KM and agility - how to survive recession

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In 2008 and 2009, the Economist Intelligence Unit surveyed 349 executives, on the topic of "Business agility" - How business can survive and thrive in turbulent times.

Although the focus of their questions was on Technology, and many of the people they talked to seem to be CIOs or the like, there were some interesting conclusions with regard to Knowledge management, and it becomes clear through the report that KM is closely behind process efficiency in driving agility.

Firstly, when asked to rank what they will do to increase agility, and the ability to make fast, correct decisions, the action of "Improving knowledge management and information sharing processes" was ranked the second highest, chosen by 38% of those interviewed, while the third choice was "Improve collaboration".

Secondly, they point to KM as a key tool for innovation and agility.

"In acknowledging the interplay between organisational agility and superior innovation, executives expect several tools to take a central role. Topping the list are knowledge management and collaboration systems, something that 81% of those polled indicate will go furthest in spurring innovation".

The report concludes as follows -

"For most companies, the path to organisational agility involves transformation, the ability to whittle away at inefficiency and regroup around what is truly core to the business. While the task may appear daunting, there are a number of steps that management can consider to lighten the burden of change:

• Optimise core processes.

• Minimise information (and knowledge) silos. Barriers to change include conflicting departmental goals and priorities, a culture of risk aversion and silo-based information. By reducing silos, business leaders can improve collaboration inside and outside their enterprise and better align departmental goals and performance measures with overall strategy.

• Integrate and automate fundamental knowledge-sharing processes. Such integration will enable IT (and KM) to advance an organisation’s ability to problem-solve, improve decision-making and convert information (and knowledge) into insight".

Italics are my addition - to move beyond the technology domain into the KM domain.

Only 8 days left to join the Knoco 2014 Global KM Survey

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At the beginning of April, we launched a wide-ranging and comprehensive survey of the current status of KM - across geographies, company sizes, and industry segments.

Already more than 300 KM professionals have shared their experience through this survey, helping us to build a common picture of the current KM "state of the art".

All contributors to the survey receive a free copy of the final report, with a target date of mid May. Contribution is free and confidential, and takes about 30 minutes.

Join us! We would love to have your contribution to the shared dataset.

Find out more, or take the survey

Dialogue, the engine that drives Knowledge Sharing

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Dialogue is the engine behind Knowledge Management - it is the primary means by which Knowledge is shared and absorbed.

We often assume that connecting people together will lead to better knowledge exchange, but connecting wires doesn't necessarily make a circuit. You need a way of ensuring conductivity as well as connectivity, and dialogue provides that conductivity for knowledge.

Dialogue is different from other forms of conversation. In a Dialogue, the participants are trying to reach mutual understanding. It is a process of exchange of views and of knowledge, of both sides asking questions and of listening to the answers. It is a combination of listening, advocacy, reasoning and consensus-seeking. It is hard to imagine effective knowledge exchange without some form of dialogue.

• Dialogue differs from argument, which is all about presentation and advocacy of views. There are no winners or losers in dialogue; you can't say "I lost the dialogue with Peter”.
• Dialogue differs from debate, which is all about testing the validity of a proposition rather than testing whether it is understood.
• Dialogue differs from interrogation, where all the questions are one-way, and only one person stands to profit from the exchange.
• Dialogue differs from discussion, which is often about analysis of detail rather than searching for common understanding.

We need dialogue because of the  unknown knowns, the deep knowledge of which people are unaware.  The person who has the knowledge (the "knowledge supplier") may only be partially conscious of how much they do know. The person who needs the knowledge (the "knowledge customer") may only be partially conscious of what they need to learn. The unknown knows and unknown unknowns are uncovered only through two-way questioning; in other words through dialogue.

Dialogue is needed, in order to

• Help the knowledge supplier understand and express what they know (moving from superficial knowledge to deep knowledge)
• Help the knowledge customer understand what they need to learn
• Transfer the knowledge from supplier to customer
• Check for understanding, and
• Collectively make sense of the knowledge

The knowledge customer can ask the knowledge supplier for details, and this questioning will often lead them to analyse what they know and make it conscious. The knowledge supplier can tell the customer all the things they need to know, so helping them to become conscious of their lack of knowledge. As pieces of knowledge are identified, the customer and supplier question each other until they are sure that transfer has taken place.

Almost all of the effective KM processes are based on dialogue. AARs, Peer Assists, Knowledge Handovers, retrospects, Harvesting interviews, Learning Histories, Knowledge exchange - all are dialogue based.

Some of the elements of dialogue can be done remotely through Web 2.0 tools, though this needs to be done deliberately. We can't assume that dialogue "just happens" over social media, any more than we can assume that a conversation will be a dialogue.

• Blogs are 95% monologue, and although some dialogue can be sparked through blog comments, it's more often debate than dialogue. However examples such as the Polymath project suggest that a structured approach of Blogs and Wikis can lead to problem-solving through dialogue
• Community discussion forums can occasionally engender dialogue, but again, debate and argument are often found in there as well. 
• Social media promote conversation, but not necessarily dialogue. The conversations in LinkedIn, for example, are mostly serial monologues and arguments, where people post their own views while seldom seeing to understand the views of others
• Wikis allow co-creation, but not through a dialogue format, which makes them difficult for really contentious or emergent topics. 

So how do we promote dialogue in our organisations?

1. We deliberately promote, even to the extent of educating people in, the behaviours of listening and questioning, as part of a Knowledge Management and Organisational Learning Culture.
2. We introduce the facilitated processes mentioned above
3. We ensure our Online communities of practice are also guided and facilitated, to promote dialogue instead of argument
4. We train the facilitators well.

We move beyond just "connecting people", and look at the nature of that connection, and the nature
of the conversations that result.

This blog is on holiday

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This blog is on holiday for a couple of weeks. Normal service will be resumed on return

How much difference does Knowledge make to performance? (the answer, up to 220%)

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How much difference does knowledge make to performance?

We can give you this answer, based on the controlled experiment that we call "Bird Island".

The answer is

• Your own team knowledge can make a 40% difference to performance
• The knowledge of your current CoP can make an 80% difference to performance
• The knowledge from "all time" can make a 220% difference to performance

Let me explain how this works, and where these numbers come from.



In the Bird Island exercise we ask the teams to build a tower, then we measure the height of their tower. We then hold an after action review (AAR) to discuss what they have learned about tower building, and after the AAR we ask them to estimate how much taller they can build, now they have knowledge and experience.

The graph to the right shows as histogram, or frequency plot, of the percentage increase they recognise. This is somewhere between 0% and 120%, with a mode of 40%. This represents the performance increase a team thinks they could gain, by learning only from themselves.




Then we hold peer assists, where the teams exchange knowledge with the other teams, rather like sharing in a Community of Practice. Now they are sharing knowledge, instead of just looking at their own learning. Then after the peer assist, we ask them to estimate how tall they could build the tower.

This next graph shows the percentage increase between the first tower and the post-peer assist estimate. Although the mode is still a 40% increase, the mean is now closer to an 80% increase. (The reason why the mode does not shift from 40%, is that the team with the highest tower rarely believes they gain any knowledge from the peer assist. So one team almost always does not improve their estimate. That's why the frequency distribution in this graph has more than one peak).


Finally we show the teams the current best practice, built from the experience of hundreds of teams over 14 years, and ask them to build the tower again. This gives them access to the current full state of knowledge about tower building, and really gives their performance a boost.

 The final graph shows the percentage increase between the first and second towers - between a state of no knowledge, and a state of full knowledge. The increase they achieve is now in the order of 220% - representing a trebling of height from the first tower to the second.

Bird Island is a test of the link between knowledge and performance in a controlled experimental environment, with a simple repeatable task, and with teams that come to the task with no knowledge.

Whether the same performance increases could be made at work, in a more complex environment, I don't know, and it is sometimes very difficult to measure. However we can certainly see a 67% increase in the speed to drill oil wells, and a 55% increase in the speed to build drilling platforms, so where performance improvements through controlled learning are measurable, they are large.

Therefore the answer has to be - Why not?

If KM materially impacts performance in the experimental setting why should it not do so in the real world?

Excellence is not an act but a habit

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Inspiration from Aristotle, by Kevin Krejci, on Flickr

We are what we repeatedly do. Excellence, then, is not an act, but a habit. --- Aristotle

Knowledge Management, the lazy way to work

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Lazy Drinker by Saul Soto on Flickr

The quote about Knowledge Management being "the lazy way to work" came from a control room operative in a chemicals plant in the USA
As far as this guy was concerned, if a problem arose at work, he would turn to the KM system to find the answer. If the answer was not there, he would ask his CoP, or pick up a phone and speak to another operative in another factory, and find their way to fix the problem. As he said -

"The lazy person will find the easiest way to do something, and if someone somewhere else has found an easier way to do something, I am going to incorporate it into my job"

We so often fixate on KM as making people's job harder - of adding to the workload - and that may be because we start at the Supply end of the equation. We start by thinking about the effort required to capture knowledge, and we decide "this seems like a lot of effort".

But if you start from the Demand side of the equation, we see that KM may actually be the lazy way to work.