【正文】 Chapter 1 Introduction: DataAnalytic Thinking 1 ? The past fifteen years have seen extensive investments in business infrastructure, which have improved the ability to collect data throughout the enterprise. ? Virtually every aspect of business is now open to data collection and often even instrumented for data collection: operations, manufacturing, supplychain management, customer behavior, marketing campaign performance, workflow procedures, and so on. ? At the same time, information is now widely available on external events such as market trends, industry news, and petitor‘s movements. ? This broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from datathe realm of data science. 2 The Ubiquity of Data Opportunities ? With vast amounts of data now available, panies in almost every industry are focused on exploiting data for petitive advantage. ? In the past, firms could employ teams of statisticians, modelers, and analysts to explore datasets manually, but the volume and variety of data have far outstripped the capacity of manual analysis. ? At the same time, puters have bee far more powerful, working has bee ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible. ? The convergence of these phenomena has given rise to the increasing widespread business application of data science principles and data mining techniques. 3 The Ubiquity of Data Opportunities ? Data mining is used for general customer relationship management to analyze customer behavior in order to manage attrition and maximize expected customer value. ? The finance industry uses data mining for credit scoring and trading, and in operations via fraud detection and workforce management. ? Major retailers from Walmart to Amazon apply data mining throughout their businesses, from marketing to supplychain management. ? Many firms have differentiated themselves strategically with data science, sometimes to the point of evolving into data mining panies. 4 The Ubiquity of Data Opportunities ? The primary goals of this book are to help you view business problems from a data perspective and understand principles of extracting useful knowledge from data. ? There is a fundamental structure to dataanalytic thinking, and basic principles that should be understood. ? There are also particular areas where intuition, creativity, mon sense, and domain knowledge must be brought to bear. 5 The Ubiquity of Data Opportunities ? Throughout the first two chapters of this books, we will discuss in detail various topics and techniques related to data science and data mining. ? The terms ―data science‖ and ―data mining‖ often are used interchangeably, and the former has taken a life of its own as various individuals and anizations try to capitalize on the current hype surrounding it. ? At a high level, data science is a set of fundamental principles that guide the extraction of knowledge from data. Data mining is the extraction of knowledge from data, via technologies that incorporate these principles. ? As a term, ―data science‖ often is applied more broadly than the traditional use of ―data mining‖, but data mining techniques provide some of the clearest illustrations of the principles of data science. 6 Example: Hurricane Frances ? Consider an example from a New York Time story from 2020: ? Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit on Florida‘s Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at WalMart Stores decided that the situation offered a great opportunity for one of their newest datadriven weapons … predictive technology. ? A week ahead of the storm‘s landfall, Linda M. Dillman, WalMart‘s chief information officer, pressed her staff to e up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes‘ worth of shopper history that is stored in WalMart‘s data warehouse, she felt that the pany could ?start predicting what‘s going to happen, instead of waiting for it to happen,‘ as she put it. (Hays, 2020) 7 Example: Hurricane Frances ? Consider why datadriven prediction might be useful in this scenario. ? It might be useful to predict that people in the path of the hurricane would buy more bottled water. Maybe, but this point seems a bit obvious, and why would we need data science to discover it? ? It might be useful to project the amount of increase in sale due to the hurricane, to ensure that local WalMart are properly stocked. ? Perhaps mining the data could reveal that a particular DVD sold out in the hurricane‘s path – but maybe it sold out that week at WalMarts across the country, not just where the hurricane landing was imminent. 8 Example: Hurricane Frances ? The prediction could be somewhat useful, but is probably more general than Ms. Dillman was intending. ? It would be more valuable to discover patterns due to the hurricane that were not obvious. ? To do this, analysts might examine the huge volume of WalMart data from prior, similar situations (such as Hurricane Charley) to identify unusual local demand for products. 9 Example: Hurricane Frances ? From such patterns, the pany might be able to anticipate unusual demand for products and rush stock to the stores ahead of the hurricane‘s landfall. Indeed, that is what happened. ? The New York Times (Hays, 2020) reported that:‖…the experts mined the data and found that the stores would indeed need certain productsand not just the usual flashlights. ―We didn‘t know in the past that strawberry PopTarts increase in sales, like seven times their no