Category: Knowledge work

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.

Culture, Process, and Practice – Effective leverage for Enterprise 2.0

The discussion about organizational culture in knowledge management and Enterprise 2.0 efforts is evolving in useful and pragmatic ways. The earliest discussions ignored culture entirely and implicitly assumed that technology would magically shape the organization as needed. The next round of discussion identified sharing as a desirable global cultural characteristic. If you were in a sharing culture, all was good. Time would ultimately reward your virtue. Those with equal need but less virtue whispered of incentives and WIIFM (what’s in it for me?). Crass and vulgar, but perhaps sufficient for most organizations.

In general these open ended discussions of culture are unsatisfying. They make gross assumptions on the basis of little data. Dave Snowden’s principles of knowledge management (see Rendering Knowledge) provide important pointers to a better answer with his emphasis on the behavior of individual knowledge workers.

Enterprise 2.0 as the extension of ERP

Michael Idinopulos of SocialText noted a shift in the debate at the recent Enterprise 2.0 conference in Boston. In a post titled The End of the Culture 2.0 Crusade?, he observed that:

Last week, the Enterprise 2.0 world turned a corner. Nobody pounded the table for cultural change. Nobody talked about incentives or change management. Nobody talked about transparency or modeling collaborative behavior.

Instead, people talked about process.

…

This is the most pragmatic shift in focus since the inception of Enterprise 2.0. It will have huge effects on the pervasiveness of social software in the enterprise, because it shows a clear path to the business value companies can realize from their implementations.

I ve been arguing for some time that social software achieves widespread adoption only when workers use it in the flow of work. Asking your colleagues to step outside their daily processes and tools to share what they know or network with others won t get you very far. (Trust me, I ve tried.) Bringing your colleagues collaborative tools and practices that make their daily processes better, faster, cheaper, and more interesting does work. It s all about process. Improve the process, you win. Don t improve the process, you lose.

This point of view squares with Ross Mayfield’s that broken business processes contribute to email overload and that

(Employees) spend most of their time handling exceptions to business processes. That s what they are doing in their inbox for four hours a day. E-mail has become the great exception handler.

While this is a very attractive position, it is incomplete in an important, and potentially dangerous, way. If you focus Enterprise 2.0 efforts solely on business process you may get scale, you will likely avoid the issue of culture, BUT you risk missing an opportunity for great leverage.

Leaving Process for Practice: Leveraging Knowledge Work as Craft

Focusing solely on Enterprise 2.0 efforts as an extension of existing business processes treats Enterprise 2.0 as a residual, clean-up, effort. It presumes that the goal worth pursuing is the efficient execution of well-defined processes. It reduces Enterprise 2.0 to an afterthought to ERP.

There is an assumption in this process-centric view that all relevant behavior can be reduced to business rules in the automated system. Exceptions are viewed as system failures or design failures. Moreover, failures in the system are attributed to resistance on the part of system users and operators. Instead of interpreting failures as resistance, what if we start by treating them simply as data?

If you start with process, your goal is uniformity at scale. Ideally, every situation should be treated in exactly the same way. This is eminently logical if you are calculating payroll withholding taxes. It is clever when you extend that logic to treating airline seats as a perishable commodity whose price might vary depending on the day of the week. Can you push in this direction forever?

Are there situations where uniformity and scale are not the appropriate criteria? Of course. The realm of art and craft is all about uniqueness and the value of limited scale. What happens if we opt to start from the art and craft end of the spectrum?

One choice is to accept the dichotomy. There is a class of problems suited to process thinking and a class of problems suited to art and craft. Another choice is to continue to force fit problems into a process view of the world. This has been a successful approach. Consider the number of problems that have been transformed into algorithmic problems well-suited to automation — inventory control and demand forecasting to name two.

A third choice is to ask how to improve art and craft without presupposing that uniform process is the goal. This was the approach started by Vannevar Bush, Doug Engelbart, and their intellectual heirs. This approach spawned much of the technology environment that we operate within today. Oddly, we’ve largely ignored their motives in creating this technology; to support more effective ways of wrestling with intellectual problems.

Engelbart, and those working on his agenda, started building new technology tools because our previous tools and methods were inadequate for the problems we had to solve. They built tools to support new kinds of problem-framing and problem-solving practices. We’ve adopted the tools without considering the practices and behaviors they were designed to enable.

Culture as the sum of shared behaviors

Those who lay the blame for failed efforts to introduce knowledge management or Enterprise 2.0 tools on organizational culture are picking up on this behavioral issue. They are doing so, however, at the wrong level of detail. Organizational culture is a convenient shorthand for the practices and behaviors that constitute "they way we work around here." Changing culture is hard because it’s an abstraction; there’s nothing to push against to get it to move.

While changing behaviors can also be hard, as anyone who’s tried to adopt a new habit can attest, it is possible. Change the right behaviors and eventually you have a new culture. The opportunity in Enterprise 2.0 technologies lies in the new behaviors that become possible. The challenge is that these technologies do not dictate a single set of obvious behaviors. In fact, it is possible to adopt the technologies and make no behavioral changes at all. 

Focusing on behavior is still a challenge. Technology opens up possibilities while setting few constraints. The activities we are interested in here are equally unconstrained by business process; we’ve defined them as the behaviors that don’t fit within the mantle of process. We need to go back to observing how work is currently being done, ask what flows smoothly, see where things get stuck, and design alternate ways to make use of the new range of available tools. We’ll visit those questions tomorrow.

LATE BREAKING LINK: As I was about to publish this post, John Tropea of Library Clips posted a lengthy piece on Have we been doing Enterprise 2.0 in reverse : Socialising processes and Adaptive Case Management. I’ve only just skimmed it, but it’s squarely part of this evolving conversation.

Observable work – more on knowledge work visibility (#owork)

My post on the visibility of knowledge work last week generated some some excellent comments and excellent blog posts around the web. For my own benefit I wanted to gather up what I’ve come across so far and put it in one place.

Recap

Greg Lloyd, CEO at Traction Software, kicked things off. pulled together some key threads of the conversation and gave us a better label – “observable work.” His initial summary:

I believe that principles of open, observable work like open book financial reporting to employees – is a simple and powerful principle that people at every level of an organization can become comfortable using. In my opinion, wider adoption of observable work principles can succeed with support and encouragement from true leaders at every level of an organization – as Peter Drucker defines that role: “A manager’s task is to make the strengths of people effective and their weakness irrelevant–and that applies fully as much to the manager’s boss as it applies to the manager’s subordinates.” Enterprise 2.0 and Observable Work

Greg also pointed to an excellent post by John Tropea at Library Clips:

why do I have close to total awareness of people in my personal life that requires low effort, but yet in the workplace I don t have this ambient awareness!

In fact it may be more crucial to have micro-blogging/activity stream networks in the workplace as we share and work on the same/similar/related goals and tasks within our teams, across teams, workgroups, and enterprise wide so the more we are aware, the more we can be on the same page, and have better coordination, cooperation and collaboration surface opportunities (emergence), have the best people on the right tasks, and generally have the ability to be more responsive and adaptive.

Balancing Uniqueness and Uniformity in Knowledge Work

Self-portrait of Leonardo da Vinci. Red chalk....

Image via Wikipedia

The essence of good knowledge work is uniqueness not uniformity. The ideal knowledge work product is exactly what your client asked for and could only have been created by you. The challenge is that we have been conditioned by the industrial economy to value uniformity and see uniqueness as undesirable variation instead of the essential quality it has become.

To deliver better knowledge work products, we need to unlearn habits and develop new perspectives. Our inappropriate habits stem from assumptions about industrial work. With industrial thinking, once you ve created a new product the goal becomes how to replicate it predictably. You specify the characteristics of the output precisely, lock down the process, or, ideally, do both. That works if you need to manufacture cars or calculate every employee s pay stub correctly. It doesn t when the goal is to create the new product. The primary challenge here is to shift focus away from the issue of replication and toward creation. The question becomes how do we manage to create this? instead of how do we produce the same thing all over again?

Creating good knowledge work has a challenge beyond simple uniqueness. It is the same challenge faced by artists who once worked at the pleasure of their patrons. You want to create your art, but ultimately, you have to please your patron as well. You need to educate your patron about the nature and qualities of the output you have been commissioned to create and you need to learn from your patron what limits and constraints they face in putting that output to work. The unique insights where value resides may need to be packaged in a document or presentation package comfortable to the organization. Or you may need to place those insights in a more familiar context that lets them be seen by the organization.

Working out a feasible balance between the unique and the familiar is more complex and more subtle than either simply delivering what the customer asks for or creating an off-the-shelf product. It is not precisely a negotiation, nor is it generally a full-bore collaboration.

A basic strategy for discovering an appropriate knowledge work output has three parts.

1. Defining a deliverable.

If you ve grown up in a professional services environment, what s the deliverable? gets tattooed on the inside of your eyeballs. It s essential in professional services because it creates something tangible for which you can get paid. In any form of knowledge work the notion is valuable in that it makes something tangible out of what can otherwise be an amorphous process. A deliverable might be a spreadsheet or a presentation, an executive workshop, or a first release of a new information system; regardless, it needs to be something that both you and your client can point to and decide that it is done.

2. Understanding how the deliverable will be used.

The most important thing to understand about a deliverable is what your customer intends to do with it. Will it be used to identify a decision to be made? Justify that decision to someone higher in the food chain? Generate or eliminate alternatives?

Each of these possible uses influences what is important about the deliverable and what is secondary or irrelevant. For example, if your customer wants to understand whether a particular technology does or doesn t work in their current environment, a lengthy analysis of the current state of the software market isn t likely to be relevant.

That same analysis may be the essential requirement of someone evaluating whether to introduce their technology to the market. Whether you invest in preparing the analysis depends first and foremost on whether someone needs it.

3. Determining how the deliverable can be created.

Understanding how a deliverable will be used sets limits and focuses your attention on how it should be created. An executive who needs an answer about whether to invest in a new distribution channel may prefer that answer delivered on a one-page memo. However, if your customer is that executive s support staff, you may need to create substantial supporting analyses. Even in that case, whether you deliver that supporting analysis as a bound report, an electronic document, or organized as a series of frequently asked questions will affect both its value to the customer and its cost (to you) to create.

This three-step process should be followed for every piece of knowledge work we create. The urge to standardize outputs or processes is a holdover from industrial practices that is inappropriate in a knowledge work environment. Applied here, it risks short-circuiting the essential interaction. We must define and deliver the unique output that integrates client needs with your ability to create an appropriately unique solution.