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Introducation to Information System

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Introduction to Information Systems

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Introducation to Information System

  1. 1. Introduction to Information Systems By Prof Saumendra Mohanty
  2. 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. 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. 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. 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. 6. Information • “Processed” Data is “Information” • There can be multiple Processes on data to get the Information • Data + Context = Information
  7. 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. 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. 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. 10. DIKW Model
  11. 11. Corporate Pyramid mapped to DIKW
  12. 12. Information Systems Pyramid mapped to DIKW/Corp Pyramid
  13. 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. 14. Decision making Pyramid
  15. 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. 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. 17. Information Systems
  18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. • There are 5 Stages in Transaction Processing System • The next slide shows the graphical layout of 5 stages of TPS
  28. 28. 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
  29. 29. 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
  30. 30. 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)
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. What is Knowledge • Knowledge is - Knowhow - Applied Information - Information with Judgment - Capacity for effective action
  38. 38. • 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
  39. 39. 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
  40. 40. 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
  41. 41. 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 .
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. 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
  46. 46. 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
  47. 47. 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
  48. 48. Expert System - Conversion of Expert Knowledge for Automatic Distribution of Advice to users
  49. 49. Expert System Components
  50. 50. Organizations using Expert System • Medical Diagnosis – ex WebMD , www.easydiagnosis.com • Games –Chess /Cards – www.chess.com • Coding • Filing Income Tax Returns
  51. 51. 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
  52. 52. 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
  53. 53. 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
  54. 54. MANAGEMENT INFORMATION SYSTEMS LESSON 4 DECISION THEORY I DECISION UNDER UNCERTAINTY I DECISION UNDER RISK
  55. 55. DECISION THEORY ELEMENTS OF DECISION THEORY TWO METHODS OF DECISIONS – UNDER UNCERTAINTY AND UNDER RISK TOPICS
  56. 56. 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
  57. 57. 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
  58. 58. 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
  59. 59. 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
  60. 60. 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
  61. 61. 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
  62. 62. 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
  63. 63. 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
  64. 64. 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
  65. 65. 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
  66. 66. 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
  67. 67. 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
  68. 68. 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
  69. 69. 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?
  70. 70. 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
  71. 71. 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.

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