Breaking the engineering paradigm

In the first of a two-part series, Dave Snowden charts the paradigm shifts in management and KM over the decades

Delivering a keynote at KMUK 2010 came with the bonus of an award for ‘the best advance of KM as a scientific discipline’ (see Inside Knowledge, June/July 2010). I will admit to being pleased at the recognition for what I and others have been attempting over the past decade and more – namely to use natural science, rather than the tail end of systems dynamics to inform both the theory and practice of knowledge management (KM). To me this has always seemed common sense. We are after all human beings not cogs in some machine. We evolve; we cannot, other than in a very limited sense, be engineered. Human beings, especially when considered collectively, have capability that cannot be replaced by technology, although it can be augmented. We have known for more than a decade that the cybernetic models that were in vogue from Turing in the 1950s are to many a modern author woefully inadequate to describe human capability.

Unfortunately KM as a discipline arose during the full pomp of process reengineering (the dominant idea of the systems period) coupled with the limited capability of early highly structured collaborative technologies. KM thus mirrored the outcome-based focus of that period, defining how things should be and then trying to close the gap. We are finally starting to realise that for organic systems you have to evolve from the evolutionary potential of the present, rather than design for an idealistic future. If you want evidence of this shift then compare the success of social computing in a few short years with what KM has achieved in over a decade. Social computing has been successful without design and with little structure. It has muddled through in a messy but coherent manner which mimics natural human (sic) practice in social settings, but fundamentally challenges the neat, tidy and ordered structures of the modern organisation.

I ranged far and wide in the keynote, but in this article I want to focus on the first part of the presentation, in which I attempted to place different approaches to management in general (and KM in particular) into a wider context. Figure 1 opposite is a simplified form of the slide I used on the day. The framework in the picture combines the well established ‘S’ curve with ideas from Moore’s seminal work, Crossing the Chasm. The basic idea is that new ideas and new ways of thinking gain early enthusiasm but quickly fall away as they challenge established orthodoxy. Those ideas which manage to cross this chasm (the small dip at the start of each curve), then become the dominant idea for a period of time, before we start to exhaust their utility. This exhaustion creates the space for the next idea to come into play, but the very dominance of the old idea makes this very difficult. Failures in the old way of doing things are generally interpreted as just not trying hard enough. In the current era we can see this with what I fondly call sick stigma or business-process reengineering, with bible-belt cultism added onto it for good measure. The high priests of cult even wear different coloured belts to indicate their status, and at the highest levels are exempt from ordinary work to enforce cult discipline.

The overlap between the curves makes a strong point. The success of the old idea suppresses the new but it can only do so for a limited period before it collapses. During major shifts this is where the dominant predators of the established period fail to see the change, until it is too late to change. I have now lived through the tail end of one major theme, the whole of the second and I’m now at the start of the third. Each period is associated with a new way of thinking, a new technology and is triggered by a recession. These three together form a perfect storm, which brings about a paradigm shift. So, let’s look at the three periods, and then look at the ways in which they are different.

Paradigm one: Scientific management – the era of the professional
This is also known as Taylorism, after its first theorist. The novel idea here was that there is science to management. The new technology was the telephone and the fax machine, which enabled the growth of international corporations. The recession? Well, do you remember the 20s? Before this period individuals owned ships or factories, they knew their business and were not yet infected with the jargon of management science. Major industries grew during this period – for example, ICI, by “serving customers internationally through the innovative and responsible application of chemistry and related science”. When the manager responsible for aviation company Boeing’s 747 development was asked for the expected return on investment, he said that while some studies had been made he couldn’t remember the result.1 People entered organisations expecting to learn over time through experience and the idea of an MBA-driven consultancy exercise was inconceivable. In a very positive sense of the phrase they muddled through, planning what could be planned, giving responsibility to people and systems that had grown from formal and informal apprentice systems. The dominant metaphor was a military one, indeed many managers were from military backgrounds so we have that fluid mix of hierarchy and delegation of authority into trained roles, which characterises military environments.

Paradigm two: Systems dynamics – the era of the engineer
Our story moves on to the 1980s, with another recession and critically the arrival of scalable computing. Until this time computers were mainframe based, expensive and very limited in application. With mini and micro computers, we could now handle information in large volumes and more quickly. This allowed a major shift from the vertical functional form of the previous era to one which was horizontally focused on process. Systems dynamics created a theoretical framework and we now see a plethora of radical new ideas, most of which promised much, delivered some but in the end proved to be limited.

A basic and fundamental characteristic of this period was the focus on outcome. Organisations aimed to define an ideal future three or five years out and then engineer their way to that future. We also saw attempts to manage the inherent uncertainty of the organisation and its environment through outcome-based targets and the whole mission and value concept that is associated with Peter Senge and others. In the same period, we saw the balanced scorecard, Porter’s five forces, the total quality movement, business-process reengineering, and what I have come to call ‘Nonaka-based KM’. The whole tenor of this period was towards codification and structure, to design human systems as if they were computers. Indeed, to replace humans at times or worst still to reduce human systems to the linear binary processing routines of machines. KM aped this approach with the febrile attempt to create explicit knowledge from tacit. Humans were corralled into centrally controlled and defined communities of practice and the initial goal of every KM project was to create a taxonomy, a word that rhymes with and is more naturally associated with the consequences of taxidermy.

The other major change here was the focus on shareholder value. ICI was taken over by the Hanson Group and its mission changed to “creating value for customers and shareholders through market leadership, technological edge and a world competitive cost base”. The share price briefly peaked then collapsed. Boeing went through a similar process and lost out to Airbus and there are many other examples before we even mention the financial sector and the banks.

Paradigm three: Cognitive complexity, the era of the situated network
So now we enter the present day. We have gained benefits from both of the earlier paradigms, but we are reaching the end of their utility. With a few honorable exceptions – and after initial success – KM programmes have tailed off into being a minor part of the IT department or a killed completely. The role of the chief knowledge officer, which once offered much promise, is now reduced to a minor role, if it exists at all. At the same time we have seen a massive technology change in the growth of pervasive computing. My watch hosts more technology than the local Technology College computer on which (via a 200 baud acoustic coupler) I wrote my first ever program (to fake the results of a physics practical). Now that we take technology for granted it can become a tool rather than a fetishistic device. We also have a recession, and a major one at that. We have an exciting new set of ideas to work with: complexity, or the science of uncertainty, cognitive science and through evolutionary psychology and anthropology, radical new insights into the nature of human ecologies. What that means and the implications for KM will form the second part of this series.

Dave Snowden is the chief scientific officer and founder of Cognitive Edge Pty Ltd. He can be contacted at dave.snowden@cognitive-edge.com

Reference
1. Examples here and later from John Kay’s excellent Obliquity

Note: To view a PDF of Dave's article including Figure 1, e-mail the editor at kclifton@waterlow.com