Chapter 6 – Decision Support Systems: Working with “Big Data”
I. Learning Objectives:
Upon completing this chapter, the student should be able to:
1. Identify the three Vs of “big data.”
2. Contrast structured and unstructured data.
Structured data can be easily contained in classic rows and columns of
3. Describe the three sources of “big data” for marketers.
4. Compare descriptive, predictive, and prescriptive analytical approaches.
• Descriptive analysis is designed to enhance understanding of available data
5. List and discuss the key challenges of “big data” integration.
Three key challenges include:
• Access to and retrieval of data
II. Chapter Outline:
A. The Three Vs: Volume, Velocity, and Variety
B. The Fourth V: Value
1. Improving Customer Retention Rates
3. Enhancing Health Care
Chapter 6 – Decision Support Systems: Working with “Big Data”
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4. Creating Personalized Promotions
Research Window 6.1: Target, Big Data, and You
C. Marketplace Sources of “Big Data”
Research Window 6.2: The Decline and Resurgence of Marketing Research:
An Interview with Don Schultz
Exhibit 6.1: Sources of Data by Era
1. Structured Data
2. Unstructured Data
a. Social Data
D. Big Data Analysis
Exhibit 6.2: Psychographic Profiles of Frito-Lay “Fans” in Wisdom Professional
1. Descriptive Analysis
3. Prescriptive Analysis
Research Window 6.3: Google.org Flu Trends
E. Key Challenges of “Big Data” Integration
Exhibit 6.4: Big Data and the Individual Consumer
1. Access to and Retrieval of Data
3. Firm Integration of Big Data
Manager’s Focus
F. Summary
III. Answers to Review Questions:
1. Between volume, velocity, variety, or veracity, variety is the most challenging for
2. The possibility of increased revenue opportunities is one value of big data.
3. Structured data are data that can be written into rows on a spreadsheet or
database based on standard column headings. Unstructured data are data that
4. Social data are unstructured data available from social media and social networking
Web–based platforms. Mobile data are both structured and unstructured data
5. Descriptive analysis is designed to enhance understanding of available data to
benefit firm performance. Predictive analysis is designed to aid both explanatory
6. Three key challenges to big data integration include (1) access to and retrieval of
quality data, (2) lack of sufficient analytical skills within the firm, and (3) issues
related to firm integration both within and between firms. The first and third
IV. Instruction Suggestions:
1. This chapter is designed to provide an in-depth discussion of an increasingly
common, but still loosely defined, term: “big data.” Perhaps the best practical
2. Next, it might be interesting to discuss the pros and cons of the Dr. Don Schultz
interview, specifically the second question and his answer. Discussion of the
collection of behavioral data leads to a subsequent discussion of consumer privacy
(at least that has been our experience). It is interesting to hear views of conceptual
comparisons between freely giving away consumer data in social media outlets
versus having consumer data captured unknowingly via some of the tools noted
here.
3. Suggest that students look at both the quantitative and qualitative Web site
4. Turn then to a discussion of the different analytical approaches. The IBM Building, a
5. Finally, Exhibits 6.3 and 6.4 help to provide a balanced perspective of the pros and