same idly plate is served to you in a better place it could by @60 INR. We may argue about taste, hygiene etc of idly from 20INR to 60INR. When one moves from basic utility to additional utilities we measure that in terms of marginal utility. Marginal utility, is the incremental benefit one gets from consumption or usage of an offering.

    We can continue from our situation of buying a four wheeler. The first question one may have is – which car – SUV, Sedan, Hatchback. Once that is decided which company which brand and what amenities such as AC, power window and music system etc. There are obviously many more questions in making a decision. The other option is hiring a local taxi or app based service. After all this analysis – what one gets is drop from point A to point B. At times pain of parking – if it is owned car or self driving. The drop from A to B is utility in this case. Marginal utility is – can you play music of your choice while driving or is the vehicle air conditioned during the trip. In the bargain of ability to play music and air conditioning you need to pay extra. This extra amount vs the additional benefit you get defined marginal utility.

    Many of our life decisions are made emotionally and justified rationally. I analyzed a lot with a decision of buying a house and renting a car. Currently, I live in a rented house and drive a car. How often do we take decision by listening to our heart and mind? Is there a rational justification to our decision? Why do we take such decision? When we have to really take a plunge with faith, we falter. When we have to take a decision with full analysis we do the opposite!

    Give it a thought – Mahavir and Buddha – both were from warrior families and were to be the kings. Why did they renounce everything, how on earth they both might have taken such step? Had they both done enough analysis possibly both had stayed back. I know more about the Buddha so can think of an incident, when he was following extreme austerity such that he could had died of hunger, he decided to eat. In short term his five best friends went away saying he has left the path. But the Buddha was right, austerity and self affliction may not necessarily help. He said to himself, if I die of hunger I wont be able to achieve what I strive for.

    In my analytics practice one of my senior’s Eron Kar used to say – “If you torture data enough it will confess to the story you want it to narrate.” Always it is not right to over analyze, at times leaders need to take a leap of faith and decide.

    Prof Mankad told me once – “Pravin, when you become a leader, sitting in the 76th floor of your corporate office when you have to make a tough choice do one thing. Leave aside all the papers and analysis. Walk toward the window. Take out a coin from your pocket, toss it and you will have the decision.” Dr Mankad continued “Pravin, sometimes you should stop over analyzing.” I need to learn a lot to do that one I have to become that big a decision maker of a company and two stopping the urge to analyze everything, I still analyze a lot!

    Image source – http://novalo.com/flat-fee-self-employed/

  • Predictive Texting







    To err is human and to typo in predictive texting is smart phones! 🙂

    This is my first effort to share my blog on LBC (Loose Bloggers Consortium). This topic was suggested by Padmum, for the weekly Friday Loose Bloggers Consortium where currently nine of us write on the same topic every Friday.  I hope that you enjoyed my contribution to that effort.  The eight other bloggers who write regularly are, in alphabetical order – Ashok, gaelikaa, Lin, Maxi, Padmum, Rummuser,  Shackman and The Old Fossil. Do drop in on their blogs and see what their take is on this week’s topic. Since some of them may post late, or not at all this week, do give some allowance for that too!

    I was aware about LBC, thought of joining the group earlier too. However, I write only once a week on my blog.  It is a big responsibility to write and on regular basis is a very tough job with the kind of engagement I have. Honestly it has been difficult to keep up with my regular blogs of Business and the Buddha.

    I was impressed with the title  – “Predictive Texting”. Reasons are a plenty.

    1. First reason is, I have been doing this mistake often. Here is one – Writing “Vegetable” instead of “Venerable” to Ramana uncleji is one. In my Whatsapp, I have written most often – *<WORD> TYPO <CORRECTED WORD>. One can do an analysis on the same and infer that one of the most frequent word from me is “TYPO” or a typo – a misspelling.

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    2. I have been doing text analytics so this field is of some interest for me. Predictive texting learns from an individual’s usage of words as well. I can save “Pravin”, “Uncleji” as right spellings and the program will learn. In fact, with more knowledge of my usage of words it may predict what is coming. The type of analytics I am doing is on Twitter feeds & text based identification of sentiments as a starter, instead of predicting text.

    Coming to the point – How did predictive texting start? Do you remember Nokia mobile phones used to have T9 feature. This was a dictionary, T9 used to do auto suggest. When user used to type a digit (in those days digits used to have 3/4 alphabats associated) T9 used to offer suggestion. The same is extended to predictive texting of current edge.

    Our machines have become smart to beat humans in Jeopardy quiz but still Predictive texting is error prone. Is also referred as “Cupertino effect” because in the initial year predictive text used to change cooperation to Cupertino :).

    We should not be blaming machines completely for these errors, after all they are not humans and in whatever case they are also learning. Machine learning is a concept where computers learn from past. Recently published news suggest what Google is trying in – Machine Learning. One thing they are doing and continue to do is – suggestions based on the user behavior. Just imagine how interesting it would be to have machines helping us in whatever endeavors

    Be patient  something worthwhile is in  the offering. Please Cupertino (cooperate) – as vegetable (venerable) uncleji did.