This isnt going to be an easy transition requiring organizations to focus on

This isnt going to be an easy transition requiring

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decision making: “in God we trust, everyone else please bring data.” This isn’t going to be an easy transition, requiring organizations to focus on change management. Imagine informing an expert clinician that the success rate based on his prescribed regimen is 40% or that the costs associated with the treatment are double the national average with an inferior outcome. Or a chief economist that the data support a hypotheses at odds with her theory? Or a veteran marketer that his proven methods no longer work? Or the sports manager of a professional basketball team that his strategy is flawed against teams that have a majority of left-handed sluggers. The list goes on. Fundamentally managers will have to adapt their information gathering and decision making strategy in this new world.
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More generally, we are moving into an era of big data where for many types of problems, computers are inh erently better decision makers than humans, where “better” could be defined in terms of cost and accuracy. This shift has already happened in the world of data-intensive finance where computers now make the majority of investment decisions often in fractions of seconds as new information becomes available. The same is true in areas of online advertising where millions of auctions are conducted in milliseconds every day, air traffic control, routing, and many types of planning tasks that require scale, speed, and accuracy simultaneously. This trend is likely to accelerate in the near future. 3. Knowledge Discovery In his provocative article titled The End of Theory: The Data Deluge Makes the Scientific Method Obsolete 1 ,” Chris Anderson drew on the famous quote by George Box that “All models are wrong, but some are useful,” arguing that with the huge amounts of data now available, we don’t need to settle for wrong models or any models for that matter. Anderson pointed out that prediction is of paramount importance to businesses, and that data can be used to let such models emerge using machine learning algorithms, largely unaided by humans. Anderson points to companies such as Google as symbolizing the triumph of machine learning over top- down theory development. Google’s language translator doesn’t “understand” language, nor do its algorithms know the contents on webpages. Nor does IBM’s Watson “understand” the question it is asked. There are dozens of lesser known companie s that likewise are able to predict the odds of someone responding to a display ad, etc., without any solid theory, but rather, based on gobs of data about behaviors of individuals and the similarities and differences among these behaviors. Anderson’s arti cle set off a vigorous debate in academic circles. How can one have science and predictive models without first articulating a theory?
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