Practice and Performance

Cpl. Derek McGee, USMC MEU15 TRAP 2013

How do you strike an effective balance between practice and performance? In many realms we draw a distinction between performing and preparing to perform. Actors and musicians rehearse. Athletes practice. Soldiers train before they fight.

In other, equally demanding, realms the boundary is fuzzy; at times non-existent. Where does a sales rep or project manager practice? Where does a brand manager practice market segmentation? When does an investment banker practice designing a deal?

The notion of an apprentice observing and mimicking a master is one proven model that blends practice and performance. What troubles me is that this model works best when it is explicit. There needs to be some recognition that some performance settings are about both performance and practice; some fraction of your focus and attention needs to be tuned to learning.

My sense is that we have abandoned the notion of practice built into apprenticeship and favor performance exclusively. If we substitute performance only in place of practice and performance, do we abandon the possibility of achieving peak performance? How do we recognize situations that call for effective apprenticeship models? How do we design organizations so that they meet their performance goals and provide the necessary practice opportunities so that tomorrow’s organization can perform as well or better than today’s?

Connected Courses Course – An Experiment in Collaboration – #CCourses

I’m carving out time to participate in what I see as a worthy experiment in collaboration. It’s been organized by some of the most interesting people working on online learning and seems to be attracting an equally interesting collection of people interesting in participating.

Here’s what they have to say:

We invite you to participate in a free open online learning experience designed to get you ready to teach open, connected courses no matter what kind of institution you’re working in. We’ll explore how openness and collaboration can improve your practice and help you develop new, open approaches.

You can mix and match — take one unit or take them all, and go at your own pace. You’ll be joined by other participants from around the world who are looking to:

  • get hands-on with the tools of openness;
  • create open educational resources, curriculum and teaching activities and get feedback from a community of your peers; and
  • connect with and learn alongside other faculty, educators and technologists.

Sign up and receive updates from the organizers. Everyone is welcome, and no experience is required. We will all learn together in this free and fun opportunity to start planning your own connected course. The instructors, award-winning university professors from around the globe, are the innovative educators behind successful connected courses such as FemTechNetds106phonar, and the National Writing Project CLMOOC.

An orientation starts Sept. 2 and the first unit starts Sept. 15, 2014 and you can sign up and find more details about the topics we’ll be exploring at

[Connected Courses Sign Up]

This is being billed as “a collaborative community of faculty in higher education developing networked, open courses that embody the principles of connected learning and the values of the open web.” I think it is something richer than that.

Paying attention is the least that you should do if you are interested in issues of collaboration, learning, and new organizational forms. Jumping into the pool with the rest of the crowd is a better idea.

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.


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.

How better thinking about deliverables leads to better knowledge work results

Deliverables should be a knowledge worker’s best friend but, like all good friendships, the relationship can get complicated. One of the most direct ways to improve the quality of knowledge work is to spend more time and effort to define appropriate deliverables.

My faith in deliverables has grown out of decades of practical consulting experience, where defining the appropriate deliverable can mean the difference between mediocrity and success. A deliverable is an identifiable work product intended for someone outside the knowledge work process that marks the end of that particular process. Examples of deliverables include:

  • A consulting report prepared by the consultant or consulting team containing recommendations to the client sponsor.
  • A list of recommended job offers to prospective employees prepared by a campus recruiting team and handed to the hiring manager.
  • Production Release 1.0 of a new application system installed and operating on the companies intranet.
  • A project work plan and business case delivered to a steering committee for approval.

Why Deliverables Are Relevant

In the world of industrial work, process outputs are well-defined. This gives organizations a straightforward process improvement path. Holding the outputs constant allows process improvement to proceed by identifying and eliminating activities that don’t add value to the output; to break down, reorder, and redesign process steps to create more standard outputs with less input; and so on.

This path isn’t available to knowledge work practices. The goal is not to produce well-defined, standardized outputs. Deliverables are the closest analogue but their value depends on how well they match the unique needs of their users. No one is interested in a spreadsheet full of someone else’s data; no teacher is likely to value a copy of a paper you’ve submitted to another class. Understanding what aspects and features of a knowledge work deliverable are most valuable to its intended user is key to focusing efforts on producing the desired deliverable.

Natural vs. Artificial Deliverables

Some knowledge work processes produce obvious and natural deliverables. A campus recruiting effort generates a list of candidates to extend offers to. There may be a variety of associated supporting documents (e.g., interview notes, assessments), but the list is the deliverable and is a natural outgrowth of the process.

Other knowledge work processes generate more amorphous or less obvious outputs. For example, what’s the deliverable for a typical project status meeting? Shared understanding among the project team? Agreement about the state of the project between the team and the project sponsor?

Consider the typical status report as a more artificial than natural deliverable and ask how much value resides in this particular item. If you can define the deliverable more effectively, is the world a better place?

Defining Better Deliverables

The better we can define deliverables, the better and more effective we can make our knowledge work. Rather than ignore the end product, we need to be systematic in extracting as much as we can about the expected deliverable that can guide our effort to create it. There are three productive paths to explore in using the deliverable to drive your knowledge work.

  • Path #1: Understand the user’s essential quality need

    Does this deliverable need to be exactly correct whatever the time or cost, as good as possible given a particular deadline/budget, or the best you can come up with on the phone? Each deliverable demands a different approach; each requires a conversation (and perhaps a negotiation) with the end user. The history of systems development is littered with examples of failing to follow this path.

  • Path #2: Balance uniqueness and uniformity

    We’ve been conditioned by one hundred years of industrial experience to value uniformity. In knowledge work, the value of uniformity is to free time and attention for the essential uniqueness we’ve defined. The folks in marketing may believe that corporate identify standards are about branding. For a knowledge worker, they let you ignore formatting decisions to focus on the content that matters.

  • Path #3: Specify stopping conditions

    There was an joke that was old even in my early consulting days that you knew the design phase was complete when the budget ran out. Possibly the biggest challenge for managing knowledge work is determining how to recognize that the deliverable is done. Think of the unfinished doctoral dissertations and manuscripts of the great American novel gathering dust on a shelf or lost in a lonely directory of a disk drive. Too many knowledge-work efforts fail simply because no one thinks about how to recognize what "done" will look like.

Focusing on deliverables means working backwards with the end in mind. Knowledge work is valuable to the extent that it produces end results, i.e. deliverables, that meet the unique requirements of a particular customer or end user. Time invested in understanding what that deliverable should look like will yield the greatest return in defining and focusing the activities that contribute to creating that particular deliverable at the right time and place.

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.


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.