Question 1

Successful BI is a tool for the information systems department,but is not exposed to the larger organization.

Accepted Answer

Successful BI should not only be a tool for the information systems department but should be exposed to the larger organization in order to improve decision-making processes and enhance overall business performance. By sharing the insights gained through BI with the entire organization, companies can foster a culture of data-driven decision making and create a more competitive advantage.

Question 2

Business intelligence (BI)is a specific term that describes architectures and tools only.

Accepted Answer

Business intelligence (BI) refers to the process of collecting, analyzing, and presenting data to support business decision-making. While there are specific architectures and tools used in BI, it is not limited to these components alone.

Question 3

The use of statistics in baseball by the Oakland Athletics,as described in the Moneyball case study,is an example of the effectiveness of prescriptive analytics.

Accepted Answer

The use of statistics in baseball by the Oakland Athletics was aimed at prescribing actions and decisions to improve team performance, making it an example of prescriptive analytics.

Question 4

Many business users in the 1980s referred to their mainframes as "the black hole," because all the information went into it,but little ever came back and ad hoc real-time querying was virtually impossible.

Accepted Answer

The statement accurately describes the sentiments of some business users towards mainframes in the 1980s.

Question 5

Managing data warehouses requires special methods,including parallel computing and/or Hadoop/Spark.

Accepted Answer

Managing data warehouses with large amounts of data requires special methods that can handle the processing and storage of the data. Parallel computing is a method that allows for processing of multiple operations simultaneously, which can improve performance and speed. Hadoop and Spark are distributed computing frameworks that are designed to handle big data processing and storage, making them popular choices for managing data warehouses. Therefore, it is true that managing data warehouses requires special methods such as parallel computing and/or Hadoop/Spark.

Question 6

In the 2000s,the DW-driven DSSs began to be called BI systems.

Accepted Answer

In the 2000s, the term BI (Business Intelligence) became more widely used to describe data-driven decision-making systems that included Data Warehouses (DW) and Decision Support Systems (DSS).

Question 7

Major commercial business intelligence (BI)products and services were well established in the early 1970s.

Accepted Answer

Commercial business intelligence (BI) products and services did not exist in the early 1970s. The term "business intelligence" was not even coined until the mid-1980s. It wasn't until the 1990s that major commercial BI vendors like Business Objects and Cognos emerged.

Question 8

Traditional BI systems use a large volume of static data that has been extracted,cleansed,and loaded into a data warehouse to produce reports and analyses.

Accepted Answer

Traditional BI systems do indeed use static data that has been extracted, cleansed, and loaded into a data warehouse. This data is then used to produce reports and analyses, which form the basis of business decisions.

Question 9

Computer applications have moved from transaction processing and monitoring activities to problem analysis and solution applications.

Accepted Answer

With the advancement in technology and Artificial Intelligence, computer applications have become more sophisticated and are now used for problem analysis and solution applications. They are not limited to just transaction processing and monitoring activities anymore.

Question 10

Computerized support is only used for organizational decisions that are responses to external pressures,not for taking advantage of opportunities.

Accepted Answer

Computerized support can be used for both responding to external pressures as well as identifying and taking advantage of opportunities. It can help organizations analyze data, identify trends, and make strategic decisions based on that information.

Question 11

During the early days of analytics,data was often obtained from the domain experts using manual processes to build mathematical or knowledge-based models.

Accepted Answer

In the early days of analytics, data was indeed obtained from domain experts using manual processes to build mathematical or knowledge-based models.

Question 12

Decision support system (DSS)and management information system (MIS)have precise definitions agreed to by practitioners.

Accepted Answer

While there are generally agreed-upon definitions for DSS and MIS, there can be variations in interpretation and use depending on the organization and context.

Question 13

Demands for instant,on-demand access to dispersed information decrease as firms successfully integrate BI into their operations.

Accepted Answer

As firms integrate Business Intelligence (BI) into their operations, the demand for instant, on-demand access to dispersed information typically increases because BI tools enable real-time data analysis and decision-making.

Question 14

Managing information on operations,customers,internal procedures and employee interactions is the domain of cognitive science.

Accepted Answer

Managing information on operations, customers, internal procedures, and employee interactions is the domain of information management or information systems, not cognitive science. Cognitive science is the study of the mind and its processes, including perception, thinking, and decision-making.

Question 15

The use of dashboards and data visualizations is seldom effective in identifying issues in organizations,as demonstrated by the Silvaris Corporation Case Study.

Accepted Answer

The statement is false. The Silvaris Corporation Case Study actually demonstrates the effectiveness of using dashboards and data visualizations in identifying issues in organizations.

Question 16

Information systems that support such transactions as ATM withdrawals,bank deposits,and cash register scans at the grocery store represent transaction processing,a critical branch of BI.

Accepted Answer

Transaction processing systems are not a branch of Business Intelligence (BI); they are operational systems that handle day-to-day transactions. BI involves analyzing data from these systems for decision-making purposes.

Question 17

The growth in hardware,software,and network capacities has had little impact on modern BI innovations.

Accepted Answer

The growth in hardware, software, and network capacities has played a significant role in modern BI innovations. For example, advances in hardware have allowed for faster processing speeds and greater storage capacity, which has enabled the analysis of larger and more complex data sets. Improvements in software have also provided more sophisticated tools for data visualization, predictive modeling, and machine learning. Additionally, advancements in network capacities have made it easier to collect, share, and access data from a variety of sources.

Question 18

Data generation is a precursor,and is not included in the analytics ecosystem.

Accepted Answer

Data generation is actually a part of the analytics ecosystem. It is the first step in the process of data analysis and is a critical part of the overall analytics process. Without data generation, there would be no data to analyze. Therefore, data generation is an integral component of the analytics ecosystem.

Question 19

Due to industry consolidation,the analytics ecosystem consists of only a handful of players across several functional areas.

Accepted Answer

The answer of Due to industry consolidation,the analytics ecosystem consists...

Question 20

BI represents a bold new paradigm in which the company's business strategy must be aligned to its business intelligence analysis initiatives.

Accepted Answer

The answer of BI represents a bold new paradigm in...

Quiz 1: An Overview of Business Intelligence,analytics,and Data Science