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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?

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