Category: Knowledge work

KM Chicago: Collaborating Minds: Solving Tough Problems with a Unique Team

David and I will be talking about the work we’ve been doing on collaboration at next week’s KM Chicago meeting. We’re looking to have a highly interactive session. 

KM Chicago: Collaborating Minds: Solving Tough Problems with a Unique Team: ” Thursday, August 30, 2012 Collaborating Minds: Solving Tough Problems with a Unique Team At 5:30 on Tuesday, September 11th, join Jim McGee and David Friedman at KM Chicago’s monthly meeting to hear a progress report on ‘Collaborating Minds’, their unique problem-solving venture. This meeting will be especially productive in person, but participation online is also available. See details on the right.   As Jim points out, we continue to make progress in developing tools to support the efforts of teams to conduct complex knowledge work. At the same time, we are deepening our understanding of what differentiates highly effective teams from average teams. But these two streams of progress rarely intersect.   Collaborating Minds is the business concept that Jim and Dave have developed that functions at that intersection of complex knowledge work and highly effective teams.    Collaborating Minds tries to answer three related questions:

1. Given what we know about high-performance teams and current social technologies, can we create a virtual high-performance team with several hundred members?

2. If such a team existed, what kinds of problems could it solve that are currently unsolved?

3. Is there an acceptable business model to sustain that team over time?

On September 11th Jim and David will tell us what they’ve learned to date and will lead participants in a design collaboration that will help shape Collaborating Minds’ next stage of development.

Headshot DavidFriedmanDavid Friedman is passionate about problem-solving and about relationship building as fundamental human activities. That’s why he’s developing Collaborating Minds. He wants people to be much more productive and enjoy themselves much more too. He writes about collaboration at Positive Structures.  David was a partner at McKinsey & Company (a global consulting firm) and through his firm Bridgewell Partners has advised professionals on growing their practices through systematic relationship building. You can contact David here.  

 

 

McGeeJimHeadshot 20120807Jim McGee is an expert in knowledge management and knowledge use. He also knows a lot about technology, and about where technology and knowledge work intersect (or should). That’s why he’s a founder of Collaborating Minds. He’s been writing about these topics since 2001 at McGee’s Musings. Jim was a founder of Diamond Technology Partners (a technology and management consulting firm) and has been, among other roles, a faculty member at the Kellogg School at Northwestern University. You can contact Jim here. Posted by KMChicago at 5:15 PM “

Richard Feynman On The Folly Of Crafting Precise Definitions

I’ve often struggled with the notion of definitions when working in organizations. On the one hand, too many of us hide our ignorance and uncertainty behind a wall of jargon and terminology. Terms fall in and out of favor and their relationship to the underlying real world is often less important than their value from a marketing perspective.

On the other hand, new terms and language can help us point to and see new ideas and new opportunities for action. Here’s a recent post from Bob Sutton that sheds light on these challenges and is worth thinking about.

One of my best friends in graduate school was a former physics major named Larry Ford. When behavioral scientists started pushing for precise definitions of concepts like effectiveness and leadership, he would sometimes confuse them (even though Larry is a very precise thinker) by arguing “there is a negative relationship between precision and accuracy.” I just ran into a quote from the amazing Nobel winner Richard Feynman that makes a similar point in a lovely way:

We can’t define anything precisely. If we attempt to, we get into that paralysis of thought that comes to philosophers one saying to the other: “you don’t know what you are talking about!”. The second one says: “what do you mean by talking? What do you mean by you? What do you mean by know?

Feynman’s quote reminded me of the opening pages of the 1958 classic “Organizations” by James March (quite possibly the most prestigious living organizational theorist, and certainly, one of the most charming academics on the planet) and Herbert Simon (another Nobel winner). They open the book with a great quote that sometimes drives doctoral students and other scholars just crazy. They kick-off by saying:

“This is a book about a theory of formal organizations. It is easier, and probably more useful, to give examples of formal organizations than to define them.”

After listing a bunch of examples of organizations including the Red Cross and New York State Highway Department, they note in words that would have pleased Feynman:

“But for the present purposes we need not trouble ourselves with the precise boundaries to be drawn around an organization or the exact distinction between an “organization” and a “non-organization.” We are dealing with empirical phenomena, and the world has an uncomfortable way of not permitting itself to be fitted into clean classifications.”

I must report, however, that for the second edition of the book, published over 20 years later, the authors elected to insert a short definition in the introduction:

“Organizations are systems of coordinated action among individuals and groups whose preferences, information, interests, or knowledge differ.”

When I read this, I find myself doing what Feynman complained about. I think of things they left out: What about norms? What about emotions? I think of situations where it might not apply: Doesn’t a business owned and operated by one person count as an organization? I think of the possible overemphasis on differences: What about all the times and ways that people and groups in organizations have similar preferences, information, interests, and knowledge? Isn’t that part of what an organization is as well? I could go on and on.

I actually think it is a pretty good definition, but my bias is still that I like original approach, as they did such a nice job of arguing, essentially, that if they tried to get more precise, they would sacrifice accuracy. Nonetheless, I confess that I still love trying to define things and believe that trying to do so can help clarifying your thinking. You could argue that while the outcome, in the end, will always be flawed and imprecise, the process is usually helpful and there are many times when it is useful pretend that you have a precise and accurate definition even if you don’t (such as when you are developing metrics). “

Richard Feynman On The Folly Of Crafting Precise Definitions – Bob Sutton:

Euan Semple on nurturing a knowledge ecology

This gem from Euan Semple made the rounds earlier this summer. I was too busy then to do more than note it.

Ten ways to create a knowledge ecology

TUESDAY, JUNE 28, 2011 AT 7:08AM

A tweet yesterday prompted me to remember sage advice from Dave Snowden which I took to heart in my work with social tools at the BBC. “You can’t manage knowledge but you can create a knowledge ecology”. I thought it might be useful to others to list the ten most important things I learned about doing this.

1, Have a variety of tools rather than a single system. Not everyone sees the world the same way or has the same needs so mixing up different tools with different strengths allows people to find one that works for them. Avoid single platforms like the plague.

2. Don’t have a clear idea where you are headed. The more fixed you are in your aspirations for your ecology the less likely you are to achieve them. Be prepared to go where people’s use of the tools takes you and enjoy the ride.

3. Follow the energy. Watch where the energy in the system is and try to copy the factors that generated it. Get others interested in why energy emerges and they will want some of it themselves.

4. Be strategically tactical. You can have an overall strategy of behaving in certain ways depending on how your ecology develops. It is possible to sell this as a strategy to those who need strategies.

5. Keep moving, stay in touch, and head for the high ground. Keep doing things, keep talking about what you are doing and why, and have a rough idea of where the high ground is.

6. Build networks of people who care. Don’t try to manage your ecology by committee but cultivate communication and trust between those who care that it works and have the commitment to do something about it – whoever they are and whatever their role.

7. Be obsessively interested. Notice everything that happens and consider why. Tell great stories about what you are observing.

8. Use the tools to manage the tools. Blog about what is going on with your corporate blogging, ask questions in your forum about security, tweet when something is changing in your ecology and ask people why it is interesting.

9. Laugh when things go wrong. If you are pushing limits and exploring new territory things will occasionally blow up in your face. Having a sense of humour and enjoyment of the absurd will help you stay sane.

10. Unleash Trojan Mice. Don’t do big things or spend loads of money. Set small, nimble things running and see where they head.

(– The Obvious? – Ten ways to create a knowledge ecology)

The paradox of organic approaches to change is that while they appear to be simple and mundane, they also appear to be the only thing that works with any regularity in complex situations. For all the rhetoric of bold plans and audacious goals, the reality is that most change occurs inch-by-inch.

Where IS Health Care Going? Technology Leader’s Presentation

Last week, JoAnn Becker  and I ran an interactive discussion with the monthly TLA Manager’s breakfast meeting here in Chicago. We had a lively and excellent debate among a group of technology executives, health care executives, and other smart people about the real challenges of successfully deploying information technology to improve productivity and quality in delivering health care in this country.

That, of course, is an immense issue and would could barely scratch the surface in the hour we had. For those who are interested, we’ve uploaded our slides to Slideshare.

 

We used two recent TV ads from GE and IBM to kick off the discussion. On the surface, each provides a sense for the promise of information technology to make health care more effective:

GE TV ad – Doctors
IBM TV Ad – “Data Baby”

In the tradition of all good technology vendor advertising, both also completely gloss over the complex organizational adaptation and evolution necessary to bring these hypothetical worlds into being. They also gloss over the existing institutional and industry complexity that needs to be understood and addressed through a combination of design, leadership, and management.

Fred  Brooks, professor of computer science at UNC and author of The Mythical Man-Month : Essays on Software Engineering, draws a critical distinction in the final chapter of the book, which is titled "No Silver Bullet," between accidental and essential complexity. His point is that software is so difficult to design and develop because it must successfully model the essential complexity of the domain it addresses. Technology and software efforts can stumble on a variety of barriers and roadblocks, but failing to understand and address essential complexity is the worst.

Health care provides its own mix of accidental and essential complexity. If the decision makers aren’t careful to draw distinctions between accidental and essential, then a great deal of time and effort will be expended without corresponding returns. On the one hand, we may simply succeed in "speeding up the mess" as my friend Benn Konsynski so liked to put it. Or, we may obliterate  essential complexities in a quest for uniformity and productivity that is blind to those complexities. Or, finally, we may invest the appropriate level of design time and talent in systems that account for essential complexity and eliminate accidental complexity.

Resources

We drew on a variety of excellent resources in preparing for this talk and wanted to make them more easily available here.

Here are several books that provide useful context and background

Here are pointers to a variety of health care related web resources worth paying attention to:

Collaboration, games, and the real world

I’ve been thinking a lot about hard problems that need multiple people collaborating to solve. There’s no shortage of them to choose from.

This TED video from Jane McGonigal makes a persuasive case that I need to invest some more time looking at the world of online gaming for insight. Watch the video  and see if you don’t come to a similar conclusion.

 

Doing and Managing Knowledge Work: TUG2010 Keynote Reflections

I’m back from last week’s Traction User’s Group meeting, TUG2010, where Greg Lloyd graciously asked me to do the opening keynote. I’ve posted the slides on Slideshare and wanted to add some further commentary here.

 

First, one caution; when I do use slides I don’t design them to be standalone documents. There are too many bullet points in the world as it is. What I’d like to do here is highlight and elaborate on some of the key points I was trying to make.

Peter Drucker first called our attention to the importance of knowledge workers decades ago. The rest of us are slowly catching up to his ideas. One shift in focus that I’ve begun to emphasize is toward the knowledge work itself and away from the notion of knowledge worker as somehow distinct from other kinds of workers. Trying to distinguish who may or may not be a knowledge worker as opposed to some other kind of worker simply perpetuates pecking order games that do little to further the mission of an enterprise. We all do knowledge work  to some degree or another, we are all doing more knowledge work than before, and the important question is how to do that work more effectively.

The notions of visibility and observability have been central to my thinking for some time now. The evidence is clear that dealing with complex problems and thinking requires a certain amount of corresponding complexity and mess in our working environments. To those whose focus is on stability and operational control, mess, of course, is disturbing. So disturbing that we ridicule those who deviate from the presumed ideal. We do so at a greater organizational cost than we realize, however, when we ignore the complexity in the environment that is driving the mess.

I introduced the following simple map to suggest just how unavoidably messy the real world of knowledge work can be. The x-axis maps the inherent structure of the knowledge “stuff” we encounter; the y-axis maps the degree to which knowledge stuff is individual or social. It didn’t take long to identify a multitude of items and objects that you might routinely encounter as you go about your work.

KnowledgeStuffMap-2010-10-19-1045

It’s tempting to simplify this reality in some way. Many years ago P&G was famed for teaching its managers to distill their arguments into one-page memos. Too many consultants and speakers opt to squeeze all of their output into slideuments; which merely transfers the problem somewhere else. Senior executives rely on staffs to filter the stream at the risk of filtering out the essential insight or data point that truly informs.

The strategy I prefer is to accept the fundamental messiness and seek ways to tame it enough to make it manageable. Part of that relies on exploiting the natural pattern-seeking, pattern-matching capabilities of the human mind. Part relies on enlisting the pattern management capabilities of the other human minds in the system to supplement your own capacity. Both of which also need to be tempered by appreciation for the limits of those same capabilities.

Taming the mess breaks into three layers of practices:

  1. Hygiene. The proliferation of objects in a physical office offer a host of clues about their contents and relative importance; size, shape, color, location on a shelf or desktop, position in a stack, etc. In a digital environment you need to provide the equivalent of those clues explicitly and consciously. Seemingly mundane decisions about the file names you choose, for example, can make large differences when you are later scanning through a page of search results. Most of today’s systems provide little real assistance in this arena; you and your teams need to develop their own standards for naming files, managing versions, and other details of the knowledge stuff they work with.
  2. Metadata. i wish there were a more homespun term for this layer. One of the central tricks to taming the flood of data and information that constitute your digital world is to add more data to the flood. The ability to tag the items you create or encounter with labels that are meaningful to you greatly leverages the other tools at your disposal. Merge those tags with the tags of those in your social network and you shorten the path to finding what you are searching for still further, either on your own or through your network’s help.
  3. Context. One of the least appreciated aspects of messiness in the physical world is the context it provides. There’s a story attached to each pile and object; a story that can be triggered by its context. The power of this context is why students do better on tests when they take them in the same classroom they took the course in than in a random room. A filename in a directory listing or a document displayed on  a monitor lack this ready context and are poorer for it. The alternative is to become more mindful of the importance of context and make an effort to capture it explicitly and contemporaneously. This is rationale behind such notions as narrating your work, and developing a digital portfolio.

There is a payoff to all of this for both individuals doing knowledge work and the organizations they contribute to. Once again Peter Drucker said it first; “the most valuable asset of a 21st century institution will be its knowledge workers and their productivity.”  Economic growth in this century depends on our ability to improve knowledge work productivity; until you can see it, you can’t improve it.

Finding knowledge work practices worth emulating and adapting

[Cross posted at FASTForward Blog]

How might we best go about improving knowledge work, both practices and outputs, in today’s complex organizational environment? Are there paths other than simple trial and error that might lead to systematic gains?

Frederick Taylor and his followers built their careers on finding the one best way to carry out a particular physical task. Later proponents of this way of thinking transferred their approach to defining the one best way to carry out information processing tasks. John Reed of Citibank launched his career by applying factory management principles to automating check handling. Reengineering essentially rebooted these approaches for a richer technology environment, but held to the premise that outputs were a given, tasks could be well-defined, and processes could be optimized.

Peter Drucker, in his typical way, pointed out that the key to understanding and improving knowledge work (PDF) was that there was no defined task to be optimized. Knowledge workers start by defining the task at hand and an output of suitable quality. This is not an approach which lends itself to conventional improvement or optimization approaches.

Two useful approaches come to mind. One would be to identify and shadow individual knowledge workers deemed to be particularly effective. Observing, understanding, and emulating their personal practices would be time well spent. A second approach would be to identify a class of knowledge workers who have been dealing with the problems of knowledge work in the modern enterprise long enough to have developed practices and approaches that might be broadly adaptable to knowledge work activities in general.

Both of these approaches are well worth undertaking. Today, I’d like to take a look at this second approach. A recent blog post by Eric Raymond prompts looking at a group I’ve often thought of as the leading edge of modern knowledge work– software developers. Over the last half-century, this group has been inventing and developing the technological infrastructure that shapes our modern enterprises. As such, they have been the first to encounter and address the challenges of knowledge work. Certainly any group responsible for the Internet and the invention of open-source software will have lessons for the rest of us as we try to bring forth our own examples of knowledge work products.

Raymond, among many other things, is the author of the excellent The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. He also maintains the always interesting and provocative blog Armed and Dangerous. In a recent post, “the social utility of hacker humor”, Raymond dives into the number of the behavioral norms that he believes characterize hackers and software developers. The entire blog post is well worth your time, but let me call your attention to the following excerpt:

…every once in a while something erupts out of them that is a game changer on a civilization-wide level. Two of the big ones were the Internet and open-source software. These two movements were intimately intertwined with hacker culture, both produced by it and productive of it. The origins of our tribe go back a bit further than either technology, but we have since re-invented ourselves as the people who make that stuff work.

And I don t mean make it work in a narrow technical sense, either. As long as there are people who laugh at INTERCAL and RFC1149 and the Unix koans of Master Foo, and recognize themselves in the Jargon File, those same people will care passionately that computing technology is an instrument of liberation rather than control. They won t be able to help themselves, because they will have absorbed inextricably with the jokes some values that are no joke at all. High standards of craftsmanship; a subversive sense of humor; a belief in the power of creative choice and voluntary cooperation; a spirit of individualism and playfulness; and not least, a skepticism about the pretensions of credentialism, bureaucracy and authority that is both healthy and bone-deep.

[Raymond, Eric S. Armed and Dangerous. “The social utility of hacker humor“]

Substitute “knowledge worker” for “hacker” and I believe we will find parallels worth exploring.

This is still in the working hypothesis stage. I can think of a number of practices that might prove worth emulating and some useful entry points into learning more.

Practices worth investigating

Serious software developers have adopted a number of practices as they’ve struggled with the challenge of designing, developing, and evolving products that are pure thought stuff. To the extent that knowledge work is also a process of developing outputs that are themselves largely thought stuff, these practices ought to have analogues. Here’s a preliminary list, in no particular order.

  • Version control/source code control. Final outputs and products grow through a process of successive refinement.