of information, the company can ad dress clients who might be at-risk, and attempt to intervene with less expensive preventative measures What other applications simila ediction of falls Student responses will vary, but could include a number of other medical d itions or types of ac 3. How would you convince a new health insurance customer to adopt healthier ple 3)? Student responses will vary, but may focus on improved customer education that is targeted at specific risk factors as well as financial or benefit inducements tied to positive changes in lifestyle 4. Identify at least three other opportunities for applying analytics in the retail valt chain beyond those covered in this section Many potential opportunities exist, and student responses will vary based on their experences 5. Which retail stores that you know of employ some of the analytics applications identified in this section? Student responses will vary based on the retail establishments they are familiar with and the applications used at the time Section 1.7 Review Questions 1. What is Big Data analytics? Typically, the data is arriving in many ditferent forms. be they structllGo unit The term Big Data refers to data that cannot be stored in a single storage unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data What are the sources of Big data? Major sources include clickstreams from Web sites, postings on social med ia, and data from traffic sensors and the weather 3. What are the characteristics of Big Data? Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading which operates in the order of microseconds; and data about consumer opinions measured from postings in social med ia What processing technique is applied to process Big Data? One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming Copyright C2018 Pearson Education, Inc.9 Copyright © 2018Pearson Education, Inc. of information, the company can address clients who might be at-risk, and attempt to intervene with less expensive preventative measures. 2. What other applications similar to prediction of falls can you envision? Student responses will vary, but could include a number of other medical conditions or types of accidents. 3. How would you convince a new health insurance customer to adopt healthier lifestyles (Humana Example 3)? Student responses will vary, but may focus on improved customer education that is targeted at specific risk factors as well as financial or benefit inducements tied to positive changes in lifestyle. 4. Identify at least three other opportunities for applying analytics in the retail value chain beyond those covered in this section. Many potential opportunities exist, and student responses will vary based on their experiences. 5. Which retail stores that you know of employ some of the analytics applications identified in this section? Student responses will vary based on the retail establishments they are familiar with and the applications used at the time. Section 1.7 Review Questions 1. What is Big Data analytics? The term Big Data refers to data that cannot be stored in a single storage unit. Typically, the data is arriving in many different forms, be they structured, unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data. 2. What are the sources of Big Data? Major sources include clickstreams from Web sites, postings on social media, and data from traffic, sensors, and the weather. 3. What are the characteristics of Big Data? Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading, which operates in the order of microseconds; and data about consumer opinions measured from postings in social media. 4. What processing technique is applied to process Big Data? One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming paradigm