Summer 2014 MIS691 Syllabus
Course Information
Room: SSW2650
Time: Tuesday/Thursday 1800 – 2140
Professor: Dr. Murray E. Jennex, P.E., CISSP, CSSLP, PMP
Office: SS3206
Phone: 594-3734
Email: murphjen@aol.com OR mjennex@mail.sdsu.edu
Office Hours:
Monday - Thursday: 1700 – 1800 or by appointment
Instant Message Office Hours: whenever online (be sure to identify yourself immediately so I
don’t ignore you)
Books:
Course Reader: Knowledge Management Systems, Murray E. Jennex
Decision Support and Business Intelligence Systems, 10th edition, Turban, Sharda, and Delen,
Prentice Hall
Other materials will be on blackboard and the teradata student network
Course Approach
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.
Course Goals
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.
Specific Objectives (Learning outcome with support objectives):
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
Explain decision modeling
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
Course Polices
Students are expected to be prepared to discuss the assigned readings and to attend class. It is
understood that there may be occasions when you will have to miss class, on these occasions I
request you send me an email letting me know prior to class. Should it be necessary that you
miss class on the night an assignment is due or the exam or presentation is scheduled I request
notification prior to the absence so that exams/presentations can be rescheduled. I will accept
assignments via email on the due date as long as a hard copy is submitted at the next class the
student is at.
Excessive absences, more than 4, or a lack of participation, or excessive unrelated conversation,
or excessive use of computers for non class work will result in a 5% grade deduction. Excessive
will be in my opinion but students will be warned and given an opportunity to improve before the
deduction will be assessed.
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.
A 10% penalty will be assigned for late assignments. No assignment will be accepted if over 2
weeks late.
All turn in work needs to be typed, have a cover page, and be single-spaced with appropriate
spacing. Be sure to include your name, the class, and what the turn in work is on the cover sheet.
Grading - Assignments
Course assessment will be based on four equally weighted assignments (see below). Grading
will be based 75% on content, 15% on organization, formatting, citations, etc., and 10% on
grammar. The grading scale is:
Grade
A
AB+
Range
>=
94%
>=
90%
>
87.5%
B
BC+
C
Cother
>=
>=
>
>=
>=
<
83%
80%
77.5%
73%
70%
70%
Assignment Descriptions:
Class participation is worth 10% of the grade. Participation is not just showing up to class.
Participation is active interaction in discussions, asking questions, answering questions, providing
context and opinion. Students who only attend class and do not participate in discussion will earn
no better than an 8/10 for participation, students who actively engage in class discussions and
attend consistently will earn scores above 8/10 depending on their level of participation.
Four practical exercises, total value of the assignment is 60%, each is worth 15%, with the write
ups for:
Practical exercise one due 6/3
Practical exercise two due 6/10
Practical exercise three due 6/17
Practical exercise four due 6/24
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 30%, to be presented and turned in on 7/1.
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; 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: MicroStrategy 9 BI Quick Warm-up Exercise >> Nenad Jukic
Prac Ex 3: 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 or the Teradata
Network: Sentiment Analysis Teaching Case
Prac Ex 4: Teradata Network: SAS Visual Analytics, Assignments 1-4.
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.
Reading Assignments
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)
Suggested readings from the Teradata Student Network are listed below
Date
5/22
Reading
TCh 1, 2, 9
5/27
5/29
6/3
TCh 3, 5
TCh 13
TCh 4, Ch
11-14
TCh 7-8, Ch
1, 15
TCh 12, Ch
2-6
Ch 7-10
Ch 16-18
Blackboard
TCh 6, 10,
11
TCh 14
None
6/5
6/10
6/12
6/17
6/19
6/24
6/26
7/1
Topics
Introduction, Decision Theory, DSS, Decision
Modeling
Decision Modeling, Data Warehousing, Data Mining
Big Data - Viz Center Visit
Crisis Response, Data Visualization
Assignments
Practical Exercise 1
Text and web mining, DSS design documentation
Knowledge Management Systems and Success
Catch up
Knowledge Management Technologies, Applications
Knowledge Society, value of decisions
Artificial Intelligence/Expert Systems/Semantic Web
Catch up
Final presentations
Practical Exercise 2
Practical Exercise 3
Practical Exercise 4
Group Project Turn In
Suggested Teradata Readings
The Teradata Student Network found at: www.TeradataStudentNetwork.com. You will need to
register first. Use the password: UnifiedDataArchitecture.
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 Seven Pillars of BI Success
What is CRM? A Primer on CRM