This document provides an introduction to decision theory and different methods for decision making under uncertainty and risk. It defines the key elements of decision theory as actions/alternatives, states of nature, outcomes, and objective variables. For decision making under uncertainty when probabilities are not known, it describes non-probability methods like maximax, maximin, and minimax regret. Maximax seeks to maximize the maximum possible outcome, maximin seeks to maximize the minimum outcome, while minimax regret takes a more balanced approach weighing both profits and losses.

1. Introduction to
Information Systems
DR SAUMENDRA MOHANTY

2. INTRODUCTION TO INFORMATION SYSTEMS IS WRITTEN PREDOMINANTLY KEEPING IN
MIND THE FIRST FORAY INTO THE WORLD OF IT (INFORMATION TECHNOLOGY) FOR
NON TECHNOLOGY BACKGROUND STUDENTS PURSUING MBA /PGDM COURSES
THE STYLE IS DELIBERATELY KEPT SIMPLE PRESENTATION TYPES WITH BULLET POINTS
APPROACH TO ENSURE THAT KEY CONCEPTS CAN BE QUICKLY ASSIMILATED
THE EFFORT IS TO ENCOURAGE STUDENTS WITH NON TECHNOLOGY BACKGROUND TO
TAKE UP IT & BUSINESS ANALYTICS COURSES AS NEXT STEP
THE E BOOK HAS 5 CHAPTERS STARTING FROM RAW DATA AND ENDING WITH
INTRODUCTION TO EXPERT SYSTEMS (ARTIFICIAL INTELLIGENCE )
CONCEPTS ARE EXPLAINED WITH DIAGRAMS AND NUMERICAL EXAMPLES WHEREVER
APPLICABLE
COPYRIGHTS WITH DR SAUMENDRA MOHANTY
PREFACE

3. TABLE OF CONTENTS
CHAPTER 1 : DIKW MODEL MAPPED TO INFORMATION SYSTEMS
CHAPTER 2 : TYPES OF INFORMATION SYSTEMS TPS, MIS ,DSS & EIS
CHAPTER 3 : DECISION THEORY WITH EXAMPLES
CHAPTER 4 : KNOWLEDGE MANAGEMENT SYSTEMS
CHAPTER 5: EXPERT SYSTEMS

4. CHAPTER 1 : DIKW MODEL MAPPED
TO INFORMATION SYSTEMS

5. DATA
INFORMATION
KNOWLEDGE
WISDOM
DIKW MODEL PYRAMID
CORPORATE PYRAMID
INFORMATION SYSTEMS PYRAMID
DECISION MAKING TYPES PYRAMID
KEY CONCEPTS

6. 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 NOW AS THERE IS “CONTEXT “ TO DATA
WHAT IS DATA

7. DATA IS COLLECTION OF RAW FACTS AND FIGURES
DATA CAN BE REPRESENTED BY ALPHABETS, NUMBERS , SPECIAL CHARACTERS, IMAGES
AND VIDEOS
A-Z , A-Z , 0-9 , @#$%&*<>>,.”;’
TAKEN IN ISOLATION “DATA “ CONVEYS NO MEANING OR CONTEXT
WHAT IS DATA

8. “PROCESSED” DATA IS “INFORMATION”
THERE CAN BE MULTIPLE PROCESSES ON DATA TO GET THE INFORMATION
DATA + CONTEXT = INFORMATION
INFORMATION

9. 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”
ABOUT BLOOD PRESSURE AS CONTEXT
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
INFORMATION & KNOWLEDGE

10. IN THE PREVIOUS EXAMPLES OF BP READINGS , HERE IS DECISION OF 3 DOCTORS
DECISION OF 1ST DOCTOR ON BP READING –TAKE REST
DECISION OF 2ND DOCTOR ON BP READING –TAKE MEDICINES
DECISION OF 3RD DOCTOR ON BP READING – GET HOSPITALIZED
KNOWLEDGE + EXPERIENCE = WISDOM
KNOWLEDGE & WISDOM

11. DATA IS CONSIDERED THE NEW “OIL” BUT IS “USELESS” OR IS LIKE “CRUDE OIL” (WHAT
IS THIS LIQUID ?)
INSIGHTS AND INFORMATION PROCESSED FROM DATA IS “REFINED OIL” (WHATS TO BE
DONE TO CONVERT TO DIESEL/PETROL )
WHERE TO USE THE OIL IS KNOWLEDGE (APPLICATION OF OIL- AUTOMOBILES,
AVIATION, INDUSTRY)
WHETHER TO USE THE OIL , CONSERVE OR SELL IS WISDOM ( PREDICTION /FUTURE
FORECAST-OPEC MODEL )
DIKW MODEL

12. DIKW Model

13. Corporate Pyramid mapped to
DIKW

14. Information Systems Pyramid
mapped to DIKW/Corp Pyramid

15. Definitrmri
on
Vision
Knowledge
Info
Data
EIS
DSS
MIS
TPS
CEO
Sr Mgmt
Middle Mgmt
Executive
Information System (IS)
mapped to DIKW Model
IS = TPS+MIS+DSS+EIS
DIKW IS Hierarchy Corp Pyramid

16. Decision making Pyramid

17. CHAPTER 2 : TYPES OF INFORMATION
SYSTEMS - TPS, MIS
DSS & EIS

18. DEFINITION OF “SYSTEM” - A SYSTEM IS A SET OF RULES, AN ARRANGEMENT OF
THINGS, OR A GROUP OF RELATED THINGS THAT WORK TOWARDS A COMMON GOAL
SYSTEM IS A GENERAL SET OF PARTS, STEPS, OR COMPONENTS THAT ARE CONNECTED
TO FORM A MORE COMPLEX WHOLE.
WHAT IS SYSTEM

19. 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
FIVE COMPONENTS OF – INFORMATION SYSTEM
(IS)

20. Information Systems

21. Information Systems
layout
User Interface
Appl s/w
(Logic)
Database
Hardware
Telecom/ Internet
(Network)
IS = TPS+MIS+DSS+EIS
Users- Executive , Middle Mgmt , Sr Mgmt & CEO

22. MANAGEMENT INFORMATION SYSTEM
WHAT IS MIS

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

24. IT and IS
Information System vs Information Technology
Information Technology
 Hardware
 Software
 Database
 Network
Information
System
Are used to
build
Marketing System
Customer Service
Payroll System
Inventory System

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. Five Stages of TPS
Use Case : Point of Sale
Data Entry
• Barcode
• Manual
Transaction Process
 Batch
 Online/Real time
Document
&
Report generation
Database
3
2 3
5
Inquiry Processing
Online Queries
(Pre defines)
Routine Query

32. Payroll System as TPS
MIS Reports
Payroll
System
D/B
Employee Data
Name
Address
Salary
DOJ
Tax
Online Queries
(Pre defined)
Employee Pay check
Pre defined Output
Example :Employees earning > Rs 2,00,000/Month – will come from MIS not TPS

33. 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)

34. 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

35. TYPICAL MIS REPORTS
2015 Ticket Sales
Movie Genre January February March April May June July August
Septembe
r October November December TOTAL
Comedy 49,832 47,232 40,002 37,283 32,910 33,829 30,102 32,111 34,921 30,293 28,392 24,192 421,099
Thriller 12,839 16,828 15,839 18,082 24,932 30,462 34,240 42,718 41,128 39,382 36,621 37,283 350,354
Documentary 9,118 9,907 7,257 7,838 6,372 5,992 5,773 5,993 6,302 8,103 9,100 9,278 91,033
Romance 14,381 14,651 11,969 14,602 13,046 14,411 13,871 14,184 13,033 14,625 12,196 13,081 164,050
TOTAL 86,170 88,618 75,067 77,805 77,260 84,694 83,986 95,006 95,384 92,403 86,309 83,834

36. TYPICAL MIS VISUALIZATION
0
10,000
20,000
30,000
40,000
50,000
60,000
1 2 3 4 5 6 7 8 9 10 11 12
Column Chart
Comedy Thriller Documentary Romance

37. 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

38. 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

39. Components of DSS
S/W Web Browser Other S/W
User Interface (UI)
Model Management Function
• Analytical Model
• Statistical Model
Data Management Function
Data Extraction, Validation, Consolidation & Replication
Operational
Data
Market
Data
Sales
Data
Customer
Data
Data Marts & Other Databases

40. 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

42. CHAPTER 3 : DECISION THEORY
WITH EXAMPLES

43. 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

44. 1. ACTIONS / ACTS /ALTERNATIVES /STRATEGIES – CHOOSE 1 OUT OF 2 OR MORE
ALTERNATIVES .USUALLY REPRESENTED AT ACTS AND REPRESENTED BY ALPHABETS A,B
,C ,D …..IN A TABLE ACTS ARE USUALLY REPRESENTED IN “COLUMNS”
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
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

45. 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

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

47. 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 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 MAX (BACKWARD) MAXI
I MAX
FIRST FIND THE MAXIMUM VALUE OF EACH COLUMN AND THEN FIND THE MAXIMUM OF
THOSE VALUES.
SIMILARLY MAXIMIN IS MAXI I MIN . FIRST FIND “ MIN ‘’ OF EACH 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

48. 3. MINIMAX REGRET CRITERIA – A REALISTIC APPROACH , TAKING CALCULATED RISK ,
WEIGHING BOTH PROFIT AND LOSS .
- IN THIS ANALYSIS, FIRST ROWS ( STATES OF NATURE) ARE COMPARED .THE HIGHEST
VALUE (OUTCOME) IS CHOSEN IN THE ROW AND EACH VALUE IN THE ROW IS
SUBTRACTED FROM THE 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

49. NOW LETS SEE THE WORKING OF CHOOSING STRATEGY USING MAXIMAX /MAXIMIN
AND MINIMAX REGRET USING PROFIT PAY OFF AND OPPORTUNITY 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

50. MAXIMAX
States of
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

51. 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

52. 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

53. DECISION MAKING UNDER RISK WILL ASSIGN PROBABLITY VALUE TO EACH OF THE
STATES OF NATURE FOR VARIOUS ALTERNATIVES
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 OPPORTUNITY LOSS ) IS CALCULATED BY CREATING A
OPPORTUNITY LOSS TABLE FROM PROFIT MATRIX AND FOLLOWING THE ABOVE STEPS
APPLYING PROBABLITY
LOWEST VALUE OF EOL IS CHOSEN AS THE ALTERNATIVE
DECISION MAKING UNDER RISK

54. 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

55. 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

56. 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)=87
Answer is A 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

57. Example
1. Convert Previous Profit Matrix to Opp Loss matrix
2. Calculate 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 0.5 150 0 180
Q 0.3 40 410 0
R 0.2 0 400 250
Q) Which Act can be chosen as the best act?
Answer ) EOL A = 87 , EOL B=203 , EOL C=140
Choose A (Lowest EOL)

58. Example
1. Convert Profit Matrix to Opp Loss matrix
2. Calculate 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
Nature
Prob 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?

59. CHAPTER 4 : KNOWLEDGE
MANAGEMENT SYSTEMS

60. 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
Knowledge Management Systems

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

62. 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

63. • 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

64. Hierarchy of Knowledge
Example: Understand Hierarchy of knowledge in a factory where machine is used to
produce a good say bottle in a factory
Common
Knowledge
Knowledge to
take decision
Domain Expert
Knowledge
Deeper
Knowledge
CASE
WHY
HOW
WHY
WHAT
Understand the social context
Stakeholders- People, customers, factory, other
external factors in addition to machine
Understand the working of machine
in details to produce the bottle
Understand what good the
machine is producing-Bottle
How to operate a machine

65. 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

66. 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 .

67. 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

68. 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
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

69. Components of Knowledge Management System
Web User
Enterprise Knowledge Person
ERP CRM SCM
Web
Internet
Intranet
Extranet
email
Enterprise
Knowledge
Base
Structured Data Source Unstructured Data Source Enterprise Knowledge

70. CHAPTER 5: EXPERT SYSTEMS

71. 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

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

73. Expert System Components

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

75. 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

76. 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

77. 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