Reasons why you should not incentivise knowledge publishing

Just incentivising knowledge publishing risks drowning the organisation in trivia.

Social media overload
by Mark Smiciklas on Flickr

I once had a conversation with a knowledge manager, who explained to me the incentive system they used for KM. My heart sank. 

In this particular organisation, people were incentivised to publish knowledge. For each article they published they were awarded "points". If someone accumulated enough points, then they could trade them in for a reward.

"Is there any quality control on the publishing" I asked? No, was the answer.

"Do you give points for re-using knowledge?"  "No"

"Do you have a way of combining, or removing, duplicate knowledge?"  "No" 

"Do people have to know there is a demand for what they publish?"  "No"

The result was an ever-expanding supply of poor quality material, with no demand. It made the job of the knowledge seeker infinitely difficult.

This reminded me of another client organisation, which required each person to publish ten items every year.  In an organisation of 100,000 people, that was a million items per year, with no quality control, no filtering, no re-use, no guidance on what to publish; no strategy other than to flood the knowledge base with documents.

Both of these organisations were falling into a common trap;

• incentivising Push (supply of knowledge) and paying no attention to Pull (demand for knowledge), and
• incentivising publishing quantity at the expense of quality.

More knowledge is not necessarily better. 

There are many cases where too much knowledge  is counter-productive, especially where this knowledge is poor quality. 

• A highly populated knowledge base with poor structure can seem like a "needle in a haystack" problem to the user, and adding more hay just makes it worse (see my blog post on "a haystack is no place to store your needle").

• A situation where a user is already bombarded by a "Knowledge firehose" will not be made better by turning up the flow.

• Providing far too many items is less valuable than providing a few simple pieces of knowledge which people can retain in "the heat of battle".

• As I found in another client, a lessons data base which is overly full of poor quality lessons is a frustrating experience for the user. "We cant find what we need, and when we do, its not helpful" was common feedback, and users seldom visited the database a second time. 

• Another client, again rewarding publishing, but with no structure and guidance, was proud to see wikis popping up all over the place, not seeming to see the problem in having 5 competing wikis on the subject of Knowledge Management, for example. 

• Here is another example taken to the extreme, where the amount of publishing created a perfect storm of futile effort which cost far more than it ever saves.

Davenport and Prusak, in their seminal "Working Knowledge" point out that over-volumed knowledge bases do not work. 

"Volume may be the friend of data management" they wrote - "but it is the enemy of knowledge management"

Too much knowledge requires the user - and each user in turn - to  filter the volume of material to find the relevant and timely knowledge, and as the volume increases, the task gets harder and harder, and people just give up. It becomes easier to reinvent the wheel, than to find the correct wheel in a junkyard.

Go for "less and better"

The lessons from these observations are relatively simple, though perhaps counter-intuitive in the early stages of KM.

• Do not incentivise knowledge sharing - incentivise knowledge seeking and re-use instead.

• Incentivise quality (usefulness, utility and demand) of published knowledge, not quantity.

• Introduce a synthesis step, so new knowledge does not accumulate like a snowdrift, but is used to refine what is already known.

• Incentivise people who update the existing knowledge; those who build on the work of others, not people who create new duplicate knowledge.

• Provide a structured just-in-time supply of knowledge for the end user - a knowledge faucet and not a firehose - a lean knowledge supply chain .

Don't incentivise knowledge publication alone - incentivise knowledge development and reuse.

Maximising use of bandwidth in KM

We all suffer from bandwidth issues in KM - generally due to the deluge of information. Here's a good principle from the military for dealing with these issues.

Information overload by SparkCBC on Flickr

The phrase - "Smart push, warrior pull" (described here). is a very useful military principle for maximising knowledge bandwidth. In the military case the bandwidth is often restricted by hardware rather than by attention, but the principle is a good one.

The US Department of Defence operates a global broadcast system (GBS) which acts as a knowledge and information transfer system to troops. Like any such communication system it suffers from bandwidth issues, so the transfer of knowledge and innovation must be strictly prioritised.  "Smart push, warrior pull", inevitably shortened to SP/WP, is a widely-applied principle to do this prioritisation. It represents the two following components;

• Intelligent transfer, from central command to the troops on the ground, of the information and knowledge they need at that moment and nothing more.
• The option for further requests for additional information and knowledge from the troops to the centre.

This is an excellent principle for a lean knowledge supply chain. 

It ensures that people get the right knowledge at the right time, with no waste. Send people what they must have, and let them pull what they might need.

The armed forces and intelligence units operate this way because of the bandwidth limitations of the GBS, but there is no reason why a similar principle cannot be used within organisations: "Smart Push, Front-line Pull" (SP/FLP) for example, where the customer facing staff, or staff conducting operations or projects, are provided centrally (or automatically) with the knowledge they need for that customer, or that operation, or that project, with the option to ask/search for more if needed. To do this effectively, the "senders" in the centre must anticipate the needs of the front line, so this "push" is probably best managed by the communities of practice and/or the domain experts.

Although organisations are less likely to have infrastructure bandwidth limitations, there is definitely an attention bandwidth issue, also known as "information overload". A principle such as SP/FLP would improve the efficiency of the KM system by reducing "knowledge waste" and minimising information overload.

4 more types of KM plan

 I wrote a blog post yesterday on 4 types of KM plan, and (too late) realised that there were more than 4. Here are another four types. 

Yesterday's blog post mentioned the following 4 types of plan, which are all at a fairly high level of granularity. These are:

• The organisational KM strategy
• The organisational KM implementation plan
• A KM plan for a project
• A KM plan for an operational department

If we drop down another level of granularity, we can see four more levels of plan:

• A KM pilot project plan
• A CoP "launch and grow" plan
• A knowledge domain plan
• A knowledge retention and transfer plan for an expert

 More detail of these additional four types of plan can be found below.

KM Pilot Project plan

Your KM implementation plan (one of the plans described yesterday), will almost certainly involve a series of KM pilot projects - projects focused on applying a simple form of KM to a business issue. These pilot projects need to be scoped carefully, and need a charter, a terms of reference, and a plan. The plan will include some or all of the following:

• Objectives of the pilot
• Key stakeholders
• Metrics (to measure success)
• Scope
• Approach
• KM framework to be applied
• Activities and milestones
• Risks and opportunities.

Community of practice development plan

Communities of practice benefit from a development plan, which can be helped considerably by tracking community maturity. Depending on the stage of the community, the plan may include some or all of the activities below:

• Appoint sponsor and leader 
• Identify core participants 
• Hold Launch meeting 
• Define charter 
• Define key knowledge topics 
• Identify experts and adopters
• Expand community membership 
• Start Community site  
• Hold community meeting 
• Transfer Quick Win practice
• Develop practice knowledge base 
• Track metrics
• Report progress to sponsor
• Run community maturity metrics
• Update CoP plan

Knowledge domain plan

The departmental KM plan, mentioned yesterday, may identify a number of knowledge domains that need to be better managed. The experts accountable for these domains will need to create their own "domain refresh" plan. This may include activities such as:

• Identify support/facilitation requirements 
• Capture lessons
• Exchange best practice
• Interview experts
• Create/update domain resources

Expert KRT plan

Many organisations develop a knowledge retention and transfer (KRT) strategy to deal with the risk of knowledge loss from departing experts. Once an expert has been identified as representing a risk of knowledge loss (e.g. holding critical knowledge in their head, and due to retire soon), then the KM team work with the expert to develop a KRT plan. This involves listing the topics which the expert knows about, deciding the best way to capture and transfer this knowledge, and then creating an action plan.

Each of the plans mentioned within this blog post is a subset of one of the higher level plans described yesterday, as shown in the graphic at the head of this post. Together, the plans represent a structured method of KM implementation.

4 types of KM plan

Knowledge Management plans exist at many scales. Here are 4 of them.

KM planning session

Implementing KM is a project, and a project needs a plan. However KM can be implemented at many scales, and many variants of KM plan may be needed. In this post we describe 4 of them:

• The organisational KM strategy
• The organisational KM implementation plan
• A KM plan for a project
• A KM plan for an operational department

KM strategy 

A KM strategy is the framework document that sets the context, direction and principles for KM implementation. The strategy ensures that the Knowledge Management implementation proceeds in a way that is aligned with the current business approaches, is targeted on the right problems, and is coordinated with other existing change initiatives.

One of the key components in the strategy is an analysis of “What Knowledge do we most need to Manage? These strategic knowledge areas are identified through discussion at the highest level (CEO if possible).

One of our clients had an excellent discussion with their senior management, while preparing their KM strategy, on this topic of key knowledge, and were given a strong steer to “focus on driving growth in new consumer markets, through knowledge sharing networks”. That decision set their KM strategy, and formed a framework for the next 4 years of KM activity.

KM implementation plan 

Once you have your strategy in place, you can start on your implementation planning. This is where you plan your activities and resources, in order to develop and embed an effective KM “system”. The plan will be based on

• the Knowledge Management strategy 
• an assessment and benchmarking of your current state of KM 
• an outline “desired end state” (KM framework) 
• a staged, change management approach 
• a full analysis of the risks to Knowledge Management delivery 

The KM implementation plan maps out the steps from the current state to the end state, guided by the strategy and the assessment. It defines your timeline, and the resource needs.

Project KM plan 

The project-level Knowledge Management plan is a device that allows KM to be fully embedded into project controls, at the same level of rigour as risk management, or document management. It allows the assignment of accountabilities to individual project team members, and allows these accountabilities to be monitored and reviewed. Some organisations also address.

 A KM plan has three main components.

1. A Knowledge Register, which defines the key areas of knowledge needed by the project (“key knowledge inputs”), and the assigned actions to make sure this knowledge is accessed. It also defines the key areas of knowledge which the project will be learning about, and which they need to share with the rest of the organisation (“knowledge outputs”), and the actions to make sure this sharing happens. 
2. A KM Protocol, which defines the system by which knowledge will be managed in the project. It defines the roles and accountabilities, the technologies (such as lessons databases) which will be used, and the processes which will be applied and when they will be applied as part of the project timeline. 
3. An implementation plan for the project, to make sure the protocol is ready to use. This will require training of staff in the tools and technologies, induction of new staff, registration of staff onto the relevant communities of practice, installation of technology onto people’s desktops, and so on. 

The plan is created at a KM Planning work-shop, early in the project, held as part of the set-up activities; about the same time the team are developing their risk management plan, their document management plan, and other front-end planning activities.

Operational KM plans 

Just as a project KM plan is built into the planning and review framework of a project, an Operational KM plan needs to be built into the operational planning and review framework. This should be done as follows;

1. During the annual planning cycle, the operation will agree its annual objectives and budget. The next step will be to create the annual KM plan. 
2. The KM plan will contain the same components as a project KM, though the key topics in the knowledge register will be set by the new operational objectives. The operational management team will get together for a KM planning workshop, and start with the question – “What do we need to know (or to learn) in order to deliver our operational objectives”?
3.  The main deadline for the operation to capture new knowledge for other operations will be at the end of the year, when they review performance against objectives, and ask “What were the causes of any deviation from planned performance (either a positive or a negative deviation), and what have we learned from these to improve next year’s performance”? They will also review the application of the KM plan through the year.
4. The operational department may also assess the level of management of key operational topics or knowledge domains, for example through a KM audit or Scan. If any knowledge domain needs to be better managed (better documented for example, or updated, or a community of practice initiated), or is at risk of loss through potential departure of key experts, then the KM plan will define the remedial actions that need to be taken.

Contact Knoco for help with KM planning at all scales

The role of reports in Knowledge Management

 Reports are poor places to keep knowledge. However they do have a role to play in Knowledge Management. 

Image from wikimedia commons
by user Coolcaesar under CC licence

Once upon a time, we relied on reports, papers and books to store our knowledge. This was before the Internet, before networked computers, when the only way to share explicit knowledge was to write it down and publish it as an item. Think of the learned societies in the 18th century, when knowledge advanced by experts reading their papers, which were then bound into journals wto be published for the readers.

In the 21st century, we are no longer bound by the same limitations, and we can question whether the old model, of publishing authored reports, papers and other items and artefacts, is still the only way to manage  knowledge.

Using reports to store knowledge has three major drawbacks:

• Reports have an author, occasionally two or three. Knowledge on the other hand is built through the interaction of very many people. Knowledge belongs to, and is born in, communities. The ownership of knowledge is collective, and to talk of an "author" for knowledge is meaningless. Already we see examples of scientific communities collaboratively creating work; the outputs from the Polymath community are authored under the pseudonym of "DHJ Polymath" for example, to reflect the massive collaborative nature of the community.
• Reports have a publication date, at which point they are frozen in time and cannot be further edited (unless they become new versions of the report). Knowledge is not frozen in time. Knowledge evolves, knowledge changes, often rapidly. It cannot have a publication date - the only really valid publication date for knowledge is "Now". A report on the other hand is outdated the moment it is written. 
• Reports (with some exceptions) seldom go back over the history of a topic. If you want to understand the current state of the art of a topic, you may have to read multiple reports and decide for yourself what is relevant and what can be ignored. 

So if we don't store Knowledge in reports, then where do we store it?

Knowledge should be stored somewhere editable and updatable, so that it can evolve and change, synthesising new knowledge with existing knowledge as contexts, circumstances and understanding change. 

• A wiki, for example. Wikipedia, for all its flaws, is widely recognised as a first-stop shop for knowledge, and a place where multiple authors can help knowledge evolve and grow. Shell make massive use of their enterprise wiki, Pfizer have the "Pfizerpedia", even the Military are using wikis to house doctrine. 
• Or a multi-author blog, where knowledge can be refined in comments and discussions. This was the Polymath approach, with discussion in blogs eventually moved to a wiki.
• Or a knowledge base where the users can edit (or at least request edits of) the content.
• Or a community discussion forum.

If you have such a technology, adopted by a community of practice, with new knowledge being used to update the collective knowledge in real time, then Knowledge can grown, can evolve, and will always have a publication date of Now (or if not Now, then at least "recently updated").

So what role do reports play in all this?

I am not saying that organisations should no longer write reports - far from it. Projects need to create reports, discrete pieces of work need reports, constultants need to write reports; reports are a necessary deliverable to document work that has been done. They are the primary means of meeting reporting requirements.  However they should not be the primary repository for knowledge. Their role is as follows:

Reports should document the evidence on which the knowledge is based. That evidence is then used by the community to update the knowledge.

In knowledge terms, reports should collect data, present data, propose insights and conclusions, and offer lessons. The lessons from reports are the increments from which knowledge can be built and refined. On wikipedia, reports are the references, bibliography and external links that you see at the foot of the page. My guess is that you often use Wikipedia, but seldom click those links. However the links and references are the paper-trail - the audit trail that allows you to look at the sources from which the knowledge has been drawn. 

In a knowledge management framework, reports are outputs of the project workstream, but are not suitable outputs for the knowledge workstream. See http://www.nickmilton.com/search/label/knowledge%20workstreamhere and here for further discussion on the two workstreams.

I hope this makes sense. Reports (with their single authors and publication dates) are needed, but should not be the primary store for knowledge because knowledge evolves and reports are static. Instead you need to store knowledge somewhere editable by the community. In the 18th century, this was not possible. In the 21st century, editable knowledge stores are easy to set up.

If you are still using reports to store knowledge, then its time to rethink the possibilities. 

How Connect and Collect work within knowledge management

A reprive from the archives - an overview of Connection and Collection as dual components of KM.

Image by Marc Smith on Flickr

There are two main mechanisms for supporting the flow of knowledge in an organisation - Connecting the people, or Collecting the content.

 These are sometimes seen as separate strategies of codification or personalisation, but both are needed as dual components of any Knowledge Management strategy. 

Here is an overview of Connection and Collection, starting with the concept of knowledge suppliers and users.

Knowledge suppliers and users

Knowledge is created through experience, and through the reflection on experience in order to derive guidelines, rules, theories, heuristics and doctrines. Knowledge may be created by individuals, through reflecting on their own experience, or it may be created by teams reflecting on team experience. It may also be created by experts or communities of practice reflecting on the experience of many individuals and teams across an organisation. The individuals, teams and communities who do this reflecting can be considered as ‘knowledge suppliers’.

In business activity, knowledge is applied by individuals and teams. They can apply their own personal knowledge and experience, or they can look elsewhere for knowledge – to learn before they start, by seeking the knowledge of others. The more knowledgeable they are at the start of the activity or project, the more likely they are to avoid mistakes, repeat good practice, and avoid risk. These people are ‘knowledge users’.

Communication, Conversation, Connection

The most direct way to transfer knowledge from suppliers to users is through direct communication and dialogue. Face to face dialogue, or dialogue via an online communication system, is an extremely effective means of knowledge transfer. This method allows vast amounts of detailed knowledge to be transferred, and the context for that knowledge to be explored. It allows direct coaching, observation and demonstration. It often allows new knowledge to be created through the interaction.

However, it is very localised. The transfer takes place in one place at one time, involving only the people in the conversation. For all its effectiveness as a transfer method, it is not efficient. For direct communication and dialogue to be the only knowledge transfer mechanism within an organisation, would require a high level of travel and discussion, and may only be practical in a small team working out of a single office where travel is not an issue (for example a regional sales team that meets on a regular basis). This may be the only practical approach to the transfer of uncodifable knowledge; that knowledge that cannot be written down (that Polyani would call “tacit”). However, it should not be the only mechanism of knowledge transfer, nor should knowledge be stored only as tacit knowledge in people’s heads.

Using people’s memories as the primary place for storing knowledge is also a very risky strategy. Memories are unreliable, people forget, misremember, or post-rationalise. People leave the company, retire, or join the competition. For example, what is the staff turnover in your team? Your division? Your company? How much knowledge is leaving your organisation in the heads of the departing people? There needs to be a more secure storage mechanism for crucial knowledge, and a more efficient means of transfer than just dialogue.

Codification, capture, content, Collection.

The less direct flow of knowledge  is through codification and capture of the knowledge, storage in some sort of ‘knowledge bank’, and retrieval of the knowledge when needed. The transfer is lower bandwidth than direct communication (perhaps 14 times lower), as it is difficult to write down more than a fragment of what you know, and the written knowledge needs to be translated back into human understanding by the knowledge user (some would argue that the written knowledge has become information, and needs to be translated back into knowledge). No dialogue is possible, and demonstrations are restricted to recorded demonstrations, eg using video files.

Transfer of knowledge by this means is not very effective. However, the knowledge need only be captured once to be accessed and reused hundreds of times, so it is an efficient method of transferring knowledge widely. The knowledge is secure against memory loss, or loss of personnel. This approach is ideal for codifiable knowledge with a wide user base. For example, the widespread transfer of basic cooking knowledge is best done through publishing cookery books, rather than creating communities of chefs.

It is also ideal for knowledge that is used intermittently, such as knowledge of office moves, or knowledge of major acquisitions. These events may not happen again for a few years, by which time the individuals involved will have forgotten the details of what happened, if it has not been captured and stored.


These two approaches to knowledge transfer are the Connect and Collect approaches.  Effective Knowledge Management strategies need to address both these methods of knowledge transfer. Each has its place, each complements the other, as summarised below.

Connection

• Advantages  - 
• Very effective 
• Allows transfer of non-codifiable knowledge 
• Allows socialization 
• Allows the knowledge user to gauge how much they trust the supplier 
• Easy and cheap
• Disadvantages - 
• Risky. Human memory is an unreliable knowledge store 
• Inefficient. People can only be in one place at one time 
• People often don’t realize what they know until its captured
• Type of knowledge for which this approach is suitable -  
• Ephemeral rapidly changing knowledge, which would be out of date as soon as its written
• Highly contextual knowledge 
• Knowledge of continual operations, where human knowledge is contatntly being refreshed and rehearsed
• Knowledge needed only by a few
• Comments - One traditional approach to Knowledge Management is to leave knowledge in the heads of experts. This is a risky and inefficient strategy other than in very small organisations

Collection

• Advantages - 
• Allows systematic capture and development of knowledge
• Allows synthesis of knowledge from many sources
• Allows knowledge to be embedded in common process, in product design, or in algorithms
• Creates a secure store for knowledge, which will therefore not be list when people leave the organisation
• Very efficient. Knowledge can be captured once and accessed many times
• Disadvantages -  
• Some knowledge cannot be effectively captured and codified.  
• Capturing requires skill and resource 
• Captured knowledge may become impersonaland decontextualised
• Type of knowledge for which this approach is suitable -  
• Stable mature knowledge 
• Knowledge of intermittent or rare events 
• Knowledge which requires input form many sources or people
• Knowledge with a large user-base
• Comments -  A strategy based only on capture may miss out on the socialization that is needed for culture change, and may fail to address some of the less codifiable knowledge.

How can governments support a knowledge economy?

Governments know all about supporting traditional economies. So why are they so poor at supporting the knowledge economy?

Image from wikimedia commons
by 401(K) 2012, CC licence

A "knowledge economy" is a focus for many many governments in the developed world. A knowledge economy is one "where distinctive know-how is vital to competitive services and products", and where knowledge is the resource from which economic value is delivered.  Governments like the idea of a knowledge economy, because it delivers high value without the need for natural resources like oil, farm land, minerals or tourism, and relies instead on intellectual assets.

So why do governments have such a difficult time understanding how to support the knowledge economy?

I think partly it is because they do not understand knowledge and how it drives value, they do not understand Knowledge Management (the public sector is behind the curve in adopting KM, and most governments see it either as a synonym for library science, or an issue solvable through the use of technology alone), and they have not yet looked long and hard about how other economies are supported, in order to take those lessons and apply them to the knowledge economy.

The UK Government, for example, sees the knowledge economy as intrinsically tied to higher education. They measure the economy partly by measuring high-tech industries and university places, and support it partly by funding higher education and partly through what they call "Knowledge Transfer" but which really is the encouragement of commercialisation of University research.

However to be successful, the knowledge economy requires knowledge management, just as the farming economy needs land management, and the fisheries economy needs fisheries management, and the forestry economy needs forestry management.

Where an economy is based upon a resource, the government should play a key role in ensuring that resource is well managed, in order to generate value for the economy.

Knowledge management ensures management of the critical knowledge resource, by ensuring that knowledge is efficiently and effectively created, discussed, developed, codified (where needed), retained, applied and continuously improved. Knowledge management is the difference between hoping an economy will arise if we have enough higher education, and giving companies the awareness and tools to ensure that an economy arises and is sustainable.

So how does a government currently support a resource-based economy such as agriculture, forestry, fisheries and tourism, and how can that be translated to the Knowledge economy?

1. The government educates. In every resource-based economy (with the current exception of the knowledge economy) the government plays a key role in educating the key players in how to manage the key resource on behalf of the economy as a whole. Whether this is managing land, managing fish stocks, managing forests or hotels or seaside towns, the government understands that good management is key to a healthy economy, and so provides awareness, training and skills development. This is not yet the case with the knowledge economy, as the government does not yet seem to fully grasp good knowledge management and how it protects the knowledge resource.


 
2. The government sets standards. In every economy (with the current exception of the knowledge economy) the government sets standards - marketing standards and labelling requirements for fisheries, standards for agricultural produce and organic farming, star ratings for hotels and "standards for responsible tourism". Now we have a set of standards - ISO 30401:2018; the ISO management systems standard for KM - and the government can and should be promoting this as a requisite for all knowledge-based industries. This move alone would go a long way towards building the knowledge economy. 



 
3. The government offers tax breaks and incentives. For example, agricultural fuel ("red diesel") is free from tax. Governments can financially incentive the tourism sector through low-interest loans, or provide free advertising, and can provide grants for forestry management programs. They do not yet provide incentives for KM development. They could do this if they chose - they could provide tax breaks for investment in KM technology (so long as this doesn't lead people to think that technology is all you need), could provide grants for the development of KM strategies. or could subsidise the development of KM skills.


 
4. The government provides specialist advisors.  Fisheries management advisors, regional tourism advisors, "Farming Advice Service" accredited advisors and so on. Where are the Knowledge Management advisors? There aren't any, because governments generally do not yet understand knowledge management, and do not yet understand the key message, highlighted above and repeated here - Where an economy is based upon a resource (in this case, knowledge), the government should play a key role in ensuring that resource is well managed, in order to generate value for the economy.
 

The first government that realises this, and that takes the 4 actions listed above. will be the next winner in the Knowledge Economy race.