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  • Data Driven Decision Making


    Decision making is the inevitable truth of the life of every practicing manager. The best of the corporate leaders constantly emphasize on the fact that the litmus test of a manager is to take correct decisions in the difficult times. Management styles of the managers have varied widely and there has been always a contest between the merit of data driven decision making and the intuitive method of decision making. So any attempt to reach a conclusion about the greater efficacy of one over the other or the chance of success of one over the other lies in accessing the arguments made in support of each of them.

    The intuitive managers would argue that this style of decision making is justified because it is not a fluke. Rather it is the gut feeling of the manager which he has acquired out of experience. Further such decision making is quick and less rigorous to reach conclusively for the decision maker. Also there are examples which prove that this method has produced unexpectedly great results. But does this certify the method beyond doubt?

     

    Even when claim of intuitive manager regarding generation of unprecedented, unexpected great result is accepted yet this argument is incomplete. This is because the frequency of the success rate in the above stated intuitive method can always be questioned. The decisions taken intuitively are equally likely to produce highly good and highly bad outcomes. On the other hand the decisions taken based on data are likely to produce outcomes with lesser variance. In their case the outcomes are expected to be far more standard. An analogy can be drawn with the individuals trained in the discipline of management and individuals who do the business based on their experience and not on the basis of tools, techniques or methods taught to them. While the latter might produce very high outcomes, the former can be expected to produce standard set of expected outcomes on all instances. The risk involved in the case of the former is bracketed and is well within bounds.

     

     

    This helps us further argue the case that the data driven decision making helps organization to create a standard process for carrying out decisions making. This makes the company less dependent on skillsets of a given manager rather it makes the skills ubiquitous through the designed processes.

     

    We have examples of successes in decision where the manager is guided by gut feeling and intuition. Hitler in the battlefield, a manager at war or Indian cricket captain M.S. Dhoni, a manager in the stadium or Steve Jobs, a manager spinning the invention wheel without any empirical evidence of demand are all such examples. But there are also examples that did just the contrary like Napoleon on the battlefield, Nate Silver in the baseball match and modern India’s entrepreneur like Snapdeal founder Kunal Bahl. It should be concluded here that data driven decision making helps create a framework which allows validation of a standard method by repetition.

     

    Data driven decision making has produced remarkable results in several domains and has helped to solve historically unsolved problems.

     

    In the field of education, the top notch management colleges have expressed concern on not being able to create the best of peer groups that could maximize peer learning. The problem was essentially a consequence of intuitively formed groups with seldom any recognition of the educational background, past academic performance; data based analysis of individual’s strength and weaknesses etc. Modern research has shown that application of decision based system has resulted in remarkable increase in the peer learning. This study was based on the assumption of keeping all the externalities equal and the examination results were taken as the proxy for the improvement in performance.

     

    In the field of crime mitigation and managing police administration the USA has shared reports that could startle any intuitive manager. The manager in this case is Anne Milgram, former Attorney General, State of New Jersey who was trying to put a check on the amount of crime rates. For all the time prior to Anne the decision was based on intuitive skills of the officers concerned. Anne revolutionized the entire methodology. She instructed to collect data in greater detail and based on the profiling of the people and the locality she redesigned the entire schedule for the police petrol and beet police. The impact was a sizable decrement in the crime rates.

     

    Another example that validates the claim is the research of Prof. David Pall of Wharton School. In relation to e-commerce the professor says that attempt to push sales by an online enterprise should be based on some empirical findings. He says that the information about the social capital and the buying behavior of the customers are positively correlated with a correlation of 0.4. This correlation might appear to be small but even this much insight into the buying behavior of the customers has far reaching consequences on the company’s strategy. This gives the sales team a sizable knowledge that where their efforts in selling goods are more likely to fructify.

     

    This example becomes clearer when we see it in the light of the failure of an e-commerce company that worked without any regard to data based decision making. This company, boo.com based in U.K. had established offices in the elitist of the locations for which it spent more money than Amazon and eBay and yet failed. The reason primarily was making decisions that weren’t corroborated by data and facts. They launched products unmindful of regulations in respective countries, did absolutely no market research on the expectations of target population in terms of getting the touch and feel of the products that they offered. The final nail in the coffin came when they expected very high volume of customer orders on the Christmas when they actually received just eight orders.

     

    This clearly shows that data based decision making has greater significance and are likely to have a strong impact on business.

     

     

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    @ Saurabh, thanks for showing interest in my article. 


     


    To answer your question, yes it is possible that an intuitive decision might occasionally produce better results. But that does not undermine the importance of data driven decision making. Intuitive decisions might at times produce very low outcomes. So the variance is high in case of an intuitive decision making. This implies high risk involvement. 


     


    On the other hand a data driven decision making offers outcomes with lesser variance and hence safeguards against the downside risk. It produces outcomes with low variance between the best and the worst of the outcomes. This results in making the decision making experience more standard and increases the worth of repeatedly using the data driven decision making methods. 


    Hope that answers your question.

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    Hari, I must say that the article is pretty informative and helpful in understanding the concept . 


     


    I know couple of e-commerce companies including Amazon which works on the leadership principles "Dive Deep" and "Bias for Action". Now, since in this digital age, there is so much data available for any website (eg No. of clicks, impressions, visits, Users' IPs, time spent on each page, etc), can you please suggest on how should one balance both these principles together? 


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    @ Akshayg, Thanks a lot for the wonderful question

     

    Prima Facie it may appear that the two leadership principles "Dive Deep" and "Bias for Action" contradict each other but factually they do not do so. 
     
    Diving Deep refers to the belief that getting deep into data analysis is imperative if one wants to take good decisions. But the focus is also on the fact that we do not get so much entangled in the data that we get lost in it and hence are unable to take the decision.
    So practically the entire problem is looked from the view point of several possible solutions. Then, we try to solve the problem, keeping "Bias for action" and shortlist 3-4 best solution choices. At this stage bulk of the non-likely scenarios are removed without too much of data analysis. 

    Then, we deep dive for only these 3-4 solutions and choose the best solution. In this fashion, we can apply both the principles together and they are principles that exhibit synergy and not contradiction. 
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    You mentioned using data in the e-Commerce industry. Though I understand that post sales analysis would involve data. But please elaborate on how data can find use in the task of selling goods? Is it not more of a personal choice?

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    @ jaiviry, thanks for this important question...


     


    Buying is definitely a choice made by an individual but to collect information about the buying behavior of people in particular and then channelizing effort towards the individual who have a greater propensity to buy is a process that involves a lot of analytics. 


     


    Even for the purpose of advertising or understanding the consumer sentiments the entire process of collecting and analyzing data becomes imperative. 


     


    One has to understand what the customers are saying, when am I (as a firm) being talked about i.e. in what is the seasonality of my company being discussed the most, on which platform i.e. where my potential or current customers are discussing about me. For all these purposes one would need huge amount of data and the decision methodology will be data driven decision making. 


     


    Hope that answers your question!

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