2. B. Information Technology (IS) CISB454: Introduction to Knowledge Management Understanding Knowledge

3. Learning Objectives At the end of this lesson, you should be able to:  define the basic knowledge-related terms  discuss the relationships between data, information and knowledge  identify the different types of knowledge  discuss the concept of knowledge as an attribute of expertise 1-2

4. Understanding Knowledge Basic Definitions of Knowledge-related Terms

5. Basic Knowledge-Related Definitions Common Sense Innate ability to sense, judge, or perceive situations; grows stronger over time FactA statement that relates a certain element of truth about a subject matter or a domain HeuristicA rule of thumb based on years of experience KnowledgeUnderstanding gained through experience; familiarity with the way to perform a task; an accumulation of facts, procedural rules, or heuristics IntelligenceThe capacity to acquire and apply knowledge 1-4

6. Data  Unorganized and unprocessed facts; static; a set of discrete facts about events Information  Aggregation of data that makes decision making easier Data, Information, and Knowledge 1-5

7. Data, Information, and Knowledge 1-6 Knowledge is de- rived from informa- tion in the same way information is deri- ved from data  it is a person’s range of information

8. Understanding Knowledge Relationships: Data, Information & Knowledge

9. Relationship between Data, Information and Knowledge 1-8 Information Data Value ZeroLow Medium High Very High Knowledge

10. An Illustration 1-9 ZeroLowMediumHighVery High Value InformationData H T H T T H H H T H … T T T H T p H = 0.40 p T = 0.60 R H = +$10 R T = -$8 n H = 40 n T = 60 EV = -$0.80 Knowledge Counting p H = n H /(n H +n T ) p T = n T /(n H +n T ) EV=p H R H + p T R T

11. Relating Data, Information, & Knowledge to Events 1-10 Knowledge Information Data Information System Decision Events Use of information Knowledge

12. 1-11 Wisdom Knowledge Information Data From Data Processing to Knowledge-based Systems Non-algorithmic (Heuristic) Algorithmic Non-programmable Programmable

13. Understanding Knowledge Categories of Knowledge

14. Shallow (readily recalled) and Deep (acquired through years of experience) Explicit (already codified) and Tacit (embedded in the mind) Procedural (repetitive, stepwise) versus Episodical (grouped by episodes) Knowledge exist in chunks 1-13

15. From Procedural to Episodic Knowledge Shallow Knowledge Procedural Knowledge Knowledge of how to do a task that is essentially motor in nature; the same knowledge is used over and over again Declarative Knowledge Surface-type information that is available in short-term memory and easily verbalized; useful in early stages of knowledge capture but less so in later stages. Semantic Knowledge Hierarchically organized knowledge of concepts, facts, and relationships among facts. Deep Knowledge Episodic Knowledge Knowledge that is organized by temporal spatial means, not by concepts or relations; experiential information that is chunked by episodes. This knowledge is highly compiled and autobiographical and is not easy to extract or capture. 2-14

16. Explicit &Tacit Knowledge Explicit  knowing-that  knowledge codified & digitized in books, documents, reports, memos, etc. 1-15

17. Explicit &Tacit Knowledge Tacit  knowing-how  knowledge embed- ded in the human mind through expe- rience and jobs 1-16

18. Illustrations of the Different Types of Knowledge 1-17

19. Understanding Knowledge Knowledge as an Attribute of Expertise

20. 1-19 An expert in a spe- cialized area mas- ters the requisite knowledge The unique perfor- mance of a know- ledgeable expert is clearly noticeable in decision-making qu- ality

21. Knowledge as an Attribute of Expertise 1-20 Experts are more selective in the infor- mation they acquire Experts are benefi- ciaries of the know- ledge that comes from experience

22. Expert’s Reasoning Methods 1-21 Reasoning by ana- logy  relating one concept to another Formal reasoning  using deductive or in- ductive methods Case-based reason- ing  reasoning from rele- vant past cases

23. Deductive and Inductive Reasoning Deductive reasoning  exact reasoning  It deals with exact facts and exact con- clusions Inductive reasoning  reasoning from a set of facts or individual cases to a general conclusion 1-22

24. Human’s Learning Models Learning by expe- rience  a function of time and talent Learning by exam- ple  more efficient than learning by expe- rience 1-23

25. Human’s Learning Models Learning by disco- very  undirected approach in which humans ex- plore a problem area with no advance knowledge of what their objective is 1-24

26. Implications for KM Emphasis on tapping, sharing, and pre- serving tacit knowledge and total knowledge base Focus on innovation and the processes that convert innovation to products and services Consider organizational learning sys- tems and systems thinking 25

27. THE END Copyright © 2012 Mohd. Sharifuddin Ahmad, PhD College of Information Technology