This document provides an introduction to information systems and key concepts in knowledge management and expert systems. It begins with defining information systems and distinguishing them from information technology. It then covers models for understanding the relationships between data, information, knowledge and wisdom. The document discusses different types of information systems like transaction processing systems, management information systems, decision support systems, and executive information systems. It also covers knowledge management concepts like explicit and tacit knowledge, and how information technology can be used to create, capture, share and distribute organizational knowledge. Finally, the document introduces expert systems, their components like the knowledge base and inference engine, and techniques like forward and backward chaining used in the inference engine. It provides examples

1. Introduction to
Information Systems
By Prof Saumendra Mohanty

2. Background
The introduction to Information Systems is designed for non technology oriented
Managers to understand the implication of Information Technology and Information
Systems in their organization so that they equip themselves with the necessary
knowhow to cope with both embracing technology in their department and also get
ready for the next wave of Information Systems – Artificial Intelligence and Machine
Learning which is getting deeply embedded in the day to day Information Systems
in the organization
This is also useful for Management Students ( PGDM and MBA) who don’t have
background in Technology to make their foundation for both Technology related
courses in Management and get them ready for the new technology job market
The style of writing this book is deliberately made in bullet point concepts approach
so that students can understand the concepts with one reading .
Author : Saumendra Mohanty
B.Tech (Electronics) NIT, Calicut, PGDM (IMI),Delhi, PhD Scholar
27th December 2019

3. CHAPTE
R 1 –
DATA TO
DIKW
MODEL
• Topic :
• Data
• Information
• Knowledge
• Wisdom
• DIKW Model Pyramid
• Corporate Pyramid
• Information Systems Pyramid
• Decision Making Types Pyramid

4. Understanding
Data
• 230975 is Data
• 23/09/75 is Data – It could be
anyone’s DOB
• DOB 23/09/76 Suresh – Data –
Could be any Suresh in the world
• DOB 23/09/76 Suresh s/o of
Ramesh – Data –could be any
Suresh s/o of Ramesh
• DOB 23/09/76 Suresh s/o Ramesh
R/o of …….is Information as now
there is “Context “ to Data

5. What is
Data
• Data is collection of Raw facts
and Figures
• Data can be represented by
alphabets, numbers , special
characters and images
• A-Z , a-z , 0-9 , @#$%&*<>>,.”;’
• Taken in isolation “Data “ conveys
no meaning or context

6. Information
• “Processed” Data is “Information”
• There can be multiple Processes on
data to get the Information
• Data + Context = Information

7. Information &
Knowledge
• 120/80 ,130/90, 140/100 , 150 /110
is Data
• BP I 120/80 ,130/90, 140/100 , 150
/110 is still Data
• BP I Date I Time I Name I 120/80
,130/90, 140/100 , 150 /110 is
“Information”
• BP I Date I Time I Name I 120/80
,130/90, 140/100 , 150 /110 I Low
,Average ,High – is Knowledge
• Information + Rules = Knowledge

8. Knowledge &
Wisdom
• In the previous examples of BP
Readings , here is decision of 3
doctors
• Decision of Doctor 1 on BP Reading
–Take Rest
• Decision of Doctor 2 on BP Reading
–Take Medicines
• Decision of Doctor 3 on BP Reading
– Get Hospitalized
• Knowledge + Experience = Wisdom

9. DIKW
Application
• Data is considered the new “Oil”
but is “useless” or is like “Crude oil”
(What)
• Insights and Information processed
from Data is “Refined Oil” (Why )
• Where to use the oil is Knowledge
(What's happening)
• Whether to use the oil , conserve ,
sell is Wisdom ( Prediction /Future
Forecast)

10. DIKW
Model

11. Corporate
Pyramid
mapped to
DIKW

12. Information
Systems
Pyramid
mapped to
DIKW/Corp
Pyramid

13. Information Systems
IS Tools
Mission
Knowledge
Info
Data
DIKM
EIS
DSS
MIS
IS Hierarchy
TPS
CEO
Sr Mgmt
Middle Mgmt
Executive
Corp Pyramid
IS=TSP + MIS + DSS + EIS (SAP)

14. Decision
making
Pyramid

15. What is
System
• Definition of “System” - A system is
a set of rules, an arrangement of
things, or a group of related things
that work toward a common goal
• System is a general set of parts,
steps, or components that are
connected to form a more complex
whole.

16. Five Components
of IS –Information
System
• Hardware – Physical devices –
computers , tablets , mobile phones
• Software – Tells the hardware what
to do
• Data –Information manipulated by
Software
• People –Users , Programmers
,Analysts , IT Dep't
• Process- steps to accomplish a
goal
• Telecommunications /Networking

17. Information
Systems

18. Information Systems (IS)
Hardware
Telecom /internet
Users Executives Middle Mgmt Sr Mgmt CEO
UI
Application
S/W (Logic)
Database
IS=TSP + MIS + DSS + EIS
Components
Software
Hardware
Telecom
People

20. Difference
between IT
and IS
• Information Technology (IT) deals
with Technology
• - Hardware
• -Software
• - Networking
• Information Systems (IS)
deals with
• -Technology
• People
• -Process
• IS Relates to Business using
Technology

21. Information System vs Information Technology
Information Technology
 Hardware
 Software
 Database
 Network
Are used to
build
Information
System
Customer Services
Payroll System
marketing System
Inventory System
Business Oriented

22. Chapter 2 –
Types of
Information
Systems
• Information Systems can be broadly
divided into following four categories:
• TPS – Transaction Processing
System
• MIS – Management Information
System
• DSS – Decision Support System
• EIS – Executive Information System

23. Types of
Business
Information
Systems
• Operations Support – Provided by
TPS
• Management Support – Provided
by MIS , DSS & EIS
• Other Business Support Systems
• - GDSS ( Group Decision Support
System )- Communications
(Chat/Mail ) , Conferencing (
Audio/Video Conferencing ) and
Collaboration ( Workflow)
• - Knowledge Management
• -Expert Systems

24. Types of Decisions
• Structured – Inventory Re order Decision
• Semi Structured – Which product lines to add
in next 1 year
• Structured – Which business to be in next 5
years

25. Transaction Processing System (TPS)
• Captures daily transactions like POS (Point of
Sale ) data in Store
• Helps in Operational Level of Management (
lower Management)
• Online (POS) or Batch Processing
• Pre defined transactions
• No decision making
• Structured problems
• Follows ACID Mode

26. ACID Principle of TPS
• All TPS model follow ACID Principle
• A – Atomicity – Transaction is Full or None
• C – Consistency – All transactions within
defined boundary ex ATM limit of 10K cash
withdraw per transaction
• I – Integrity – All Credits and Debits will be
done simultaneously
• D- Durability – Maintain Log Reports of who
used the system , when

27. TPS Classifier -ACID Test for TPS
• A – Atomicity- TPS is complete in full or not
• C-Consistency- set of operating rules and
constraints of Database Mgmt.
• I – Isolation – Each TPS is different even if they
are at same time
• D –Durability – TPS once done cannot be
undone

28. • There are 5 Stages in Transaction Processing
System
• The next slide shows the graphical layout of 5
stages of TPS

29. 5 Stages of TPS
• POS (Point of Sales)
• Payroll System- Pay slip generation
Data Entry
Transaction Processes
• Batch
• Online/real time
Documents &
Report generate
Database
Inquiry
Processing
Online Query
(Pre defined)
Routine query
3
2 41
5

30. Payroll System as TPS
Employee data
Payroll
Simple Calculation
D/B
Name
Address
Salary
DOJ
Tax
Payroll
System MIS Report
Employee Paycheck
(Pre defined output)
Online Queries
(Predefined)
Employee earning > 2 lac/m-will come from MIS not TPS

31. Management Information System
(MIS)
• Captures data from TPS
• Summarizes TPS Data
• Provides “Organization Performance Reports”
• Mostly Structured –Pre Defined decision
making
• Little Analytics
• Example : Grade Sheet of Class Term Result is
MIS (Performance of Class)

32. 4 Types of MIS Reports
1. Periodic and Scheduled Reports – provided on
regular basis – daily /weekly/fortnightly/monthly
ex Sales Reports ,Production & Inventory Reports
2. Exceptional Reports – Only in exceptional
conditions , may be periodic and non periodic.
Periodic to decrease info overload , non periodic
ex exceeding Credit limit
3.Demand and Response –Available on demand –
Customized Reports , Web Based , RDBMS query
4.Push Reports – Automatically pushed on desktops
ex Newsfeeds of competition, stock prices

33. Decision Support System (DSS)
• Non Routine Decisions
• Semi Structured Decisions – 50/50
• 2 Categories
-Model Based
- Data Based
• Used infrequently only when problems
/opportunity analysis

34. DSS
• Model Based – Use of Statistical Models – result
is know , arrive at correlation between 2 variables
– ex behavioural analysis – Cross and Up Sell in E
Commerce
• Data Based – Use Data from TPS /MIS to slice
/dice/ consolidate /replicate and arrive at new
knowledge which was not known
ex Yield Management System ( Dynamic ticket
pricing of Airlines ) based on optimization model

35. Components of DSS
Tally S/W Web Browser Other S/W
UI
Model Management Function
• Analytical Model
• Statistical Model
Data Management Function
Data Extraction, Validation, Sanitation, Consolidation & Replication
Operational
Data
Market
Data
Sales
Data
Customer
Support
Data
Data Marts & other Databases

36. Executive Information System (EIS)
• Decisions taken at Top level
• Totally unstructured decisions based on data ,
insights, intuition and experience
• Data is 2 types
- Internal Data – From DSS
- External Data – Sensex , Standard and Poor
type reports , Govt Policies , Industry Reports
• Dashboard is Graphical /Charts

38. Topics:
• Understand concept of Knowledge
• Hierarchy of Knowledge
• Types of Knowledge – Explicit & Tacit
• Knowledge Types conversions
• Value of Knowledge
• Organizational Knowledge – Single & Double loop
• Use of Information Technology in Knowledge
• Introduction to AI – Expert Systems
• Expert Systems – Forward and Backward Chaining
Chapter 3 – Knowledge Management &
Expert Systems

39. What is Knowledge
• Knowledge is
- Knowhow
- Applied Information
- Information with Judgment
- Capacity for effective action

40. • Lets understand the Hierarchy of Knowledge
with reference to manufacturing of some
product using a machine in factory
• Here each level of personnel has a different
knowledge know how with context to his role

41. Hierarchy of Knowledge
Example: understand Hierarchy of Knowledge in a factory where machine is used to
Produce a good say bottle.
WHY
WHAT
HOW
Knowledge to take decision
• Understand the social context
• Stakeholders-people, customer, factory, other
external factors in addition to machine
CASE
WHY
Domain expert Knowledge
Deeper Knowledge
Common Knowledge
Understand the working of machine
in details to produce the bottle
Understand what good the machine is
Producing-Bottle
How to operate a machine

42. Types of Knowledge
Knowledge basically is of two types
1 . Explicit Knowledge – Can be expressed in
words and figures , essentially this knowledge
can be documented
2.Tacit – This knowledge cannot be documented
For organization to grow by continuous
innovation, Tacit knowledge has to be
continuously converted to Explicit knowledge

43. Knowledge Types Conversions
• Tacit to Explicit – for continuous innovation , Ex
Expert Systems
• Explicit to Tacit – Ex PhD Research –Start with
Literature review , find gaps and to tacit research
for further innovation
• Explicit – Explicit – Organizations copy best
knowledge practices from each other , ex use of
same type of Payroll System
• Tacit –Tacit –Two subject matter experts talk to
each other to increase the Tacit knowledge of
each other .

44. Value of Knowledge
• In knowledge economy , Knowledge has “money
“ value – Valuations of Startups in determined by
their Innovation (Knowledge).This knowledge has
to be continuously stored in Knowledge
Management System
• Newspapers /Websites provide information and
not knowledge .They in turn earn money from “
Ads” which is giving someone else’s information
• Knowledge creates wealth in today's economy
and is the greatest asset of any organization

45. Organizational Knowledge
• Organizational Learning Strategy is different for different
organizations .It creates new standards for operating
processes
• There are two types of Organizational learning
1. Single Loop – Get into deeper understanding of “Cause” in
the “Cause and Effect “ theory.Ex Earthquakes kill people
.Here you will get into understanding of Cause of
Earthquake and find solution
2.Double loop –You challenge the “ Cause “ . Ex Earthquakes
don’t kill people , Falling buildings do . Earthquakes don’t
kill people in Japan and US , but they still do in Indonesia
and other countries

46. Information Technology in Knowledge
Management
• Technology is used in Knowledge
Management in 4 ways
1. Create Knowledge – Use simulation and
design tools like CAD/CAM software , Virtual
Reality
2.Capture & Codify (Automate ) knowledge by
using Artificial Intelligence (AI) like Expert
Systems

47. Information Technology in Knowledge
Management
3. Share Knowledge – Use of GDSS ( Groupware
Software ) to share and increase knowledge
4.Distribute Knowledge – Using Office
Automation Systems , Intranets etc
Points 3 & 4 are part of all Information Systems
Point 1 & 2 are specific to Knowledge
Management Information System

48. Components of Knowledge
Management in Organization
Web users
Enterprise Knowledge
Portal
Structured data
source
Understand data
source
Enterprise
Knowledge
ERP CRM SCM Email Web
Internet
Intranet
Extranet
Enterprise
Knowledge
base

49. Expert System
• The process of transfer of human expert
knowledge to a computer and thereafter
taking inputs of the expert advice from the
computer is called Expert System
• The components of Expert System as
described in next slides are
- Knowledge Base
- Inference Engine
- User Interface

50. Expert System - Conversion of Expert Knowledge
for Automatic Distribution of Advice to users

51. Expert System Components

52. Organizations using Expert System
• Medical Diagnosis – ex WebMD ,
www.easydiagnosis.com
• Games –Chess /Cards – www.chess.com
• Coding
• Filing Income Tax Returns

53. Components of Expert Systems
• Inference Engine – Use of “Rules “ , “ What if
Analysis “ – This is “brain” of Expert System.
Apart from Rules , its other function is to “Search
“ the Knowledge Base.
• Knowledge Base – Domain Experts (ex Doctor)
provides knowledge to Knowledge (Data)
Engineer who codifies the knowledge in
Knowledge Base
• UI – Uses web , Text to Speech and Speech to Text
to get the expert advice to non expert user

54. Forward and Backward Chaining in
Inference Engine
• Inference Engine uses Forward and Backward
Chaining techniques for framing Rules and Search
from Knowledge Base
• Forward Engine – Starts with known facts and
asserts new facts
• Backward Chaining – Starts with goals and works
backwards to determine what facts must be
asserted so that goals can be achieved . It
essentially does hypothesis testing

55. Forward and Backward Chaining
Example
• A is initial condition – No one is in
Management Institute today
• A->B ( A implies B ) – Rule – If no one is in
Institute today , it must be holiday
• B (Result ) – It is holiday today
Forward Chaining – Given A and A->B , find B
Backward Chaining – Given B and A->B , find A

56. MANAGEMENT INFORMATION
SYSTEMS
LESSON 4
DECISION THEORY I DECISION
UNDER UNCERTAINTY I
DECISION UNDER RISK

57. DECISION THEORY
ELEMENTS OF DECISION THEORY
TWO METHODS OF DECISIONS – UNDER
UNCERTAINTY AND UNDER RISK
TOPICS

58. DECISION THEORY IS A STATISTICAL
TOOL OR TECHNIQUE WHICH IS USED TO
SELECT THE BEST WAY OF DOING WORK
HELPS IN DECISION BY SELECTING THE
BEST OUTCOME OUT OF MANY
ALTERNATIVES USING DATA
DECISION THEORY

59. 2.STATES OF NATURE – MUTUALLY
EXCLUSIVE AND EXHAUSTIVE CHANCE
EVENTS. AT A TIME ONLY 1 CHANCE
EVENT WILL HAPPEN OUT OF FINITE
NUMBER OF EXHAUSTIVE EVENTS. IN A
TABLE , STATES OF NATURE USUALLY
ARE REPRESENTED AS “ ROWS”
ACTS OR ALTERNATIVES ARE UNDER
OUR CONTROL WHILE STATES OF
NATURE ARE NOT UNDER OUR CONTROL
EXAMPLE – “ STOCK” IS ALTERNATIVE
WHILE “DEMAND” IS STATE OF NATURE
FOUR ELEMENTS OF DECISION THEORY

60. 3. OUTCOME /RESULTS – THE SET OF
CONSEQUENCES RESULTING FROM ALL
ACTS & STATES OF NATURE
4. OBJECTIVE VARIABLES – QUANTITY
USED TO MEASURE AND EXPRESS THE
RESULT OF A DECISION PROBLEM LIKE
“PROFIT “ , “ LOSS”
THE 4 ELEMENTS ARE REPRESENTED IN
A TABLE CALLED PAY OFF TABLE (
PROFIT) OR OPPORTUNITY LOSS ( OR
REGRET ) TABLE
FOUR ELEMENTS OF DECISION THEORY

61. DECISION MAKING IS OF TWO TYPES
1. DECISION UNDER UNCERTAINTY-
HERE PROBABILITY IS NOT USED
2. DECISION UNDER RISK –HERE
PROBABILITY IS USED
METHODS OF DECISION

62. ARRANGE ALTERNATIVES & SCENARIOS
IN A TABLE – CALLED PAY OFF MATRIX /
REGRET MATRIX
ALTERNATIVES ARE USUALLY IN ROWS
AS A,B,C,D..
STATES OF NATURE ARE USUALLY IN
COLUMNS AS P,Q,R
CELLS OF TABLE CONTAIN THE PROFIT
(IN PAY OFF ) OR OPPURTUNITY LOSS (
IN REGRET ) TABLE
DECISION UNDER UNCERTAINTY

63. THREE TYPES ARE MOSTLY USED FOR ANALYZING THE PAY OFF TABLE TO
CHOOSE STRATEGY..
1 MAXIMAX – MAXIMIZE THE MAXIMUM POSSIBLE OUTCOME . ATTITUDE IS TO
TAKE RISK – OPTIMISTIC APPROACH .PICK THE HIGHEST OUTCOME
(RESULTS) OF EACH ALTERNATIVE AND THEN PICK THE BEST OUT OF THE
BEST OUTCOMES ACROSS ALTERNATIVES
2 MAXIMIN – MAXIMISE THE MINIMUM POSSIBLE OUTCOME – AVOID RISK –
PESSIMISTIC APPROACH. PICK THE LOWEST OUTCOME OF EACH
ALTERNATIVE AND THEN PICK THE HIGHEST AMONG THE CHOSEN ACROSS
ALTERNATIVES
3 MINMAX REGRET
IN BOTH THESE APPROACHES , ONLY COLUMNS ( ACTS ) ARE COMPARED TO GET
THE MAXIMUM OR MINIMUM OUTCOMES
TRICK OF ANALYSIS : BREAK THE WORD IN 2 PARTS AND GO FROM BACKWARD
TO FORWARD ANALYSIS OF TABLE .EXAMPLE MAXIMAX IS MAXI (FORWARD ) AND
MAXI (BACKWARD) MAXI I MAX
FIRST FIND THE MAXIMUM VALUE OF EACH COLUMN AND THE N FIND THE
MAXIMUM OF THOSE VALUES.
SIMILARLY MAXIMIN IS MAXI I MIN . FIRST FIND “ MIN ‘’ OF ACH COLUMN AND THEN
FIND “MAX” OR MAXIMUM OF THOSE VALUES
STATES OF NATURE ( ROWS ) ARE NOT CONSIDERED FOR MAXIMAX AND MAXIMIN
ANALYSIS
DECISION UNDER UNCERTAINTY

64. HIGHEST VALUE .THE RESULT IS
REGRET OR OPPORTUNITY LOSS VALUE
.THIS EXERCISE IS DONE FOR EACH
ROW
- THEN THE SAME PROCESS AS
MAXIMAX /MINIMAX IS APPLIED . IN
MINIMAX USING BACKWARD/FORWARD
RULE , FIRST THE MAXIMUM VALUE OF
EACH COLUMN ( EACH STRATEGY
INDIVIDUALLY ACROSS STATES OF
NATURE) IS FOUND , AND THEN GOING
FORWARD , THE MINIMUM VALUE IS
CHOSEN AS THE STRATEGY
DECISION UNDER UNCERTAINITY

65. LOSS TABLES IN THE NEXT SLIDES
ALTERNATIVES ARE REPRESENTED AS
A,B,C AND D WHILE STATES OF NATURE
ARE REPRESENTED AS P,Q,R AND S
WE HAVE TO FIND THE ALTERNATIVE
(A,B,C OR D) UNDER MAXIMAX , MAXIMIN
AND MINIMAX REGRET CRITERIA
THE NEXT 3 SLIDES SHOW THE
WORKING OF THE EXAMPLE
EXAMPLE OF DECISION UNDER
UNCERTAINITY

66. MAXIMAX
Standard
Nature
A B C D
P 8 13 21 18
Q 7 12 6 11
R 14 13 12 16
S 27 22 18 8
1) Maximax- Optimistic
Rule a) Maxi Max
Front Back
Rule b) Top down column wise (Alternatives)
Pay off Matrix (Profit) Act
Acts Pay off
A 27
B 22
C 21
D 18
Answer -A

67. MAXIMIN
Acts Pay off
A 7
B 12
C 6
D 8
Maxi Min
P B
Answer-B
3) Mini Max Regret -----
 Here we consider both row wise (State of Nature) & also column wise (Alternatives)
 Start with Row wise
 Regret –loss/Give up
 Convert Profit Matrix to Opportunity Loss Table by following steps
 Pick the Max value Row wise
 Subtract that value from each column row
(that value is regret value)
 Best option is 0 regret which is Max value in row

68. MINMAX REGRET
States of
nature
A B C D
P 21-8=13 21-13=8 21-21=0 21-18=3
Q 12-7=5 12-12=0 12-6=6 12-11=1
R 16-14=2 16-13=3 16-12=4 16-16=0
S 27-27=0 27-22=5 27-18=9 27-8=19
Acts
Regret Table (Opp Loss)
A B C D
P 13 8 0 3
Q 5 0 6 1
R 2 3 4 0
S 0 5 9 19
Acts Pay off
A 13
B 8
C 9
D 19
Steps->Choose MaxMin
Answer-B

69. PROBABLITY IS POSSIBILITY OF THE
CHANCE EVENT (RISK OF NATURE )
HAPPENING
EMV – EXPECTED MONETARY VALUE IS
CALCULATED MY MULTIPLYING THE
PROBABLITY WITH RESPECTIVE
ALTERNATIVE OUTCOME AND ADDING
ALL THE OUTCOMES IN ONE COLUMN (
FOR EACH ALTERNATIVE)
SAME STEP IS TAKEN FOR ALL THE
COLUMNS (ACTS)
HIGHEST VALUE IS CHOSEN UNDER
PROFIT PAY OFF TABLE
SIMILARLY EOL (EXPECTED
DECISION MAKING UNDER RISK

70. THE FOLLOWING SLIDE SHOWS THE
WORKING OF THE EXAMPLE ON HOW TO
CALCULATE THE EMV AND EOL
PROBABLITY P IS ASSIGNED TO EACH OF
THE STATES OF NATURE
EXAMPLE OF DECISION MAKING UNDER
RISK

71. Example
1. Convert Profit Matrix to Opp Loss matrix
2. Calculate EMV & EOL of each alternative
Pay offs of three Acts/Strategies-ABC
States of Nature are P,Q & R
Probability is given for each State of Nature
Solution:
Acts----
States
of
Natur
e
Pro
b
A B C
P 0.5 -50 100 -80
Q 0.3 150 -
220
190
R 0.2 600 200 350
Q) Which Act can be chosen as the best act?

72. EMV for A = (-50X0.5)+ (150X0.3)+(600X0.2)=140
EMV for B= (100X0.5)+(-220X0.3)+(200X0.2)=24
EMV FOR C=(-80X0.5)+(190X0.3)+(350X0.2)=1230
Answer is C Alternative or Act as it has highest EMV
EOL : Create a Regret or Opportunity Loss Table and using
similar steps as above choose lowest EOL as Alternative
/Strategy

73. About the Author
Saumendra Mohanty , B.Tech (Electronics) NIT Calicut , PGDM (IMI)
Delhi and PhD Scholar (Sharda University) has 30 years experience in
Technology and IT Sector . He has extensive experience in MNCs , has
been a serial entrepreneur in Technology with successful fund raising and
exists and is a Visiting Professor of Information Systems in Management
Schools.