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MIS691, Decision Support Systems
Summer 2016
San Diego State University
College of Business Administration
Department of
COURSE INFORMATION
Professor: Murray E. Jennex, Ph.D., P.E., CISSP, CSSLP, PMP
Class Days: Monday/Wednesday
Class Times: 1800-2140
Class Location: EBA341
Office Hours Times (and by appointment):M-Th 1600-1800
Office Hours Location: SSE 3206
Units: 3
Course Overview
MIS691, Decision Support Systems, is an elective in the MSIS program and focuses on creating systems to support
organizational-and personal decision making as well as implementing organizational knowledge management initiatives that
meet ISO9001-2015 certification requirements.

Description from the Official Course Catalog - Design, implementation, and integration of computerized decision
support systems into business management. Problem representation, modeling, and simulation. (Formerly numbered
Information and Decision Systems 691.).

Description of the Purpose and Course Content The objective of this course is to train the student in the process of
decision-making and to provide the student with the ability to design systems to support decision-making. To do this
we will discuss decision theory and the technologies and processes used in the creation and management of decision
support systems, research decision support system literature, and create individual and group decision support
systems. Course topics include decision theory, decision modeling, group decision support systems, expert systems,
artificial intelligence, knowledge management, and data warehousing and mining. Students completing the course
will be prepared to analyze decision processes and design and specify decision support systems to support those
processes. They also will be prepared to build individual decision support systems using Microsoft Excel and Access
and will be familiar with the research resources available to Decision Support Systems students.

Real Life Relevance – Decision Support is an applied discipline. This course is relevant for anyone wishing to improve
their managerial decision making through uses of Big Data, analytics, business intelligence, decision models,
knowledge management, and/or expert systems/artificial intelligence. Additionally, the course is relevant for
accountants, financial managers, and management as it prepares students to make informed decisions.

Relation to Other Courses: This course supports the business analytics career track of the MSIS program. Additionally
it builds on MIS695, Business Systems Analysis and Design, by applying the systems analysis approach to designing
and building decision support systems.
Program Learning Goals
MSIS students will graduate being able to:

Design and use technology-supported solutions to improve decision making and create value

Create value through the development of data, information or knowledge (DIK) strategies and the management of
processes and projects

Demonstrate business professional skills
MIS691 contributes to these goals as well as MBA learning goals through its student learning outcomes . . .
1. Describe decision theory

Describe the attributes of a satisficer

Describe the attributes of a bounded rational decision maker

Describe the attributes of an optimizer

Discuss how risk impacts decision making

Identify and describe various decision making processes
2. Explain decision modeling
3.
4.
5.
6.
7.
8.
9.

Describe and create decision trees

Describe and create decision tables

Describe and create decision models reflecting uncertainty

Describe and create decision models using various statistical techniques such as AHP, etc.
Identify and define Knowledge Management terms and concepts

Define and discuss what knowledge is including tacit, explicit, and organizational knowledge

Define what a Knowledge Management System is and is not

Define Organizational Memory and Organizational Learning and discuss their relationship to Knowledge
Management

Discuss the Knowledge Life Cycle
Explain how Knowledge Management impacts an organization

Discuss how Knowledge Management applications such as Customer Relationship Management, Supply Chain
Management, and Data Warehousing impact organizational effectiveness

Discuss how Knowledge Management can improve organizational and individual decision making

Describe how to build and implement a Knowledge Management System

Discuss technologies used in Knowledge Management such as Web Portals, XML, Ontologies, Taxonomies, and
Topic Maps

Discuss the functions and goals of a Knowledge Management System

Discuss knowledge repositories with respect to structure and codification schemes

Describe recommendations from research as to the construction of a Knowledge Management System
Explain Knowledge Management/Knowledge Management System Success

Discuss the need for measuring KM/KMS success

List and describe KM/KMS Success Factors

List and describe KM/KMS Effectiveness Models
Identify and Discuss issues affecting Knowledge Management

Describe issues affecting knowledge transfer, flow, and use in organizations

Describe the impact of Knowledge Management strategy on KM and KMS success

Describe how Knowledge Management System use and knowledge re-use impacts KM and KMS success

Describe how Communities of Practice implement KM
Define and Explain decision support systems

Describe decision making under stress

Describe a crisis response/emergency information system

Identify and describe the components of a DSS

Discuss when to do a DSS
Explain decision support technologies

Describe a data warehouse and how it improves decision making

Describe data mining and how it improves decision making

Describe a Group Decision Support System and how it improves decision making

Describe how Business Intelligence and Business Analytics are used to support decision making.
Enrollment Information
Information about enrollment for the course:

Prerequisites – none

Recommended – MIS686, Enterprise Data Management

Adding/Dropping is through web portal
Course Materials
Two books are required for the course:

Course Reader: Knowledge Management Systems, Murray E. Jennex

Decision Support and Business Intelligence Systems, 10th edition, Turban, Sharda, and Delen, Prentice Hall
Additional materials will be posted on Blackboard and on the Teradata Student Network
Course Structure and Conduct
MIS691 is a combination seminar and lecture based course. Students are expected to be prepared for class and to contribute
to class discussions. Class nights are broken into three sections:
The first part of the class will be dedicated to DSS in the news. This section is to make students aware of how widespread and
common DSS use is in everyday activities. Students are expected to watch the media and bring in examples of DSS use.
Discussion will focus on why the example is a DSS, how it is used, how effective is it, and any issues the DSS raises.
The second portion of the class is dedicated to answering questions on the assigned reading. Lecture/discussions will not focus
on going over the reading assignments. Students are expected to read assignments prior to class and come prepared to use the
readings to support class discussion. This portion of the class is for students to ask questions about portions of the readings
they do not understand or want clarification on.
The third portion of the class is dedicated to the topic of the night. The topic of the night will be some aspect of the reading
material that the instructor feels needs expanding. This may be specific issues, applications, or related topics not covered by
the readings..
Students with Disabilities
If you are a student with a disability and believe you will need accommodations for this class, it is your responsibility to contact
Student Disability Services at (619) 594-6473. To avoid any delay in the receipt of your accommodations, you should contact
Student Disability Services as soon as possible. Please note that accommodations are not retroactive, and that accommodations
based upon disability cannot be provided until you have presented your instructor with an accommodation letter from Student
Disability Services. Your cooperation is appreciated.
Academic Honesty
The University adheres to a strict policy regarding cheating and plagiarism. These activities will not be tolerated in this class.
Become familiar with the policy (http://www.sa.sdsu.edu/srr/conduct1.html). Any cheating or plagiarism will result in
Cheating is defined as the effort to give or receive help on any graded work in this class without permission from the instructor,
or to submit alterations to graded work for re-grading. Any student who is caught cheating receives an F for the class, will be
reported to Judicial Procedures, and be recommended for removal from the College of Business.
Plagiarism will not be tolerated and rampant or repeated plagiarism will be treated as cheating. Plagiarism is claiming other’s
work for your own. This can be done by not properly citing or referencing other’s work in your papers, copying other’s work
into your own (even if cited and referenced), and/or copying other’s work into your own without citing or referencing the
source. Citation and referencing errors will result in grade deductions for the first offense, repeated offenses will result in
reduction by a full grade on the assignment, an F for the assignment, or an F for the class depending upon the severity and
intent of the offense.
Examples of Plagiarism include but are not limited to:

Using sources verbatim or paraphrasing without giving proper attribution (this can include phrases, sentences,
paragraphs and/or pages of work)

Copying and pasting work from an online or offline source directly and calling it your own

Using information you find from an online or offline source without giving the author credit

Replacing words or phrases from another source and inserting your own words or phrases

Submitting a piece of work you did for one class to another class
If you have questions on what is plagiarism, please consult the policy and this helpful guide from the Library
Turnitin
Students agree that by taking this course all required papers may be subject to submission for textual similarity review to
Turnitin.com for the detection of plagiarism. All submitted papers will be included as source documents in the Turnitin.com
reference database solely for the purpose of detecting plagiarism of such papers. You may submit your papers in such a way
that no identifying information about you is included. Another option is that you may request, in writing, that your papers not
be submitted to Turnitin.com. However, if you choose this option you will be required to provide documentation to
substantiate that the papers are your original work and do not include any plagiarized material.
Assessments and Grading
Course grades will be assigned in accordance with San Diego State University policy (see Graduate Bulletin, pp. 62-64).
Graduate grades shall be: A (outstanding achievement, available for the highest accomplishment), B (average, awarded for
satisfactory performance), C (minimally passing), D (unacceptable for graduate credit, course must be repeated), F (failing).
Table 1. Your course grade will be based on the following weighted components
Component
Weight
Individual exercise/paper generating a decision
tree and extracting rules/heuristics
15
Individual exercise/paper using Microstrategy BI
and Tableau
15
Individual exercise/paper using SAS Visual
Analytics
15
Individual exercise/paper using Freemind and a
mini-MOOC
15
Group exercise designing a DSS
25
Class participation is not a direct input to your grade. However, my perception of your participation influences grades that are
on the border. Good participation gets the higher grade, poor participation the lower grade. Participation is not just showing
up to class. Participation is active interaction in discussions, asking questions, answering questions, providing context and
opinion, and participating with your team exercise.
Five practical exercises, total value of the assignment is 75%, each is worth 15%, with the write ups for:
Practical exercise one due 6/1
Practical exercise two due 6/8
Practical exercise three due 6/15
Practical exercise four due 6/22
A Decision Support System to be designed and prototyped by student teams. This DSS is to support a wide variety of users and
should be able to be tailored to those individuals’ needs. The project is worth 25%, to be presented and turned in on 6/29.
The practical exercise portfolio is to be done using a variety of resources including the data warehouse resources on the
teradata website. The practical exercises to be done are:
Prac Ex 1: Teradata Network: Expert System Homework Assignment (Level: Difficult) >> Viji Kannan and Rule/Heuristic
Extraction. Using the decision tree branch and multiple criteria with thresholds and scoring example provided on Blackboard
identify the rules and heuristics used.
Prac Ex 2: Teradata Network: Tableau on Data Visualization >> Kakoli Bandyopadhyay; and Teradata Network: BI Reporting and
Basic OLAP Using the MicroStrategy 9 Customer Analysis Module>> Barbara Dinter
Prac Ex 3: Teradata Network: SAS Visual Analytics, Assignments 1-4.
Prac Ex 4: Use Freemind (free download at: http://freemind.sourceforge.net/wiki/index.php/Main_Page) as a graphical tool
and create a taxonomy and ontology for your personal knowledge management system and complete the mini-MOOC at
https://www.canvas.net/browse/columbiau/courses/collaborative-knowledge-services on collaborative knowledge services.
Each practical exercise may have questions that need to be answered and turn in material. Additionally, each assignment
needs to include an approximate 2 page write up that answers the following four questions:
What did the student do?
What were the results?
What did the student learn?
How does the exercise relate to the material that was covered in class?
This write up is graded using a good, ok, poor scale. Good is achieved by:
Question: what did you do?
Provide a description of what you did. I know you probably followed the directions provided so don't do a step by step account
of the directions. What I am really interested in are the actual data manipulations and actions you performed, any problems
you encountered, what you did to overcome them, and any insights you learned about the technology. Finally, in most cases
the value of DSS is in the journey more than the result, same here, the better/clearer/insightful your write up is the better it
scores.
Question: what were the results?
Provide any printouts of products produced, this could be a report, a map, a table, etc. To improve the score on this section
you should also explain what the printouts mean. What is the logic for its organization, and in particular, how would you use it?
What questions/decisions are supported by the printout? Remember that I value the journey, so take the time to tell me the
story and determine the value of your printouts.
Question: what did you learn?
I can't tell you what you learned. What I will say is that I reward insight. Insights are aha moments (a term in use long before
Oprah wanted to copyright it). If you see new ways of doing things, new insights to your thought processes, potential future
applications be they personal or work related, crossovers to other topics, these are what I reward more than just telling me you
learned lots. I expect you to learn lots but it isn't till you explain where and what that I see that you really did. Ok, so
sometimes you don't learn much. I'll still grade this area high if you tell me why, what you know, how this works on what you've
done in the past, etc. Sometimes when you start doing this you see that what you've learned is reinforcing what you've done
and sometimes you even have small aha moments. Bottom line is to be reflective, think a few minutes or overnight about what
you've done and how it fits into your nomological net (your personal set of knowledge base structure, those theories and
beliefs that guide how you evaluate and use knowledge). Then write the section, when I see this done I always score the section
higher.
Question: how does it relate to the material that was covered in the class?
As a minimum discuss specific topics that relate to what we've done and at least mention the obvious ones. Be specific, cite the
section/chapter/reading it comes from. Also cite the topics/presentations that relate. The top scores come from also citing
articles from the suggested readings and outside readings.
The group Decision Support System is a team project and is to be a system designed to support a work group or project team in
some decision process. The system can be designed to use any available tool but the team is expected to build a working
prototype to be presented to the class. The team should discuss the project with the professor and get his concurrence before
starting the project. The deliverables include:
1. A write-up using the simplified DSS Specification template that describes the purpose and requirements for the system and
that includes the decision models
2. A presentation
3. A prototype that has sufficient data in it so that it can be demonstrated to the class
A 10% late penalty will be assigned for late assignments. Nothing will be accepted if over 2 weeks late.
All turn in work needs to be typed and have a cover page. Be sure to include your name(s), the class, and what the turn in work
is on the cover sheet.
Course assessment will be based on the assignments discussed above. Grading will be based 75% on content, 15% on
organization, formatting, citations, etc., and 10% on grammar. The grading scale is:
Grade
A
AB+
B
BC+
C
Cother
Range
>=
>=
>
>=
>=
>
>=
>=
<
94%
90%
87.5%
83%
80%
77.5%
73%
70%
70%
Grade of Incomplete. A grade of Incomplete (I) indicates that a portion of required coursework has not been completed and
evaluated in the prescribed time period due to unforeseen, but fully justified, reasons and that there is still a possibility of
earning credit. It is your responsibility to bring pertinent information to the instructor and to reach agreement on the means by
which the remaining course requirements will be satisfied. The conditions for removal of the Incomplete shall be reduced to
writing by the instructor and given to you with a copy placed on file with the department chair until the Incomplete is removed
or the time limit for removal has passed. A final grade is assigned when the work agreed upon has been completed and
evaluated. An Incomplete shall not be assigned when the only way you could make up the work would be to attend a major
portion of the class when it is next offered. Contract forms for Incomplete grades are available at the Office of the Registrar
website
Tentative Course Schedule
Reading assignments will come from 2 sources:
The reader which is indicated by the chapter, CH; The Text which is indicated by the chapter, TCh
Supplemental readings are provided on Blackboard (readings are in course documents listed by night and/or topic and are
expansions on the book readings)
Table 2. The course schedule, including topics and class activities listed by week, is presented in the following table
Week
Topics
Activities
5/23
TCh 1, 2, 9
5/25
TCh 3, 5
Introduction, Decision Theory, DSS, Decision
Modeling
Decision Modeling, Data Warehousing, Data
Mining
5/30
6/1
6/6
6/8
None- Memorial Day
TCh 4
TCh 7, 8, 13
Ch 1, 11-15
6/13
6/15
6/20
TCh 12, Ch 2 - 6
Ch 7-10
Ch 16-18
6/22
Blackboard
6/27
TCh 6, 10, 11, 14
6/29
None
Data Visualization, Exercise 1 turn in
Big Data, Text and Web Mining
Crisis Response, DSS design documentation,
Exercise 2 turn in
Knowledge Management Systems and Success
Catch up, Exercise 3 turn in
Knowledge Management Technologies,
Applications
Knowledge Society, value of decisions, exercise
4 turn in
Artificial Intelligence/Expert Systems/Semantic
Web
Final presentations, project turn in
Changes to the course schedule, if any, will be announced in class.
Suggested Teradata Readings
The Teradata Student Network found at: www.TeradataStudentNetwork.com. You will need to register
first. Use the password: Analytics.
A Data Mining Primer for Data Warehouse Professionals
Business Intelligence: Past, Current, and Future
Business Intelligence 2.0
Business Intelligence Project Pitfalls
Competing on Analytics
Cooking up a Data Warehouse
Dashboard Design: Why Design is Important
Dashboards and Scorecards
Data Warehouses, Metadata, and Middleware
Decision Support Systems: To Buy or Build
Designing Executive Dashboards Part 1 and 2
Expert systems and Australian Taxation administration (Case Studies Section)
Expert Systems for Fraud Detection
Harrah’s High Payoff from Customer Information (Case Study Section)
Information Visualization for Business: Past and Future
Mycin – Expert System (Case Studies Section)
Recent Developments in Data Warehousing
Ten Worst Practices of the Unsuccessful Data Warehouse Project Manager
The Decision Support Sweet Spot
The Limits of Analytics: The Tale of the Red Sox, Hippos, and Groundhogs
The Seven Pillars of BI Success
What is CRM? A Primer on CRM