Tuesday, October 18, 2022

An Ecosystem Approach to Blend Learning and Systems

In the previous blog post, I touched on the need to blend systems and learning for a successful Knowledge Management program.  When I teach Knowledge Management, I talk a lot about methods, tools, approaches and the like, but in the real world of KM in organizations, there are very few people who speak that language, so the KM practitioner needs to work with practical examples, preferably focusing on processes that have an immediate and clear impact on the business' performance. 

Here is a task example:  

Jane is a relatively new employee. She is climbing the learning curve really fast, with some help.  She is working on a proposal team and she has been given the task of writing the past experience section.  She has never done this task before.  She needs to find information about relevant company past experience that can be used in the proposal. She has been given some general advice about where to look (past proposals, the repository of project documents, etc...) and the names of a couple of past projects and proposals she should look up because they are likely to be relevant.

This is not a simple task of retrieving some existing documents and doing a cut-and-paste job.  It requires some understanding of the proposal itself to transform existing information into an accurate, yet tailored rewriting to meet the specific needs of the proposal.

In the process of completing this task, Jane will gain valuable experience in at least three key areas:

  • Searching company databases
  • Combining/recombining information to create a new, tailored version of that information
  • Receiving and integrating feedback from proposal team members

Assuming well organized Knowledge Management systems, Jane will be able to spend a limited amount of time locating and retrieving relevant documents from the databases, and more time applying critical thinking to create the tailored version of the document.  

Assuming a learning-driven organization, Jane will optimize her learning by a) attending training for this  task (if available) and/or reviewing existing written guidance for this task as soon as he/she is given the task; b) receiving the support of a task-specific mentor throughout the process; c) reflecting in action; d) reflecting on action. 

Reflecting in action might mean that while Jane is search for a past proposal, she comes across another proposal that looks similar and might also be of interest. She pauses briefly and realizes that the proposal team lead who gave her the list of other proposals to look up may not be aware of this information she has come across.  She needs to decide whether to stick to what she has been asked to focus on or dig a little deeper into what she has come across.  Reflection in action may result in a quick insight that results in a slight change in direction, a decision made while she is completing the task.

Reflecting on action happens after the task has been completed and allows a look back to reflect on what has already happened and what could have been done differently.  In a learning-driven organization Jane will participate in the proposal team debrief of After Action Review (AAR), which will cover the entire proposal process. However, there is a lot that Jane can learn about her own task and how she handled it that will not necessarily be discussed in the team debrief. If Jane didn't realize that other projects might be relevant while she was gathering information (during the action), she might have that insight when she pauses to reflect after the action, perhaps in preparation for her participation in the team debrief.  

Assuming the proposal team debriefs are done consistently across the organization, the collection of debrief notes is a gold mine for analysis, to identify pain points and improve processes, existing guidance documents, and training, and continuously update information found in the "systems".  In most cases, systems are not self-sufficient and do not update themselves automatically with fresh information.  Jane, or someone on the proposal team, is going to be responsible for uploading the final version of the proposal and past experience section to the appropriate repository and add the necessary metadata according to a well-thought out taxonomy.  

Assuming Jane brings up her insight about other projects to the team debrief, it might be added as a good practice in the existing guidance.  It could be integrated as guidance to the proposal lead:  When guiding the past experience writer to specific projects and proposals, don't limit yourself to projects and proposals you know personally.  And it could be integrated as guidance for the past experience writer to "use best judgement when looking up information and don't limit yourself to the proposals and projects you have been told to look up."  

We want information to be available at the click of a button.  That gives us more time for processing that information, applying critical thinking and transforming information into actionable knowledge.  Most of the technology we use today to help organize our information so that it is easily accessible is helping with efficiency and speed in retrieval.  The technology does not do the thinking for us.  With AI and machine learning, some of that initial thinking, parsing and filtering of information sources will be more automated. 

Continuing with Jane's task example,  the proposal lead and others on the team may guide Jane towards specific existing proposals or projects that are likely to be relevant to the task.  These recommendations are based on one or two individuals' knowledge of prior projects and proposals.  In a larger organization, that could be very incomplete knowledge.  There is significant potential for missing out on relevant information.  Jane, during the search of the databases could encounter new information, but it would be totally dependent on her to engage in reflection in action and to pro-actively bring it up to the team.  That simple action will be dependent on the organizational culture and psychological safety the team.  Proposal teams can be high pressure, and even if the organizational culture promotes psychological safety, there can be subcultures that are more intimidating.

In the near future, the organization could use AI and Machine Learning to efficiently and automatically read the RFP, identify key components, then search the company's databases for all the relevant documents, and either spit out a list of all documents, sorted by relevance, or even draft a summary of the information that could serve as Jane's first draft.  Obviously, there is still need for a human applying critical thinking to decide how to adjust this first draft, but over time, assuming machine learning works as it should, the first drafts would become better and better.  The AI might even learn the organization's writing style if it is given training data based on the organization's existing materials.

Jane's task was relatively simple, yet exposed many connections between people and systems.  Expanding the task to the broader proposal process would expose hundreds of tasks and associated connections between systems and the people who maintain and use them.  And the proposal process is not totally isolated from the rest of the organization since as we saw from Jane's task, it is connected to past project experience and organizational knowledge. There is a broader, dynamic organization learning ecosystem.

All this requires a comprehensive framework for thinking through the tasks that Jane and other employees across the organization have to complete, establishing clear processes, defining roles and responsibilities, setting up user-friendly, integrated systems, and an overall governance that allows for seamless embedding of learning processes and systems. 

Sunday, October 16, 2022

Three Recent KM Books => two different approaches to Knowledge Management

I don't read a lot of books anymore.  Shame on me!  Still, I invested in three knowledge management books in 2022:
  • Design Knowledge Management System: A Practical Guide for Implementing ISO30401 KMS Standard, by Shanthosh Shekar

  • The Learning-Driven Business: How to Develop and Organizational Learning Ecosystem, by Alaa Garad and Jeff Gold.  

  • Making Knowledge Management Clickable: Knowledge Management Systems Strategy, Design, and Implementation, by Joseph Hilger and Zachary Wahl.
This post will focus on the last two listed above.  When I first scanned through Design Knowledge Management Systems, I found it difficult to absorb and I didn't give it a solid chance.  I will have to try again. 

I just received The Learning-Driven Business and suddenly and I had a aha! moment, an insight.  The Learning-Driven Business and Making Knowledge Management Clickable are two completely different books and reading them, one might think that they take diametrically opposite views on Knowledge Management or that they cannot possibly both be about Knowledge Management.  

The Learning-Driven Business is focused on how people learn in organizations, whether at the individual level, in teams, or at the organizational level.   Making Knowledge Management Clickable is about the systems that need to be put in place in a modern, efficient organization, so that employees don't waste their time looking for knowledge and find what they need faster. I'm oversimplifying. Neither approaches is simple or easy to implement well. To oversimplify even further, I could say that one is about systems (understood primarily as technology) and the other is about people.  

Two insights:

1. Find the Balance:  A successful Knowledge Management program must find the right balance between those two approaches.  This is not news or a new insight, but somehow organizations often don't get this right.  Any organization that focuses on the systems will soon discover that without a culture of learning and embedded learning processes, the systems will be underutilized (and then the technology will be blamed and the organization will seek to replace the technology... just to repeat that mistake). Any organization that focuses on learning without addressing the inefficiency of its systems will struggle to find the time for learn.

For example, the less time is wasted looking for information, the more time is available for critical thinking, reflection, team learning, etc... However, without a culture of learning and learning processes in place, the time saved with efficient systems isn't necessarily spent on learning.

2. Blend and Integrate:
  Before trying to figure out what the appropriate balance is between the two approaches, how can we think about how the two are connected? Perhaps the answer is not in the ratio of one approach vs. the other but more in carefully embedding both approaches in a coherent framework and understanding how they interact and reinforce each other. Perhaps an ecosystem approach or a systems thinking approach is warranted.  This is also relevant when the organization discovers that however efficient individual "systems" are, they are not well integrated and require employees to waste time switching from one system to the other. 

In the next blog post, I will explore these issues with a realistic, practical example with a specific process.

Friday, October 14, 2022

Perennial Questions in Knowledge Management

Anyone who has worked in or around Knowledge Management for a while has encountered the perennial questions.  Perennial questions are like perennial plants, they keep coming back.   These perennial questions around KM keep coming back because they are the wrong questions or they are questions for which there are no clear answers and the best answer is "it depends".

Here are two of these questions?

Is Knowledge Management dead, dying, being revived?  None of the above.  I would rather ask "What's happening in KM?"  It's constantly evolving to adapt to changes in the environment and in particular, to adapt to technology changes. The fundamentals of Knowledge Management are not changing and need to be brought back to the surface regularly. To some extent, technology is evolving to respond to new challenges brought about by technology advances.  We now have access to so much data and information that we need new technologies to process the data and information to use them.

Where does Knowledge Management belong in the organization?  The typical answer to this question is "it depends", as long as it's not in IT.  Based on my own experience, this is a question that cannot yield a complete answer unless it is asked slightly differently.  It also depends on what the top level of KM is?  If the top KM position is a relatively low level position, it won't matter where it is in the organization, it is buried.  Another aspect to this topic is the KM office/team's ability to work collaboratively with other key departments across the organization.  This may depend on the processes for strategic planning and annual work planning.  Organizational boundaries can become an obstacle to the development of a coherent comprehensive organizational framework for KM.   Therefore, to succeed, the KM team needs to be positioned such that a) its focus is strongly aligned with organizational strategies; b) its highest level staff is able to influence strategic decision making; c) organizational processes allow the KM team to work/collaborate across organizational boundaries. 

A related recent blog post from Enterprise Knowledge:  Where does KM leadership and governance belong (9/9/2022). 

Saturday, October 08, 2022

Insight Management vs. Knowledge Management

I wrote about insight mapping in the previous blog because I came across the Insight Management Academy (IMA) website.  I was looking up "insight management". 

In some ways, insight management looks very much like Knowledge Management. In fact, I came across some definitions of insight management that were literally well-accepted definitions of knowledge management. 

If I were to try to explain the difference, I would say that insight management is a form of knowledge management that is very market-oriented, future-oriented, or forward looking. While Knowledge Management can take on many different forms in an organization, insight management is more focused. 

It is not focused on lessons learned or best practices.  It is looking at the latest, most relevant data and information available in the market and internally to make decisions; it is looking to innovate and bring new solutions to the market.  It is focused on competing ahead of the market.  It's asking "what are the solutions of tomorrow?" rather than "what has worked well in the past that we can repeat?" 

To say that insight management is not paying attention to lessons from the past is an exaggeration.  As a KM professional I do not mean to say that we should ignore lessons from the past.  Lessons learned should be a foundation, a starting point, but they are not enough and unless they are continuously updated, they become obsolete rather quickly.  For more on that point, "Best Practices are Stupid" is a good reminder that sticking with so called 'proven solutions' cannot lead to new solutions and new, evolving problems require new solutions.  

Thursday, October 06, 2022

What is Insight Mapping?

 Insight mapping is the title of this blog.  Why?  What does it really mean?

First, let me clarify that this blog, which started in 2003, was not always named "insight mapping".  It's been called insight mapping since around 2017 I believe.  I had been working on variations of concept maps which we called either knowledge maps or conversation maps.  These were visual representations of Pause and Learn conversations held for NASA teams.  The Pause and Learn is the NASA equivalent to an After-Action-Review in many ways, but the process of mapping these conversations was rather unique to the Office of the Chief Knowledge Officer at the Goddard Space Flight Center.  

I learned the approach from the CKO there, Dr. Ed Rogers.  Then I evolved the approach by turning collections of individual conversation maps into a web of maps and therefore, a collection of insights.  The conversation approach itself wasn't changed and the first step of the documentation of insights in maps wasn't changed, but the way maps were constructed to be more easily aggregated into a web of map was new.  

At the time, I struggled to stick to a single name for the maps.  They were based on the idea of concept mapping, but reflected the flow of conversation rather than concepts.  They key elements were the takeaways of the Pause and Learn session, embedded in all the relevant contextual information. The key takeaways were a mix of lessons, recommendations, and insights.  In truth, most takeaways, which were visually indicated on the map, were important insights but did not readily lend themselves to being captured as lessons or recommendations.

It is only when I left NASA and worked as a consultant for a while, having more time to work on this blog, that I called it Insight Mapping and decided to rename the blog after it.  The insight maps included in the map section are not NASA insight maps, they are little illustrations of individual maps. 

The value, however, was in having a large collection of maps and therefore a large collection of insights which were tagged based on an evolving taxonomy. Each emerging key topic would become its own map, gathering the [insert a topic] insights across 100s of maps. Thanks to a maze of hyperlinks embedded in the text of the individual insights, it was easy to maintain quick access to the context for an insight in the original conversation/insight map.  

Therefore, while the individual maps provided contextualized insights, the topic map provided insight into a topic by gathering all related insights into a single map.  

Conceptually, I still think this was very interesting.  In practice, the technology and level of effort involved in constructing the maps was just not sustainable and the skills were not easily transferable.  I suspect this could be revived with a different technological solution.  The concept of insight mapping and aggregating insights to generate valuable "insight" remains valid.

I was reminded of all this while listening to the Insight Management Academy (IMA) podcast: Transforming Insight. I just started the series.  The first podcast provides a useful definition of "insights" and "insight" .  Insight is the accumulated understanding built from many insights.  It's the bigger picture that emerges by connecting the dots, making the connections across hundreds or thousands of insights.  Individual insight maps highlight insights within the context in which they emerge.  Topic maps aggregate individual insights and make it possible to get "insight" into a topic.

Sunday, October 02, 2022

The brain, expertise, and knowledge management

The brain undergoes rewiring after 40. No kidding. I would hope that the brain is constantly rewiring, but over time, the nature of the rewiring changes as we age.  And the employees within an organization who have the most expertise are likely to be over 40, the people who have decades of work experience, decades worth of learning, whether they stayed in one industry, one company, or they diversified their experiences.  These are also in many cases the people who are in the higher levels of management, making decisions. 

I came across this article:  The Brain Undergoes a Great Rewiring after age 40:  The aging brain is wired differently.  

"University in Australia swept through the scientific literature, seeking to summarize how the connectivity of the human brain changes over our lifetimes. The gathered evidence suggests that in the fifth decade of life (that is, after a person turns 40), the brain starts to undergo a radical “rewiring” that results in diverse networks becoming more integrated and connected over the ensuing decades, with accompanying effects on cognition."  https://bigthink.com/neuropsych/great-brain-rewiring-after-age-40/

The younger brain is partitioned and highly connected within the partitions.  It is building specialized networks.  The older brain is more connected across networks.  In the fourth and fifth decade of life is when you have the greatest within network and across network connectivity.  

The rewired brain is a function of the overall body's need to adjust to declining functions.  The body slows down and the brain needs to adjust.  Of course, exercise and a healthy diet can help slow down the decline.  I would like to think that continuous use of one's brain for intellectual pursuits is key to maintaining brain cells to.  By "intellectual pursuits" I mean more than sudoku and crossword puzzles, though.  My aging brain wants to connect things. Here we go:

How can emerging knowledge of how our aging brains are rewired impact how we understand expertise and knowledge retention in organizations?

A good understanding of how those brain cells are being rewired might also help put the remaining brain cells to good use and seeing where there might be some advantage to a widely-networked brain. In other words, while accepting the fact that one's brain is changing and decline is somewhat inevitable, perhaps a wiser brain is emerging.  Let's just assume this hypothesis is plausible, otherwise it's depressing.

Going back to knowledge management, how can we handle the knowledge of experts in their 50s and 60s, knowing that their neural pathways are changing?  Instead of thinking about it as declining brain functions, what are the strengths we can expect from broader connectivity among networks in the brains of the more experienced professionals in an organization?  

And are there any useful parallels between neuron networking in the brain and people networking in organizations?

A young brain is specializing within sections of the brain.  The equivalent in an organization might be specialized communities of practice.  They are building expertise within narrow knowledge domains but not connecting well with the broader system.  An older brain connects more across networks.  In an organization, the equivalent might be crosscutting networks that make much greater use of systems thinking and see how the narrowly defined communities of practice are or should be connected. 

To be more specific, the individual communities in a Yammer (enterprise social) network are the specialized segments of the organizational brain while the Yammer network itself is the more broadly integrated, aging brain.  The younger employees might feel more comfortable engaging in the specialized communities of practice.  The more seasoned employees might be at ease engaging across communities, or making the connections across the communities.

While this resonates personally, I can't say that I have any evidence that this is indeed happening.  Considering that the aging brain isn't very flexible, it makes it challenging to ask the so-called "experts" to engage in new ways, using new communications platforms like Yammer. Knowing that, how can we still leverage the aging brains' valuable expertise and strengths in seeing the bigger picture, the broadly networked picture, the whole system?