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Knowledge Management

The Central Knowledge Management Dilemma: Assets or Relationships

It was during a peer assist call that I was reminded of the central dilemma of knowledge management. A group of knowledge managers were in a call to discuss working out loud as a concept with John Stepper – who wrote the book Working Out Loud – and other leaders in the movement. The conversation was organized as a way of addressing the concerns that had been raised by a few of the knowledge managers who had seen it implemented poorly and were skeptical. The comment that John made that resonated was, “For me, it’s about relationships.” While he was describing the make up of the groups, it connected a set of concepts like lightning connects the clouds to the earth.

Tacit vs. Explicit

The knowledge management community is largely enamored with the argument about whether knowledge can be made explicit. There are conversations about different types of tacit knowledge and their ability to be transformed into explicit knowledge and what is lost during the conversion process. You see this central theme in Lost Knowledge and The New Edge in Knowledge.

There are also discussions about what is lost when you convert from tacit, context-rich knowledge into contextless, explicit knowledge. The work of Gary Klein around recognition-primed decisions is discussed. (See Sources of Power and Seeing What Others Don’t for more.) The discussion bounces back and forth around what many believe is the central question.

More work is done to codify knowledge and to develop expertise location systems, both of which are seen as slightly competitive to the other yet needing to coexist. However, John’s comments made the real question clear. Are we in the business of relationships, or are we in the business of protecting corporate assets?

Silver Tsunami

It’s the disaster that large organizations are planning for. The Silver Tsunami is the rapid retirement of the most experienced and senior leaders in the organization. Baby boomers moved through the ranks of employment, like a snake eating a watermelon, and they’re ready to start leaving the workforce in droves. That means that organizations are wondering what it will be like to be left without the steady hands that have silently and not-so-silently guided the organization for decades.

Many, but not all, knowledge management programs are based on the fear of the upcoming Silver Tsunami and how they’ll cope. Often, these programs are focused on knowledge capture and conversion. Capture whatever is in the head of the most experienced and preserve it so that it can be used for future generations. The problem with this, as knowledge managers know, is that capturing the knowledge is not easy, and making it useful is harder. (See Sharing Hidden Know-How for more.)

Asset Overload

The knowledge capture process creates as its outcome a set of assets. Those assets range from the relatively unprocessed video interview with a subject matter expert speaking of their experiences to the fully converted explicit document or training that codifies what the expert knew as best as possible. However, both add to the growing challenge of finding the information that is being sought. Technology and search approaches continue to improve but not fast enough to keep pace with the increase in available information.

Working out loud itself creates additional assets to be searched – which is a good thing. The problem is that soon the noise from the knowledge capture activities, working out loud, and other approaches become a deafening noise that makes it hard to make any sense of the situation or find positive values to the noise itself.

The truth is that we’re all drowning in information and are struggling to make sense. (See The Information Diet and The Signal and the Noise for more about information overload.) Alvin Toffler was the first to characterize too much change as future shock. (See Managing at the Speed of Change) Many see the same challenges in the overload of information. (See The Organized Mind.)

However, there may be a different approach and answer that still allows for the creation of knowledge assets through capture and additional information through working out loud but is instead focused on the relational aspects of the knowledge management problem.

Working Out Loud Circles

It’s a dimly lit room in the basement of a church. Men and women saunter in with a mix of familiarity and trepidation. They’re identifying old friends and sizing up the people they don’t know. As they grab a cup of coffee, drop in a bit of money into the box, and find a seat, they continue to monitor the room as they start to shift their thinking insider their own heads – something that they know is both dangerous and necessary.

Out of nowhere, a man says, “Hello, I’m Bill, and I’m an alcoholic.” Shaken from their thoughts they answer in near unison, “Hello, Bill.” Groups like this meeting of Alcoholics Anonymous (AA) meet across the world as a way of forming a safe space for those struggling with an addiction to alcohol to share and be in a judgement free community. There are similar groups for every other kind of addiction that you might struggle with.

The program functions with a mixture of trust, respect, humility, and understanding. (See Why and How 12-Step Groups Work for more.) While rejecting the negative stereotypes of addiction, working out loud circles are designed to create the same sense of safety. They’re designed to create opportunities for folks to share what they’re working on and what they’re struggling with in an environment where they know they’ll get caring feedback and no – or at least little – judgement. The result of this environment is often trust and connectedness. (See Trust=>Vulnerability=>Intimacy, Revisited for more on how this works.)

This creates relationships – relationships that people feel comfortable leaning on when they need an answer, or they need help. Relationships are at the core of these groups – even when they rarely start out that way.

Affinity Groups

The best answer to creating a knowledge network are small groups. Working on a project or taking part in a working out loud circle are great ways to do that. However, this necessarily creates a scalability problem. It’s difficult for the right people to know what it is that you know. That’s where the traditional working out loud work and the search engines come into play.

Sharing your work makes it easier for people to become aware that you have the answers you seek. Your relationship is built through your mutual connections – or, more often in large organizations, through your shared identity. You build a modicum of trust, because you’re both working for the same organization. Direct relationships are best, but often you can leverage the fact that you work for the same organization as sufficient reason to help one another. (See The Deep Water of Affinity Groups for more.) After you’ve helped someone, you feel related to them – and vice versa. Getting to Yes notes that Benjamin Franklin would often ask for the loan of a book and would promptly return it. The act of him asking for a favor which the other person granted made Franklin seem slightly indebted to the other person, and this often fueled the start of relationships.

The Answer

The ultimate answer to the dilemma isn’t “either-or.” The ultimate answer is “and.” Working out loud can and should create explicit artifacts. Working out loud circles should generate deeper relationships. Working for the same organization should create an affinity that people can leverage. Perhaps the best way to create value as a knowledge manager is to take the best of both worlds and create positive spirals of trust and relationships that feed the engine of the organization.

Book Review-KNOWledge SUCCESSion

One of the most interesting things about Arthur Shelley, the author of KNOWledge SUCCESSion, is the mixture of academic and pragmatic practice. Having a foot in both worlds creates a rigor in research that isn’t found in business books and a sense of what’s actually happening in the world that is all too often lost in academic writing.

Knowledge is Power

Shelley’s and my worlds – despite being on opposite sides of the planet – intersect in multiple ways. We share the same friends and the same interests in knowledge management and are thus involved in many of the same communities. At the heart of these intersections is the passion for finding ways for organizations to get more value out of the knowledge they have. By encouraging better knowledge capture and transfer, we both hope to improve corporate outcomes and the trajectory of humanity.

Despite the focus on knowledge, Shelley is clear that knowledge is only as useful as our ability to use it effectively. That is, when it’s shared and applied.  It means nothing to know something that you can’t use when you need it.

Learning Today

One of Shelley’s criticisms of learning today – and one I share – is that we’re teaching people to memorize facts so they can answer standardized questions instead of teaching people to think for themselves and form their own opinions. We have maintained the historical learning model where the teacher is the fount of information, and they pour it into the heads of the students. The problem with this method is that it doesn’t work.

Malcolm Knowles et al. in The Adult Learner made it clear that adults learn differently from children, and using a pedagogical approach on adults doesn’t work. You must provide them context and relevance to what they learn. Going further than Knowles’ work, we realize that, today, we expect we can type a question into Google and instantly be presented with an answer.

The problem is that we often accept the first response in Google as THE response. That is, we fail to see that, for every situation, there are multiple opinions. Shelley likes to say that for every PhD, there’s an equal and opposite PhD. While this may be going too far in the direction of the uncertainty of truth, it is not an inaccurate statement. Many highly educated people vehemently disagree with one another in their field of study. What looks like certainty is Fractal Along the Edges.

Shelley advocates for an experiential learning approach. Klein’s work in Sources of Power indicates the real value and expertise can come from experience. Recognition-primed decisions (RPD) in particular are powerful ways that fire commanders convert their experience into life-saving guidance for the firemen under their command. However, Klein also admitted that recreating the conditions that would allow for this same experience to be produced more quickly proved to be difficult. Therefore, experiential approaches can be time-consuming.

Learning Organizations

The oldest blog post in my drafts folder is about learning organizations. It’s a collection of ideas that never quite fit together. Despite the subtitle of The Fifth Discipline, it never really explained how to create a learning organization. The reason for the struggles may be that organizations don’t learn, people do. While the idea that you structure your organization to create learning at every level is an ideal state, it’s not clear that anyone ever really gets there.

Instead of learning organizations, we seem to fall into defensive routines (see Dialogue), and we fail to fully explore others’ points of view for fear that they may be right and our perspective may be wrong. (See also Mistakes Were Made (But Not By Me).)

Adapting Knowledge

Two underlying themes in books on creativity and innovation (see Creative Confidence and The Innovator’s DNA) are that creativity and innovation exists inside of all of us and that our creativity is unleashed with a variety of experiences. In most cases, we don’t create new knowledge or innovations as much as we extend and adapt the knowledge from other areas and find new ways of applying it to solve new problems.

Knowledge has value directly for the problems that it’s designed to solve, and it can also be leveraged in new ways to create more value.

Perhaps you’ll find the knowledge you need to be successful in KNOWledge SUCCESSion.

Project Cortex – Knowledge Management powered by Search, Graph, and AI

At Microsoft Ignite 2019, Project Cortex was announced. It’s Microsoft’s leap into better enabling customers to use information by leveraging their substantial expertise in search, social network analysis, and artificial intelligence. While Microsoft bills Project Cortex as a knowledge management platform, I stop short of this. I believe that, while Project Cortex has the power to reduce the friction to getting to knowledge, I’m not sure what they’re accomplishing is really knowledge management.

Why You Should Care

Before explaining the pieces that drive Project Cortex, it’s important to understand the interactions that Microsoft is already speaking of. First, there’s the idea of topics. The system determines that there’s a topic in the organization, and it assembles a page that collects what it knows about the topic, including descriptions, related topics and resources, and the people who seem to know the most about the topic. This topic page is something that you can curate and revise if the system doesn’t get it exactly right.

Having topics is interesting – but it’s not where the power is. The power is that, when you’re reading anywhere – in email, in a document, or on the web – you see topics get underlined in your messages. If you hover over that topic, you’ll see a topic card pop up with a short summary of the topic. If you click on the topic card, you’re taken to the topic page for the topic.

This substantially lowers the friction for someone to learn about the various acronyms and topics in the organization. If you’ve ever read on a Kindle and didn’t know what a word is, so you tapped it and got a definition, you know how much this frictionless approach leads to a better reading experience. The author doesn’t need to write information about a topic if the person doesn’t know about it. They can just trust that the system will flag the topic, and the reader can research if they need to.

How Does It Work?

Microsoft hasn’t explained the details yet, but let’s look at some of their investments and infer some of what is going on behind the scenes. The power seems to be coming from search, social network graphs, and artificial intelligence.


Microsoft’s been making significant investments to take what they’ve learned with Bing and SharePoint’s search engine. SharePoint’s search engine itself is largely an enhanced version of the search engine from their FAST acquisition several years ago. The short version is that Microsoft has a deep expertise in search across several platforms. Microsoft search unifies the experience across the board. It brings searching one set of information to every platform and application from a corporate intranet, to Bing and to the Office applications. It is one set of search results displayed in the context you run search from.

On the other end, Microsoft is enabling more search connectors, so that you can get information from all sorts of new sources. SharePoint Search has had the capacity to index Exchange and file shares for some time. New indexing support was announced for Salesforce, Box, MediaWiki, ServiceNow, and other providers. This means that search has an even greater capacity to look across applications to present users with a single search view no matter where the information is stored – in other words, the enterprise search that we’ve been looking for.

Social Network Graphs

The next component that helps to make Project Cortex possible is the work that has grown from the Yammer acquisition. Microsoft got a community building platform, but it also acquired nascent technology for building social network graphs over lightweight signals, which eventually found its way into Microsoft Graph and was surfaced via Delve. Social network graphs are a representation of the relationships that connect users to one another.

Where LinkedIn encourages you to explicitly identify people in your network in an active and intentional way, Microsoft Graph looks at the actions you’re already taking, and it infers who you’re working with. It looks at the files you open or modify, the meetings you attend, and about a dozen other things. These signals are converted into edges – or relationships – between people. The beauty of this is that it happens completely transparently.

Users just do their work, and the system watches what they do to see whom they’re working with and therefore have a relationship with. When you turn this model loose to content and not just people, you get an interesting opportunity to identify relationships between not just people but content as well.

Artificial Intelligence

Artificial intelligence (AI) is a powerful thing. When most people are talking about AI, most of the time they mean machine learning (ML). That simplifies down into an intelligent implementation of Bayes theorem, which is an application of statistics. In short, most AI is about predicting what is and isn’t something based on continuous learning and correction.

In addition to ML, there is natural language processing that attempts to extract meaning from our written language. You see this in action as Word and PowerPoint try to help you correct your grammar. The system begins to recognize the way sentences should be structured and coaches you when you don’t get it right.

In the context of Project Cortex, AI is necessary to determine what should be topics. In a social network graph, you know that people are the objects. In content, you don’t know which things are important and which aren’t. If you can identify what the likely topics are, then you can start to evaluate their connections and start to build a graph of the topics and how they relate.

Knowledge Management or Not?

Historically, knowledge management has been focused on two things. First, capturing knowledge before it leaves the organization for good. Second, enabling people to connect with one another to share knowledge. That’s built on the understanding that there are three types of knowledge (or two types with one having two sub-types) someone can have. Explicit knowledge can be written down – or has already been written down.

Implicit knowledge is knowledge that can’t be easily articulated. Sometimes called tacit knowledge, it frequently has two sub-categories. The first is for that knowledge that can be articulated but for which no investment has yet been made to do so. The second is the category of knowledge where it’s not believed to be possible to convey the information no matter how much work is put into trying to convert the knowledge.

For the tacit knowledge that can be converted, consider cooking a dish that’s never had a recipe. It’s possible to write it into a recipe, but it just hasn’t been done yet. Also consider the idea of riding a bike; it’s hard to put into words exactly how to do this. That’s a kind of implicit knowledge that may never be able to be accurately conveyed – or at least is very difficult.

Knowledge managers often look at explicit knowledge as the tip of the iceberg. It’s the explicit knowledge that Project Cortex has access to. Often knowledge managers are trying to use the explicit knowledge as an indicator that there’s a wealth of implicit knowledge beneath the surface. That’s why communities of practice are useful and why retiring executives are encouraged to record videos that are later transcribed. The belief is that if there’s something interesting in what the executive said, someone in the organization should be able to reach out to them and get access to their knowledge – even after they’ve left the organization.

The problem with calling Project Cortex a knowledge management solution is that knowledge management is much more about building communities and enabling people to talk to other people.

If you want to learn more about how the knowledge management industry thinks about things, check out my white paper, The Road Ahead: Knowledge Management and Records Management Converge with Office 365.

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