DATA, INFORMATION, KNOWLEDGE AND COMPETENCE

1. DATA, INFORMATION, KNOWLEDGE AND COMPETENCE Valdemar W. Setzer Dept. of Computer Science, University of São Paulo, Brazil www.ime.usp.br/~vwsetzer

2. TOPICS  1. Introduction  2. Concepts  3. Competence matrices  4. Uses of a competence system  5. Example of a system 6. Competence centers: social considerations  7. Conclusions

3. 1. Introduction • In 1999, PROMON Eng. (revenues of about US$ 1 bi) wanted to build up a Competence Center on Information Technology • What is a company organized around Competence Centers

4. 1. Introduction (cont.) • The big problem was: • What does it mean to be competent on I.T.? • What does it mean to be competent? • E.g., what does it mean to be competent on English? • To answer this question, it is necessary to know what knowledge means • But knowledge has to do with information • What is information? • What is the difference between information and data?

5. 1. Introduction (cont.) • These concepts make it possible to build a system to help assessing employees’ competencies and selecting professionals according to desired competencies • Example of a system developed in 2001 for PRODESP, the State of São Paulo DP company (1,000 professionals on I.T.) • Considerations on implementation and assessment of competencies • Competence Centers - social issues

6. 1. Introduction (cont.) • What is information? • What is the difference between information and data? • What does it mean to be competent in English?

7. 2. Concepts - Data • DATA • A sequence of quantified or quantifiable symbols • E.g.: texts, pictures, recorded sound, animation • Mathematical “objects” • Purely syntactic • May be inserted into a computer, and processed by it • Everything represented in a computer is data

8. 2. Concepts - Information • INFORMATION • An informal abstraction in the mind of a person, representing something of significance to her • E.g. “Paris is a fascinating city” • In the literature, also associated to messages • Attention: what is transmitted is data and not information! • The recipient receives the data and eventually transforms it into information

9. 2. Concepts - Information (cont.) • Example: A table of cities and local temperature • In Chinese: pure data (may be formatted, sorted, etc.) • In English: information (makes sense) • Information cannot be stored into or processed by a computer! • What is processed is its representation as data • E.g. “fascinating” must be quantified: 0 to 4.

10. 2. Concepts - Information (cont.) • Information may be obtained without data • E.g. feeling how cold or warm it is • E.g. feeling pain • Data is always incorporated by a person as information - as long as it is understood • “Understanding,” “significance”, “meaning” cannot be defined • Mental association between concepts or between perception and concept • Thinking is an organ for the perception of concepts

11. 2. Concepts - Information (cont.) • Information contains semantics • Semantics cannot be formalized • It is impossible to introduce semantics into a computer (a syntax machine!) • Problem with Searle’s “Chinese Room”: he does not say what semantics is • Claude Shannon did not develop an Information Theory, but a Data Theory! • Does Information Technology exist?

12. 2. Concepts - Knowledge • KNOWLEDGE • A personal, inner abstraction of something that has been experienced by someone • E.g.: a person who visited Paris has some knowledge about it • Cannot be described • Information can, through data • It’s in the purely subjective realm of humans and animals

13. 2. Concepts - Knowledge (cont.) • Infants may have knowledge, but no information (they don’t associate concepts); the same with animals • Knowledge cannot be stored into a computer! • “Knowledge databases” are in fact databases! • Knowledge is always practical • There may exist information without knowledge (purely theoretical) • E.g. reading a travel guide about Paris

14. 2. Concepts - Knowledge (cont.) • Data  syntax Information  semantics Knowledge  pragmatics

15. 2. Concepts - Competence • COMPETENCE • The capacity of executing some (socially) useful task in the “real world” • Data  syntax Information  semantics Knowledge  pragmatics Competence  physical activity • Examples: • Delivering speeches • Mathematician (creating and transmitting new concepts, giving classes, etc.)

16. 2. Concepts (cont.) • Data  objective Information  objective/subjective Knowledge  subjective Competency  subjective/objective

17. 2. Concepts (cont.) • KNOWLEDGE IN INTELLECTUAL FIELDS • In our characterization, a mathematician or a historian would have no knowledge! • Not a problem for technical areas • Way out (not accepted by everyone): • “Experience” of the Platonic world of ideas • A “universal memory” in that world

18. 3. Competence matrices Ex: competencein ENGLISH • Understanding written language • Understanding spoken language • Speaking • Writing • Writing translations • Simultaneous translation SKILLS KNOWLEDGE AREA

19. 3. Competence matrices (cont.) Therefore, COMPETENCE refers to a SKILL exercised over a KNOWLEDGE AREA

20. 3. Competence matrices (cont.) This leads to a matrix representation, the COMPETENCE MATRIX Lines:knowledge areas Columns:skills • In each cell one enters a DEGREE OF COMPETENCY

21. 3. Competence matrices (cont.)

22. 3. Competence matrices (cont.) The concept of competency matrices lead to the construction of COMPETENCE SYSTEMS

23. 4. Use of competency systems • Selection of professionals with specific profiles • Knowledge dissemination (who is competent on, knows about or has information on what) • A part of knowledge management! • Selecting professionals for • Project teams • Filling positions in the enterprise • Giving interviews • Social projects and activities • Artistic activities • Receiving specific visitors • Testimonies in judicial processes • Judicial reports

24. 4. Uses of compet. systems (cont.) • Counting how many professional have certain competencies • Discovering weak areas in the enterprise or departments • Evaluating what is the enterprise’s expertise • Representing required in-house core competencies

25. 4. Uses of compet. systems (cont.) • Helping dept. of human resources with training programs • Planning courses • Selecting participants for training activities • Base for promotions • Curriculum systematization and maintenance • Automatic updating upon completion of training activities (if integrated with training database)

26. 5. Example of a system • Developed for PRODESP (1,000 IT professionals) • Tested with about 50 professionals • Implemented in Delphi for Oracle • Any number of matrices • Two levels of knowledge areas • Any number of skills per matrix, two levels • Any number of competency degrees per matrix

27. 5. Example of a system (cont.) • 5 competency matrices: • Technical competencies in IT • Systems produced by PRODESP (hundreds) • Administrative competencies • Education • Foreign languages

28. 5. Example of a system (cont.) • Degrees of competency (vary by matrix) • IT and administrative competencies • Theoretical knowledge (information) • Personal learning, courses without practical exercises • Practical knowledge (knowledge) • Theoretical knowledge plus practical exercises or accompanying some project without effective production • Basic competency • Up to 2 years of effective production • Advanced competency • More than 2 years of effective production

29. 5. Example of a system (cont.) • Competencies on developed systems • Short participation (up to 2 years) • Medium participation (2-5 years) • Long participation (more than 5 years) • Foreign languages • With difficulty (needs constant help) • Well (needs sporadic help) • Very well (fluent)

30. 5. Example of a system (cont.) • Education • High school • Professional (technician) • College degree (incomplete) • College degree • Graduate studies • Master’s degree • Doctor’s degree

31. 5. Example (cont.) - TI matrix

32. 5. Example (cont.) - Systems matrix

33. 5. Example (cont.) - Administrative matrix

34. 5. Example (cont.) - Foreign languages matrix

35. 5. Example (cont.) - Education matrix

36. 5. Example (cont.) - Assigning competencies

37. 5. Example (cont.) - Assigning competencies (cont.)

38. 5. Example (cont.) - Registering a professional

39. 5. Example (cont.) - Competency vitae

40. 5. Example (cont.) - Selecting professionals

41. 5. Example (cont.) - Selection results

42. 5. Example (cont.) - Counting professionals

43. 5. Example (cont.) - Access security • 4 levels (types of users): • Generic (any non-registered person) • May select professionals • May register (gives password) • Personal (already registered) • May select professionals • Reads and changes his/her registration and competencies

44. 5. Example (cont.) - Access security • Supervisor • May select professionals • Reads and changes his/her registration and competencies • Reads competencies of other people • System administrator • May read and change anything

45. 6. Competency centers - social issues • Advantages • Optimizing allocation of human resources • Greater flexibility • Interaction with peers • Disadvantages • Disruption of social integration (no more long-term contacts within a department) • Lack of personal identity with a business department

46. 7. Conclusions • Characterizations of information, knowledge and competency worked very well in interviews for competency assessment in 2 enterprises • Professionals were grateful for the systematized competency curriculum • Computer selects possible candidates • A subjective assessment must follow, otherwise professionals are handled as data (things)

47. 7. Conclusions (concl.) • Problems when assessing competencies with our method • Homogenizing criteria among professionals • At PROMON: just one interviewer • Not feasible with hundreds of professionals • At PRODESP: self-assessment followed by homogenization by employee’s manager • Does not take into account the quality of a project developed by a professional • This would have to be assessed by managers • Social problems • No behavioral matrix (leadership, communication, etc.) • Should also be done by managers

48. 7. Conclusions (cont.) • Main application: Knowledge Management Dissemination of personal knowledge: who knows what