Ch3: The Origins of Knowledge

1. Ch3: The Origins of Knowledge The Knowledge Management Toolkit Amrit Tiwana

2. Knowledge • Knowledge is a fluid mix of framed experience, value, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms. (Davenport and Prusak)

3. Knowledge • Knowledge is simply actionable information. • Actionable refers to the notion of relevant and being available in the right place at the right time, in the right context, and in the right way so that anyone (not just that producer) can bring it to bear on decisions being made every minute.

4. Knowledge • Knowledge is the key resource in intelligent decision making, forecasting, design, planning, diagnosis, analysis, evaluation, and intuitive judgment. • It is formed in and shared between individual and collective minds. • It does not grow out of databases but evolves with experience, successes, failures, and learning over time.

5. Information verse Knowledge • Knowledge in the business context is nothing but actionable information • Knowledge allows for making prediction, casual associations, or predictive decisions about what to do, unlike information, which simply gives us the facts.

6. Information verse Knowledge • Knowledge is not clear, crisp, or simple. • Instead, it’s muddy, intuitive, hard to communicate, and difficult to express in words and illustrations, and a good chunk of it is not stored in databases but in minds of people who work in your organization. fuzzy, partly structured, and partly unstructured.

7. Information verse Knowledge • Only a minuscule portion of this tacit knowledge gets formalized in databases, books, manuals, documents, and presentations; the rest of it stays in the head of people.

8. Information verse Knowledge • Knowledge is supported by both formal and informal processes and structures for its acquisition, sharing, and utilization. • Knowledge workers or employees broadly communicate and assimilate values, norms, procedures, and data, beginning with early socialization and proceeding through ongoing formal and informal group discussions and exchanges.

9. Information verse Knowledge • Data and information are essential, but it is knowledge that can be applied, experience that comes into context, and skill that are used at that moment that make the difference between a good decision and a bad decision.

10. From data to knowledge • As firms are beginning to get comfortable with both data and information, the next challenge that comes into the picture is that of making sense of this overwhelming amount of information itself. • Where does this process begin?

11. The origins of Data • From a perspective of a firm, data is a set of particular and objective facts about an event or simply the structured record of a transaction. • When we talk about managing data, our judgment is mostly quantitative. • Qualitative measures are considered secondary.

12. Illusions and Data • The quantity of data captured often gives firms a false illusion of rigor and accuracy. • The often false belief that firms tend to stick with is that having collected a lot of data means that ensuing decisions will be good, accurate, objective, and rational.

13. Illusions and Data • Data, in itself, possesses no inherent meaning. The more of it you have, the harder it gets to make sense of it. • Data, although important to firms, has little use by itself unless converted into information.

14. Information and noise • Drucker describes information as data endowed with relevance and purpose. • It is the recipient of this information who decides whether it is truly information or purely noise. • What qualifies as useful information in different situations is a subjective judgment.

17. The data-rich and information-poor society • This exactly is the problem with many organizations and businesses. • They tend to collect a lot of data, hoping that it will give them that imaginary esoteric notion of the fountainhead of competitive advantage that any firm yearns for. • Management of knowledge, not data or information, is the primary driver of a firm’s competitive edge.

18. The big slip between the swipe and the refrigerator • The collection of too much data all too easily. • The result is that firms end up with overwhelming volumes of data – so overwhelming that they have a hard time figuring out just what to do with it.

19. Knowledge integration across levels • Very often, when data is shared and distributed among people within a firm, it starts to become increasingly useful as some people perceive it as useful. • The fundamental mistake that companies repeatedly make is that of equating information and knowledge.

21. Knowledge integration across levels • EX. HP pursued a strategy of making its best practices available throughout the company. Although HP was quite successful in identifying its best practices, it was not successful in moving them from one location to another.

22. Knowledge integration across levels • When an intranet, for example, moves the knowledge without the practice, what actually gets moved is the know-what without the know-how. • Managing knowledge means adding or creating value by actively leveraging know-how, judgment, intuition, and experience resident within and outside the company.

23. Classifying knowledge

24. Categories of knowledge • Tacit knowledge is personal, context-specific knowledge that is difficult to formalize, record, or articulate; it is stored in the heads of people. • Explicit knowledge is that component of knowledge that can be codified and transmitted in a systematic and formal language: documents, databases, webs, e-mails, charts, etc.

25. Components of knowledge • Truth • Judgment • Experience • Values, assumptions, beliefs. • Intelligence.

26. Components of knowledge • Truth • Projects and investments made in companies are often based on a set of assumptions: These assumptions might be about markets, customers, the business environment, consumer preference, competition, etc. • Often, the entire set of decision that might have been made earlier might not hold in future situations, because these assumptions might have changed.

27. Components of knowledge • Truth • Discovering, recording, and maintaining these assumptions and the ability to do a what-if analysis akin to scenario analysis with spreadsheets is a critical components of a complete KM system. • The problem, however, is that these assumptions are often deeply embedded in individuals from a specific functional area; they almost seem so natural and obviously ingrained that they never explicit surface.

28. Components of knowledge • Judgment • Very unlike information, which is facts, and data, which is factoids; knowledge has a component of judgment attached to it. • Judgment allows knowledge to rise above and beyond an opinion when it reexamines itself and refines every time it is applied and acted upon.

29. Components of knowledge • Experience • Knowledge is largely derived from experience. • Being able to transfer knowledge implies that a part of experiential knowledge also gets transferred to the recipient.

30. Components of knowledge • Rules of thumb, Heuristics, and Tricks of the Trade • As people’s experience in their jobs increases, they begin to figure out shortcut solutions to problems they have seen before. • When they see a new situation, they match it to compare pattern that they are aware of.

31. Components of knowledge • Rules of thumb, Heuristics, and Tricks of the Trade • With experience, these scripts guide our thinking and help avoid useless tracks that we might have followed earlier. • Such rules of thumb or heuristics provide a single option out of a limited set of specific, often approximate, approaches to solving a problem or analyzing a situation accurately, quickly, and efficiently. • They play out as scripts. Our experience teaches us these scripts.

32. Components of knowledge • Values, assumptions, beliefs. • Very often, business processes are based on a set of deeply ingrained but oblivious and unarticulated assumptions. • Companies, however, are often shaped by the beliefs of a few key people working there. • Such values, assumptions, and beliefs are integral components of knowledge.

33. Components of knowledge • Values, assumptions, beliefs. • And knowing, capturing, and sharing this component of knowledge can make all the difference between complete knowledge and incomplete, unactionable information. • Until more can be done about this area in a systemic manner, we must rely on people who hold the critical beliefs that drive processes. • It is for this reason that you will see repeated emphasis on providing systemic pointer to people holding such components.

34. Components of knowledge • Intelligence. • When knowledge can be applied, acted upon when and where needed, and brought to bear on present decisions, and when these lead to better performance or results, knowledge qualifies as intelligence. • When it flows freely throughout a company, is exchanged, grow, is validated, it transforms an informated company into an intelligent enterprise.

35. Integration of knowledge source • So, what are the primary feed to a KM system? • From where does knowledge come into the system?

37. The three fundamental processes • Knowledge acquisition • Knowledge sharing • Knowledge utilization

39. Knowledge acquisition • The process of development and creation of insights, skills, and relationships. • Data-capture tools with filtering abilities, intelligent databases, keyboard scanner, note-capture tools and electronic white boards are examples of IT components that can support knowledge acquisition.

40. Knowledge sharing • Disseminating and making available what is already known.

41. Knowledge utilization • Whatever is broadly available throughout the company can be generalized and applied, at least in part, to new situations.

42. Levels of professional knowledge • The four levels of professional intellect, in the decreasing order of importance are, • Know-what • Know-how • Know-why • Care-why

44. Levels of professional knowledge • Know-what • This level represents cognitive knowledge. • This is an essential but insufficient basis for competing.

45. Levels of professional knowledge • Know-how • Know-how represents the ability to translate bookish knowledge into real-world results. • Professional know-how is developed most rapidly through repeated exposure to real-world, complex problems. • Any networking or knowledge support system that intends to move workers from this level to the next must enable extensive exposure to problem solving.

46. Levels of professional knowledge • Know-why • A system’s understanding represents the know-why aspect of knowledge. • It’s the deep knowledge of the complex slush of cause-and-effect relationships that underlie an employee’s range of responsibilities. • This knowledge enables individuals to move a step above know-how and create extraordinary leverage by using knowledge, bringing in the ability to deal with unknown interactions and unseen situations.

47. Levels of professional knowledge • Know-why • To be able to move knowledge worker from the know-how level, a KM system must support extensive discussion and conversation so that the participants and employees get a feel for the problems, rather than simply apply well-know rules that have worked in most situations. • Value creation from knowledge is enhanced if experimentation in the course of problem solving increase know-why and if incentive structures stimulate care-why, rather than solely focusing on know-how.

48. Levels of professional knowledge • Care-why • Care-why represents self-motivated creativity that exists in a company • Care-why explains why highly motivated, creative, and energetic groups and companies outperform large corporations with more money and resources.

49. The collaborative nature of knowledge • Collaborative problem solving, conversations, and teamwork generate a significant proportion of the knowledge assets that exist within a firm. • This focus on collaboration and collaborative support is perhaps one of the primary distinguishing factors that differentiates knowledge support systems from information systems.