BIS 445 DeVry All Week Discussions
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<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>All</strong> <strong>Week</strong> <strong>Discussions</strong><br />
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<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>All</strong> <strong>Week</strong> <strong>Discussions</strong><br />
<strong>BIS</strong><strong>445</strong><br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 1 Discussion 1<br />
Decision Support (graded)<br />
Discuss the similarities between Decision Support Systems and Business Intelligence (BI) methodologies. What are<br />
the benefits of each system, under what circumstances would you use one over the other, and how does the Internet<br />
play a role in decision support?<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 1 Discussion 2<br />
Enterprise Systems (graded)<br />
Today's organizations have a variety of information systems used to manage their operations. Pick one of ERP, CRM,<br />
SCM, MRP, and Knowledge Management systems. Do some research on the Internet and describe a popular vendor<br />
for such a system, and how such a system improves bottom-line results.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 2 Discussion 1<br />
Implementing Decision Support Systems (graded)<br />
Discuss the main components of a decision support system and the issues facing an implementation of DSS. What<br />
factors must a company address to insure a successful implementation and use of a DSS? Give an example of a<br />
successful or unsuccessful implementation of DSS.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 2 Discussion 2<br />
Data Warehouse Architecture (graded)<br />
What are the different types of data warehouse architecture a company can use in its design, and what issues should<br />
be considered when deciding which architecture to use in developing a data warehouse. List an example of one issue<br />
or important factor a company must consider when implementing a data warehouse.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 3 Discussion 1<br />
Business Performance Management (graded)<br />
Describe the technical, organization, and management issues that companies have to address in developing and<br />
implementing a business performance management methodology. What are some of the approaches they can take,<br />
and how do balanced scorecards assist in this capacity?
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 3 Discussion 2<br />
Digital Dashboards (graded)<br />
What are the management benefits of digital dashboards? Describe how a manager can use a digital dashboard to<br />
increase overall performance. If you were to use a digital dashboard, what are some examples of the types of<br />
information you would want to view in your dashboard?<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 4 Discussion 1<br />
Business Analytics (graded)<br />
Business Analytics covers a broad area of tools and techniques used as part of business intelligence. Discuss the<br />
different types of models used in decision support. For example, what is meant by optimization model? Discuss how<br />
software application, such as Excel, can assist in the modeling and business analytics.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 4 Discussion 2<br />
Data Mining (graded)<br />
Discuss data mining and the major application areas for it. Is data mining a new technology or has it simply not been<br />
popular? Discuss the different data mining methods.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 5 Discussion 1<br />
Neural Networks (graded)<br />
Discuss the different types of design parameters used in developing a neural network. What are the different software<br />
components of a neural network, and how can we apply traditional software such as Excel to a neural network?<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 5 Discussion 2<br />
Predictive Analysis (graded)<br />
Describe ways that prediction could be used in your work, in the stock market, and to improve business performance<br />
in general. What are some limitations of statistical techniques, like linear regression, in making future predictions?<br />
Under what circumstances would you use a technique such as this one with confidence?<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 6 Discussion 1<br />
Group Decision Making (graded)<br />
Today’s environment requires more use of collaborative thinking and group decision making. What are the advantages<br />
of using group decision making? Discuss the difficulties associated with sharing and collaboration in two processes,<br />
one with computer-mediations technology and the other without it.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 6 Discussion 2<br />
Knowledge Management (graded)<br />
Discuss why knowledge management is crucial to the success of an organization. How does a company implement<br />
knowledge management and what are the processes for implementing it? Discuss the different components in a<br />
knowledge-management system.
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 7 Discussion 1<br />
Expert Systems (graded)<br />
Discuss what an expert system is and how it is used by organizations. What are the main components of an expert<br />
system? Search the Internet and provide an example of a real world expert system and how it is used.<br />
<strong>BIS</strong> <strong>445</strong> <strong>DeVry</strong> <strong>Week</strong> 7 Discussion 2<br />
Advanced Intelligent Systems (graded)<br />
Advanced Intelligent Systems make up other systems not classified as Expert Systems but do not necessarily mean<br />
they are more advanced. Discuss some of these systems or components. For example, voice recognition and casebased<br />
reasoning. What are the limitations of using such systems?