Understanding Data, Information, and Knowledge in Management Information Systems
This chapter delves into the distinctions between data, information, knowledge, and intelligence within the context of Management Information Systems (MIS). It explains how raw data transforms into meaningful information through context, and then into actionable knowledge guided by principles and experience. Moreover, it emphasizes the importance of quality, relevance, and timeliness in information for effective decision-making. Practical illustrations, such as rainfall statistics and forecasting, demonstrate the application of these concepts. The chapter also discusses parameters for assessing the adequacy and value of information for better organizational outcomes.
Understanding Data, Information, and Knowledge in Management Information Systems
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Presentation Transcript
Chapter 7 Information and Knowledge Jawadekar: Management Information Systems, 3/e
Data to Knowledge to Intelligence • Data – No Character • Information – Data with context • Knowledge – Information backed by principles , practices & experience • Know how – Ability of applying knowledge to specific problems • Wisdom – Judicious Use of know how • Intelligence –Ability to develop new insight & then applying knowledge in a new situation
Illustration • Rainfall statistics:Data • Analysis of data by seasons :Information • Developing a Rainfall pattern Model:Knowledge • Know-how:Ability to predict the rainfall • Intelligence:Knowledge & Models used for rain forecast • Dr. Govarikar’s forecasting Model:Intellectual capital
Characteristics of Information • Improves representation of an entity • Updates the knowledge level of user • Has an element of surprise (value) • Reduces uncertainty • Supports strategic and tactical decision making
Attributes of Information • Accuracy in representation • Complete in content • Form of presentation • Frequency of reporting • Scope of coverage • Sources of collection • Time dimension: Past, current & future • Relevance & utility for DM • On time when needed • Just in Time
Measures of Quality • Utility:Form, Time, Availability, Access • Satisfaction:No of users using the information& have expressed satisfaction • Error:Data measurement, collection, processing, checking, verification, validation, presentation • Bias:Built by factors creating bias at the stages of collection, processing, presentation
Parameters of Quality Improvement • Source of data:unbiased & authorised & valid • Impartial:Collection without pre conceived view, prejudice or with motive • Validity:Is data appropriate for its purpose of use or application? • Reliability:Data not coming from right source, doubtful on correctness, completeness and coverage. In short, bad raw data • Consistency:Source, period, coverage, processing method and presentation same • Age of information:Should be latest, current, real time
Classes of Information • CurrentversusInformation of perceived value : Time • RecurringversusNon recurring: Frequency • InternalversusExternal: Source
Class: Application of Information • Planning • Control • Knowledge • Decision induced information
Class: By Users of information • Organization Information:Used by all • Functional Information: Used by business function managers • Status Information:Used by planning managers for strategic purpose • Operational Information:Used by staff & line managers • Performance Information:Used for strategic planning by Senior managers
How to Judge the 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
Value of Information • The concept of value of information is linked to its impact importance on the decision making performance • If DM performance would improve significantly, the value is high • Actually, value is not measured in absolute terms but in incremental terms • What we seek is the value of additional information
Illustration: value of additional Information • Your score in examination based on present level of subject information & knowledge is 80% @ the cost of Rs 100 thousand. The chance of admission in IIM is 20 % at this grade • If You join coaching class & expect to raise the score to 95% at the cost of Rs 200 thousand. Then chance of admission would raise to 98% • V1 = Rs 500 thousand, V2 = Rs 800 thousand C1 =Rs100 thousand, C2 = Rs200 thousand VAI = (800 – 500) – (200 – 100) = 200 Where V = value gain, C = Cost • Since, the difference is positive it is worth joining the coaching class to gain additional knowledge
How to use the concept of Value of Information? • Present value of Information = V1 • The cost of generating the information = C1 • The cost of adding more information & value = C2 • The value of new information then is = V2 Hence value of additional information ( VAI ) is VAI = (V2 – V1) – (C2 – C1) If VAI > 0,& If VAI is significantly high then one should seek additional information
Methods Of data collection for processing to generate information • Observation • Experiment • Survey • Estimation • Processing of data/transactions & extraction • Purchase • Publications: Govt & Private bodies
