Category: Knowledge work

Idea Management as an Abundance Problem

The best way to have a good idea is to have lots of ideas

 Linus Pauling

I suppose it’s fitting that I have struggled with this blog post far more than most. It began with a desire to improve the rhythm and cadence of completing and published writing deliverables. Having just passed nineteen years writing this blog, you would think I was beyond fits of teenage angst. Maybe the onset of blogging adulthood is more daunting than I realized. 

I’ve always liked the Pauling quote. Having lots of ideas has never been a particular problem for me, so I’ve trusted that some reasonable portion of them would be good enough to share. For a long time, I attributed my occasional struggles to focus on a particular train of thought on my ADD. Bright shiny objects always promise a dopamine hit, but I’ve felt like I’ve been able to keep it enough under control. 

It occurs to me, however, that my ADD simply serves as an early introduction into a world that we all now live in. We all swim in an overwhelming abundance of ideas. Our training and practice focuses on turning individual ideas into a desired deliverable, whether that is a blog post, a client presentation, or a spreadsheet analysis for our boss. 

While there’s much to be learned about that idea to deliverable evolution, there’s another layer of knowledge work practice that we must tackle. That thread of idea to deliverable is one element of a collection of threads and you have to manage the collection as something distinct from any one thread. 

Simply splitting the problem into two layers is a step forward for me. I have a reasonable handle on the first layer; I know how to take an idea,  develop it, extend it, and polish it into a deliverable. As a creative task, however, that process is rarely linear. Ideas are not widgets, you can’t simply plow ahead from idea to deliverable. You often need to set things aside and let them cook. 

Which is where the second layer comes into play. What do you pick up when you set the first idea aside? Presumably another idea that you set aside earlier or a new idea trying to seduce you. You now have a management problem as well as a creation problem. 

The management problem is about selecting ideas, monitoring progress, switching, sequencing, timing, and cadence. This is operating at a different level of abstraction from the creation process. 

At small scales, you can likely manage organically. There aren’t so many threads that you can’t keep most or all of the management issues in your head. With time and the accumulation of a body of work, it’s worthwhile to externalize the management problem and not try to rely on the limits of memory. At the same time, the management process must be subordinate to the creative process. 

Over the past eighteen months or so, I’ve been gradually retooling my baseline creative practices around a more disciplined note-centered practice. Like any retooling, this has led to a temporary drop in output. As committed to improvement as I may be, there’s still muscle memory to be overcome. Even bad habits are still habits that require extra energy to break down and replace. Some markers of this journey that have risen to things worth sharing include:

Early on, what management of the process I did was on the proverbial back of an envelope. Keep a list of ideas as they came to me and pick something off the list when I sat down to write the next piece. Look back at the last few blog posts and write a follow up piece. Do this for any length of time, however, and the envelope gets pretty full

My next thought was to take the list off the back of the envelope and formalize it. I dug into the approaches of other writers who’ve gone through this evolution. Among the appealing approaches I ran into were:

These approaches, however, don’t scale well. As your body of work grows, you risk spending more time maintaining the management control system than you do creating new work. That misses the point entirely. 

Some Zettelkasten advocates claim that the necessary tools and structure emerge as you gain more experience and grow your collection of notes:

This hasn’t played out for me. Setting aside the hypothesis that this simply reflects my personal limitations, what’s missing? 

This comes back to the distinction between creating and managing. Most of the discussion and advice I’ve been able to review is focused almost exclusively on creation. It either ignores managing the process as a whole or presumes that what needs to be managed is trivial relative to making creation work more smoothly and reliably. 

To manage the overall process you need to get above the details of individual work in process (WIP) items. You want to collect just enough data about each item to not have to read the entire piece while you are trying to manage a collection of multiple WIP items. And you need to track the status of each piece of WIP relative to its transition from WIP to final deliverable. Is this item a new idea? A draft? In need of editing? Ready to publish? Published? There’s a life cycle to be defined. This is the metadata you need to make informed decisions about the overall process. Do you have enough WIP to feed your deliverable goals? How does the mix of materials look?

This is a classic data management problem that would seem to call for a simple spreadsheet as DBMS solution. Or a multi-column outline of some sort. Both of those approaches failed relative to the goal of keeping the management system subordinate to the creative system. As I continue the transition to a note-centric creation system, the challenge is to embed the pertinent metadata in the individual notes and create some method of querying the metadata to generate the schedules and lists that will help me manage the creative process. Now that I’ve got a handle on the basic requirements for managing my WIP, the next step is to discover or create the reporting tool. 

McGee’s Musings turns 19 today

McGee’s Musings turns 19 today. I started this outpost on the Web nineteen years ago while I was on the faculty of the Kellogg School. It was a way to share ideas with my students. It also grew out of my abiding interest in doing knowledge work effectively.

Recently, there’s been a resurgence of interest in how to best apply technology to knowledge work. Some of this is about developing software tools. More interesting to me has been a set of new ideas about how to use tools. Notes, for example, have taken on new roles and new importance. What does it mean for a note to be “atomic” or evergreen?” Why is that a useful distinction?

What is a Zettelkasten? Should I care? How about a “digital garden?”

One of the key concepts I’ve been working out for myself has been the idea of making knowledge work observable. This spot has been one element of that ongoing effort.

I’m beginning to work on how to improve this experiment both for myself and those who’ve been following along. There are two key questions I need to address. The first is where to draw the line between what gets shared and what isn’t yet fully baked. When in the creation process does it help to reveal the current state of progress to make still more progress? That’s largely an emotional decision about how exposed I want to feel.

The second question is what other elements should be part of the design of this place? What would make this site more useful for you? What’s missing? I’ve got some ideas. I’m researching others. What would you recommend?

Identifying Knowledge Work Practices

I’m continuing the quest to understand and improve my work practices. Several terms/ideas have been holding my attention and I’d like to work out how they might fit together;

After some iteration, I put them together in the following diagram;

Let’s start with signals and noise. Noise is a problem in pretty much any communications environment. Social media of late is an environment with a lot more noise than signal, for example. Claude Shannon pretty much invented the field of information theory in his work at Bell Labs in the 1940s. There might have been a point when I understood the math back in my youth. Today, I’m content to settle for a metaphorical approach and think about my work as uncovering or creating signal out of noise.

I’ve been running up against the limits of deliverable and working backwards. Over-focusing on deliverables limits your thinking when you are early in the process and don’t yet have clear notions of deliverables. I’ve found the notions of sensemaking and solving for pattern effective ways to counterbalance that focus.

Another notion I am playing with is a distinction between Intake and Pre-Intake. I appreciate the warnings of some in the Zettelkasten crowd about the “collector’s fallacy,” but not enough to stop my picking up shiny things. I value my magpie tendencies and I’m reluctant to trade faux-productivity for creativity.

As I thought through this broad flow from initial inputs to deliverables, it occurred to me that it was worth thinking of process/practice management as a layer distinct from the working layer I typically operate within. One hypothesis I’m exploring is that there is a useful distinction to be made between process and practice. “Process” feels a bit too rigid for most knowledge work; practice seems to better capture an appropriate degree of flexibility and adaptability.

Regardless of how the process/practice distinction evolves, separating the managing layer from the doing layer is helping. It allows me to investigate activities and practices that may be missing from my current repertoire. As a first approximation, I’ve identified several practice management elements to investigate:

Some of the pieces exist–reference management, archive management–and could stand improvement. What is most evident to me is that I don’t have effective practices for managing WIP and reading. Now, I’ve got some shape on what to work on next.

Building A Bespoke Knowledge Work Environment with Off-the-Rack Tools

Photo by cottonbro from Pexels

I’m still slogging through the mess I spoke about last time. I just checked and I’ve written over 10,000 words since then; none of which yet rise to the threshold of being worth sharing. Some will before too long, I hope.

One of the thoughts that leaked out of my fingertips during that time is the seed of an idea that provoked this post.

As a knowledge worker, one permanent task on your to do list is to build a bespoke knowledge work environment using the off-the-rack tools  available.

I am in the midst of unpacking that idea for myself and decided it would be useful to share that process. I won’t expose all of the mess; the daily journal entries where snippets of this post first surfaced, the list of previous posts that I searched out and reread, the bullet point outline I am working from right now. But I will try to reflect the essentials.

I’ve organized this around the following four questions:

1. Why do you/I need a bespoke environment?

2. Why are most of us constrained to leverage off-the-rack tools?

3. Why is this perspective useful?

4. Where should you/I start?

Why do you/I need a bespoke environment?

Off-the-rack is a concept that didn’t exist before the industrial revolution. All products and services were bespoke. If you needed a new shirt, you made it yourself or had someone make it just for you. The industrial revolution and the industrial era were built on a different promise; accept some level of standardized product in exchange for a huge leap in average quality and a proliferation of choices. This has been such a successful strategy, that it’s easy to forget that it is based on a tradeoff.

Knowledge work and the products of knowledge work return us to a bespoke world. Value in knowledge work is correlated with uniqueness. I’ve written about this before (Balancing Uniqueness and Uniformity in Knowledge Work). If your goal is to pursue unique insights and contributions, you will want to tweak and tune your work environment in any and all of the ways that contribute to achieving that uniqueness.

Why are most of us constrained to leverage off-the-rack tools?

Inventing and building new tools is an order of magnitude more difficult than using existing tools. We tend to forget this when we reach for tools in an existing environment; you aren’t likely to think about what it takes to make a carpenter’s hammer as you pick one up to drive a nail.

Until recently, the fundamental tools of knowledge work were simple and readily available. Paper and pencil, blackboard and chalk, can take you far down the path of creating a knowledge work artifact of value.

Knowledge work, however, is fundamentally symbol manipulation in various guises. Communications and computing technologies are power tools for manipulating symbols. And, they are complex artifacts in their own right. You would no more think of writing your own word processing tool than you would think of forging your own hammer.

That does not, however, absolve you from learning how to choose and use available tools effectively.

Why is this perspective useful?

Adopting the perspective that the goal is a tailored environment and the available building blocks must be selected from what’s available sets up a tension that can be used to drive the design process.

Perhaps most importantly it provokes a recognition that simply picking tools is the least challenging task before you. That should establish an appropriate degree of skepticism about the blandishments of tool vendors and tool enthusiasts.

I took a look at this problem some time ago (Building Your Knowledge Workshop). That advice still applies. Your goal is an effective work environment, primarily for yourself. Individual tools come and go in your knowledge work environment much the same as tools accumulate and evolve in a conventional workshop. You need to grasp the work you are doing and the materials you are working with. And you need to learn what each tool can and cannot do in support of that work.

Where should you/I start?

There’s a decision to be made at the very outset of this process. It’s a choice about attitude or approach. You can elect to treat tools the way a new apartment dweller might. Get a starter set of basic tools assembled by some marketing intern at Home Depot and call the building super when you run into trouble. Or, approach the problem like a homeowner with a strong do-it-yourself bent. Investing in and learning to use professional tools for the most part. Knowing when to reach out to more experienced experts from time to time. Or, finally, you can offload your problems to a collection of experts and their tools. This increases your costs and leaves you beholden to the expertise and ethics of the experts you rely on.

I’m an advocate for the middle approach. That starts with getting a better handle on the work you do, followed by more diligently investing in learning how to use your existing tools. We don’t encourage either step in most organizational settings. Software development seems to be the only arena where practitioners routinely think about and invest in their tooling and practices.

Embrace the mess if you want to do better knowledge work

I’ve been deeply immersed in the recent profusion of new ideas, apps, and initiatives in the knowledge work  space. I’ve been working to make sense of a host of terms and concepts and discern their relevance to my own work. A partial list of those concepts (with some pointers to good entry points) includes:

There’s also a recent uptick in applications and services offering a path to implementing these ideas. These new apps are also fighting for mindshare with a set of existing apps. A very partial list (basically those apps I have experimented with or use with some regularity) includes:

Software developers, entrepreneurs, and evangelists of all stripes have to make the spine of their application, service, or approach clear and compelling. You’ve got to be a believer if you’re going to put in the time and effort to build something new. Early adopters also tend to be believers.

I tend to be an early adopter in many settings. But I’m also an old fart, so I’ve been jilted many times. Scar tissue provides perspective.

One of the drivers behind this surge in new work is the inexorable shift to knowledge work. Knowledge work is different from so much of the work that organizations have learned to manage and control. No matter what the bean-counters and compliance managers would like, knowledge work is inherently messy.

There’s a distinction in the world of early AI research that is useful in this context. The early world of AI research broke into two camps on the nature of intelligence; the “neats” and the “scruffies.” I took a look at this argument a number of years back in an earlier blog post on the realm of knowledge work–Knowledge management: the latest battle between the neats and the scruffies.

I once aspired to being a “neat”–business school is fundamentally targeted towards those who cherish and desire to impose order. The reality, linked no doubt to my ADD, is that I will always be a “scruffie.”

Fortunately, the world now aligns more closely with my “disorder.” You can’t get to “neat” without traveling through “scruffie.”

The challenge is that nearly all of the evangelizing and advice about new ideas is packaged as though that journey is over or, at least, easy. We get a “neat” picture of the destination. The journey is left as an exercise for the reader.

Even if the developers and early adopters acknowledge that there is a journey to be made, they gloss over the messy parts. If they share any details of the necessary hero’s journey, they offer just enough of the ugly parts to burnish their story. Preparing you for what you will encounter just gets in the way of the next chapters of their stories.

The absolutely essential step if you want to travel the path to being more effective as a knowledge worker is to accept that you have to walk the path for yourself. Seeking out more honest accounts of those who have traveled before you can help. Finding guides who can walk with you and help you avoid the quicksand and tar pits is even better.

But you’re still going to get dirty.

Building knowledge work toolsets

Swiss Army KnifeI first encountered the notion of “affordances” in Don Norman’s excellent The Psychology of Everyday Things (which was renamed The Design of Everyday Things a couple of years later).  From the world of design, an “affordance” is a characteristic of an object that offers clues about how to use or interact with that object; the design of a chair tells us it is something for sitting on, a pitcher is for holding and pouring things. Affordances can be difficult to design in a world of more complex physical objects; they can be nigh on impossible in the design of software tools and applications. One of the things that made early computer systems so confounding was that they offered no affordances to latch onto. Graphical user interfaces were a huge step forward in making software user friendly (or at least not overtly user hostile).

Affordances and the design thinking that goes into crafting good ones are rooted in the physical world; dexterity, reach, visual acuity, strength, all factor in to design. This is the world of ergonomics or human factors. Transferring that knowledge to the abstract world of software and the objects in our environment with significant software elements is no simple task. You need only think of the remote control for your DVR and set top box or the user interface of your bank’s ATM to appreciate how difficult this design work can be.

One failure (limitation?) of modern application design is affordances that run out of steam before users grasp the full capabilities of an application. If all chairs looked like a Shaker chair, how would we ever figure out what to do with a BarcaLounger; or the pilot’s cockpit seat in an F-16? The menus and window layouts of your typical desktop application (Word, Excel, Powerpoint, Outlook) get everyone up and running quickly, but they do nothing to hint at, much less reveal, any deeper capabilities or opportunities. So, for most users, most of the time, those opportunities go unrecognized and the capabilities never get exercised.

What separates power users from the rest of us, then, is a willingness to dig into users manuals (RTFM as a career advancement strategy) and to experiment and play with their tools to figure out what is possible. Otherwise smart people take marketing hype about “easy and intuitive” software seriously and judge themselves deficient when good software tools are often neither. The opportunity wasted is tremendously sad.

So, what do we do to help more people get more value out of their software? My specific interests happen to be about knowledge work. What can we do to help knowledge workers push their tools farther and better? Put another way, why do we settle for knowledge workers leveraging such limited subsets of the tools they already use or avoiding tools or techniques that might unlock significant new insights and outputs?

Although I’ve been talking about affordances I don’t think the blame or the answer lies exclusively with software designers. More realistic claims from software marketers wouldn’t hurt although I’m not holding my breath. As software users, one thing we can do is invest time to suss out more of the design models in the heads of the software developers who build the tools we are seeking to leverage.

Word processing software offers examples of what I’m thinking about.

Microsoft Word is likely the dominant word processor on the planet. You can find it installed on most any desktop or laptop computer you encounter. It ushered in the world of WYSIWYG computing where one goal of the interface was to represent your work in as close to final form as possible. Implicit in that design was that the “final form” was a printed page.

A consequence of that design choice was that you now had one tool that spanned what had once been a series of discrete stages in the process of bringing an idea to the printed page. Writing, editing, design, and layout are quite different cognitive activities. In a pre-WYSIWYG world, each of these steps had its own set of professionals, its own set of standards and practices, and its own set of tools. With Word you now have a single tool that can serve at all stages (at least for 80% of cases).

Whether that is a good thing is another question. Having one tool makes it more difficult to see that the activities in each stage are quite different. When you’re working out the structure of an argument, there’s little point to be worrying about what typeface and font size works best for a section heading. But a single tool blurs that distinction and boundary. You can be enticed into playing with those problems or thinking they are important to deal with now while your fundamental argument or storyline is still a mess.

A tool that is suitable across all these steps may be valuable within an organization. But at the price of obscuring the differences between different process stages. You could manage that problem if you made an effort to make the different stages of the process more explicit. But now the organization and its people need to work at cross purposes to the tools. You have a single tool obscuring the differences in the process while you try to highlight those same differences as you manage the development and evolution of any particular deliverable.

Scrivener is a popular word processing tool, especially among authors dealing with longer form writing projects. Its user experience embodies a very different model of the writing process than Microsoft Word. Scrivener makes the various stages of writing–drafting, editing, design, layout–visible and identifiable in its user interface. In particular, WYSIWYG is a secondary or even tertiary goal in the user experience.

Moreover, Scrivener was designed in a time when the printed page is only one of many target forms for final deliverables. It also supports multiple formats for electronic books, PDF files, and the web. To that end, the design of Scrivener separates the activities for structuring a draft output from formatting it for final output.

For users accustomed to the WYSIWYG model embodied in Microsoft Word this is a source of frequent confusion. Learning how to invoke specific functions and features in Scrivener isn’t terribly helpful until new users grasp the fundamentally different process model embedded in the design of the application.

Software marketers don’t like to acknowledge that software users require multiple individual software tools to handle the complexities of modern knowledge work.It is not their job to figure out how to fit multiple tools together to get work done.

This is an element of your job description that hasn’t been acknowledged or addressed in the average organization. You will likely be issued a starter set of basic tools. But don’t expect useful guidance on how to use them in concert to accomplish the work expected of you. It is yours if you hope to be an effective knowledge worker.

Learning to See-Improving Knowledge Work Capabilities

My wife is a photographer. Quite a good one, in fact. One sure way to annoy her is to ask what kind of camera she uses after admiring one of her photos.. It’s her eye, not the camera, that recognizes the perfect shot. The tool may well be the least important element in the mix.

My own photography has gotten better courtesy of time spent in apprentice mode by her side. Photography is also an example of a knowledge work capability that can shed light on performance improvement in a knowledge context. The primary performance metric is whether you can capture the image you envision. Secondary metrics might include meeting time, budget, and other constraints on the image. In some settings, you may also need to be able to articulate the logic for why the image you eventually capture meets the criteria set.

If your goal, for example, is to capture a simple selfie to demonstrate that you were there at Mt. Rushmore, anything with both you and the mountain in frame and in focus will suffice. As you goals evolve, you also acquire new concepts and vocabulary; composition, depth of field, light conditions, focal length. exposure.

Meeting those goals may lead you to exploring and adopting new tools. A better camera might well enable you to capture images that weren’t possible with starter tools. But the functions and features of more sophisticated tools might just as well not exist if you don’t have the corresponding concepts to work with.

These concepts and the tools all need to be in service to creating the images you imagine. You don’t learn them in theory or in isolation. You learn them by doing the work and getting feedback. Over time, you also learn to give yourself better feedback.

Ira Glass has an excellent series of short videos on storytelling that fit here and fit knowledge work in general. The whole series is worth your time and attention–Ira Glass on Storytelling – This American Life. The nut graf, however, is something to keep close at hand as you work at your craft:

Nobody tells this to people who are beginners, I wish someone told me. All of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, it’s just not that good. It’s trying to be good, it has potential, but it’s not. But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. A lot of people never get past this phase, they quit. Most people I know who do interesting, creative work went through years of this. We know our work doesn’t have this special thing that we want it to have. We all go through this. And if you are just starting out or you are still in this phase, you gotta know its normal and the most important thing you can do is do a lot of work. Put yourself on a deadline so that every week you will finish one story. It is only by going through a volume of work that you will close that gap, and your work will be as good as your ambitions. And I took longer to figure out how to do this than anyone I’ve ever met. It’s gonna take awhile. It’s normal to take awhile. You’ve just gotta fight your way through.

There is craft behind all knowledge work. You get better at craft work by being intentional about getting better. And by accepting that craft lies in the mix of tools, techniques, practices, mentors, and peers. It’s a mistake to remain wed to the first tool you pick up, It’s equally a mistake to confuse changing tools with improving your craft.

Getting Outside Your Head – Managing the Mess

Managers do not solve problems, they manage messes
– Russell Ackoff

I’ve long been a fan of the late Russell Ackoff. This was one of his observations that continues to stick with me. As much as we like to think of the world as a set of discrete problems to be solved, reality insists on  being interconnected and messy. As I seek to understand and improve my own knowledge work practices, this is one of the pieces of wisdom I try to keep in mind.

One of the sources of mess that has been on my mind lately is scale. Things that seem obvious and simple always get messy as they get bigger. For example, I’ve just wrapped up a course on how to do requirements analysis. We run it as a field based course which means we work with local organizations to tackle a real problem. But it has to be a small enough problem that we can fit it into a semester’s worth of work.

I can require student teams to prepare the same work products that would be expected of them on the job but the problems aren’t big enough to make the need for some of those work products to be evident. Students do the work I ask of them, but they don’t really believe me when I assert that all of those products are relevant and important.

I’m probably irretrievably tainted by my early days in public accounting when I hung out with auditors. Now, this was so long ago that spreadsheets were still actual sheets of paper with rows and columns preprinted on them. Audits generated piles and piles of paper. The most natural thing in the world for an auditor faced with managing a stack of spreadsheets was to prepare another spreadsheet to serve as an index into the stack.

The physical scale of stacks of paper made this solution fairly obvious. Turn those stacks into bits, however, and the need to manage that scale problem disappears or, at least, fades into the background. If you can no longer see the mess, it won’t occur to you that it needs to be managed.

On a project team, there is often enough friction in dealing with multiple team members trying to coordinate their work that you can impose some level of control over the growing collection of digital materials being produced. But the usual pressures to “get to done” work against efforts to manage the mess. Persuading team members to give some thought to what they name that Excel file gets lost in the rush to work on what’s inside the file.

A sufficiently OCD project manager might prevail over a project team. Certain regulated environments can force the mess to be managed. But for individual knowledge workers, there are few incentives to deal with, or even recognize, the problems of managing the mess that is a digital work environment.

Getting work outside of your head is only a first step. Doing so gives you the capacity to take on problems that are too big to fit inside your head. Your reward for increasing your capacity is a set of bigger messes to manage.

Getting Outside Your Head

I’ve been on a quest over the past year or so to understand the importance of getting outside of your head if you want to be more effective as a knowledge worker. The inciting incident for this quest was reading How to Take Smart Notes by Sonke Ahrens (my review is at Unexpected Aha Moments – Review – How to Take Smart Notes). I think I’m past the “refusal of the call” but I don’t know that there is a mentor to be found, although there do seem to be many others walking similar paths. Ahrens tells a story about Nobel physicist Richard Feynman that I traced back to James Gleick’s biography of Feynman (Genius: The Life and Science of Richard Feynman). Gleick tells it this way:

[Feynman] began dating his scientific notes as he worked, something he had never done before. Weiner once remarked casually that his new parton [In particle physics, the parton model is a model of hadrons] notes represented “a record of the day-to-day work,” and Feynman reacted sharply. “I actually did the work on the paper,” he said. “Well,” Weiner said, “the work was done in your head, but the record of it is still here.” “No, it’s not a record, not really. It’s working. You have to work on paper, and this is the paper. Okay?”

This is what my math teachers would label a “non-trivial” insight. However, if they made that point when I was studying math, it sailed right past me. Sure, you could sometimes salvage credit on a problem set by “showing your work” but it never occurred to me that “showing” and “doing” your work was the same thing. I always felt that the work was supposed to be going on inside my head, that the goal was to get everything inside my head before exam time rolled around. Certainly the testing and evaluation systems reinforced the notion that you were supposed to keep the important stuff in your head; storing it elsewhere was likely to land you in serious trouble if you got caught referring to that external storage during the exam.

Some of this is the problem of “toy problems.” In teaching settings, you need to work with problems that can fit into class sessions and semester-long projects. With most of these you can get away with lazy practices; you can manage it all in your head. If you’re lucky, the course designer may try to force you to follow good practices above and beyond simply finding the “right answer.” As a student, you’re still likely to miss the point of learning the supporting practices. [As an aside, this is now something I’m working on improving in my course design and delivery]

Once you start to look for it, you do see that smart people have been offering good advice about how to deal with the limitations of your unaided memory and brain. Think of Anne Lamott’s advice to write “shitty first drafts,”  Peter Elbow’s practice of “freewriting,”  Tony Buzan’s advocacy of “mind mapping,” or John McPhee’s ruminations on “Structure.” All of these are the kinds of techniques and practices that can make us more effective at creating quality knowledge work artifacts. But it isn’t clear that we encounter this advice as early or effectively as we should.

If we do stumble across this category of advice and fold it into our work practice, we can gain a meaningful edge. We’ve taken elements of the work out of our heads and into our extended work environment. We’ve increased the range and complexity of material we can now draw on to create better deliverables.

I’m in the midst of working this out for myself. I actually think that this is something that each knowledge worker is going to have to design for themself. I’m suspicious of claims that someone’s new tool or application contains the secret answer. Right now, I’m investigating various sources with an eye toward identifying design principles and ideas worth extracting or reverse engineering.

Some of the more interesting trains of thought include:

Task Zero – Well Begun is Half Done

The late Peter Drucker continues to be a source of insight and inspiration for me.  In 1999, he published “Knowledge-Worker Productivity; The Biggest Challenge” in the California Management Review. I’ve written about it before (Knowledge work and productivity). I want to explore the following observation:

The crucial question in knowledge-worker productivity is: What is the task? It is also the one most at odds with manual-worker productivity. In manual work, the key question is always: How should the work be done? In manual work, the task is always given.

I’ve started to think of this question as “Task Zero.”

The invention of zero was one of the great advances in mathematics; perhaps we should respect that power. One of the curious things you learn as a computer programmer is to start counting from zero rather than one. Certain things become easier when you do.

One of the reasons I’m drawn to starting at zero is that it frees up my thinking. If you think of yourself as starting from zero on a map or a coordinate system, you are free to move in any direction. Starting at one immediately commits you to a direction.

That’s tempting because a direction gets you moving. There’s a great observation from Cory Doctorow that reflects this:

Start at the beginning,” he said. “Move one step in the direction of your goal. Remember that you can change direction to maneuver around obstacles. You don’t need a plan, you need a vector.

― Cory Doctorow, Homeland

We like movement; it feels like progress. But they’re not the same thing. Take a closer look at Doctorow’s quote; he’s advocating movement in “the direction of your goal.”

Clarifying the goal is Task Zero. It is the “what problem are we trying to solve” question that grizzled consultants pose to annoy eager young MBAs and impatient clients.

It’s worth giving this task an identity separate from the tasks that follow. It’s like that pregnant moment at the end of a countdown to launch just before movement begins.