?Chapter 8

1) How does prescriptive analytics relate to descriptive and predictive analytics?

2. Explain the differences between static and dynamic models. How can one evolve into the other?

3. What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?

4. Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Chapter 9

1. What is Big Data? Why is it important? Where does Big Data come from?

2. What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

3. What is Big Data analytics? How does it differ from regular analytics?

4. What are the critical success factors for Big Data analytics?

 5. What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Answer:

Chapter 8

1) Perscriptive analytics is releated to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps the model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.

2) A static model does not account for the element of time, while a dynamic model does it. Static models typically use static aging techniques, changing certain certain variables on the original microdata file to produce a file with the demographic and economic characterstics expected in the future year.

3) An optimistic descission maker consider the most favourable outcome where as a pessimistic descision maker is very conservative in his approach. In making decision under uncertainity, the decision maker should assess not only the most likely outcome from a decision but also the outcome that will arise if the worst problem happens.

4) Decision making under uncertainity and risk entails the selection of a course of action when we do not know with certainty the results that each alternative action will lead. Because when solving a problem under certainty, the decision maker thinks about the situation in various possible results for each and every action.

Chapter 9

1) Big Data is a definition that is hard to pin down. Several experts and publications have a different idea of what big data actually means. Generally big data is a term for incredibly large, varied and complex sets of data. It is important because it speed up and automates tasks that when done manually are slow and ineffecient. The bulk of the big data generated comes from three primary sources: social data, machine data and transactional data.

2)  The future advent of Big Data has a harmful impact because it serves the majority while diminishing the minority and ignoring important outliers. Overall, the rise of Big Data is a big negative for society in nearly all respects.  It will lose its popularity to petabytes, zetabytes, and beyond.

3) Big data analytics generally refers to the strategy of analyzing large volumes of the data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The main difference between the big data and the data analytics is that the big data is a large quantity of complex data while data analytics is the process of examing, transforming and modeling data to recognize useful information and to support decision making.

4) The critical success factors for Big Data analytics are:

• Get support from the corporate culture.
• Start at the top, work from the bottom, and meet in the middle.
• Confirm and handle the truth.
• Start by thinking of the answer or outcome.
• Build an analytics narrative.

5) The big challenges that one should be mindful of when considering implementation of Big Data analytics are:

• The Uncertainty of Data Management.
• Talent Gap in Big Data.
• Getting Data into Big Data Structure.
• Syncing Across Data Sources.
• Extracting information from the Data in Big Data Integration.