Quantified KM value story number 119 – finding knowledge at Accenture

One of the ways in which KM adds value is through helping poeple do work faster and better.  Here is a story of howAccenture estimated that value.

Accenture make documented knowledge available to their staff through a portal known as KX. IN order to estimate the value delivered through KM they decided to focus on one component of Knowledge Management –the use of KX – and to focus on one single benefit – the savings in time delivered through the use of knowledge gained through the KX portal.

They estimated these savings through a survey, which asked the following question

“Please estimate the amount of your time that you saved during the last two weeks as a result of this knowledge.

“During the last 2 weeks, this information saved me AT LEAST:”
“During the last 2 weeks, this information saved me AT MOST:”

The results from this survey were used to calculate average time savings, and thus average cost savings.  They found that for annual KX costs to the sample population of $170,000 they were delivering savings of $2,400,000.

This equates to a 2500 percent Return on Investment

View Original Source (nickmilton.com) Here.

The shrinking half-life of knowledge, and what that means for KM

Knowledge has a half-life, and that half-life is getting shorter every year.

When John Browne was CEO at BP, he talked about “the shrinking half-life of ideas”. This always struck me as a very interesting concept; one which was fundamental to Browne’s approach to corporate KM. I have since found that he was quoting an older idea from 1962 concerning the shrinking half-life of Knowledge, which has now been popularised and explored by Sam Arbesman (see video) among others.
The idea of a half-life comes from nuclear physics, and originally applied to the decay of radioactive nucleii. In knowledge terms it refers to the observation that, as this article tells us

“What we think we know changes over time. Things once accepted as true are shown to be plain wrong. …. But what’s really interesting is that studies of the frequency of citations of scientific papers show they become obsolete at a predictable rate.  

Just as with radioactive decay, you can’t tell when any one ‘fact’ will reach its expiry date, but you can predict how long it will take for half the facts in any discipline to do so. In medicine, for example, ‘truth’ seems to have a 45-year half-life. Some medical schools teach students that, within a few years, half of what they’ve been taught will be wrong – they just don’t know which half. In mathematics, the rate of decay is much slower: very few accepted mathematical proofs get disproved

Not all knowledge has a short half-life – sometimes the knowledge is linked to the technology, and if you are running a nuclear power station using 1960s control software, then the half-life of the knowledge of the software has to exceed the life of the power station. However in most other areas, where knowledge is evolving and changing, and your competitive advantage lies (at least partly) in having the best and most valid knowledge, then hanging on to old knowledge which is past it’s half-life can be competitively dangerous. And the faster the speed of change, the shorter the half-life of knowledge and the greater the danger of using obsolete knowledge.

Where knowledge has a short half-life, Knowledge Management is not so much about documenting and protecting “what you know”, it is about how fast you can know something new, and how easily you can let go of the old. That’s what will win you the battle with the competition.

Knowledge which has been captured must constantly be re-examined in the light of new lessons and new experiences, and obsolete knowledge must be constantly updated, lest your conpetitors overtake you.

Colonel Ed Guthrie of the US Army used to liken this constant learning to the aerial dogfights in world war 1. “In those days” he used to say, “It was about getting inside the other guy’s turning circle. That’s what would win you the engagement. Now it’s about getting inside the other guy’s learning circle”.

So be aware of the shrinking half-life of knowledge, and be prepared for constant knowledge update.

View Original Source (nickmilton.com) Here.

The knowledge cycle as you have never seen it before

We are used to seeing pictures of knowledge cycles, but there is one cycle you never see, and it’s very important.

You can find very many versions of the knowledge cycle, and they all seem to work the same way.

They start with “Create” or “Capture”, and progress through “Store”, “Share” etc until they get to “Use” or “Apply. Some have as few as 3 steps, some have 8 or more steps, and the 4-step basic has even made it into the Stock Photo collections. However all these cycles work in the same way, as all of them are “Push” cycles.

By “Push Cycle” I mean a cycle that is driven by knowledge supply, and describes how that supply of knowledge works through various stages until the knowledge is used again. It is a supply chain model, and people use the cycle to put in place roles and processes to move knowledge along the steps in the supply chain.

However Supply is only half the story, and you need to look at Demand as well.

The diagram shown here is a cycle driven by knowledge demand – a “Pull cycle” – and it works like this.

  • The cycle starts with a problem, and the identification of the need for knowledge to solve the problem (the “need to know”)
  • The first step is to seek for that knowledge – to search online, and to ask others
  • Seeking/asking is followed by finding
  • However generally we tend to “over-find”. Unless we are lucky, or there is a very good KM system, we fInd more than we need, so the next step is to review the results and select those which seem most relevant in the context of the problem.
  • This found knowledge then needs to be integrated into what is already known about the problem, and integrated into solutions, approaches, procedures and plans.
  • Finally the integrated knowledge needs to be applied to the problem.
So why do we never see this Pull cycle in diagram form?

  • Is it because Pull (Demand) is less important than Push (supply)? Surely not! Most people would see them as equally important, and there is an argument that Pull is in fact a bigger driver of knowledge transfer than Pull
  • Is it because the Pull cycle is less useful than the Push model?  Surely not! If we can generate knowledge pull, and a demand for knowledge, we can spark knowledge supply. 
  • Is it because the Pull cycle is more difficult to work with than the Push cycle? Maybe this is one reason. Asking is less of a natural behaviour and more of a cultural barrier than sharing, so sharing may be the easier option. But ignoring barriers wont help you in the long run.
  • Is it because the Pull cycle is less measurable? The Push cycle is often linked with the creation of documents, and this is something that can be measured. Leaving aside the question about whether anyone is looking for these documents, and whether these documents are useful when found, it is easier to measure the first couple of steps in a Push cycle than it is to measure similar steps in a Pull cycle. However you can also measure searches, and measure questions in a community forum.
  • Is it because people only want one diagram? Yes, probably, but we know that KM cannot be reduced to a single and simple diagram; it is far too nuanced for that.
  • Is it because everyone else draws their cycles this way? Probably yes. But just because everyone else does it, doesn’t make it correct or sufficient.

There are many places where this Pull cycle can be applied very well.

  • Each individual uses this cycle when searching for knowledge. Most of the steps are done in the individuals head, but it may be useful to talk them through with a manager or colleague,. 
  • You can apply the cycle within a Peer Assist meeting, and the format of the meeting can follow the entire cycle from asking the questions, to reviewing the answers, to integrating them into the forward workplan.
  • You can apply it within a Community of Practice forum. Someone asking a question on the forum could  be asked to give feedback on the answers they received, the knowledge they selected from these answers, how they integrated this knowledge into their plans and (ideally) how it helped solve the problem. 
  • You can apply it as part of KM planning. A project team can identify their knowledge needs, conduct a search/ask activity, then get together to discuss how they will select and integrate the knowledge they have found. 
Being more conscious and explicit about the Pull cycle gives you more ways to create and stimulate knowledge demand in your organisation, and helps drive a Knowledge Seeking culture

Please do not focus only on the Push cycle for KM – its only half the story. Make sure your KM Framework incorporates the Pull cycle as well. 

View Original Source (nickmilton.com) Here.

Why "knowledge sharing" cannot replace "knowledge management"

Can we use the term “knowledge sharing” as better replacement for the term “Knowledge Management? There are two good reasons not to do so.

image from Wikimedia Commons

The terminology debate continues to rumble on in the KM world, with many people preferring the term “knowledge sharing” over the term “knowledge management”. This is partly due to a distrust of the concept of management, or use of the “management” term especially when used in conjunction with the word Knowledge.

As Tom Davenport wrote in his article “Does Management mean Command and Control?”

“I have a problem with overly simplistic characterizations of knowledge management, and management more generally. …. The term “management” is apparently a synonym for “command and control,” and we know that’s bad. “Command and control” is top-down, mean and nasty, and headed for extinction; “sharing” is bottom-up, nice and friendly, and the wave of the future. Maybe the Yale School of Management, for example, should become the Yale School of Sharing”. (However)…if your organization really cares about creating, distributing (I’m sorry–“sharing”), and applying knowledge, you need to manage it”.

But irrespective of whether you think Management equates to Command and Control or not, there are still 2 good reasons why you cannot replace “Knowledge Management” with “Knowledge Sharing”.

Firstly, sharing is not the end of the process of knowledge transfer and application.

There is a common misconception that sharing is the be-all and end-all; that people should first Capture and then Share their knowledge (and Sharing is often taken as meaning posting a document into a repository), and that this constitutes an effective transfer of knowledge.

However KM does not work like that. KM is not about one person with knowledge making it available to others; transferring knowledge as if you were transferring a can of beans from one person to another as in the image above. Knowledge is not transferred, it is co-created.

Once knowledge is shared, as a post on a discussion forum, a lesson in a lesson management system or a comment on a wiki, then it can be questioned, tested, combined with knowledge from other sources, and synthesised into new and better knowledge through discussion and dialogue. After sharing comes synthesis.

And after synthesis comes re-use. Even if knowledge is captured, and shared, and synthesised into up-to-date, valuable reference material, it still adds no value unless someone looks for it, finds it, and re-uses it.

All to often a “knowledge sharing” approach is strong on capture of knowledge, strong on some form of sharing (usually by publishing in a public repository), but weak or absent on synthesis and re-use.

Secondly, sharing deals only with supply and not with demand.

The common approached to knowledge sharing, and to the development of a “knowledge sharing culture” tend to focus only on the supply of knowledge. They assume knowledge will be captured and shared, creating a constant supply of new knowledge, and that this is enough.

But it is not enough.

To make any exchange work, you need demand as well as supply.  In parallel with knowledge sharing you need knowledge seeking, and in parallel with a knowledge sharing culture you need a knowledge seeking and re-use culture. A constant supply of new knowledge is a waste of time unless there is a constant demand for new knowledge.

In fact knowledge seeking is actually a better place to start than knowledge sharing (even though both are needed as part of a Knowledge Management Framework). Seeking stimulates sharing, and as McKinsey found, “direct requests for help between colleagues drive 75 to 90 percent of all the help exchanged within organizations“.

You could draw the whole knowledge cycle from a seeking point of view if you want – starting with seeking, then finding, reviewing, synthesising with existing knowledge, and applying, rather than starting with capture and sharing – which can give you a different way to look at KM.

Knowledge Management is therefore much more than knowledge sharing.

Knowledge Management includes Knowledge Sharing, as well as Knowledge Creation, Knowledge Capture, Knowledge Synthesis, Knowledge re-use, Knowledge seeking, Knowledge finding, and so on. To focus only on Knowledge Sharing is to underestimate the topic, and runs the risk of creating only a partial solution.

Beware of a focus only Knowledge Sharing. Focus on Knowledge Management instead.

View Original Source (nickmilton.com) Here.

How to select a successful KM pilot project

Knowledge Management pilot projects are a crucial part of any KM implementation. But how do you select a good pilot?                                                   

A KM pilot project is an opportunity to test KM in a small part of the business; to see if it works, to use it as a testbed to adapt and improve your KM framework, and to deliver success stories to use in the roll-out phase.

But what makes a good pilot?

First of all, a pilot project needs to use KM to solve a business problem.  The pilot must be problem-led, not solution-led.

  • So “testing a Sales portal” is not an effective pilot, but “using KM to improve our sales figures in Germany” is.
  • “Testing a better search engine” is not an effective pilot, but “using KM to reduce costs in our new product production line” is.
  • “Setting up a Geologists community of practice” is not an effective pilot, but “using KM to improve our geological predictions” is.

The focus of the pilot is on business issues, as the purpose of Knowledge Management is to solve business problems, and the purpose of the pilot is to test and demonstrate that KM can do what it is supposed to do. In most cases, your pilot will cover multiple divisions, or multiple projects, and will look at ways of developing, sharing, transferring and re-using knowledge to solve business issues.

Please note that you do not need to use a very sophisticated KM Framework to solve the pilot. Maybe you can use simple approaches and build a “minimum viable” version of the framework which you can use for testing purposes, and then improve and enhance the framework using experience from the pilot.

How do you find a suitable business problem to solve? The problem must somehow be knowledge-related, if KM is going to help, and there are four

  • Where there is a business critical activity which is new to one part of the organisation, where rapid learning will deliver business benefits. If it is new to only one part of the organisation, then transferring learning from where it has been done before, will give huge benefits.
  • Where there is repetitive activity, and where continuous improvement is needed, in which case knowledge management can help drive down the learning curve.
  • Where there is activity which is carried out in several locations, and where performance level varies, in which case knowledge management can help exchange knowledge from the good performers, to improve the poor performers.
  • Finally where there is an area of the business which is stuck due to lack of knowledge, in which case knowledge management can help develop the knowledge needed to get unstuck.

When you start looking around, you will find very many business opportunities for KM piloting. Your “opportunity jar” will soon be full to overflowing, and you will need to find a way to compare and rank these piloting opportunities. We have a set of ranking criteria we have been using for about 15 years now, which includes looking at the following questions;

  • If the project is successful, can we measure the value, and so demonstrate that the pilot has “worked”? 
  • Is there is strong management support for the pilot, and for knowledge management, within the potential pilot area?
  • If we create knowledge, is it purely for the pilot team or can others use it across the business, allowing us to leverage the results and spread the benefits? 
  •  Finally, can we practically complete the pilot in the required timeframe and with the resources available (money, staff, KM support resource etc)? 

Any pilot where you can answer a strong YES to all of these questions, will be a top-ranking pilot, suitable for selection as part of your KM program.

View Original Source (nickmilton.com) Here.

Future Workforce: Reworking the Revolution

“Bots need humans as much as humans need bots”Great discussion on leadership role and ongoing tactics of developing the human component of your future/present workforce in the age of digital/robotic intelligence. Panelists include: Rick Ambrose, EVP Space, Lockheed Martin; Ellyn Shook, Chief Leadership and HR Officer, Accenture; John Donovan, CEO AT&T Communications; and Yvonne Wassenaar, CEO, Airware. Key takeaways include: implementing competency based training courses; automating work while retaining human assets as automation architects; democratizing knowledge of the private sector for a more collaborative public/private front end of talent supply chain; training people for project environments rather than specific functions; and training people to be data scientists. 


Army definitions in Lesson Learning

The Army talk about building up lessons through Observations and Insights. But what do these terms mean?

Chinese character dictionaryLesson learning is one area where Industry can learn from the Military. Military lesson learning can be literally a matter of life and death, so lesson learning is well developed and well understood in military organisations.

The Military see a progression in the extraction and development of lessons – from Observations to Insights to Lessons – and we see a similar progression within the questioning process in After Action Reviews and Retrospects.

On Slide 7 of this interesting presentation, given by Geoff Cooper, a senior analyst at the Australian Centre for Army Lessons Learned, at the recent 8th International Lessons Learned Conference, we have a set of definitions for these terms, which are very useful.

They read as follows (my additions in brackets)

Observation. The basic building block [for learning] from a discrete perspective. 

  • Many are subjective in nature, but provide unique insights into human experience.
  • Need to contain sufficient context to allow correct interpretation and understanding.
  • Offer recommendations from the source
  • [they should be] Categorised to speed retrieval and analysis

Insight. The conclusion drawn from an identified pattern of observations pertaining to a common experience or theme.

  • Link differing perspectives and observations, where they exist.
  • Indicate recommendations, not direct actions,
  • Link solid data to assist decision making processes
  • As insights relay trends, they can be measures

Lesson. Incorporates an insight, but adds specific action and the appropriate technical authority.  

Lesson Learned. When a desired behaviour or effect is sustained, preferably without external influence.

What Geoff is describing is a typical military approach to lesson-learning, where a lessons team collects many observations from Army personnel, performs analysis, and identified the Insight and Lesson. As I pointed out in this post, this is different from the typical Engineering Project approach, where the project team compare observations, derive their own insight, and draft their own lesson.

The difference between the two approaches depends on the scale of the exercise. In the military model there can be hundreds of people who contribute observations, while in a project, it’s usually a much smaller project team (in which case it makes sense to collect the observations and insights through discussion). If you are using the military model, these definitions will be very useful.

View Original Source (nickmilton.com) Here.

How the BBC learned from their Olympic coverage

Here is a case study of one organisation – the BBC – learning from experience. 

The  Olympics is was a massive event, on a scale that is unprecedented in peacetime. It’s the biggest project a country will ever undertake, other than a war. I have already blogged about the Olympic Games KM program, but its not just the Games organisers that need Knowledge Management, it’s everyone involved, especially those involved in new or development areas.

One such area was Digital Broadcasting.  The London Olympics were the Digital Olympics, with more HD broadcasts, web feeds, twitter feeds etc than any other Games before. And to deliver the Digital Games, the country’s main broadcaster, the BBC, needed to develop and apply a raft of new technologies.

 This post, from the BBC Internet Blog,shows how they used lesson-learning, in a structured and planned way, to ensure these products were delivered on time and to specification, and also to ensure that subsequent exercises will learn from this one.

I quote the relevant section

Lessons Learned 
 We captured the lessons from the programme as we went along, from end of sprint retrospectives and the rich data captured in our information systems above. At the end of the Olympics the project managers facilitated workshops to capture additional successes and improvement opportunities and share these with their colleagues.  

From these on-line surveys and interviews with stakeholders, over 300 lessons were captured in our project register. The key lessons touched on above were the importance of organising and planning the work amongst self-directed, multi-disciplinary teams, with a layer of information and communication support provided by the management team. The ability to prioritise the scope and deliver it incrementally with frequent opportunities to test at scale and in a live environment, contributed to the success of a once-in-a-lifetime sporting event for the BBC’s on-line audiences. 

 The experience and lessons learned in delivering this exciting programme will be carried forward by the team members into their next projects, while the environment and process limitations identified, will drive improvements in technology provision and uptake of best practices.

We can see in-project learning (end of sprint retrospectives) and post-project learning (at the end of the Olympics  – workshops to capture additional successes and improvement opportunities) – both activities built into the work program.

We worked with the BBC “live and learn” team about 10 years ago to introduce some of these learning principles, and they have subsequently been MAKE award finalists for many years. This blog shows that KM and learning practices are still alive and thriving at the BBC.

View Original Source (nickmilton.com) Here.

The heart and soul of a Community of Practice

What makes a group of people into a Community of Practice?

Recently I read a document which asserted that the employees within an organisation are naturally a community of practice, because they all work together in service of the same organisational goal.

That immediately struck me as wrong, but why was it wrong?

Certainly the employees fall under Wenger’s definition of a Community of Practice – namely “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.” Employees in a company may be passionate about what they do, they may learn to do it better, and they may interact regularly with each other as part of their work. But for me, this alone is not enough for them to be a community of practice.

The extra point that is needed, is a sense of group identity based around practice.  

The members of a community of practice identify with the domain of practice, and therefore with the other practitioners. They talk about “we” when they talk about practitioners in the knowledge domain. That’s a different “we” from “we who work for this company”- its “We, the engineers”, “We, the geologists” and “We, the admin assisitants”.

This sense of identity needs to be supported by interactions that are specifically about the domain. Even if you say “we, the engineers”, there is not necessarily a community of practice of engineers unless there is an interaction among that engineering community, related to the practice of engineering, and concerned with engineering issues. The interactions, these conversations about engineering, make the community a reality.

So the employees within an organisation are not naturally a community of practice, until they develop a sense of identity around a domain, and interact within each other to discuss and improve their practice withuin that domain. Without these two factors, they remain individual contributors, reliant on their own knowledge.

Of course there is more to being successful as a CoP than just identity and interaction (see my post on the 10 success factors for CoPs), but interaction and identity are the heart and soul.

View Original Source (nickmilton.com) Here.