Deck 6: Decision Support Systems: Working With Big Data

Question 1

The most challenging of the four dimensions of Big Data is considered to be

A)volume
B)veracity
C)velocity
D)variety
E)All dimensions are equally challenging.

Answer 1

C

Question 2

Applications of Big Data in the real world might include which of the following?

A)Improving customer retention rates
B)Dealing with negative word of mouth
C)Creating personalized promotions
D)All of these are valid applications of Big Data.
E)Only b and c are valid examples of Big Data.

Answer 2

D

Question 3

Which of the following does NOT represent the purpose of Big Data or what it is all about?

A)Understanding current business practices better
B)Generating more data inputs
C)Seeking new opportunities to enhance future performance
D)Establishing the processes to yield insightful outcomes
E)All of these are consistent with the value of Big Data.

Answer 3

B

Question 4

A study of hundreds of C-level executives reveals that a fifth dimension of Big Data may be

A)visibility.
B)variability.
C)value.
D)vision.
E)vigor.

Question 5

A major restaurant chain wishes to understand consumer sentiment about its brand,so it analyzes social media comments,receipt survey data,call center conversation summaries from its CRM system,and even reviews from websites like Urban Spoon.This illustrates which dimension of Big Data?

A)Volume
B)Veracity
C)Velocity
D)Variety
E)Value

Question 6

The auto insurance company that uses an app to monitor your driving behavior,collecting thousands of data points in the process,as part of providing you a policy quote that illustrates which dimension of Big Data?

A)Volume
B)Veracity
C)Velocity
D)Variety
E)Value

Question 7

Which of the following represent valid sources of Big Data?

A)Survey responses
B)Transactions details
C)Social media references
D)Location data
E)All of these are valid types of data.

Question 8

The velocity dimension of Big Data refers to

A)the amount of data being collected.
B)the pace of data flow,both in and out of a firm.
C)the consistency of the data collection process.
D)the diversity of types or forms of data.
E)the speed at which data is transmitted through the company network.

Question 9

Data such as transactional data collected by banks,airlines,and retailers is known as _______ data.

A)structured
B)random
C)collected
D)unstructured
E)big

Question 10

Data such as blogger reviews or social media comments is known as _____ data.

A)structured
B)random
C)collected
D)unstructured
E)big

Question 11

Businesses like banks and airlines have more data than firms in other industries due to the _______ nature of their businesses.

A)seasonal
B)consumer
C)stagnant
D)transactional
E)services

Question 12

The value of linking data from several contexts (e.g.,omni-channel transactional data)is that it can provide

A)big data sets for analysis.
B)a complete view of in-store purchasing behavior.
C)a complete view of online purchasing behavior.
D)location mapping data.
E)a 360-degree view of purchasing patterns.

Question 13

Analytical techniques,when applied to large sets of data,can

A)describe consumer behavior.
B)predict future consumption actions.
C)prescribe courses of action for a firm and its management.
D)All of these are correct.
E)None of these are correct.

Question 14

A Big Data application of using location-based mobile data from call records is

A)voice-of-the-customer insights.
B)a 360-degree view of purchasing patterns.
C)location-based marketing in real time.
D)optimized website design.
E)All of these are enabled by location-based mobile data.

Question 15

Big Data is the process of ____________ large and varied data sets.

A)capturing,understanding,and distributing
B)detecting,merging,and analysing
C)capturing,merging,and analysing
D)securing,validating,and storing
E)researching,analyzing,and storing

Question 16

The variety dimension of Big Data refers to

A)the amount of data being collected.
B)the pace of data flow,both in and out of a firm.
C)the different storage device capacities available for storing Big Data.
D)the diversity of types or forms of data.
E)the variety of businesses that utilize Big Data.

Question 17

Omni-channel retailing recognizes that sources of transaction data that provide purchase insights are available from

A)brick-and-mortar.
B)e-commerce.
C)mobile.
D)in-store pickup.
E)Omni-channel retailing could include transaction data from all of the above.

Question 18

The volume dimension of Big Data refers to

A)the amount of data being collected.
B)the pace of data flow,both in and out of a firm.
C)the consistency of the data collection process.
D)the diversity of types or forms of data.
E)the capacity of the storage units on which data is stored.

Question 19

Which of the following is NOT one of the four dimensions of the framework for understanding Big Data?

A)Volume
B)Vastness
C)Velocity
D)Variety
E)All of these are dimensions of the framework.

Question 20

A great source of "Voice of the Customer" (VOC)data is

A)structured.
B)mobile.
C)omni-transactional.
D)unstructured.
E)social.

Question 21

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

Question 22

The analysis that discovers interesting relationships between items purchased on a single ticket (e.g.in the same shopping cart)of consumers is

A)data harmonization.
B)data mining.
C)data fusion.
D)neural networks.
E)visualization.

Question 23

Creating charts,graphs,images,diagrams.and even word clouds that allow for better communication of data is an example of

A)data fusion.
B)visualization.
C)neural networks.
D)harmonization
E)data mining.

Question 24

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.

Question 25

A key term in prescriptive analysis is

A)randomization.
B)differentiation.
C)optimization.
D)experimentation.
E)amortization.

Question 26

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.
E)To determine if a customer can be cross-sold or up-sold a product at the point of purchase.

Question 27

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

Question 28

Combining real-time sales data with real-time social media mentions in order to better understand consumer sentiment toward an advertising campaign is an example of which data analysis technique?

A)Neural networks
B)Affinity
C)Data mining
D)Data fusion
E)Harmonization

Question 29

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.

Question 30

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.

Question 31

_________ analysis tries to uncover explanatory and predictive models of business performance based on the relationship between data inputs and business outcomes.

A)Regression
B)Descriptive
C)Predictive
D)Perceptive
E)None of these are correct.

Question 32

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.

Question 33

The analysis technique to consider when searching for nonlinear patterns in data is

A)visualization.
B)data fusion.
C)data modeling.
D)data mining.
E)neural networks.

Question 34

Which of the following is NOT considered a descriptive analysis technique?

A)Data harmonization
B)Data mining
C)Data fusion
D)Neural networks
E)Visualization

Question 35

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.

Question 36

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

Question 37

Recognizing patterns,such as identifying fraudulent insurance claims made by otherwise consistent policyholders,is an example of which kind of descriptive analysis technique?

A)Data screening
B)Visualization
C)Neural networks
D)Data fusion
E)Data mining

Question 38

The descriptive analysis technique whose goal is to integrate and analyze data from various sources as opposed to relying on only a single source is known as

A)data harmonization.
B)data mining.
C)data fusion.
D)neural networks.
E)visualization.

Question 39

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.

Question 40

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 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

Question 41

Define Big Data and describe the framework for understanding its key elements,the four "Vs",explaining what each of the "Vs" represents.

Question 42

Data mining is a descriptive analysis technique used for finding nonlinear patterns in the data.

Question 43

The sources of Big Data are not highly variable.

Question 44

One way of understanding structured data is as filling rows of data on a spreadsheet.

Question 45

Social network analysis studies social connections where the leader in a network is a hub and the multiple followers are nodes on the spokes.

Question 46

The veracity dimension of Big Data is considered the most challenging of all.

Question 47

Firms investing in the capture,storage,and analysis of large and varied data sets are forward-looking and cutting edge.

Question 48

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.

Question 49

Voice of the Customer (VOC)data are largely structured social media posts.

Question 50

Barcode transaction data captured at a grocer's cash register is an example of unstructured data.

Question 51

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.

Question 52

It is generally accepted that Big Data is four-dimensional.

Question 53

Predictive analysis focuses on future-oriented,potential behaviors as opposed to merely classifying past behaviors.

Question 54

One of the key challenges of Big Data integration is the ability to merge unstructured and structured data.

Question 55

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.