From Data, with Love

That most hallowed of mental models and glib explanations, the Data-Information-Knowledge-Wisdom hierarchy has taken a bit of a beating this week. It started in an innocent enough way when, in a discussion about knowledge sharing and generation on the KM4Dev listserve, somebody cited the DIKW model as a way of describing how knowledge is generated in organisations. This provoked Dave Snowden into some sharp but illuminating posts (by the way, if you ever get bored and feel like doing some Dave-baiting, get yourself a false identity, sign up to one of the listserves he frequents, and make an enthusiastic post about DIKW, wisdom management, Six Sigma, Ayn Rand or KM certification - or any combination thereof):

“I would reject the DIKW pyramid, aside from the fact it’s just plain wrong, it’s difficult to explain and leads to bad labels. Better to think that KNOWLEDGE is the way we create INFORMATION from DATA. If we share knowledge then we can understand information.”

“Aside from being linked to a particular period of systems thinking approaches, which we are hopefully moving on from, its very culturally specific.  It fails entirely to account of shamanistic knowledge, or the narrative traditions of Sufi philosophy and others.  I could go on, but the you get the point; the DIKW pyramid is a culturally limited and inadequate model which has done more harm than good. The SECI model with its de facto focus on codification comes a close second, as I said the other day it’s the model that launched a thousand failed knowledge management initiatives.  The main problem is its tendency to get people to think of knowledge as a thing rather than as a flow.”

Dave has posted in the past at greater length on DIKW here and here, and so have I.

However, one worried comment from a listserve member that DIKW was a “well-understood idea within the community” struck me, and prompted a further reply from me - because indeed this hierarchy is extremely well entrenched in the KM (and information science) literature. It’s about as sacred as a sacred cow can get. Why? And should that make it immune to attack posts?

Here’s my reply, slightly modified for a wider audience:


It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.

Why did the hierarchy become received “wisdom” in KM?

(1) It became received wisdom very quickly in computer sciences and information science literature, because it was a legitimising model – such models become entrenched very quickly.

(2) The weaknesses of the model in relation to knowledge and wisdom were never tested in its first decade by which time it had become entrenched in the literature.

(3) Writers for new knowledge management journals in the 1990s – as in any new discipline – suffered from “citation poverty” and so fell back on the received literature and mental models from their parent disciplines, without adequately questioning their applicability in this new context.

(4) If you don’t actually try to do anything based on the model, it serves a quite useful function in proffering a glib explanation of the distinctions between data information and knowledge and makes a pleasing nod at wisdom, so it seems like it has utility.

(5) If you do try to structure your KM work using the model, you get rapid support from the technology side of KM (so the model must be ok), and when you run into problems with ground adoption and usability, it’s easy to chalk this up to human intransigence and change resistance, rather than the poverty of the model as a framing device.

The pyramid form implies that data – at the base – is more abundant than information, which is more abundant than knowledge, which is more abundant again than wisdom, at the very tip. Indeed, from an enterprise perspective that might seem to be the case, but I happen to think it’s mistaken. My own view is that in most cases there’s a lot more knowledge (in and around the people) than information, and even less data. I won’t comment on wisdom.

One constructive way to read the DIKW pyramid is in terms of the VISIBILITY and TANGIBILITY of the different elements, which is a different thing from their presence. From that perspective, the visual representation of the pyramid makes sense. We do see a lot more data, it’s easier to figure out where it is, and what to do with it. Information is less transparent and more complex to audit and map, knowledge is much more opaque, and wisdom auditing (people do actually sell this!) would be either a work of opinion or divination. So if DIKW just made claims about visibility it would have some use.

That may be another reason why the DIKW pyramid has seemed so attractive: the visual form focuses us first on the more manageable (visible and tangible) elements and encourages us to work on those first as foundational elements – providing a visual justification for a quick win bias. The problem is that the knowledge ecosystem is more complex than DIKW allows, and focusing energies and effort on the easier stuff frequently fails to meet the most critical needs. The critical stuff is just not “seen” through a DIKW lens.

It’s interesting to note that some information scientists have recently been reassessing their attachment to their offspring: