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Page 12
ANSWER:
b
RATIONALE:
This is an example of visualization. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:57 AM
DATE MODIFIED:
7/26/2017 1:59 AM
28. The analysis that focuses on future-oriented, potential behaviors as opposed to classifying past behaviors is known
as
a.
future analysis.
b.
predictive analysis.
c.
descriptive analysis.
d.
presumptive analysis.
e.
speculative analysis.
ANSWER:
b
RATIONALE:
This type of analysis is known as predictive analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:59 AM
DATE MODIFIED:
7/26/2017 2:01 AM
29. _________ analysis tries to uncover explanatory and predictive models of business performance based on the
relationship between data inputs and business outcomes.
a.
b.
c.
d.
e.
ANSWER:
c
RATIONALE:
Predictive analysis tries to uncover explanatory and predictive models of business
performance based on the relationship between data inputs and business outcomes. See
6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
Copyright Cengage Learning. Powered by Cognero.
Page 13
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:01 AM
DATE MODIFIED:
7/26/2017 2:04 AM
30. The analysis technique that involves seeing how a dependent variable might change when one or more independent
variables changes, such as to see if a customer can be cross-sold or up-sold at the point of purchase is known as
a.
purchase analysis.
b.
regression analysis.
c.
descriptive analysis.
d.
time series analysis.
e.
simulation analysis.
ANSWER:
b
RATIONALE:
This analysis technique is known as regression analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:04 AM
DATE MODIFIED:
7/26/2017 2:06 AM
31. Discovering data-based trends by analyzing sequences of data over successive times to not only recognize the data
pattern but forecast how the data will extend into the future is the goal of
a.
regression analysis.
b.
trend analysis.
c.
time series analysis.
d.
simulation analysis.
e.
spectral analysis.
ANSWER:
c
RATIONALE:
This represents the goal of time series analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:06 AM
DATE MODIFIED:
7/26/2017 2:08 AM
Copyright Cengage Learning. Powered by Cognero.
Page 14
32. Taking multiple, random samples from an existing data set and running thousands of “what if” analyses, each with
different assumptions about market conditions and other marketplace dynamics, is an example of which of the
following?
a.
Regression analysis
b.
Trend analysis
c.
Time series analysis
d.
Simulation analysis
e.
Spectral analysis
ANSWER:
d
RATIONALE:
This is an example of simulation analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:09 AM
DATE MODIFIED:
7/26/2017 2:11 AM
33. Which of the following best describes what prescriptive analysis is?
a.
To recommend the best course of action among the firm’s various options.
b.
To predict a future outcome with some degree of confidence.
c.
To understand the current context.
d.
None of these are correct.
e.
All of these are correct descriptions of descriptive analysis.
ANSWER:
a
RATIONALE:
Predictive analysis seeks to recommend the best course of action among the firm’s
various options. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:12 AM
DATE MODIFIED:
9/20/2017 2:01 PM
34. What statement best describes what prescriptive analysis is used for?
a.
To uncover explanatory and predictive models of business performance based on the relationship between
data inputs and business outcomes.
b.
To understand business performance.
c.
To extract patterns from large datasets.
d.
To determine a set of high-value alternative actions for the purposes of improving business performance.
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Page 15
e.
To determine if a customer can be cross-sold or up-sold a product at the point of purchase.
ANSWER:
d
RATIONALE:
Prescriptive analysis is used to determine a set of high-value alternative actions for the
purposes of improving business performance. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:15 AM
DATE MODIFIED:
7/26/2017 2:21 AM
35. A key term in prescriptive analysis is
a.
randomization.
b.
differentiation.
c.
optimization.
d.
experimentation.
e.
amortization.
ANSWER:
c
RATIONALE:
Optimization is a key term in prescriptive analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:39 AM
DATE MODIFIED:
7/26/2017 2:41 AM
36. The analysis that Major League Baseball uses to create its schedule each year, to optimize stadium commitments,
travel, hotel, sponsorships, and other factors is an example of
a.
descriptive analysis.
b.
prescriptive analysis.
c.
chronological analysis.
d.
predictive analysis.
e.
spatial analysis.
ANSWER:
b
RATIONALE:
This is an example of prescriptive analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
Copyright Cengage Learning. Powered by Cognero.
Page 16
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:42 AM
DATE MODIFIED:
7/26/2017 2:44 AM
37. IBM’s Slamtracker, which analyzes years of Grand Slam tennis data to compare historic head-to-head statistics of
competitors to predict keys to winning a match, is an example of which kind of analysis?
a.
Descriptive
b.
Competitive
c.
Prescriptive
d.
Historical
e.
Predictive
ANSWER:
e
RATIONALE:
This kind of analysis is known as predictive analysis. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 2:44 AM
DATE MODIFIED:
7/26/2017 2:47 AM
38. A key challenge of Big Data is integration. Which of the following is NOT one of the areas in which companies are
challenged with Big Data integration?
a.
Integration of a firm’s internal transaction data
b.
Access to data
c.
Analytic skills
d.
Retrieval of data
e.
Integration of data within and between firms
ANSWER:
a
RATIONALE:
All of these are key challenges of Big Data integration except the integration of a firm’s
internal transaction data. See 6-5: Key Challenges of “Big Data” Integration.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.05 – List and discuss the key challenges of “big data” integration.
DATE CREATED:
7/26/2017 2:47 AM
DATE MODIFIED:
7/26/2017 2:50 AM
39. A wealth of barcode transaction data is collected by merchants when customers make purchases. Which one of the
following scenarios does NOT represent one of the typical Big Data integration challenges in using these transactional
Copyright Cengage Learning. Powered by Cognero.
Page 17
data?
a.
Getting merchandising ideas by doing a market basket analysis
b.
Linking the transaction data to a decision support system for ease of in–store price changes
c.
Creating personalized promotions using the store’s loyalty program
d.
Combining barcode transaction data with customer Facebook status updates about service quality
e.
All of these represent typical Big Data integration challenges
ANSWER:
b
RATIONALE:
All of these represent possible scenarios of Big Data integration challenges except linking
the transaction data to a decision support system for ease of in-store price changes. See
6-5: Key Challenges of “Big Data” Integration.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.05 – List and discuss the key challenges of “big data” integration.
DATE CREATED:
7/26/2017 2:50 AM
DATE MODIFIED:
7/26/2017 2:53 AM
40. Which of the following statements best represents the analytics skills problem of Big Data integration?
a.
Big Data analytics is a discipline too new to provide meaningful employment opportunities.
b.
Most companies have not yet accumulated enough data to have a need for Big Data analytics.
c.
There is too much data and companies lack the right skills to manage data effectively.
d.
The level of skill required for Big Data analytics is too low to attract quality job candidates into the
profession.
e.
There is no analytics skills problem associated with Big Data.
ANSWER:
c
RATIONALE:
There is typically too much data, and companies lack the right skills to manage data
effectively. See 6-5: Key Challenges of “Big Data” Integration.
POINTS:
1
DIFFICULTY:
Easy
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.05 – List and discuss the key challenges of “big data” integration.
DATE CREATED:
7/26/2017 2:53 AM
DATE MODIFIED:
7/26/2017 2:56 AM
41. Even if all data integration issues are addressed, the problem that remains for most companies when it comes to
integrated data is
a.
finding ways to store it all.
b.
backing up the data properly.
c.
controlling access to the data.
d.
finding the hidden insights within the data.
e.
auditing the data regularly.
ANSWER:
d
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Page 18
RATIONALE:
Most companies struggle to find hidden insights within the data. See 6-5: Key Challenges
of “Big Data” Integration.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.05 – List and discuss the key challenges of “big data” integration.
DATE CREATED:
7/26/2017 2:57 AM
DATE MODIFIED:
7/26/2017 2:59 AM
42. It is generally accepted that Big Data is four-dimensional.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 –
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 2:59 AM
DATE MODIFIED:
7/26/2017 5:06 AM
43. The sources of Big Data are not highly variable.
a.
True
b.
False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 –
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 3:01 AM
DATE MODIFIED:
7/26/2017 5:08 AM
44. Firms investing in the capture, storage, and analysis of large and varied data sets are forward-looking and cutting
edge.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
Copyright Cengage Learning. Powered by Cognero.
Page 19
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 – Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 3:06 AM
DATE MODIFIED:
7/26/2017 5:09 AM
45. One way of understanding structured data is as filling rows of data on a spreadsheet.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 –
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 3:09 AM
DATE MODIFIED:
7/26/2017 5:11 AM
46. Barcode transaction data captured at a grocer’s cash register is an example of unstructured data.
a.
True
b.
False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 –
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 3:11 AM
DATE MODIFIED:
7/26/2017 5:12 AM
47. One of the key challenges of Big Data integration is the ability to merge unstructured and structured data.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 –
Contrast structured and unstructured data.
Copyright Cengage Learning. Powered by Cognero.
Page 20
DATE CREATED:
7/26/2017 3:15 AM
DATE MODIFIED:
7/26/2017 5:13 AM
48. Data mining is a descriptive analysis technique used for finding nonlinear patterns in the data.
a.
True
b.
False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 3:17 AM
DATE MODIFIED:
7/26/2017 5:14 AM
49. Predictive analysis focuses on future-oriented, potential behaviors as opposed to merely classifying past behaviors.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 –
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 3:19 AM
DATE MODIFIED:
7/26/2017 5:15 AM
50. The veracity dimension of Big Data is considered the most challenging of all.
a.
True
b.
False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 –
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 3:21 AM
DATE MODIFIED:
7/26/2017 5:16 AM
51. Voice of the Customer (VOC) data are largely structured social media posts.
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Page 21
a.
True
b.
False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 –
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 3:23 AM
DATE MODIFIED:
7/26/2017 5:17 AM
52. Social network analysis studies social connections where the leader in a network is a hub and the multiple followers
are nodes on the spokes.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 – Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 3:25 AM
DATE MODIFIED:
7/26/2017 5:19 AM
53. Starbucks creating over 800 “geofences” in the UK to send geotargeted text messages to customers is an example of
using location-based services of mobile data.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 – Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 3:27 AM
DATE MODIFIED:
7/26/2017 3:46 AM
54. Apple uses data in one context that is linked to data in another context, and another and another, to get a 360–
degree view of customer purchasing. This is an example of omni-transactional data.
a.
True
b.
False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy
QUESTION TYPE:
True / False
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 – Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 3:29 AM
DATE MODIFIED:
7/26/2017 3:46 AM
55. Define Big Data and describe the framework for understanding its key elements, the four “Vs”, explaining what each
of the “Vs” represents.
ANSWER:
Big Data is the process of capturing, merging, and analyzing large and varied data sets for
the purpose of understanding current business practices and seeking new opportunities
to enhance future performance. The first dimension of Big Data is Volume, which refers to
the amount of data being collected. The second “V” is Velocity, with refers to the pace of
data flow, both into and out of a firm. The third “V” or dimension of Big Data is Variety
and refers to the fact that data can take many forms, both structured and unstructured.
The fourth “V” is Veracity and refers to the accuracy and trustworthiness of the data
collected.
POINTS:
1
DIFFICULTY:
Medium
QUESTION TYPE:
Essay
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 –
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 3:31 AM
DATE MODIFIED:
7/26/2017 3:33 AM