미국대학원_과제_4차 산업 혁명_애널리틱스, 빅데이터 3.0_Columbia Univ_Analytics 3.0 by Tom Davenport

 1  미국대학원_과제_4차 산업 혁명_애널리틱스, 빅데이터 3.0_Columbia Univ_Analytics 3.0 by Tom Davenport-1
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미국대학원_과제_4차 산업 혁명_애널리틱스, 빅데이터 3.0_Columbia Univ_Analytics 3.0 by Tom Davenport에 대한 자료입니다.
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The authors characterization in analytical stages are evident. The construction company I worked for in the past, and many of the suppliers I had transactions with fall into somewhere around analytics 1.0. They agree that data analysis is important and utilizes it to some extent. However, they tend to focus their resources more on sales and project operations because they believe sales and operations fulfillment are more important in their immediate profitability and survival. This is especially the case for the small companies I worked with. They have almost no interest in investing in infrastructures for analytics. Instead, they are much more interested in expanding production line, and hiring more engineers and sales representatives. However, companies such as Google and Amazon stepped in and showed the world the power of analytics and data science. With their analytical methods, the user experience from their services are so much greater than that of others.
Now, as the author pointed out, many companies throughout all industries utilize analytics. Walmarts research team devised a system, using mathematical modeling and heuristics, that optimizes the efficiency of cartoning process minimizes material, labor, and shipping costs. The company observed $2 million annual cost savings throughout its distribution center network. Sun Microsystems enhanced its sales forecasts by using Bayesian methods that combines its sales staffs judgmental priors and statistical analysis, and keeps improving itself with accumulating priors and actual sales data. The difference in profitability and operational efficiency between the companies at 3.0 and 1.0 is evident.
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