Introduction to Information Management

1. IMDR IMDR IM 91 Waman Jawadekar

2. Scope of the Course: IM • Introduction: Need Of IM • Data, Information, Knowledge, Business Intelligence • Information: Context: Business/Decision Making/Performance Measure & Control. • Management Information Systems. • Concepts: Information, Decision Making, & System • Strategic Design Of MIS • Knowledge Management • Information Quality • Information Security • Information Dissemination: Delivery & Sharing • Case: HLGS

3. Books to Read & Refer • Management Information Systems by WamanJawadekar, Tata McGraw Hill Publication • Management Information Systems: Managing a Digital firm by Laudon & Laudon • Management Information Systems by O’Brien & Marakas

4. What is Information Management? Information Management begins with identification of information needs of the organization & its users, and plans systems for its generation, maintenance, & dissemination ensuring quality & security of the information.

5. Mission & Goal Sense Customer Needs Build Flexible Structure Information Driven Participative Management External Signals From Environment Operational Signals Information Driven Strategy WhyInformation Management is important now? • Business is an Open System • Strategy: Sense & Respond proactively • Information of customer needs & the competition is necessary for growth Information Management System Evaluate Strategy Evaluate Achievements

6. Importance of Information Management We need Information for: • Business Operations • Operations Control • Decision Making • Business Management: Strategy & Implementation • Performance Measure: Targets, Budgets, KPIs • Performance Control • Achieving Goals & Targets

7. Importance of Information Management • Business & Industry is information driven • Needs to know JIT status on chosen aspect, • Needs to know instantaneous event occurrence. • Needs to know Exceptions any where • Needs prompts in terms of Alert, Attention, Action. • Needs information support to DSSs, embedded or otherwise Need a Information Management System to process data, events, transactions producing the information to achieve business goals & performance targets.

8. IMS Model Input Data, Standards, Specifications Information, Knowledge, Rules, Policies, Practices, Strategy Output Data Information, Knowledge, Decisions, Results, Exceptions Information Management System (IMS) Output Display

10. A set of Information & knowledge entities, backed by systems, tools & technology, organized to provide insight in the scenario. DATA No Character & No Value INFORMATION Has a character & Value KNOWLEDGE • A information set built using principles, laws, experience etc. It is an asset of high value. • Know how: Ability to apply knowledge in practice • Wisdom: Judicious Use of know how & knowledge BUSINESS INTELLIGENCE

11. Facts without context is ‘Data’ • Data processed in context is‘Information’ • Information processed in context is ‘Knowledge’ • Knowledge in context of application is ‘Know-How’ • Know-how in context of right use is a ‘Wisdom’ • Data/Information/ Knowledge/ Knowledge Assets & supporting infrastructure of systems, and Tools to use it is a ‘Business Intelligence’

12. More on Data / Information / Knowledge • Data: Data comes about through research, creation, gathering, and discovery and through transaction processing. Census data, market research, Purchase sales transactions. • Information: Data, when processed with context, is an Information. Data is turned into information by organizing it so that we can easily draw conclusions. Data is also turned into information by "presenting" it, such as making it visual or auditory. Census data on education processed with gender attribute gives information on education divide. • Knowledge: Knowledge is built from scratch by the learner through experience of application of information or by its analysis. knowledge is dynamic as it lives within us and changes with experience. On analysis of this information, we come to know the reasons of education divide between genders.

13. Illustration Data / Information / Knowledge • Rainfall statistics: Data • Analysis of data by seasons : Information • Rainfall Pattern Model: Knowledge • Knowledge & Models used for rain forecast: Business Intelligence, Dr. Govarikar’s forecasting Model

14. Methods & Sources of Data and Information Collection • Observation • Experiment • Survey • Estimation • Processing of Data/Transactions/ Events • Purchase • Source from Publications: Govt & Private bodies

15. Tata Motors Vehicle sales by month Jan 10,000 Feb 9,000 Mar 12,000 Apr 13,000 May 12,000 • DATA • No character • Has no value • No meaningful response • of any kind from the reader

16. Tata Motors Vehicle sales by month: Context: Model How would you increase the value further? • This is ‘Information’ • Context is model • Gives sales by model x month • A better representation of sales • Generates Mental Response • Has a value for marketing

17. Vehicle sales by month: Context:Model & Previous year sales Tata Motors How would you improve the value of sales Information? Total Sales • More meaningful due to comparison • More revealing on model • Evokes managerial response

18. Customer Segment: Indica The information value increased by adding comparative dimension by customer segment. This information evokes mental response, may prompt action.

19. Customer Preferences & Perception Vs Competition Information processed with wider context by including competition Provides better insight in the car market in terms preferences & perceptions of customers. Information value is increased tremendously. This information when linked to marketing strategy, it reveals that Positioning strategy of Indica as a family car is failing. Needs re- look into strategy. Information is elevated to knowledge.

20. When Information Value Increases? Low value • Improvesrepresentation of an entity • Updates the current Information & knowledge of the user • Has an element of surprise( value) • Reducesuncertainty. Brings clarity in expectations, belief, perceptions • Evokes a mental response. Triggers thinking. May lead to action • Supports strategic and tactical decision making High value

21. Organisation & Information: DM Perspective External Low Source of Information Structured Information Internal High

22. Organisation Vs Data to Information to Knowledge to Business Intelligence Possessed by expert judgmental, not transferable TOP Top Less judgmental, contextual, and can be transferred through learning Middle Coded, contextual, easily transferable and can be shared Middle Processed data with context and purpose. Transferable and sharable Lower Lower Facts and Figures with no context Fig. 7.4 Knowledge Hierarchy

23. Attributes of Information • Accuracyin representation • Completein content • Formof presentation to grasp quickly • Frequencyof generation & reporting • Scopeof coverage/contents • Sourcesof input data • Time: Past, Current & Future • Relevance & utilityfor DM • Availabilitywhen needed • Accessibilityto the user

24. Illustration: Vehicles Sales • Accuracyin representation: Covers all deliveries & Returns for the period • 2. Completein content: Covers all information of the model • Formof presentation to grasp quickly: Orderly, Highlighted, Graphical, Analytical • 4. Frequencyof generation & reporting: weekly, daily • 5. Scopeof coverage/contents: Gross, Net, taxes, discounts by model & all models • 6. Sourcesof input data: Delivery Note/Invoice/ Credit & Debit notes • Time:Past, Current & Future • 8. Relevance & utilityfor: Analysis & DM in sales Strategy • 9. Availabilitywhen needed: Available on line to authorized users • 10. Accessibilityto the user: Offered by rights to access

25. Quality of Information: How do we measure? Quality Attributes & Measures • Utility: Form, Time, Availability, Access, & for DM • Satisfaction: Number of users, expressed confidence in its use • Error free: Accurate & Complete and processed through checking, verification, validation, & presentation • No Bias: Built by using unbiased methods of collection, processing, & presentation

26. Error free Information Processing System Error free Input Processing system Output Inputs capturing Start Tested code Input Checking, Verification, validation System Ensure Error free inputs

27. Producing ‘Error free’ Information Monthly Report on car sales in number: • Definition of Sales: Invoiced sales less returns • Process: Aggregation of quantity mentioned in invoices of the month less quantity in Goods Return Notes • Checking:Check whether all Invoices & GRNs of the month equals number of invoices & GRNs processed in the sales reporting system Focuses on completeness of inputs to the system • Verification:Verify the total sales figure for the month is same in Excise Report. Number of students vs. Course chosen vs. Fees paid, Age vs. Birth date Focuses on correctness of the input of the system • Validation: Processed after examining the Order, Specification, Price ensures that invoices of prototype demo models developed for test run are excluded. It is not a commercial sale. Studentname vs. student name in Admission list Focuses on authenticity (reliable, trustworthy, genuine) of data, transaction itself

28. Classification of Information Key: Application: How it is used? • Planning:‘Information’ enabling the planning of business entity Marketing Planning: Segment, Forecast, Preferences • Control:Information which aids performance measure, tracking & control: Benchmark, Budget, Targets, & KPIs • Decision Making: Information demanding the decision action:Decisions Options, Cost & Benefits, Probabilities of occurrences • Strategy Development: Information that builds strategy. Goals, Competition Information, Strength & weakness, emerging markets

29. Classification of Information Key: Users or User Groups • All users: Organisation:Telephone Number, Product specifications, Employee lists • Function managers: Functional:Production, sales, Returns, Rejections, Payables Receivables, Drawings, Capacity • Line managers: Operational: Pending Orders, Customer complaints, Material awaiting inspection, stocks, queries

30. Classification of Information Key: Character/Nature • Confidential: Authorized accessto few in a small group. Process design, Knowledge assets, R&D plans • Confidential: Restricted Access: Price, Stocks, Order book, HR profiles • Not confidential: Accessible to all: Web site information, Product literature • Not confidential but restricted access: Instruction manuals, engineering drawings, BOMs

31. Classes of Information • Time:Currentversus Futuristic • Frequency:RecurringversusNon recurring • Source: Internal versusExternal

32. Organisation Pyramid & Information

33. Adequacy (?) of Information • Difficult to set a standard for adequacy • The degree of adequacy differs from person to person • The information could be adequate at a point of time • With time changes, information scope & content would change • Hence we need one measure of judging the adequacy of information When do you stop to improve adequacy of the information? Check on Value of additional information

34. Discussion Application/Users/Character Kingfisher Airlines ICICI Bank Identify data, Information by application, users, character

35. Value of Information • The concept of value of information is linked to its impact importance on the decision making performance • If Decision Making performance improves significantly, or brings more quality thevalue of information is high • Actually, value is not measured in absolute terms but in incremental terms • What we seek is the value of additionalinformationto judge the adequacy of current information

36. Illustration • Your CAT score on present subject information & knowledge is expected to be 80% @ the cost of Rs. 100 thousand • The chance of admission in the B grade institutions with this score is 80%. And the return to you is Rs. 500 thousand • You want to improve your score to ensure the admission in the A grade institutions. You can improve your information & knowledge by joining a coaching class. The cost of this is Rs. 200 thousand. And the return is Rs. 800 thousand • You join coaching class & expect to raise the score to 95% at the cost of Rs. 200 thousand. Then chance of admission to A grade institution would raise to 98% • V1 = Rs. 500 thousand, V2 = Rs. 800 thousand C1 =Rs. 100 thousand, C2 = Rs. 200 thousand VAI = (800 – 500) – (200 – 100) = 200 VAI is positive & high it is worth joining a coaching class

37. Value of additional Information • Present value of Information = V1 • The cost of generating present information = C1 • The cost of adding more information = C2 • The value of increased information then is = V2 Hence Net value of additional information is VAI = (V2 – V1) – (C2 – C1) If VAI > 0, & If VAI is significantly high then one should seek additional information

38. Information Overload • Too much information creates confusion • Human mind does not respond • Goal oriented, specific, user specified information does not create overload

39. Methods to contain Information Overload • Filtering by levels, type of users and usage • Grouping & coding • Highlighting through presentation • Analyzing for essence • Making more and more contextual • Use of analysis & predictive tools

40. Processing • Models • Systems • Methods • Processes Conceptual over view of processing of input

41. Model of DPS Keyboard, Code Readers Camera capture Voice Recorder Data transfer

42. Data Processing DP Model Inputs Data: Numeric Text Picture Images Drawings Output Data: Numeric Text Picture Images Drawings Data Processing System Data store Source Authentication Controlling Validation Verification Editing Aggregation Computing

43. Customer Order Data Processing DP Model Inputs Order Data: Customer Product Quantity Price Delivery- date, terms Output Data: Valid order data Data Processing System Customer Order Record Source Authentication Controlling Validation Verification Editing Aggregation Computing

44. Transaction Processing System TPS Model:Customer order processing & acceptance E-mail To Customer Customer Order: Mail/E-mail Customer Order data processing Customer Order Transaction Processing System (TPS) Trigger Order Acknowledgement Customer Order Through Web Update Order book Customer Type Check Credit Terms Check Credit Limit check Delivery date check Reserve stock Business Rules based processing

45. Model of TPS Order Acknowledgement Current Records Product Customer Inventory Business Rules Accepted Orders

46. Customer Order Delivery Application Processing AP Model: Delivery, Invoicing & accounting Applications Customer Order Acceptance Invoice Order Delivery Planning Delivery Note Processing Customer Invoicing E-mail to customer Customer Order Record Picks Orders For Delivery Delivery Instructions Execution Processing Delivery Record Processing For billing Delivery Intimation To customer Finished Goods Inventory Business Rules based Application processing

47. Model of Application Processing System Customer Order acceptance, Order book Customer, Product, Accounts Delivery Note, Invoice Rules for: Picking, Scheduling, Tax & Charges

48. Model of Business Processing System Sales Register, Sales tax Register, Sales Analysis Two way, Order Book analysis ……. Order Processing, Order Delivery, Invoicing, Accounting Order Ack files Delivery Record Invoice Records Business System Processing Rules Linking Rules Order, Delivery, Invoice, Statutory rules On taxes & duty

49. Model of Information Processing System

50. Identification of different class of Information