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Kongkan

Sparks Nov'13
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About Kongkan

  • Rank
    Newbie
  • Birthday 05/20/1988

Profile Information

  • Name
    Kongkan Siakia
  • Company
    IIM Calcutta
  • Designation
    Student
  1. Decision Decision is something that every organization has to deal with every day. From the smallest scale, like labor distribution, to the largest, as in targeting new markets, it is often the major reason for success or failure at that particular level and beyond. In modern times the managers and executives, who control the future and destination of the companies, are facing more and more complicated situations where they need to make decisions considering numerous criterion within limited time period. At the same time the business competitors are coming up with more and more innovative ways to deal with the situation. DDDM The information explosion from increasingly sophisticated computer tracking of shipments, sales, suppliers and customers, as well as e-mail, web traffic and social-network is a huge challenge for some and for others it is a great opportunity. The later base their decisions on the analysis of data rather than purely on intuition, this practice is called Data-driven decision making. The benefits of data-driven decision making have been demonstrated in a study conducted by the economist Erik Brynjolfsson and his colleagues from MIT and Penn's Wharton School. They developed a measure of DDDM that rates firms as to how strongly they use data to make decisions across the company. They show statistically that the more data-driven a firm is, the more productive it is—even controlling for a wide range of possible confounding factors. And the differences are significant: 4–6% increase in productivity. DDDM also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal. Drawbacks of Purely Intuitive Decision Making Complex situations. Pure intuition may be fine for small decisions where your mind processes part or all of the information automatically, quickly, and without awareness of any details. But as the complexity increases it becomes impossible for human mind to process all the scenarios individually. Time to build intuition. Good intuition takes a long time to build. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns. Specific to environments. Some environments provide accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Feedback from the environment is information about what worked and what didn’t. Intuition doesn’t work well in environments where the results of decisions are vague and takes time to materialize. Inconsistency in application. A person following intuitive decision making often makes largely different decisions for the relatively similar situations and if one decision works on a particular situation there is no guarantee that it will work the same in others. Bad judgments. Intuition can easily be biased by overreaction to available information. Psychologists call this availability bias, which although the most prevalent is not the only form of bias that might exist, for eg. there is confirmation bias when people have a hard time believing and remembering evidences that contradict their beliefs. The decision makers could be a victim to many such biases. Origin of idea. There’s no to know if a spontaneous idea is the result of legitimate expertise or of a pernicious bias. The Way Forward Intuition and DDDM are not necessarily at odds. There are many times when decision-makers don't have very good data because something is new, the situation has changed or because what might seem like intuition is actually well-rehearsed behavior that comes from years of experience at something, so even though people can't articulate the pattern they recognize, they still are acting on a huge body of experience and knowledge. And on the very other side of experience there are virtues to the gut reaction of naive people, as those who are not properly brainwashed may see things and come up with ideas that expertise drives out of their brains. The tools and techniques provided by data science to process data can contribute a great deal by the following ways: Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages. Evaluating data results requires careful consideration of the context in which they will be used. The relationship between the business problem and the data analytics solution often can be decomposed into tractable subproblems via the framework of analyzing expected value. Information technology can be used to find informative data items from within a large body of data. Entities that are similar with respect to known features or attributes often are similar with respect to unknown features or attributes. Provides close attention to the presence of confounding factors, possibly unseen ones. Finding a balance between intuition and data DDDM is important not just for the top mangers and executives but throughout the organization. For eg. using data analysis in marketing would allow the business to provide the most desirable products to its prospective customers. Many data focused companies employ data-science teams to bring advanced technologies to increase revenue and to decrease costs. Companies like Facebook and Twitter, along with many other “Digital 100†companies,5 have high valuations primarily due to data assets they are committed to capturing or creating. Conclusion Using data analytics for business processes is important, but we must think beyond the algorithms, techniques, and tools in common use. We must think about the core principles and concepts that foster success in data-driven decision making. Success in today's data-oriented business environment requires being able to think about how data analytics can be applied to particular business problems. Understanding this process and its stages helps structure problem solving, makes it more systematic, and thus less prone to error. Note - Visitors shall not be able to comment on this article until they are logged in.
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