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.

School as a Place to Learn Knowledge Work Practices

19th Century Classroom

I was usually good at the  stuff that gets rewarded in school. Good enough to  get away with also being a bit of a smart ass. The problem was that school came easily enough that I rarely had need to develop good study habits. Not a problem that I worried about at the time. The bigger problem was that I never saw the connection between study habits and effective work habits after you left the classroom and wandered into real life.

For a long time, this wasn’t actually a problem because there wasn’t a deep connection between schooling and real life. The only habits taught in school that mattered for later were to show up on time and do what you were told.

An odd thing about the knowledge economy, however, is that almost everything you do in school is a form of knowledge work. It’s a place where you could be laying down habits and practices you can call on in the future. Instead, we’re evaluated and rewarded for demonstrating our mastery of content. No one pays attention to how we developed that mastery.

Today, there’s increasing attention to the notion of learning how to learn and there’s lots of advice and material on note-taking techniques, or memorization tricks, or thinking habits. But, it’s all secondary. We let the superficial differences between the classroom and the real world obscure the deeper alignment we could be exploiting.

That’s a missed opportunity. The content we learn will become obsolete, but the practices will retain their value. The classroom should be where we develop the habits and practices we could continue to employ when doing knowledge work for a living.

Preaching and Practicing Better Knowledge Work Habits

Moving classes online back in March triggered a new emphasis on the notion that the knowledge work we do is better and more easily done when you get it out of your head and somewhere in front of you where you can see it and improve it. This is a position I’ve come to only after a long process of fighting the notion in practice and gradually coming around to that notion and working to update and adapt my own work habits and practices.

Practicing what you preach is always much harder than the preaching part. As part of convincing myself to work harder on improving my practices, I went back and gathered up some of the key moments in the evolution of my thinking if only to shame myself into more practice and less preaching. It occurs to me that assembling these pieces in one place may be useful to others as well.

Deliverables and the downside of working backwards

In 1992, Larry Prusak and I were working for Ernst & Young exploring the links between information technology and strategy. We thought it would be a good idea to organize what we knew and turn it into a book. We put together a short proposal, pitched the idea to the powers that be, and landed a contract with John Wiley & Sons to deliver a manuscript. Being a diligent project manager, I sat down to plan the effort. We had worked out a chapter outline, so I started with that classic planning strategy of working backwards from the deliverable.

I wish I could lay my hands on that outline and the plan I wrote from it. We reorganized the book from top to bottom multiple times between that first outline and the final manuscript we delivered. My first reaction was to blame myself for being a much worse project manager than I claimed. My second reaction was to focus on the estimating error of not factoring in the multiple revisions and rewrites that happened with each chapter.

It took a long time to recognize the deeper lesson hiding in plain sight. Successfully working backwards from a target deliverable depends on how clear a picture you have of that deliverable. There are always pressures trying to convince you that your picture is sharper than it is.

The temptation whenever you start a new consulting project is to generate the outline of the final report as part of your proposal efforts. That, however, applies an industrial, mass-production, mindset to a process that ought to be about addressing the unique problems and situations of this client in this moment. On the other hand, clients come to you because you have a reputation for solving problems similar to the problem they believe they have .They are paying for your expertise.

How do you draw on the work you have delivered in the past, without succumbing to the temptation to phone it in? How do you fold in knowledge of prior deliverables without blinding yourself to this problem’s new, and possibly unique, questions? Rely on prior deliverables too heavily and all you do is create variations on a theme. Ignore the lessons contained in prior work and you are no more valuable than any random smart person.

Assuming you bring a threshold level of professionalism to the task, what else can you do to avoid being led astray by old answers when you are facing a new problem? One thing is to distinguish between how far you can see and how clearly. Working backwards is a useful strategy, but that doesn’t mean you are in a position to work backwards from the finish line. Thinking through deliverables is only one tool in your kit.

Some pointers to prior thoughts on deliverables

I’ve written about deliverables multiple times on this blog. Here are a few of the more immediately relevant entries.

Knowledge work improvement – black box, white box, and deliverables

Deliverables – the fundamental secret to improving knowledge work

How better thinking about deliverables leads to better knowledge work results

Getting a better handle on knowledge stuff

Back when I was a Chief Knowledge Officer, I struggled with the problems of how to better tap the collective knowledge and experience of an organization filled with extraordinarily smart people. There were the technical problems of what to collect and how to organize it. There were the organizational change problems of how to persuade those same smart people that sharing their expertise my way was in their best interest.

I had an epiphany when I came to see knowledge management less as an organizational problem and more as a problem for individual knowledge workers. From that problem, the first task was to figure out how to get better at sharing knowledge with yourself. Which led me to the notion of observable work. Dave Winer’s thinking on narrating your work was an excellent entry point to that train of thought.

During that process, I sketched the following diagram as I collected my thoughts:

Scarcely rocket science but it was helpful to me. The next step was to think about what larger scale patterns or clusters might be discernible That led to this picture:

I thought it could be useful to revisit this and unpack it from the perspective of more experience and insights culled from other smart people. Let’s start with generating a new list of “Stuff” that seems to have something to do with knowledge:


10K Data Flow Diagram Log Speech
10Q Data Record (row) Lyric Spreadsheet
Abstract Database Manuscript Stanza
Acronym Database Query Map Statistic
Affinity Diagram Database Schema Mathematical model Statistical Model (Regression)
Agenda Dataset Melody Statistical Test
Annotated Bibliography Descriptive Statistic Message Story
Appointment Dialog map Mindmap Subroutine/Module
Bibliography Dictionary Musical Score Syllabus
Block diagram Directed Graph Network Diagram Synopsis
Blog Email Note Term Sheet
Blog post Encyclopedia Notebook Theory
Blueprint Equation Orchestration Threaded Discussion
Book Federated Wiki Outline Time Series Analysis
Book Series File Poem Timeline
Bookmark File Folder Phone Call TLA, ETLA
Bug Report Financial Schedule Phone Number Transcript
CAD Drawing Flowchart Photo Trip Report
Calendar Forecast Podcast Tuple
Card Catalog Formula Précis Tweet
Case study Gantt Chart Presentation Tweet Thread
Catalog Glossary Presentation Slide Url
Chat message Graph RACI Matrix Use Case
Chat log Group Calendar Reading List Voice Recording
Chord Progression Hyperlink Report/White Paper Video Clip
Citation Index Resume/CV Video/Movie
Code Infographic RSS/News Feed Webpage
Code Repository instant message Scene Website
Computer Program Interview Notes Schedule Wiki
Concept map Issue Script Work Breakdown Structure
Concordance Issue Inventory Search query Work Plan
Conference Call Journal Article Search results Working Draft
Consulting Report Journal Entry Simulation model Working Paper
Contact record Journal/Diary Sketch
CRUD Matrix

You could continue to add to this list. Or, you could explore what subsets organized by discipline—mathematics, economics, programming—might reveal. I think the broad distinctions between notes, working papers, and deliverables remains useful I’ve been looking at those questions myself of late (notes, working papers, deliverables). My advice used to be to start with deliverables and work your way backwards. That grew out of years of consulting work focused on the needs of clients.

Lately, I’ve been wrestling with the problem of managing at scale either as an individual or as a larger group. Techniques and practices that work for a single project or a handful of clients/projects break down as both time passes and numbers grow. Organizations address these problems by dedicating resources to them. They create and enforce the rules that annoy individual knowledge workers who haven’t yet run into scale problems in their own work.

These kinds of problems often surface in data management settings. The simplest example that springs to mind is sorting a list of names and addresses. Someone setting up a spreadsheet to invite friends to a surprise birthday party might set up one column for name and another column for phone number. Simple enough. Someone who’s been burned before will start by splitting the name into separate columns for first name and last name.

The point is that the “obvious” solutions to knowledge work data management problems have lots of unanticipated flaws that don’t surface until you cross scale thresholds. It seems perfectly reasonable to file project deliverables away by client and project until you have a hundreds of clients and projects and none of that information helps you track down the regression analysis you did sometime last year for one of three clients but you can’t recall just which.

Zettelkasten advocates make a compelling argument that the classification schemes natural to a knowledge manager actively inhibit the creativity meant to be the defining characteristic of knowledge work (good tags and bad tags). There is more worthy thought and discussion about categories of notes and emerging structural layers of notes.

The conclusion that is slowly emerging for me is that part of being an effective knowledge worker is a need to design your own knowledge management environment and that this design needs to anticipate that your needs and understanding are a continually evolving driver of that design.

Scoping the knowledge work data layer

I’ve been nibbling around the notion of the data layer of knowledge work for some time. We’re accustomed to talking about deliverables in knowledge work. I’ve argued about the problems of visibility in knowledge work and the benefits of making knowledge work observable. Lately, I’ve started to explore the realm of working papers and intermediate knowledge work artifacts.

Three broad layers are emerging in my model of the data layer:

  • Deliverables: Presentations, reports, infographics, e-books, or any other self-contained package designed to be left in the hands of a client or the public. The notion of a deliverable is a powerful organizing idea, although it sometimes implies that anything not a deliverable isn’t relevant.
  • Working papers: Spreadsheets, process flows, trip reports, design sketches, use cases, statistical models, simulations or any other intermediate work product useful to an individual knowledge worker or to a project team. Working papers may or may not be shared with others.
  • Notes: Everything else. Interview notes, reading notes, journal entries, outlines, mindmaps, whiteboards, marked up placemats from breakfast meetings, or any other external representation that helps a knowledge worker capture or elaborate an idea.


Since we’re thinking about data, we also have to deal with what metadata is necessary or useful to maintain. If you work down from the deliverables layer, the default choice is to group deliverables by project. If you do knowledge work for any length of time, project information grows complex. Any given project might be part of ongoing work for a particular client.

On the other hand if you work up from notes, other metadata questions surface. While you might be able to identify a project, notes and note taking often happen before you have a specific project in mind. Do you collect or preserve metadata about the source of a note—is a note about an interview with a client, your thoughts about something you’re currently reading, or connected to some ongoing topic of interest? ‘

Working papers can be carved out of bigger deliverables or bubble up from the notes layer as your thinking develops. Which suggests that these metadata requirements will be a blend of the layers above and below.

The point of collecting this metadata is to make the proliferation of materials in the data layer manageable. Extracting and reporting on metadata ought to make it easy to monitor the developing status of the collection of notes, working papers, and deliverables that comprise an active project. How many interviews have we completed? Have we tracked down the data for the regression analysis we are about to perform? How many uses cases have we identified? How many have been written? Reviewed?

Formality and Explicitness

For one person working on a single project, this seems to be an inordinate amount of fuss and worry about something you can keep track of in your head or informally by reviewing an inbox or browsing a file directory. For large scale efforts by large teams, it might pay to invest in full time staff and formal systems.

For the middle arena where we spend the bulk of our time, the temptation is to rely on informal methods and practices. That’s a mistake. A better choice is to invest some thought and effort into making these distinctions and introducing a modicum of formality.


I’ve worked on these notions in a number of place. There’s a lot of other, excellent, work in this realm; I’ll save that for another day and another blog post. This list is chronological:

Thinking about the data layer of knowledge work

Early in my education as a computer programmer I encountered Niklaus Wirth’s seminal Algorithms + Data Structures = Programs. The fundamental insight was that algorithms and data structures have to be fashioned in concert; a good choice of data structure can simplify an algorithm, a clever algorithm might allow a simple data structure.

An example from the pandemic environment we are all living through is working with exponential functions (an algorithm). You quickly learn that expressing the data as logarithms (a structural choice) greatly simplifies much of the analysis. Complex curves turn into simple straight lines.

If you’re a good engineer, computer programmer, or data scientist, you’re trained to think about these tradeoffs in a systematic way. In the realm of knowledge work, we have lost sight of this useful distinction. We spend the bulk of our time and attention talking about the equivalent of algorithms.

What is the opportunity to simplify or improve our effectiveness at knowledge work if we devote more attention to the data layer of knowledge work? What kinds of tradeoffs should we be looking for when doing knowledge work? What choices about how we organize and manage data might improve the quality or effectiveness of doing knowledge work?