• David Griffiths

Knowledge management Insights: The 4-4-2 High-Impact Knowledge Management principles

High-Impact Knowledge Management principles

Every good Knowledge Management programme we have encountered has been built on strong foundations; we call these the 4-4-2 high-impact Knowledge Management principles. Our experience tells us that if you are not tending to these principles, you will likely see KM fail in your organisation.

4-4-2 High-Impact Knowledge Management Principles | The first 4

The secret to successful Knowledge Management lies in value creation. To create value Knowledge Management needs to focus design decisions around FOUR core operational functions (you might be interested to know that KM ISO 30401 doesn’t explicitly acknowledge all four core operational areas):

  1. The anticipatory and responsive acquisition of tactical and strategic enterprise knowledge, which becomes embedded through…

  2. the sharing of knowledge, the usefulness of which is demonstrated, and becomes deeply embedded, through…

  3. The application of knowledge, which exposes gaps in enterprise knowledge and creates opportunities for…

  4. The creation of new enterprise knowledge through invention, innovation, intrapreneurship and incubation

If Knowledge Management is not consciously designing responses for all four operational areas, then the value of Knowledge Management is limited – see this blog for more info on creating eye-catching KM value reports.

The technical and managerial skills required to design and deliver a high-value Knowledge Management programme are often limited by a focus on IT-driven KM functions that miss the fundamental truth that knowledge is a human condition linked to learning. If knowledge is a human condition, then it logically follows that the secret to successful Knowledge Management design lies in the understanding of adult learning design and development principles (e.g. associationist, cognitive and situative learning principles).

As an example, the inability of an employee to retrieve or recall knowledge at a time when it is needed suggests incomplete, insecure or shallow learning – in other words, the organisation is vulnerable to knowledge loss. Technology-led KM design often misses the opportunity to create feedback loops that sense such vulnerability – it is blind to the problem – which limits value creation and the longterm viability of Knowledge Management within the organisation.

4-4-2 High-Impact Knowledge Management Principles | The second 4

The four core operational functions of Knowledge Management take place in FOUR enterprise knowledge domains: simple, complicated, complex and chaotic (adapted from Snowden & Boone’s Cynefin framework).

For Knowledge Management to be considered a high-value function, it needs to acknowledge the multitude of design decisions associated with each domain – too many design decisions are made unconsciously without any articulated view of the issues being addressed or the tradeoffs involved.

For Knowledge Management to create high-value outputs, design decisions MUST be consciously considered and not left to instinct alone.

If KM programme designers do not consciously engage in such KM design decisions, where they fail to anticipate and respond to the specific needs of each domain, then if KM succeeds it will be by luck and not design.

Importantly, where KM designers rely on instinct, the potential for disorder increases – the KM design solution does not sync with the governing priceless of the enterprise knowledge domain – along with the unintended consequences from the operationalisation of insecure, shallow and incomplete organisational knowledge.

Does your KM function consciously consider the needs of the FOUR enterprise knowledge domains?

1: The simple enterprise Knowledge domain

The simple enterprise knowledge domain is a stable environment, where outcomes can be known before action is taken, and cause and effect are apparent to all; this a domain of repeating patterns and best practice.

In the simple domain, there is only one right answer (e.g. which side of the road should you drive on when in England?), which is self-evident, widely accepted or undisputed. Here, we are speaking of low interactivity between variables that influence outputs, which allows for a KM design approach that accelerates learning toward one right answer (e.g. a Q&A portal).

2: The complicated enterprise knowledge domain

The complicated enterprise knowledge domain is a space where outcomes can again be known before actions are taken. However, this is a branching dimension, where at each decision point, there are better or worse workable solutions to a given context. Cause and effect is not clear to everyone but can be known, and therefore subject matter experts are used to guide people toward better responses or practice (e.g. under typical conditions, which is the fastest route when driving from New York to Los Angeles? In response, a person could engage a logistics expert who regularly negotiates such a challenge).

As with the simple domain, the sequencing of knowledge, where variables are known and structured, takes a hierarchical KM design approach (e.g. a lessons learned portal), where each learned step progresses toward knowledge of more complicated tasks.

3: The complex enterprise knowledge domain

The complex enterprise knowledge domain is one of the emergent answers, dictated by a dynamic (changing) environment with a high level of interaction between variables. Cause and effect are unclear, interactions between variables are non-linear, and outcomes cannot be known beforehand.

The non-linear nature of the domain creates an environment of emergent outcomes. The use of a single subject matter expert limits understanding of this domain, where a single field of view cannot provide the holistic view required to make sense of the world. Instead, multiple experts or feedback loops are necessary to identify variables, their interactions and potential outputs (e.g. predict who will win any given sporting event at the outset of a tournament or season).

“Though a complex system may, in retrospect, appear to be ordered and predictable, hindsight does not lead to foresight because the external conditions and systems constantly change” (Snowden & Boone, 2007, p. 70)

In the complex space, Knowledge Management must account for the needs of multiple roles and perspectives in the construction of knowledge to develop security, depth and completeness of knowledge. The challenge with many Knowledge Management functions is that they apply the linear (hierarchical) knowledge and learning approaches used in the simple and complicated domains, which fundamentally do not sync with the governing principles of the knowledge domain – dissatisfaction and failure quickly follow!

4: The chaotic enterprise knowledge domain

The chaotic enterprise knowledge domain is one where the search for right answers is pointless (e.g. predict the weather in London, for December, five years from now):

“the relationships between cause and effect are impossible to determine because they shift constantly and no manageable patterns exist – only turbulence” (Snowden & Boone 2007, p. 71)

The chaotic domain creates problems for Knowledge Management, where too much resource is expended in the hunt for cause and effect where none exist.

As a note, Snowden and Boone’s argument that the chaotic domain produces novel practice is compelling, but potentially misleading in the context of KM design. For example, there could be an argument for emergent practice in the complex domain producing novel outcomes that also require novel practice, which muddies the characteristics used to describe the governing principles of the complex enterprise knowledge domain. Nevertheless, the Cynefin framework is undoubtedly a powerful tool for KM design, which is why it is put forward here.

5: Avoid disorder through conscious, evidence-based KM design decisions

Knowledge Management design decisions MUST work to limit the risk created by disorder, which can be achieved by using the many tools available to help Knowledge Management designers – e.g. The International Risk Governance Council ‘s Risk Governance (stakeholder involvement) Framework, which can be mapped against the Cynefin framework above).

IRGC Risk Governance Framework
IRGC (2012, p. 20) stakeholder involvement framework (adapted)

A question to ponder…

What might the impact be if your Knowledge Management function fails to consciously consider the governing design principles of each of the four enterprise knowledge domains that exist in your organisation?

4-4-2 High-Impact Knowledge Management Principles | The 2

For decades enterprise Knowledge Management functions have, in the main, focused on distilling knowledge – providing people with the one best answer to their problems or question – delivering the definitive response to any decision-making/problem-solving challenge. In such cases the starting condition for KM design is one of convergence – i.e. there is only one right answer.

As an example, if an employee has a question, automated Knowledge Management solutions respond by making sure the likes of SharePoint is deployed to deliver enterprise knowledge to the employee’s device of choice. However, KM designers often miss the fact that, even with a convergence-led approach, the ability to effectively deliver enterprise knowledge is reliant on people correctly inputting and cataloguing their knowledge in the first place.

The secret to high-impact Knowledge Management lies in a move away from a convergent design focus

In the convergent world, enterprise knowledge can be distilled to find the one best answer. Here, the assumption is that people always operate in a simple enterprise knowledge domain, where any divergence from the one right answer only serves to delay decision-making, decrease efficiency and increase costs.

Consider the simple enterprise knowledge domain again, the variables at play are assumed to be low: the content (visual, auditory, symbolic, semantic, behavioural) consists of low numbers of variables, with limited and visible interactivity. In such circumstances, the operations (cognition, memory recording, memory retention, divergent production, convergent production, evaluation) required to deal with such content is low, and the products (possible outputs) from the content and operations are limited (convergent).

However, more-often-than-not, the variables at play are higher: the content (data and information) consists of high numbers of variables, with unknown levels of interactivity and low visibility. In these circumstances, the operations (intellectual process) required to deal with such content is high, and the products from the content and operations are variable (divergent).

Square pegs in round holes: convergence-led design in a divergence-led enterprise knowledge domain

Divergence is a condition of the complex enterprise knowledge domain and this where Knowledge Management often fails because much of today’s KM practice focuses on simplifying complexity – basically, the KM practice or solution doesn’t fit with the needs of the domain, and KM design creates disorder. Why? Because technology-driven KM solutions can’t handle divergence – the delivery of enterprise knowledge becomes messy, which doesn’t fit with Knowledge Management’s traditional tech/data/info-driven view of the world.

The secret to successful Knowledge Management means anticipating and responding to the human challenge

The challenge for Knowledge Management is that the latest Future of Jobs Reports, issued by the World Economic Forum (WEF), shows that Artificial Intelligence/Robotic solutions are eroding the human advantage in the workplace.

What this means is that the higher the level of knowledge convergence in a task, the greater the risk to the role, where 50% of organisations surveyed in the latest report expect to reduce their workforce by 2022.

WEF Future of Jobs Report
The Future of Jobs Report (WEF, 2018, p. 12)
The rise of workplace automation in its many forms has the potential to vastly improve productivity and augment the work of human employees. Automation technology can help remove the burden of repetitive administrative work and enable employees to focus on solving more complex issues while reducing the risk of error, allowing them to focus on value-added tasks The Future of Jobs report (WEF, 2018, p.11)

The only way that we as humans thrive in workplace 4.0 is by playing to our strengths as divergent thinkers – this is what is meant by “value-added tasks” in the WEF Report. The question is whether Knowledge Management designers are ready to deliver value at the divergent end of the enterprise knowledge spectrum?

by 2022, the skills required to perform most jobs will have shifted significantly. While these skill shifts are likely to play out differently across different industries and regions,31 globally, our respondents expect average skills stability—the proportion of core skills required to perform a job that will remain the same—to be about 58%, meaning an average shift of 42% in required workforce skills over the 2018–2022 period. The Future of Jobs Report (WEF, 2018, p. 12)

4-4-2 High-Impact Knowledge Management Principles | Conclusion

To deliver ongoing enterprise knowledge advantage, you, the Knowledge Manager, need to make conscious 4-4-2 design choices.

Ask yourself, what is the cost of not consciously considering such fundamental enterprise Knowledge Management design principles in your organisation?

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If you want to know more about the human factors that influence knowledge and learning in organisational systems, or if you have any questions on Knowledge Management value creation, drop me a line (david@k3cubed.com) and start a conversation.