| |September 20209tions flows back as Feedback. Culture means that the organi-zation has the patience to wait for results and not be trigger happy on making changes to al-gorithms, models or logic.5. Causality over Correla-tion ­ In any data set there will always be interesting variables which show high correlation to the result but questionable to nil causality. In my own expe-rience I have seen a situation where we saw that consumers who buy Whirlpool fridges tend to take Personal Loans. We used this data point and actively tried to convert every consum-er who wanted to buy any other brand of fridges to Whirlpool in the hope that they will eventu-ally take a personal loan! The plan backfired!!One of the best examples of correlation WITH causality is perhaps how continued educa-tion (effect) of the girl child in rural areas (especially the most backward of areas economical-ly) is a direct sign of improved quality (correlation) of life due to easier access to micro finance (cause). It is important for the Analytics team to identify Cause- it helps identify which on which variables to nudge consumer behaviour.6. Awareness of `Small Data' ­ Access to Hadoop Clusters, AWS Cloud computing etc. means we can crunch unstruc-tured data will never before. This is what we call `Big Data'. My experience as a Consumer Behaviourist taught me the im-portance of `Small Data'. Small Data is basically going back to consumers who exhibit a particular behaviour and sim-ply asking an important ques-tion ­ Why? This is an exercise which An-alytics Managers / Heads in fact all P&L managers must routinely do- step out in the field and sim-ply send time with consumers to understand why they behave so. This makes the analytics far more potent.As the CMO of Fino Payments Bank I often wondered why a Mi-cro ATM offering merchant in the middle of Jaunpur district did extra ordinary business. No data point could support the outcome. We simply spent time with him to figure his secret ­ He opened his shop earlier than the market and shut 2 hours after the market closed (was hence perceived to be always available) and greeted ev-ery customer who walked into his shop with a smile and asked for their well-being. This simple act endeared him to folks who wanted to withdraw money and they re-peatedly visited him! No data point in an excel sheet would have ever captured that.7. Simply. Simplify and Simpli-fy. ­ To most things mathematical are an anathema and hence threat-ening. To take the business teams along and get their support an Analyst should have the skills to simplify his/ her craft. Once busi-ness understands the basis for pre-dictive modelling, use cases for AI etc., they appreciate the solution, impact. Else it becomes a case of `I told you so' when projects fail and many will fail.All the points above are the softer but critical aspect of Analyt-ics which often get ignored in the quest for more compute power, more variables to be added. But all the above are irrelevant if the basic quantitative skills are absent and if the company does not invest in the right infrastructure. ANALYTICS IS SEEING WHAT OTHERS SEE, HEARNING WHAT OTHERS HEAR, READ WHAT OTHERS READ, BUT BE ABLE TO COME UP WITH THOUGHTS AND ACTIONABLES THAT OTHERS DON'T
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