Introduction to mis5101 at Temple University Fox School of Business taught by Prof. Steven L. Johnson in Fall, 2010.

1. MIS5101: Business Intelligence
Key Slides Weeks 1-3
PROF. STEVEN L. JOHNSON
Twitter: @StevenLJohnson
http://stevenljohnson.org
http://community.mis.temple.edu/mis5101fall10/

2. Week 1: Introduction
 Introduction to...
 Content
 Format
 Each Other
 Expectations
Photo by conorwithonen

3. Information Technology Management
MIS5001 MIS5102 MIS5108
MIS5101
Management Business Information
Business
Information Process Systems
Intelligence
Systems Improvement Strategy
Understand issues in successful IT Introduction to Fundamentals of Strategic Value of Data, Information
Information Business Process Information and Knowledge
strategy implementation Technology Analysis Technology management
Apply business
Evaluate IT investments and IT- Understand role of Identify and
intelligence
IT in improving N/A evaluate strategic
enabled business opportunities. firm performance value of IT
solutions to solve
business problems
Form a business case for a Learn common Communicate IT Develop business Create value
justifications for IT value to business case for IT-related realization plan for
technology initiative. initiatives audience initiative bus. intelligence
Analyze a current business process N/A
Learn business
analysis and
Identify business
Integrate data
across multiple
and improve it (BPM). consulting skills opportunities. sources
Analyze Develop
Plan transition to new systems N/A
requirements for
Develop a plan for
organization
information
IT-enabled security policies
and processes. business change
change
and plans

4. In-Class Exercise #2
“According to Russell Ackoff, a systems theorist and professor of organizational
change, the content of the human mind can be classified into five categories:
• Data: symbols
• Information: data that are processed to be useful; provides answers to
"who", "what", "where", and "when" questions
• Knowledge: application of data and information; answers "how" questions
• Understanding: appreciation of "why”
• Wisdom: evaluated understanding.
Ackoff indicates that the first four categories relate to the past; they deal with what
has been or what is known. Only the fifth category, wisdom, deals with the future
because it incorporates vision and design.”
From: http://www.systems-thinking.org/dikw/dikw.htm
Photo credit: TheAllNewAdventuresOfMe

5. Coming Attractions
 Check website: discussion question in next 24-48
hours
 Post a response
 Read and reply to other’s comments, too
 Read assigned readings
 Consider discussion questions in syllabus
 Bring “Weekly Summary” to class with
 One copy to turn in and one copy to keep

6. Week 2 Agenda: Data Access
 Case Discussion
 Reading
 Recap: Blog Discussion Question
 Upcoming Assignments
 Case Study Analysis
 Group Project

7. Case: Balancing Access with Accuracy... in Tanzania
Accuracy is measured by: sensitivity and specificity
3 barriers to access are: cost, facilities, personnel
3 product concepts are: strip, squeeze, paper filter
Highest access / lowest cost: strip test
Highest accuracy & cost: paper filter
3 major $$$ stakeholders: IANGOs, other donors, govt.
Other major stakeholders: CBOs, hospitals, patients
Govt. Favors most accurate: to maintain test credibility
Photo by

8. Case Discussion Questions
 What test(s) do you recommend for initial roll-out in
Tanzania? Why?
 Assume one of the three tests is the sole choice for
roll-out (choose one). What specific communication
strategy do you use among key stakeholders to gain
support for this test?
 What is another example you can think of with a
trade-offs between information access and accuracy?
When is it better to favor access? When is it better to
favor accuracy?

9. Case Study Analysis
 How
 Pick a case from weeks #4,5,7
 Pick ONE discussion question
 Write a one-page answer
 See syllabus for format, advice and evaluation criteria
 When
 First in weeks #4,5,7; Second in weeks #8-13
 Email to Prof. by Monday at 5pm
 Post to blog between Monday at 5pm and Tuesday at 5pm

10. Individual and Group Project
 How
 Group of 3 or 4 members each
 Analyze a technology, application, or project that supports the
creation, identification, acquisition or use of knowledge.
 When
 Week #3 (9/15) – Deadline to Form Groups
 Week #4 (9/22) – Deadline to Propose Individual and Group
Project Topics
 Week #9 (10/27) – Individual Project Due
 Week #14 (12/8) – Group Project Due, Group Presentations

11. In-Class Exercise
 Divide into 4-person teams
 7 minutes: brainstorm to identify 10 examples of
technologies, applications, or technology vendors
that support the creation, identification, acquisition
or use of knowledge.
 Report 3 examples back to the class
 7 minutes: pick 3 high-level examples and identify
4-5 individual related components

12. Week 3 Agenda: Data Quality
 Announcement: Classroom Capture
 Discussion of Reading
 Recap: Blog Discussion Question
 Data Integration Exercise

13. Factual Review
7 types of IS are... TPS, MIS, DSS, EIS, OLAP, Data mining, BI
Two measures of information sources:
hardness and richness
Information satisficing (raises or lowers) decision
making quality lowers
Organization memory is... fragmented
Two types of knowledge: explicit, tacit

14. Definitions from Watson (2005)
 Data are raw, not summarized, not analyzed.
 Information is data processed into a meaningful
form.
 Knowledge is the capacity to use information.

15. Discussion Questions
 Looking back on last week’s case study, how could
you describe the roll-out scenarios in terms of data,
information, and knowledge?
 Consider an organization you work for now (or have
worked for recently). How would you describe its
organizational memory?
 How do senior management needs for data typically
differ from the needs of the person who entered the
data?
 What are typical challenges in integrating
information?

16. Blog Discussion Question
 As there’s more data available in organizations,
there’s also more bad data. What kind of problems
have you run into before with missing, incorrect, or
incomplete data? What do you think can be done to
improve data quality?
 Cost of bad data
 Role of legal and regulatory environment
 Who owns the problem?
 Incentive alignment... problem vs. solution... pain vs. gain

17. For More Information
PROF. STEVEN L. JOHNSON
EMAIL: STEVEN@TEMPLE.EDU
Twitter: @StevenLJohnson
http://stevenljohnson.org
http://community.mis.temple.edu/mis5101fall10/