Knowledge: Definition, Types, Usefulness
I don't think we need to define knowledge
. Still, we do expect a set characteristics from this or that knowledge, to wit:
- accuracy: knowledge must be correct, up-to-date, accurate
- availability: inaccessible knowledge is no knowledge to the subject who is kept in the dark (even if in violation of copyright?)
- usefulness: applicable, relevant, actionable
We often demand that knowledge be structured, that bits of knowledge be related to one another in some structure, hierarchy, scheme, tree. We expect a tree of knowledge. We don't want to go about learning this, then that, and being satisfied with learning any this or that. Or, instead of a tree of wisdom, we expect our knowledge to be based on a vocabulary of concepts. We appreciate knowledge above mere data.
We also want to learn, or attain, wisdom, the ultimate actionable knowledge. On a small scale, we believe that a good (efficient) doctor or engineer have achieved some type of wisdom.
The Knowledge Pyramid: Data, Information, Knowledge, and Wisdom (DIKW)
(From https://en.wikipedia.org/wiki/DIKW_pyramid)
The Knowledge Pyramid represents a model, actually any models, of possible structural and functional relationships between a set of components, usually four: data, information, knowledge, and wisdom.
The point is that information
gets gradually refined from raw data, through actionable knowledge, to some general or strategic knowledge (wisdom).
Earlier versions of the Knowledge Pyramid excluded data (how do we tell between data and information?), while later ones excluded or downplayed wisdom (possibly as something too vague or spiritual). Still other versions include additional components. Also, this model is not always presented as a pyramid but as a chart, a chain or flow diagram, sometimes even as a continuum.
In 1987, Czechoslovakia-born Milan Zeleny mapped the components of the hierarchy to knowledge forms:
- data matches
know-nothing
,
- information matches
know-what
,
- knowledge matches
know-how
, and
- wisdom matches
know-why
.
Let's try to provide an example of the distinction between data, information, and knowledge...
Data versus Knowledge
In the context of DIKW-type models, data is conceived, according to Zins' 2007 formulation, as being composed of symbols or signs, representing stimuli or signals, which, in Rowley's words (as of 2007), are of no use until ... in a usable (that is, relevant) form
, that is until they become information.
Data is as yet uninterpreted, meaningless in itself, whereas information consists of interpreted data (or the interpretation of data).