Question 1

OLAP technology is primarily used for transaction processing.

Accepted Answer

OLAP technology is primarily used for online analytical processing, meaning it is used for data analysis and decision-making rather than transaction processing.

Question 2

_____ is the process of using computers to identify hidden patterns in and to build models from large data sets.
A) Data harvesting
B) E-discovery
C) Optimization
D) Report canning
E) Data mining

Accepted Answer

Data mining is the process of using computers to identify hidden patterns in and to build models from large data sets. Data harvesting is the act of gathering data, e-discovery refers to the process of finding electronic data for legal purposes, optimization is the process of finding the best solution to a problem, and report canning is not a commonly used term in data analysis.

Question 3

While spreadsheets are popular tools, they cannot effectively be used for "what-if" analysis.

Accepted Answer

Spreadsheets can effectively be used for "what-if" analysis through the use of functions such as goal seek and scenario manager.

Question 4

A(n) _____ is an AI system that examines data and hunts down and exposes patterns, in order to build models to exploit findings.
A) Hadoop
B) canned report
C) data aggregator
D) neural network
E) e-discovery

Accepted Answer

A neural network is an AI system designed to analyze data and uncover patterns, which can then be used to build models to exploit those findings. Neural networks use deep learning algorithms to process data and can be used in a variety of applications, from image recognition to natural language processing.

Question 5

Computer-driven investment models can be very effective when the market behaves as it has in the past. However, in terms of historical consistency, they are vulnerable to failure in the face of:
A) brute force attacks.
B) black swans.
C) zero-day exploits.
D) calendar rivalry metrics.
E) distributed denial of service.

Accepted Answer

Computer-driven investment models rely on historical data and patterns to make predictions about future market behavior. However, events like black swans, which are unpredictable and not accounted for in historical data, can disrupt these models and lead to failures in investment predictions. Therefore, black swans are a significant vulnerability for computer-driven investment models. The other options listed - brute force attacks, zero-day exploits, calendar rivalry metrics, and distributed denial of service - are unrelated to the vulnerability of computer-driven investment models.

Question 6

Data mining is leveraged by some firms to determine which products customers buy together, and how an organization can use this information to cross-sell more products or services. This area of application of data mining is referred to as:
A) market basket analysis.
B) expert systems.
C) customer churn.
D) customer segmentation.
E) vertical integration.

Accepted Answer

Market basket analysis is the specific application of data mining that focuses on identifying patterns in customer behavior, such as which products are frequently purchased together. This information can then be used to improve cross-selling strategies and increase revenue.

Question 7

What is data mining? What are the key areas where businesses are leveraging data mining?

Accepted Answer

The answer of What is data mining? What are the...

Question 8

_____ is a method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube.
A) Ad hoc reporting
B) E-discovery
C) Data aggregation
D) Online analytical processing
E) Data adjacency

Accepted Answer

Online analytical processing (OLAP) is a method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube. OLAP provides multidimensional analysis of data and allows users to view the data from different perspectives.

Question 9

Describe how L.L. Bean's Big Data efforts have changed and improved firm interactions with customers.

Accepted Answer

The answer of Describe how L.L. Bean's Big Data efforts...

Question 10

What are data warehouses and data marts? What is their significance in terms of data organization?

Accepted Answer

The answer of What are data warehouses and data marts?...

Question 11

Random occurrences in data mining results can be detected by dividing the data and building a model with one portion and using another portion to verify the results.

Accepted Answer

Dividing the data and building a model with a portion and using another portion to verify the results is known as cross-validation, which helps to detect random occurrences in data mining results.

Question 12

One reason L.L. Bean moved to an unstructured, NoSQL, big data environment was because the firm's customer engagement had evolved to include some 30 different customer engagement channels

Accepted Answer

Yes, it is true. L.L. Bean moved to an unstructured, NoSQL, big data environment because it needed to handle data from its 30 different customer engagement channels.

Question 13

What are the issues to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts?

Accepted Answer

The answer of What are the issues to be addressed...

Question 14

_____ put(s) users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters.
A) Canned reports
B) Ad hoc reporting tools
C) Dashboards
D) Data cubes
E) Online analytical processing

Accepted Answer

Ad hoc reporting tools are designed to allow users to create custom reports by selecting specific parameters and criteria. Canned reports, on the other hand, are pre-designed reports that are generated by the system and cannot be customized. Dashboards, data cubes, and online analytical processing are all tools used for data analysis and visualization, but they do not necessarily put the user in control of custom report generation like ad hoc reporting tools.

Question 15

Skittish and untrusting managers should realize that the first findings of analytics always reveal an optimal course of action.

Accepted Answer

The statement is false. The first findings of analytics may reveal insights, but it is unlikely to immediately provide an optimal course of action. It usually requires further analysis, interpretation, and context before determining the best course of action. Additionally, skittish and untrusting managers may need more time and persuasion to trust data-driven insights.

Question 16

A data cube refers to a:
A) secure, cloud-based off-site location used for data storage, analysis, and reporting.
B) heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics.
C) special database used to store data in OLAP reporting.
D) firm that collects data with the intention of reselling it to others.
E) combination of fields used to uniquely identify a record, and to relate separate tables in a database.

Accepted Answer

A data cube is a special type of database used for OLAP (Online Analytical Processing) reporting. It stores data in a multidimensional format, allowing users to analyze data from various perspectives.

Question 17

_____ are model building techniques where computers examine many potential solutions to a problem, iteratively modifying various mathematical models, and comparing the mutated models to search for a best alternative.
A) Expert systems
B) Ad hoc reporting tools
C) Iterative mutations
D) Sampled alliterations
E) Genetic algorithms

Accepted Answer

The technique described in the question is known as genetic algorithms, which involve the iterative modification of mathematical models to find the best solution. Therefore, the correct choice is E, genetic algorithms.

Question 18

Which of the following conditions is essential for data mining to work?
A) The data must be collected from proprietary sources and not from data aggregators.
B) The organization must leverage standard relational databases as opposed to inferior hierarchical and analytical databases.
C) The events in the data should reflect current and future trends.
D) The data mining software must necessarily incorporate ad hoc reporting tools and dashboards.
E) The data should have passed the Diehard suite of stringent tests for randomness.

Accepted Answer

For data mining to work effectively, it is essential that the data being analyzed reflect current and future trends. This will allow patterns and insights to be discovered that can be used to make informed decisions and predictions. The other options listed are not necessary for data mining to work, although leveraging standard relational databases may help with data storage and management.

Question 19

A(n) _____ refers to a heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics.
A) interstitial
B) embassy
C) canned report
D) prediction interface
E) dashboard

Accepted Answer

A dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so that the information can be monitored at a glance.

Question 20

Why would a firm choose Hadoop for a big data project instead of using conventional relationial databases?

Accepted Answer

The answer of Why would a firm choose Hadoop for...

Quiz 15: The Data Asset: Databases, Business Intelligence, Analytics, Big Data, and Competitive Advantage

OLAP technology is primarily used for transaction processing.

_____ is the process of using computers to identify hidden patterns in and to build models from large data sets.

While spreadsheets are popular tools, they cannot effectively be used for "what-if" analysis.

A(n) _____ is an AI system that examines data and hunts down and exposes patterns, in order to build models to exploit findings.

Computer-driven investment models can be very effective when the market behaves as it has in the past. However, in terms of historical consistency, they are vulnerable to failure in the face of:

Data mining is leveraged by some firms to determine which products customers buy together, and how an organization can use this information to cross-sell more products or services. This area of application of data mining is referred to as:

What is data mining? What are the key areas where businesses are leveraging data mining?

_____ is a method of querying and reporting that takes data from standard relational databases, calculates and summarizes the data, and then stores the data in a special database called a data cube.

Describe how L.L. Bean's Big Data efforts have changed and improved firm interactions with customers.

What are data warehouses and data marts? What is their significance in terms of data organization?

Random occurrences in data mining results can be detected by dividing the data and building a model with one portion and using another portion to verify the results.

One reason L.L. Bean moved to an unstructured, NoSQL, big data environment was because the firm's customer engagement had evolved to include some 30 different customer engagement channels

What are the issues to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts?

_____ put(s) users in control so that they can create custom reports on an as-needed basis by selecting fields, ranges, summary conditions, and other parameters.

Skittish and untrusting managers should realize that the first findings of analytics always reveal an optimal course of action.

A data cube refers to a:

_____ are model building techniques where computers examine many potential solutions to a problem, iteratively modifying various mathematical models, and comparing the mutated models to search for a best alternative.

Which of the following conditions is essential for data mining to work?

A(n) _____ refers to a heads-up display of critical indicators that allow managers to get a graphical glance at key performance metrics.

Why would a firm choose Hadoop for a big data project instead of using conventional relationial databases?