The Poetics of Thought

The Data Information Knowledge Wisdom Hierarchy

Posted in Uncategorized by Fred McVittie on December 15, 2009


Where is the Life we have lost in living?

Where is the wisdom we have lost in knowledge?

Where is the knowledge we have lost in information?

(Eliot, 1934)

.

The ‘Data Information Knowledge Wisdom Hierarchy’ is an epistemological system usually associated with Russell Ackoff (Ackoff, 1989)  although elements of it are prefigured in the work of Milan Zeleny (1987), and in more poetic form in T.S. Eliot (above) and in the lyrics of a song by Frank Zappa[1].

To introduce this model, a brief description of what is meant by the ‘Data Information Knowledge Wisdom Hierarchy’ is in order. As indicated in the name, the model organises the range of epistemological phenomena into four categories, these are:

  • Data – this indicates the set of individual facts, figures, sensory impressions, etc.  Data is regarded as essentially meaningless, although it is the raw material from which meaning is derived.
  • Information – is regarded as data which has undergone some kind of organisation.  Data sets may be divided into categories according to some criteria; individual data items may be linked together according to some salient feature.
  • Knowledge – this is, essentially, information which has been internalised by the person such that they might put it to use. An important feature of knowledge is that, whereas information and data may reside in texts, objects, and events, knowledge acquisition, ownership, and transfer can only be effected by human agents.
  • Wisdom – this is seen as the possession of knowledge such that one is able not only to observe patterns of information within data and make intelligent connections between different patterns, but also to feel the principles which underlie the patterns themselves.  Wisdom allows one to see these various patterns in their contexts and to be able to remain independent of immersion in that context oneself.

What I want to argue is that this model draws on certain key metaphors.  These are partially spatial metaphors which, I will argue, map coherently onto those outlined in my previous analysis of ‘tacit’ and ‘explicit’ knowledge in the work of Michael Polanyi (and indeed to the less formal ideas of ‘objective’ and ‘subjective’ knowledge).  In addition though, a close reading of the Data Information Knowledge Wisdom distinctions reveals a set of metaphors drawn not only from the properties of space but also to the properties of objects, specifically the substantial properties of hardness and softness, lightness and heaviness, liquidity, granularity, and evanescence.

Data

Data is understood primarily as a physical resource, and the metaphorical form of this resource has a number of properties which distinguish it from information and knowledge. Firstly it is conceptualised as a large number of individual, separate, atomistic, entities, like an aggregate of small stones, or a pile of leaves blown by the wind.  Items of data have an ontological irreducibility which prevents their being understood as composites themselves; just as when one is collecting pebbles from the beach one would not think to increase one’s collection by splitting each pebble in half, so individual datum cannot be divided.  Data is also understood as pre-existing any efforts to effect its collection; we conceive it as simply ‘out there’ waiting for some kind of exploratory practice to discover it.   Such entities might be ‘collected’, ‘mined’, ‘gathered’, or ‘stored’; on the other hand, because items of data are unconnected to every other item, they might also easily be lost, fall away from one another, disaggregate, or slip through the cracks.


[1] The 1979 song ‘Packard’s Goose’ by Frank Zappa, on the Album ‘Joe’s Garage Act II and III contains the lines: Information is not knowledge/Knowledge is not wisdom/Wisdom is not truth/Truth is not beauty/Beauty is not love/Love is not music/and Music is THE BEST.(Tower Records, 1979).

http://www.youtube.com/watch?v=m2cORmA4MD8


ACKOFF, R. L. (1989) From Data to Wisdom. Journal of Applied Systems Analysis, 16, 3-9.

ELIOT, T. S. (1934) The Rock, Faber & Faber.

ZELENY, M. (1987) Management Support Systems: Towards Integrated Knowledge Management. Human Systems Management, 7, 59-70.

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2 Responses

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  1. Kaposvári Márk said, on December 15, 2009 at 9:02 pm

    this is an important hierarchy, and a really interesting issue,
    what I would add to wisdom is irony or ironic distance,
    plus the ability to recognize how meaning is contingent on a (tacit) sense of coherence! and understanding how this coherence is structured, and how different people relate to the same phenomena through different “coherences”,
    so “understanding” is I think more than knowledge, it’s more of a wisdom, because
    it involves the readiness and willingness to abandon ones own manner of looking at the world to accommodate to others’ coherences, yes, this also brings to mind schema theory, which is barely related but the sequence of successful adaptation is I think similar to the process of understanding other people’s attitudes toward phenomena, this is: accretation tuning and restructuring, but
    ok, now I stop 🙂
    great post,
    thanks for your work,
    as always,
    best,
    M

  2. Kaposvári Márk said, on December 15, 2009 at 9:46 pm

    on second thought, (I’ll try and not be “bewildering” here)
    if we deal with data-info-knowledge-wisdom in spatial terms
    I think it makes sense to say that this continuum
    is going from an external to an internal domain,
    and this might be abstract to say, but I believe that
    knowledge that is amalgamated with other knowledges, which is wisdom,
    is a process when external affordances are internalized,
    which means that the way we relate to ideas (not objects)
    could be understood as reflecting “conceptual affordances”,
    my attitude toward a social norm, for instance, could be seen as evincing
    the conceptual affordances that that specific notion elicits in me,
    and so to realize the proportions of saliences I accord to ideas
    has a kind of spatial sense to it,
    when I manage to understand a demanding essay, for instance,
    I kind of “see” or suddenly understand a spatially explicit schema
    that underlies the logic of the text, I see the emphases and the hierarchy of distinctions…
    I hope this makes sense,


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