Data, Information, Knowledge, and Wisdom
Bellinger, Durval Castro,
There is probably no segment of activity in the world attracting
as much attention at present as that of knowledge management.
Yet as I entered this arena of activity I quickly found there
didn't seem to be a wealth of sources that seemed to make sense
in terms of defining what knowledge actually was, and how was
it differentiated from data, information, and wisdom. What follows
is the current level of understanding I have been able to piece
together regarding data, information, knowledge, and wisdom. I
figured to understand one of them I had to understand all of them.
According to Russell Ackoff, a systems theorist and professor
of organizational change, the content of the human mind can be
classified into five categories:
- Data: symbols
- Information: data that are processed to be useful;
provides answers to "who", "what", "where",
and "when" questions
- Knowledge: application of data and information; answers
- Understanding: appreciation of "why"
- Wisdom: evaluated understanding.
Ackoff indicates that the first four categories relate to the
past; they deal with what has been or what is known. Only the
fifth category, wisdom, deals with the future because it incorporates
vision and design. With wisdom, people can create the future rather
than just grasp the present and past. But achieving wisdom isn't
easy; people must move successively through the other categories.
A further elaboration of Ackoff's definitions follows:
Data... data is raw. It simply exists and has no significance
beyond its existence (in and of itself). It can exist in any form,
usable or not. It does not have meaning of itself. In computer
parlance, a spreadsheet generally starts out by holding data.
Information... information is data that has been given
meaning by way of relational connection. This "meaning"
can be useful, but does not have to be. In computer parlance,
a relational database makes information from the data stored within
Knowledge... knowledge is the appropriate collection
of information, such that it's intent is to be useful. Knowledge
is a deterministic process. When someone "memorizes"
information (as less-aspiring test-bound students often do), then
they have amassed knowledge. This knowledge has useful meaning
to them, but it does not provide for, in and of itself, an integration
such as would infer further knowledge. For example, elementary
school children memorize, or amass knowledge of, the "times
table". They can tell you that "2 x 2 = 4" because
they have amassed that knowledge (it being included in the times
table). But when asked what is "1267 x 300", they can
not respond correctly because that entry is not in their times
table. To correctly answer such a question requires a true cognitive
and analytical ability that is only encompassed in the next level...
understanding. In computer parlance, most of the applications
we use (modeling, simulation, etc.) exercise some type of stored
Understanding... understanding is an interpolative and
probabilistic process. It is cognitive and analytical. It is the
process by which I can take knowledge and synthesize new knowledge
from the previously held knowledge. The difference between understanding
and knowledge is the difference between "learning" and
"memorizing". People who have understanding can undertake
useful actions because they can synthesize new knowledge, or in
some cases, at least new information, from what is previously
known (and understood). That is, understanding can build upon
currently held information, knowledge and understanding itself.
In computer parlance, AI systems possess understanding in the
sense that they are able to synthesize new knowledge from previously
stored information and knowledge.
Wisdom... wisdom is an extrapolative and non-deterministic,
non-probabilistic process. It calls upon all the previous levels
of consciousness, and specifically upon special types of human
programming (moral, ethical codes, etc.). It beckons to give us
understanding about which there has previously been no understanding,
and in doing so, goes far beyond understanding itself. It is the
essence of philosophical probing. Unlike the previous four levels,
it asks questions to which there is no (easily-achievable) answer,
and in some cases, to which there can be no humanly-known answer
period. Wisdom is therefore, the process by which we also discern,
or judge, between right and wrong, good and bad. I personally
believe that computers do not have, and will never have the ability
to posses wisdom. Wisdom is a uniquely human state, or as I see
it, wisdom requires one to have a soul, for it resides as much
in the heart as in the mind. And a soul is something machines
will never possess (or perhaps I should reword that to say, a
soul is something that, in general, will never possess a machine).
Personally I contend that the sequence is a bit less involved
than described by Ackoff. The following diagram represents the
transitions from data, to information, to knowledge, and finally
to wisdom, and it is understanding that support the transition
from each stage to the next. Understanding is not a separate level
of its own.
Data represents a fact or statement of event without relation
to other things.
Ex: It is raining.
Information embodies the understanding of a relationship of
some sort, possibly cause and effect.
Ex: The temperature dropped 15 degrees and then it started
Knowledge represents a pattern that connects and generally
provides a high level of predictability as to what is described
or what will happen next.
Ex: If the humidity is very high and the temperature drops
substantially the atmospheres is often unlikely to be able to
hold the moisture so it rains.
Wisdom embodies more of an understanding of fundamental principles
embodied within the knowledge that are essentially the basis for
the knowledge being what it is. Wisdom is essentially systemic.
Ex: It rains because it rains. And this encompasses an understanding
of all the interactions that happen between raining, evaporation,
air currents, temperature gradients, changes, and raining.
Yet, there is still a question regarding when is a pattern
knowledge and when is it noise. Consider the following:
- Abugt dbesbt regtc uatn s uitrzt.
- ubtxte pstye ysote anet sser extess
- ibxtedstes bet3 ibtes otesb tapbesct ehracts
It is quite likely this sequence represents 100% novelty, which
means it's equivalent to noise. There is no foundation for you
to connect with the pattern, yet to me the statements are quite
meaningful as I understand the translation with reveals they are
in fact Newton's 3 laws of motion. Is something knowledge if you
can't understand it?
Now consider the following:
- I have a box.
- The box is 3' wide, 3' deep, and 6' high.
- The box is very heavy.
- The box has a door on the front of it.
- When I open the box it has food in it.
- It is colder inside the box than it is outside.
- You usually find the box in the kitchen.
- There is a smaller compartment inside the box with ice in
- When you open the door the light comes on.
- When you move this box you usually find lots of dirt underneath
- Junk has a real habit of collecting on top of this box.
What is it?
A refrigerator. You knew that, right? At some point in the
sequence you connected with the pattern and understood it was
a description of a refrigerator. From that point on each statement
only added confirmation to your understanding.
If you lived in a society that had never seen a refrigerator
you might still be scratching your head as to what the sequence
of statements referred to.
Also, realize that I could have provided you with the above
statements in any order and still at some point the pattern would
have connected. When the pattern connected the sequence of statements
represented knowledge to you. To me all the statements convey
nothing as they are simply 100% confirmation of what I already
knew as I knew what I was describing even before I started.
theWay of Systems
- Ackoff, R. L., "From Data to Wisdom", Journal of
Applies Systems Analysis, Volume 16, 1989 p 3-9.
- Gadomski, Adam Maria, Information,
Preferences and Knowledge, An Interesting Evolution in Thought
- Sharma, Nikhil, The
Origin of the Data Information Knowledge Wisdom Hierarchy
Copyright © 2004 Gene Bellinger