Part A: Briefly explain the issue(s) as presented in the reading. Position yours
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Question
Part A: Briefly explain the issue(s) as presented in the reading. Position yourself as a manager facing similar conflicts between technology and human decision making. Discuss how issues of this nature can be or perhaps should be handled in decision making. Discuss any problems which might arise in a reliance on solely technology or solely human decision making.Chapter 12 Enhancing Decision Making 467 NTECHNOLOGY The Tension Between Technology and Human Decision Makers Big data and analytics have swept the business world and the professional sports industry is no exception. Aocording to Glants bench coach Ron Wotus, mam- bers really can't tell the whole story about the qual- ity of the player. So the Giants integrate statistical Baseball, football, soccer, hockey, tennis, and even sallboat racing are finding ways to analyve data about data with scouting, coaching, and player expertence, players and competing teams in order to improve performance. Baseball has its own statistical analysisNational League the Giants do not see regularly especially when dealing with opponents outside the pline called sabermetrics, first popularized in Being able to exploit an individual player's strengths the popular book and film Moneyball comes more from knowing the player and his ability Given the huge disparities in Major League Base- as opposed to the statistics, Wotus believes. Short- ball (MLB) team budgets, wealthier teams typically have the advantage in recruiting the best players. Moneyball describes how Oakland Athletics man- ager Billy Beane was able to turn the underdog A's into a winning team by using advanced analytics to tistically related, but statistics help when you don't guide decisions about which players to recruit and cultivate. Rigorous statistical analysis had demon- strated that on-base percentage and slugging per- centage were better indicators of offensive success (and cheaper to obtain on the open market) than more historically valued qualities such as speed and and data-driven decisions comes from the 34th contact. These observations often flew in the face of conventional baseball wisdom and the beliefs of many baseball talent scouts and coaches. Beane rebuilt the A's based on these findings, producing a Decisions about how to sail the boats were based consistently winning team for a number of years by on a continual stream using advanced analytics to gain insights into each stops with good arms can play farther from home plate than normal at times, while fast runners can play closer to home plate than usual. There are nuances to defending the opposition that are not sta know players well enough to know what to expect from them. The instinct of the player and what the player is seeing on the field often override the statis- Another example of the tension between human- America's Cup race pitting Oracle Team USA against Emirates Team New Zealand in October 2013. This was the most high-tech sailboat race in history of data from the boats, which were 72-foot twin-hull catamarans capable of speeds player's value and contribution to team success that exceeding 50 miles per hour. Controlling these wick wealthier teams had overlooked edly sleek sailing machines required a lightning-fast collection of massive amounts of data, powerful data management, rapid real-time data analysis, quick Beane and his data-driven approach to baseball had a seismic impact on the game. After observing the A's phenomenal success in 2002, the Boston Red decision making, and immediate measurement of Sox adopted Beane's strategy, only with more money. the results. For Team USA, this meant using 250 sen- Two years later, they won the World Series. To vary ing degrees, every Major League Baseball team today data on pressure, angles, loads, and strains to moni- uses data and deep analytics to support decisions about many aspects of the game. sors on the wing, hull, and rudder to gather real-time tor the effectiveness of each adjustment. The sensors tracked 4,000 variables, 10 times a second, producing Nevertheless, some teams are not fully commit-90 million data points an hour, which were transmit- ted to using sabermetrics to drive their decisions. Many baseball experts continue to believe that tra ditional methods of player evaluation, along with gut instinct, money, and luck, are still key ingre- dients for winning teams. Take the San Francisco Giants, for example, who have won more games and have more outstanding players in the Baseball Austin data center for more in-depth analysis. Each ted on a wireless network to crew member wrist displays. The data were wirelessly transferred to a tender ship running Oracle 11g database manage- ment software for nearly real-time analysis using velocity prediction formulas geared to understand- ing what makes the boat go fast and also to Oracle's Hall of Fame than any team in U.S. baseball history. USA crew member wore a small mobile handheld The Giants use statistics but also base their payer recruitment decisions on the opinions of scouts and computer on his wrist to display data on the key performance variables customized for that person's responsibilities. The captain and tactician had data coa
Explanation / Answer
The tug war between the technology-based decision-making and the traditional human decision-making is increasingly seen in all the sectors. Sports are no exception to this clash. This case study gives some glimpses of specific sports like baseball and sailboat race. In the instances quoted one team used the advanced data analytics, statistical analysis to gain insights into recruiting players, driving them in the game, forecasting the player's ability to play rather than using coaches or trainers help to find the best sportsperson for the team. This data-driven approach has some advantages as well as disadvantages as 100% replication of the model do not assure winning the game but can aid in better judgement. Similarly, in the second case with completely guided by the integrated system of advanced technology, Team USA lost the match as they were completely depended on the technology. It is agreed that technology helps in increasing the efficiency but completely depending on the technology will underutilize the human capabilities.
As a manager, I would suggest maximum utilization of data-driven technologies but also should incorporate the human instinct. The decision-making should be insight-driven as well as intuition-driven. This is the middle path to achieve success for long-run survival and sustenance. As our conscious awareness of our surrounding will lead to better decision-making. Although data analytics substantiates with fast and quick data, it may be wrong. Data are one perspective for decision-making, but ignorance of other factors can add to poor insights. There are risks in statistical analysis as they are made with some basic assumptions and it can misinterpret the manager's decision.