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eCKM1.03 Define Knowledge: KM Assessment Tool Knowledge Attributes

According to Holsapple*, An attribute is a dimension along which different instances of knowledge can vary. In

any particular instance, the knowledge being studied or used can have multiple attributesAn appreciation ofmultiple knowledge attributes enriches ones understanding of knowledge resources and their processing

Attributes do not tell us what knowledge is or what knowledge resources exist. They do, however, help us to

understand the many qualities of knowledge. Every attribute dimension is a potential variable for investigation

a potential lever for practitioners to wield in their KM efforts.

Principle: Attributes highlight facets of Knowledge you might consider in designing and overseeing a KM

initiative.

Attribute Description __________________________________

1. AGE Emerging or new/innovative knowledge vs. conventional wisdom.

2. APPLICABILITY Knowledge can be universally applicable or localized for special circumstances or

contexts. Certain types of knowledge gained from experience may be relevant to a very specific application or

in a very constrained context. Other knowledge may be more universally applicable, from locale to region upto full global applicability. The key will be to know the applicability of the subject knowledge and to handle

that knowledge accordingly.

3. ACTIONABILITY Knowledge is Power. It either enables action or not. Knowledge is the power to know

what to do with the data and information.

4. CLARITY An essential to comprehension. It has to do with the amount of interpretation necessary to

comprehend the knowledge and make it ready for use. If not clear, or much effort is necessary for

comprehension, usefulness is marginalized.

5. DOMAIN The subject area of the knowledge

6. FLOWS Knowledge transferal from one stock to another and knowledge flow from a stock into

itself. The concept of knowledge flowing into itself is a way of describing innovation, the creation of newknowledge, or the concept of learning, though learning may also be said to occur when a stock receives

knowledge from another stock.

7. IMPORTANCE The capstone. Few would or should invest much in the creation of a repository of clear,

meaningful and relevant knowledge, if that knowledge was not important to solving high-value problems.

8. LOCATION Proximity to need (in space not time)

9. MEANINGFULNESS Ability to be accurately interpreted.

10. MEASURABILITY What metrics are possibleamount, quality, cost to acquire, etc.

11. MODES Knowledge can be either tacit or explicit. Tacit (and implicit) knowledge reside primarily

in the heads of knowing individuals, including mental models, and both know how and know why. Visibleknowledge. Some tacit knowledge is able to be converted into explicit knowledge in the form of notes, formal

documents, procedure manuals, training lessons, software code, designs, and the like. Unlike tacit knowledge,

once codified, it is much more easily transferred to others. This is the key value proposition of explicit

knowledge. A major challenge will be to strike a balance between the demands and advantages of each of

these two modes of knowledge Connect & Collect.

12. PERISHABILITY Shelf life of the knowledgetendency to be obsolete.

13. PRACTICALITY Degree of usability or value might be measured in terms of speed, accuracy, and/or

satisfaction with the outcome. A strong case can be made for imposing a threshold of usability. If the state of

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the stuff under consideration is usable, then it can be considered knowledge, if not usable, then it is merely

data or information to you.

14. PROFICIENCY Level of expertise.

15. RELEVANCE Pertinence to the problem at hand. Much meaningful knowledge may be available, but a

glut of irrelevant knowledge creates overload, undue processing time, with resultant delays and higher costs.

16. STOCKS An inventory of knowledgemany issues pertinent to physical inventories come into

play for stocks of knowledge (e.g., replenishment, perishability, holding costs, design, quality assurance,

tracking, planning, etc.17. STATES From data to decision (Van Lohuizen) and from data to wisdom (Barabba and Haeckel).

Bloom defined various (qualitative) levels of knowledge, or of the more knowledgeable, from the simple ability

to recall something (data and information for instance), up to the ability to evaluate based on personally

possessing progressively higher levels of knowledge. In Blooms hierarchy, knowledge seems to be defined as

having its own state, something fundamentally different and higher than data and information.

18. SOURCE Origin. Knowledge originates in the mind, but it may be transferred from one location to

another. The original source must be known to validate or seek future clarification, for instance.

19. TYPES Knowledge can be descriptive, procedural, or provide the ability to reason. Data and

information are descriptive, e.g., the measurements taken by a surveyor. The surveyors results describe some

state of the world. But, depending on your viewpoint of the data, information, knowledge hierarchy, descriptive

(or declarative) knowledge might not be knowledge. In the activity model perspective, it is definitely not

knowledge, merely data or information. It has no power. The ability to acquire it if non-existent, or to recall it(Blooms Taxonomy) or to get ready access to it if it is known, are certainly key components of the KM

initiative. So, whether we call it information or descriptive knowledge, its involved in the solution and

understood as knowledge. Procedural knowledge is fundamentally different from descriptive knowledgethis

is knowledge about how to do something or how something occurs (why)step-by-step procedures for

handling various tasks (how-to-do) or explaining various happenings (why) Examples of procedural

knowledge include:a cookbook, or procedure manual in an organizational environment, algorithms embedded

in software code, and all forms of policies and plans and even the methods to create each of these. Reasoning

considers assumptions and cause-and-effect outcomes to determine actions to be taken in a given context.

Reasoning might be especially needed when complexity, uncertainty, or frequent change overcomes the ability

to proceduralize something. According to Holsapple, reasoning tools include logic, correlation analysis

(regression analysis), analogy, and causalityprinciples. Inference - The use of reasoning knowledge to

reach such conclusions is referred to as inference, a term introduced in the Barabba and Haeckel model, as the

power that enables information to be converted into intelligence. Principles - In our context, once a principle isuncovered and validated, we hope to proceduralize it as much as we can, given the constraints of its original

context and applicability to another context, which introduces the concept of applicability.

20. USABILITY Of knowledge or representations of knowledge. Some representations may be unclear, ill-

defined, or unwieldy and hence not usable. Some knowledge, such as in the sciences is typically very usable, as

it is translated into engineering methods and practices; some science is not usable in that sense, but might yield

deeper understanding. The degree of usability (value) might be measured in terms of speed, accuracy, and/or

satisfaction with the outcome. A strong case can be made for imposing a threshold of usability. If the state of

the stuff under consideration is usable, then it can be considered knowledge, if not usable, then it is merely

data or information to you.

21. UTILITY Utility can vary according to clarity, meaning, relevance, and importance.

22. VALIDITY Confidence in its accuracy and consistency.

23. VELOCITY Measure of speed knowledge can move through organization (speed of a rumor). Tacit

may not be capable of moving as fast as explicit (email).

24. VISCOSITY Richness or thickness of knowledge the quality ofabsorption. Relevance, validity,

volatility and locality of knowledge might affect viscosity, but absorptive capacity might relate mostly to ability

of recipient to comprehend based on similarity of the knowledge to the domain of expertise of the recipient.

25. VOLATILITY How subject to change the knowledge is.

Knowledge and Its Attributes, by Clyde W. Holsapple, Chapter 9 Handbook on Knowledge Management,Springer-Verlag, 2003.