Chapter 6 Case Study - How Reliable is Big Data - MIS_Laudon PDF

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Case study for Chapter 6 - Management Information Systems - Laudon...

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CHAPTER 6: Case Study – Big Data 6-13 What business benefits did the organizations described in this case achieve by analyzing and using big data? Technology companies like Amazon, YouTube, and Sportify have flourished by analyzing the big data they collect about customer interests and purchases to create millions of personalized recommendations. Thus, they are able to enhance customer experience, attract more new users and generate more revenue. Besides, a number of online services are also benefited from big data to help consumers, including services for finding the lowest price on autos, computers, mobile phone plans, clothing, airfare, hotel rooms, and many other types of goods and services. Big data also helped the UK National Health Service (NHS) save about 581 million pounds (U.S. $784 million) by creating dashboards identifying patients taking 10 or more medications at once, and which patients are taking too many antibiotics. Compiling very large amounts of data about drugs and treatments given to cancer patients and correlating that information with patient outcomes has helped NHS identify more effective treatment protocols.

6-14 Identify two decisions at the organizations described in this case that were improved by using big data and two decisions that big data did not improve. Decisions improved by big data: -

The NHS made better decisions on treatment protocols thanks to collecting and analyzing patients´ historic health data.

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New York City analyzed all the crime-related data to lower the crime rate. They created CompStat crime-mapping program, and the data can be displayed on maps showing crime and arrest locations, crime hot spots, and other relevant information.

Decisions not improved by big data: -

The company that had generated most of its sales leads and eventual sales from trade shows and conferences did not take advantage of big data to measure its website traffic in relation to the number of mentions on Twitter.

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In 2016, when the US presidential election happened, Clinton supporters were misled from the predictive models about her assured victory, but turnout the models were lacked context in explaining potentially wide margins of error.

6-15 List and describe the limitations to using big data. 

The rush to start big data project without first establishing a business goal will not bring any benefit to the company. Because they do not know which types of information they are looking for, they are amassing mountains of data with no clear objectives. This will result to a waste of time and resources.

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There is a possibility of misleading interpretation from big data analysis result. For example, examining big data might show that from 2006 to 2011 the United States murder rate was highly correlated with the market share of Internet Explorer, since both declined sharply, but that doesn’t necessarily mean there is any meaningful connection between the two phenomena.

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Big data predictive models sometimes are not ideally to show the future trend. When the system uses an outdated modeling technique, the recommendations from the software would be useless because they could not predict properly and accurately.

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Data sets and data-driven forecasting models could be misled by flawed assumption and insufficient attention to context. For example, Google had developed a model to forecast the flu trends, but it was based on a flawed assumption, and the algorithm only looked at numbers, not the context of the results.

6-16 Should all organizations try to collect and analyze big data? Why or why not? What management, organization, and technology issues should be addressed before a company decides to work with big data? In my opinion, collecting and analyzing big data should be done only by those companies whose business is highly dependent on data and information like Netflix, YouTube, Sportify, etc. And since each organization has a unique business model and goals, not all of them should adapt this technology. Management factor: -

Management should firstly establish the goals for the business to start working with big data, as well as consider several alternatives to find out the best cost-effective method.

Organization factor: -

Organization should fully understand the business model and its most successful sales channels, which can be through social media or physical stores and events, in order to decide to adapt big data projects.

Technology factor: -

Data analysts need some business knowledge of the problem they are trying to solve with big data, in order to interpret the result in the right way.

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Firms need to choose the efficient maintenance and software providers to ensure the system will work properly....