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56 Part I • Decision Making and Analytics: An Overview

Application Case 1.6 (Continued)

Questions for Discussion consulting solutions to the businesses in employ-
1. How can analytical techniques help organiza- ing prescriptive analytical solutions. It is equally
tions to retain competitive advantage? important to have proactive decision makers in the
2. How can descriptive and predictive analytics organizations who are aware of the changing eco-
help in pursuing prescriptive analytics? nomic environment as well as the advancements
3. What kinds of prescriptive analytic techniques in the field of analytics to ensure that appropriate
are employed in the case study? models are employed. This case shows an example
4. Are the prescriptive models once built good of geographic market segmentation and customer
forever? behavioral segmentation techniques to isolate the
profitability of customers and employ optimization
What We can Learn from this application techniques to locate the branches that deliver high
case profitability in each geographic segment.

Many organizations in the world are now embrac- Source: X. Wang et al., “Branch Reconfiguration Practice Through
ing analytical techniques to stay competitive Operations Research in Industrial and Commercial Bank of China,”
Interfaces, January/February 2012, Vol. 42, No. 1, pp. 33–44; DOI:
and achieve growth. Many organizations provide 10.1287/inte.1110.0614.

analytics applied to different domains
Applications of analytics in various industry sectors have spawned many related areas or
at least buzzwords. It is almost fashionable to attach the word analytics to any specific
industry or type of data. Besides the general category of text analytics—aimed at getting
value out of text (to be studied in Chapter 6)—or Web analytics—analyzing Web data
streams (Chapter 7)—many industry- or problem-specific analytics professions/streams
have come up. Examples of such areas are marketing analytics, retail analytics, fraud ana-
lytics, transportation analytics, health analytics, sports analytics, talent analytics, behav-
ioral analytics, and so forth. For example, Application Case 1.1 could also be termed as
a case study in airline analytics. Application Cases 1.2 and 1.3 would belong to health
analytics; Application Cases 1.4 and 1.5 to sports analytics; Application Case 1.6 to bank
analytics; and Application Case 1.7 to retail analytics. The End-of-Chapter Application
Case could be termed insurance analytics. Literally, any systematic analysis of data in a
specific sector is being labeled as “(fill-in-blanks)” Analytics. Although this may result in
overselling the concepts of analytics, the benefit is that more people in specific industries
are aware of the power and potential of analytics. It also provides a focus to professionals
developing and applying the concepts of analytics in a vertical sector. Although many of
the techniques to develop analytics applications may be common, there are unique issues
within each vertical segment that influence how the data may be collected, processed,
analyzed, and the applications implemented. Thus, the differentiation of analytics based
on a vertical focus is good for the overall growth of the discipline.
analytics or data science?

Even as the concept of analytics is getting popular among industry and academic circles,
another term has already been introduced and is becoming popular. The new term is data
science. Thus the practitioners of data science are data scientists. Mr. D. J. Patil of LinkedIn
is sometimes credited with creating the term data science. There have been some attempts
to describe the differences between data analysts and data scientists (e.g., see this study at
emc.com/collateral/about/news/emc-data-science-study-wp.pdf). One view is that

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