Aggregated enterprise architecture wisdom
How far can general principles of asset management be applied to data? In this post, I’m going to look at some of the challenges of putting monetary or non-monetary value on your data assets.Why might we want to do this? There are several reasons why p…
@bengoldacre has produced a report for the UK Department for Education, suggesting some lessons that education can learn from medicine, and calling for a coherent “information architecture” that supports evidence based practice. Dr Goldacre notes that in the highest performing education systems, such as Singapore, “it is almost impossible to rise up the career ladder of teaching, without also doing some work on research in education.”
Here are some of his key recommendations. Clearly these recommendations would be relevant to many other corporate environments, especially those where there is strong demand for innovation, performance and value-for-money.
Clearly it is not enough merely to create an information architecture or knowledge infrastructure. The challenge is to make sure they are aligned with an inquiring culture.
to be continued …
Ben Goldacre, Teachers! What would evidence based practice look like? (Bad Science, March 2013)
@bengoldacre has produced a report for the UK Department for Education, suggesting some lessons that education can learn from medicine, and calling for a coherent “information architecture” that supports evidence based practice. Dr Goldacre notes that in the highest performing education systems, such as Singapore, “it is almost impossible to rise up the career ladder of teaching, without also doing some work on research in education.”
Here are some of his key recommendations. Clearly these recommendations would be relevant to many other corporate environments, especially those where there is strong demand for innovation, performance and value-for-money.
Clearly it is not enough merely to create an information architecture or knowledge infrastructure. The challenge is to make sure they are aligned with an inquiring culture.
to be continued …
Ben Goldacre, Teachers! What would evidence based practice look like? (Bad Science, March 2013)
Once upon a time, people thought of an information model as defining the structure of the stuff you want to remember. Nowadays, this definition is too restrictive: it might possibly be adequate for a system/database designer, but is not adequat…
Here’s an analogy for the so-called hierarchy of Data, Information, Knowledge and Wisdom DIKW).
Note that Information isn’t made solely from Data, Knowledge isn’t made solely from Information, and Wisdom isn’t made solely from Knowledge. See also my post on the Wisdom of the Tomato.
That’s enough analogies. Let me now explain what I think is wrong with this so-called hierarchy.
Firstly, the term “hierarchy” seems to imply that there are three similar relationships.
as well as implying some logical or chronological sequence
and quantitative relationships
But my objection to DIKW is not just that it isn’t a valid hierarchy or pyramid, but it isn’t even a valid schema. It encourages people to regard Data-Information-Knowledge-Wisdom as a fairly rigid classification scheme, and to enter into debates as to whether something counts as “information” or “knowledge”. For example, people often argue that something only counts as “knowledge” if it is in someone’s head. I regard these debates as unhelpful and unproductive.
A number of writers attack the hierarchical DIKW schema, and propose alternative ways of configuring the four elements. For example, Dave Snowden says that “knowledge is the means by which we create information out of data”. Meanwhile Tom Graves suggests we regard DIKW not as ‘layers’, but as distinct dimensions in a concept-space.
But I don’t see how any of these DIKW remixes escapes the most fundamental difficulty of DIKW, which is a naive epistemology that has been discredited since the Enlightenment. You don’t simply build knowledge out of data. Knowledge develops through Judgement (Kant), Circular Epistemology and Dialectic (Hegel), Assimilation and Accommodation (Piaget), Conjecture and Refutation (Popper), Proof and Refutation (Lakatos), Languaging and Orientation (Maturana), and/or Mind (Bateson).
What all of these thinkers share is the rejection of the Aristotelian idea of “one-way traffic” from data to knowledge, and an insistance that data must be framed by knowledge. Thus we may validate knowledge by appealing to empirical evidence (data), but we only pick up data in the first place in accordance with our preconceptions and observation practices (knowledge). Among other things, this explains why organizations struggle to accommodate (and respond effectively to) weak signals, and why they persistently fail to “connect the dots”.
And if architects and engineers persist in trying to build information systems and knowledge management systems according to the DIKW schema, they will continue to fall short of supporting organizational intelligence properly.
References
Updated 8 December 2012
An architect looks at a valley and imagines a viaduct. She then describes this imaginary viaduct in great detail. As a result of her imagination, and the efforts of many engineers and other workers, when we visit the valley ten years later we too can s…
@tonyrcollins asks if any healthcare IT system can provide a Single Source of Truth (SSOT)? In his blog (13 December 2010), he discusses a press release claiming that an electronic healthcare record system from Cerner Millennium Solutions is a “single …