This chapter is from the book
This chapter is from the book
This chapter is from the book
This chapter is from the book
Review Questions
- What is Big Data? Discuss the factors that led to the era of Big Data. Compare and contrast the paradigm shift from traditional operational databases to the Big Data analytical data warehousing.
- What is business analytics? What is the difference between analytics and statistics? Briefly describe the domain of the four major fields of business analytics: databases and data warehousing, descriptive, predictive, and prescriptive analytics.
- Compare and contrast descriptive, predictive, and prescriptive analytics in terms of tools and techniques used, data input and output, and their use in the decision-making process. Which type of analytics is more important for an organization?
- The traditional management science techniques have been revitalized in the era of Big Data and have become the basis for many prescriptive analytics models. Explain the change in the nature of management science to accommodate the need to process large amounts of data.
- The majority of companies today are using data analytics to gain a competitive advantage. Describe various ways in which business analytics can be used to lower costs, improve customer experience, and increase productivity. Make sure to support your argument with examples reported in newspapers, magazines, and online sources.
- Select a company where you are a regular customer. Think of the potential data this company stores about you. Also, consider other data sources your company might use (for example, demographics, market information, consumer information such as credit score). How do you think the business uses this information to make you a more valuable customer? For example, how does your bank know to send e-mails to you with discount offers for a loan product? How does your favorite restaurant generate a free appetizer coupon to be used during your next visit?
- Using the same company from the previous question, brainstorm ideas about how the company can improve its operations by lowering costs or improving productivity. For example, how does the bank identify the optimal interest rates for their new loan offerings? How does the restaurant decide the menu prices and happy hour discounts?
- What is volume in the Big Data definition? How does the high volume of Big Data impact descriptive, predictive, and prescriptive analytics? Provide examples to illustrate your ideas.
- What is velocity in the Big Data definition? How does high velocity of Big Data impact descriptive, predictive, and prescriptive analytics? Provide examples to illustrate your ideas.
- What is variety in the Big Data definition? How does a high variety of Big Data impact descriptive, predictive, and prescriptive analytics? Provide examples to illustrate your ideas.
- Discuss challenges that organizations face when trying to analyze Big Data. Make sure to include in your discussion one or more challenges such as privacy invasion, financial exposure, mistaking noise for the signal, and poorly defining business problems.
- How important is it to have optimal versus good but practical solutions? Discuss the importance of spreadsheet modeling in this comparison. List other modeling software and compare it with spreadsheets.
- Explore advantages and disadvantages of Excel modeling for prescriptive analytics. Consider in your discussion the ability of spreadsheets to process large amounts of data as well as the potential use of add-ins.
- Good analytics models can sometimes lead to bad business results, conclusions, and recommendations. List at least three reasons why this might happen. For each reason, offer practical recommendations to avoid erroneous conclusions. Provide examples to illustrate your ideas.
- Discuss challenges faced by practitioners when exploring Big Data with management science models. Suggest practical solutions to these challenges.