posted 2 Jul 2002 in Volume 5 Issue 10
The new simplicity
Context, narrative and content
Rather than fading into anonymity like previous management fads, KM has stood the test of time. According to Dave Snowden, however, the discipline is in danger of heading towards an evolutionary dead end. Here he draws on the theory surrounding complex adaptive systems to outline an approach to knowledge management that represents a new level of simplicity, and that promises to liberate the discipline from its current constraints.
Knowledge management has proven itself to be remarkably resilient. It is seven years now since the publication of the first popular publication of the SECI model by Nonaka and Takeuchi, with its four transformations between tacit and explicit knowledge – the two-by-two that launched a thousand knowledge initiatives. By now, if we were to follow the normal life-cycle for management fads, KM should have faded or become an embedded part of standard organisational process but, considered as a whole, it has done neither. There are at least three reasons for this:
- KM has attracted a community of true believers, often existing outside the normal power structures of the organisation, who champion their passion through many adversities. Many of these people are not in powerful roles, but they plug away year after year preaching the message, getting sponsorship for the odd initiative and generally making things happen;
- KM as a discipline had many origins, rather than a single defining influence or guru, as in the case of business process re-engineering (BPR) and the concept of the learning organisation. Its genesis has therefore been a gradual evolution from many different sources rather than a big bang. SECI may have been the trigger, but there were many pre-existing strands and thinkers/practitioners who have maternal and paternal claims;
- Knowledge as a subject addresses substantial needs. It brought together everyone who had been oppressed by the tyrannies of a mechanistic and increasingly inhuman approach to management, often instantiated in the worst excesses of BPR.
This last point is important, as it holds the key to determining if knowledge management has a future. A dead end evolutionary route is opening up, in which the tools, techniques and, critically, the objectives and measures of process efficiency are being applied to KM initiatives. Now that knowledge management is mainstream, it is being made to fit the old models. This is nothing new: the teaching masters of the Sufis captured the issue well in a story about Nasrudin: his experience of birds has so far been confined to pigeons. He has never seen a hawk, but when he finally does, his reaction is to make it like a pigeon, for the sake of his own peace of mind but also with the best of all possible intentions (see separate textbox elsewhere on this page).
Too many corporate initiatives are clipping the talons, straightening the beak and trimming the feathers, in turn losing all that can be valuable in a KM programme. Partly this represents a desire for the directive control that was relevant for process and quality management, but which is impossible in knowledge management – knowledge can only be volunteered; it cannot be conscripted. This is a deeply pragmatic reality: while I can measure compliance with a process, I can never know if someone is truly using their knowledge. Neither can I rely on loyalty: two decades of treating staff as a disposable commodity has produced a downside for organisations, in that their staff now lease their knowledge to the highest bidder, reserving loyalty for their private networks.
Properly understood, knowledge management challenges over 30 years of consultancy practice and 100 years of management science (the fact that 30 and 100 are numbers associated with fratricidal wars in European History may just be a coincidence, but one that nevertheless offers hope of peace!). It requires us to re-think some of the basic presumptions of reductionism and analytical management theory and practice as to the nature of the way we know things, make decisions and evolve systems.
Complex and complicated
In order to do this, we need to look towards a new science. The current paradigm of management thinking can be traced back to the ideas of Frederick Taylor, who took the ideas of Newtonian physics and applied them to management. In this universe, cause and effect relationships are either known or are knowable, given sufficient time and resources for a thorough investigation. We can see much of current inauthentic KM practice as failing in part because of this presumption. For example, the belief that there is such as thing as best practice is predicated on an ontological assumption that cause and effect can be linked, and that, as a consequence, repetition will produce the same verifiable results. It is this assumption that gives rise to problems in KM. It’s not that this idea it is not true, but rather that it is not universally true. Just as Newtonian physics remains useful now that we know it boundaries, so best practice, process re-engineering and the like remain not only useful, but imperative, at least within certain boundaries. However, to manage knowledge we have to move beyond those boundaries into the realms of uncertainty, and here we need a new scientific base to replace that of Newton. For many of us, this can be found in complex adaptive systems (CAS) theory. To understand this, we need to make a distinction between a system that is complex and one that is complicated.
An aircraft is a complicated system; all of its thousands of components are knowable, definable and capable of being catalogued, as are all of the relationships between those components. If necessary, the aircraft can be deconstructed to further explore the nature of its components and their relationships. Cause and effect can be separated, and by understanding their linkages we can control outcomes. The system can be optimised by optimising its parts, as the whole is nothing more or less than the sum of those parts.
Human systems are complex. A complex system comprises many interacting agents, an agent being anything that has an identity. We all exist in many identities: this author can be son, father or brother in different contexts. The same applies to work group identities, both formal and informal, as well as to various social groupings. As we move among identities, we subconsciously observe different rules, rituals and procedures. In such a complex system, the components and their interactions are changing and can never be entirely pinned down. The system is irreducible. Cause and effect cannot be separated because they are intimately intertwined. The system is not the sum of its parts – it can be more, is frequently less and is optimised by allowing the sub-optimal behaviour of its components. It cannot be taken apart conceptually or in reality without changing its nature. Two examples make this clearer:
- Consider what happens in an organisation when a rumour of re-organisation surfaces. Typically, the complex human system starts to mutate and change in unknowable ways, and new patterns form in anticipation of the event. On the other hand, if you walk up to an aircraft with a box of tools in your hand, nothing changes;
- A feature of a complex system is the phenomenon of retrospective coherence, in which the current state of affairs always makes logical sense, but only when we look backwards. The pattern is logical, but at the same time is only one of many patterns that could have formed, any one of which would be equally logical.
Organisations tend to study past events to create predictive and prescriptive models for future decisions based on the assumption that they are dealing with a complicated system in which the components and associated relationships are both discoverable and manageable. This stems from the assumptions associated with ‘old’ science and has been reinforced by a whole industry built between business schools and consultancies in which generalised models are created from analytical study of multiple case histories. These approaches served well in the revolutions of total quality management and business process re-engineering, and continue to be applicable in the domain of the complicated. However, for knowledge and learning we need to use a new science: the science of patterns.
Decision making is one of the primary uses of human knowledge and the easiest way to manifest what people know. One of the best techniques for knowledge mapping involves mapping decisions in order to create a meaningful context for knowledge disclosure: we only know what we know when we need to know it. Without contextual stimulation, knowledge capture is only partial at best.
Moreover, there is a mismatch between the way that we make decisions and the way that we say we make them. In reality we make decisions based on previously entrained patterns. When faced with a given situation, we match incoming stimuli with the first fit (not the optimal fit) pattern of our previous experience. That experience may not be personal, but cultural, based on the stories we hear and believe. This is both good and bad. It allows us to use collective prior experience to make decisions quickly, but it can also result in our not seeing things that fail to fit the pattern, representing a form of stereotyping. Interestingly, when people are questioned after they have made a decision, they will generally explain their decision as a choice between rationally evaluated alternatives, although more occasionally it will be described as gut feeling. However, neither answer reflects reality. A similar process can be observed in group decision making, where competition takes place to create a common pattern by reference to past experience or possible futures. Once such a pattern resonates with the group, the decision will be rationalised.
In human systems we can further entrain patterns through the use of artefacts such as processes and procedures, increasing the inherent conservatism of a system. We can also see this phenomenon in group formation. When a group forms all sorts of possibilities are open, and specific roles are unclear. After a period of time, roles will stabilise. These roles in turn will become a constraint on the system, and expectations are built into the system as the pattern of interaction stabilises, leading to increased stability over time unless the structure of the system is radically disrupted. (This is not the same thing as being threatened, however – an external threat, real or perceived, is more likely to reinforce roles.)
These insights, which come from naturalistic decision theory and work in neuroscience, are key to understanding how knowledge management can make a real contribution to the future of organisations. Specifically, KM can help in managing the flow of patterns in an organisation, stabilising those that are desirable, destabilising the undesirable and, under controlled conditions, disrupting entrained thinking through perspective shifts to enable the emergence of new insights and understanding.
Implications for knowledge management
The insights that we achieve from CAS theory and from an understanding of patterning in human systems allow us to approach knowledge management in a different way. They also allow a new and extremely effective take on the primary focus of KM, namely decision support and the enabling of innovation. Now that we have a deeper insight into the nature of decision making, we can start to manage the patterns of collective understanding, either to reinforce those patterns under stable situations or to actively disrupt them to sensitise decision makers to a new situation in which old models will no longer work.
Based on funding from the US government in connection with the management of asymmetric threat, the Cynefin Centre has developed a set of tools and techniques that go beyond scenario planning to manage the new dynamics of strategy. This work is provoking a rapid response from senior executives who constantly face conditions of extreme uncertainty. I have lost count of the amount of questions I have heard that run along the lines of, ‘How do I get executive sponsorship for our KM programme?’ One of the most effective solutions is to demonstrate how KM practice can impact upon the policy of an organisation. Do this, and you immediately become ‘strategic’.
With innovation, the issue is how to disrupt entrained patterns of thinking that blind experts to new ideas. One of the problems with traditional approaches to communities of practice is that they tend to reinforce current practice and punish deviants by exclusion. Again this is acceptable within boundaries, but when we cross a boundary to attempt to create a ‘eureka’ moment this natural conservatism becomes very dangerous. Various approaches to innovation have been explored in previously published articles in Knowledge Management.
This understanding requires a re-examination of what knowledge management actually means. My own definition is that managing knowledge is the creation of shared context, without which no flow of information is either meaningful or practical. However, we need to go beyond this. One way to structure what we can call the post-Nonaka period in KM is to look at the third of the heuristics listed in the textbox elsewhere on this page: we always know more than we can say, and we will always say more than we will write.
The focus on written knowledge, which dominates KM thinking, is only a partial view of the totality of knowledge that needs to be managed. Ever more desperate attempts to force people to codify their knowledge or to seek salvation in technology are doomed to failure if they are used as anything other than incomplete tools. We need to separate what we know from what we can say and from what we can write down. This is the separation of KM as a discipline in three parts: context, narrative and content.
Content management is a hygiene factor: you have to manage documents, you need places for electronic conversations and all the taxonomies, search engines and other tools (too frequently just toys) that abound at any trade show. Context and narrative management on the other hand are relatively new.
Context is key
Trust and understanding are preconditions for creating a shared context and are a part of the patterning that builds in organisations through social networks. Imagine three different situations:
- Someone you have known for several years contacts you with a question. You share the context of many experiences and you know not only what the question means, but that the person will understand your answer. Additionally, you know how far you can trust the person with the answer. As a result, knowledge exchange is effective and has a minimum impact on time. You are also building and reinforcing an existing relationship, which provides additional motivation;
- Now, the same question but from someone with whom you have no prior experience. They work for the same organisation so there is some obligation to answer, but your shared context is limited. You will ask questions and interpret their answers to create increased shared context, all of which is time consuming and often frustrating, although sometimes rewarding. When you do transfer knowledge, it is qualified: ‘Do this and then contact me’; ‘Why don’t I come and help you for the first couple of days’; etc;
- Some idiot with ‘knowledge’ in their job title asks you to write down what you know in the absence of a specific question. This is impossible, or only an approximation of reality. To do this properly you would have to anticipate as yet unknown contexts.
Both in terms of motivation, and in terms of practicality, we respond best to questions asked both in the context of a historical situation and in the context of a human relationship.
Context management, then, is about building and maintaining links in the informal as much as the formal organisation. There are some interesting new ideas in this field, which is all about managing the channels through which knowledge flows, rather than managing the knowledge itself. One broad and previously published approach here is loosely titled ‘just in time’ KM. Here the informal or shadow communities that comprise the reality of organisations are allowed to self-organise and self-manage their knowledge, with the organisation focusing on just in time stimulation of knowledge flow from the informal to the formal, when it is needed – which is to say, in the context of its need. This type of approach can radically transform the effectiveness of systems (but not their efficiency) by using the energy of the informal, sub-optimal behaviour of the agents to create a holistic and optimal system. This is reasonably well established as an approach, but the field continues to develop. Two tools developed within the Cynefin Centre illustrate the type of new thinking that is emerging in this arena.
Social network stimulation (SNS)
It has been known for some time that the more networked a company, the more knowledge flows and the higher the probability that new ideas and thinking will come to the fore when it is needed. High levels of network density normally take years to achieve as people move between jobs, work on different projects, meet people in social settings and so on. Such networks also build on the basis of personal likes and dislikes – we cannot be made to trust someone. SNS aims to intervene in an organisation in such a way as to reduce years of casual relationships into months or weeks. It does this by replicating the informal social processes by which such networks form through reward-based team actions in which teams self-select based on heuristic frameworks designed to channel the natural energy of informal trust networks into the achievement of a goal. Uses include:
- In KM programmes, focusing on the channels through which knowledge will flow, rather than trying to manage knowledge itself;
- In organisation change, fleshing out the details of change by seeing how people self-organise around a general framework before going into the details (most organisational change programmes fail not on the broad vision, but on the details);
- In innovation programmes – most organisations are aware that someone somewhere knows something, but the problem is knowing precisely who this is.
Metaphor is a powerful narrative tool that allows people to think about and confront issues in a safe environment. Dealing with issues directly is not only hard, but generally results in both camouflage and self deception. For example, in the 18th century, scientists focused on astronomy as the source of a solution to the problem of measuring longitude, ignoring a rural carpenter who had produced a workable solution – a clock that keeps accurate time on board a ship. Wonderfully told in Longitude by Dava Sobel, this story can be given to a board of executives, who can then be asked to consider whether they have ever treated their staff in the same way as the scientists treated the carpenter. This is a question they can answer, as the metaphor provides the stimulation they need to think about the issue in a different way, and also provides a ‘safe’ space in which such discussions can take place. The Grendle game extends this principle: anthropologists spend a week studying an organisation and a metaphorical space is constructed based on their findings. These can use historical situations, science fiction or other fictional settings. For example, using science fiction allows us to work with real scientists who create alien environments for science fiction writers. Such scientists have whole databases of alien life forms, which allow them to create consistent eclogues. Managers from the organisation are then landed in an alien ecology, and must learn how to survive in an alien environment, which is actually the culture of their own organisation. This tool enables learning, and reveals how people are able to solve problems in an alien environment as a means of creating new understanding of how those problems could be solved in real life. This tool can also be used in mergers, where a variation of the ‘prisoner’s dilemma’ can be introduced to two teams from the two merging organisations. In this scenario, the teams compete to gain better prison conditions for themselves or co-operate to escape. By seeing how teams escape, it is possible to gain new insight into how to manage the merger itself. The uses of these technique are many, various and extremely powerful.
Narrative (which is not the same thing as storytelling) is emerging as one of the most exciting approaches to knowledge management. There are many uses for narrative, but the most relevant to the topics covered in this paper is the use of narrative databases. This lies somewhere between content and context management, in that narrative carries or creates its own context, but at a high level of ambiguity. Its use is closer to the natural patterns of knowledge acquisition in organisations, because:
- It is easier and less onerous to capture than written knowledge. For example, I can record to a video camera in ten minutes what would otherwise take two weeks to get around to spending a hour or so writing up;
- It is a natural process, in that when we face a new task or encounter a problem we go and find people to talk to in order to ask questions so that they can provide context-sensitive answers and advice that cannot be provided by past project reviews or idealised statements of best practice.
Narrative databases provide a discourse mechanism across time and space. When we join an organisation it can take some time before we know enough to find our way around. Induction courses and work-related training all help, but it is not until we build a social network of people who have experience of the organisation and, as importantly, have heard their stories, that we really understand ‘how things are done around here’. Few people study a best practice database in advance of starting a new project; most will first seek out experienced individuals: friends or mentors whose opinions they respect or to whom they are referred by a trusted source. The problem with this is that we are restricted to our own social networks and the organisation is restricted to its current employees. Narrative databases derive in part from oral history techniques, but also pick up on some of the failures of early KM approaches, in particular those that focus on best practice. In building a narrative database, it is vital to do two things:
- Hold the material in its raw form. Any attempt to censor or interpret the material, in particular attempts to enhance or improve it, will reduce the authenticity of the material and possibly engender distrust in the eventual user;
- The material needs to be indexed based on emergent properties of the native story material. Most commonly these are underlying themes and archetypes that can readily be brought out in workshops.
Narrative databases are remarkably cheap and simple to build. The lack of any need to interpret the material and simple self-indexing of incoming material allow rapid development. They are also often used as a quick and easy entry point into KM, and as a new approach for existing systems. The usage patterns of a narrative database can also be used to prioritise development of more formal codification approaches, based on actual rather than perceived needs. It’s the equivalent of planting grass and seeing where people walk before you invest in the extra cost of building paths.
The separation of context from narrative from content can liberate knowledge management from the tired constraints of methods appropriate to complication but not complexity. This approach means that KM is able to make a visible contribution to decision making and innovation without the massive costs of a more traditional systems-based approach. It also delivers more quickly, as it uses the natural contours of the landscape rather than trying to impose a mechanical construction onto organic reality. For many of us, the use of CAS theory represents a new simplicity. It can be difficult and dangerous, because it flies in the face of entrenched practices in management science and threatens the revenue streams associated with ‘recipe book’ consulting but, once understood, it is elegant in its simplicity and deeply pragmatic.
Dave Snowden was formerly a director of the Institute for Knowledge Management and is now director of the Cynefin Centre for Organisational Complexity, membership of which is open to individuals and organisations. The centre not only develops methods but also runs a series of participatory research programmes in the application of CAS to management. He can be contacted at: firstname.lastname@example.org or via www.ibm.com/services/cynefin
1. Snowden, D., ‘Organic knowledge management’ in Knowledge Management (Vol. 3, Iss. 7; Vol. 3, Iss. 9; Vol. 3, Iss.10, Ark Group, 2000)
Nasrudin found a weary falcon sitting one day on his windowsill. He had never seen a bird like this before.
“You poor thing,” he said. “How ever were you to allowed to get into this state?”
He clipped the falcon’s talons and cut its beak straight, and trimmed its feathers.
“Now you look more like a bird,” said Nasrudin.
From Shah, I., The Exploits of the Incomparable Mulla Nasrudin and The Subtleties of the Inimitable Mulla Nasrudin (Octagon Press, 1985)
Governing heuristics of KM
- Knowledge can only ever be volunteered it cannot be conscripted;
- We only know what we know when we need to know it;
- We always know more than we can say, and we will always say more than we will write.