2018 - Page 4 of 6 - McGee's Musings

Trust, Verify, and Triangulate

“Trust but verify” is no longer an effective strategy in an information saturated world. When Ronald Reagan quoted the Russian proverb in 1985, it seemed clever enough; today it sounds hopelessly naive. If we are reasonably diligent executives or citizens, we understand and seek to avoid confirmation bias when important decisions are at hand. What can we do to compensate for the forces working against us?

It’s a cliché that we live in a world of information abundance. That cliché, however, has not led to the changes in information behaviors that it implies. We still operate as though information were a scarce commodity, believing that anyone who holds relevant data is automatically in a position of power and that information seekers depend on the holder’s munificence.

As data becomes abundantly and seemingly easily available, the problem for the information seeker changes. (It changes for the information holder as well, but that is a topic for another time.) The problem transforms from “Can I get the data?” to “What data exists? How quickly can I get it? How do I know I can trust it? How do I evaluate conflicting reports?” It is no longer simply a question of getting an answer but of getting an answer whose strengths and limits you understand and can account for.

The temptation is to fall into a trust trap, abdicating responsibility to someone else. “4 out of 5 dentists recommend…” “According to the Wall Street Journal…” “The pipeline predicts we’ll close $100 million…”

There was a time when we could at least pretend to seek out and rely on trustworthy sources. We counted on the staffs at the New York Times or Wall Street Journal to do fact checking for us. Today we argue that the fact checkers are biased and no one is to be trusted.

Information is never neutral.

Whatever source is collecting, packaging, and disseminating information is doing so with its own interests in mind. Those interests must be factored into any analysis. For example, even data as seemingly impartial as flight schedules must be viewed with a skeptical eye. In recent years, airlines improved their on-time performance as much by adjusting schedules as by any operational changes. You can interpret new flight schedules as an acknowledgment of operational realities or as padding to enable to reporting of better on-time performance.

If we are a bit more sophisticated, we invest time in understanding how those trusted sources gather and process their information, verifying that their processes are sound, and accept their reports as reliable inputs. Unfortunately, “trust but verify” is no longer a sufficient strategy. The indicators we once used to assess trust have become too easy to imitate. Our sources of information are too numerous and too distributed to contemplate meaningful verification.

Are we doomed? Is our only response to abandon belief in objective truth and cling to whatever source best caters to our own bias. Fortunately, triangulation is a strategy well matched to the characteristics of today’s information environment.

In navigation, you determine your location in relation to known locations. You need at least three locations to fix your current position. Moreover, you need to account for the limits of your measurement tools to understand the precision of your fix. In today’s world of GPS navigation, that precision might well be very high but still imperfect.

In organizational (and other) settings where you are attempting to make sense of—or draw useful inferences from—a multitude of noisy and conflicting sources, the principles of triangulation offer a workable strategy for developing useful insights in a finite and manageable amount of time.

In navigation, the more widely and evenly dispersed your sightings, the more precisely you can fix your position. Focus your data collection on identifying and targeting multiple sources of input that represent divergent, and possibly conflicting, perspectives. Within an organization, for example, work with supporters and opponents, both active and passive, of a proposed reorganization or systems deployment to develop an implementation strategy. When evaluating and selecting a new application, seek out a wider assortment of potential references, vendors, and analysts.

Triangulation also helps counteract the simplistic notion of balance that undermines too many narratives. Triangulation is based on living in a three-dimensional world; it cannot tell you where you are with only two fixes. We should be at least as diligent when mapping more complex phenomena.

A data collection effort organized around seeking multiple perspectives and guidance risks spinning out of control. Analyzing data in parallel with collection manages that risk. Process and integrate data as it is collected. Look for emerging themes and issues and work toward creating a coherent picture of the current state. Such parallel analysis/collection efforts will identify new sources of relevant input and insight to be added to the collection process.

Monitoring the emergence of themes, issues, and insights will signal when to close out the collection process. In the early stages of analysis, the learning curve will be steep, but it will begin to flatten out over time. As the analysis begins to converge and the rate of new information and insight begins to drop sharply, the end of the data collection effort will be near.

The goal of fact finding and research is to make better decisions. You can’t set a course until you know where you are. How carefully you need to fix your position or assess the context for your decision depends on where you hope to go. Thinking in terms of triangulation—how widely you distribute your input base and what level of precision you need—offers a data collection and analysis strategy that in more effective and efficient than approaches we have grown accustomed to in simpler times.

Chaos players: knowledge work as performance art

Stage - Auditorium. Photo by Monica Silvestre from Pexels

All the world’s a stage, And all the men and women merely players; They have their exits and their entrances, And one man in his time plays many parts
As You Like It, Act II, Scene VII. William Shakespeare

I’ve been thinking about the role of mental models for sense-making. While we do this all the time, I think there is significant incremental value in making those models more explicit and then playing with them to tease out their implications. Organization as machine is a familiar example, one that I believe is largely obsolete. Organization as ecosystem or complex adaptive system has grown in popularity. It has the advantage of being richer and more sensitive to the complexities of modern organizations and their environments. On the other hand, that mental model is a bit too appealing to to academic and consulting desires to sound simultaneously profound and unintelligible. It fails to provide useful guidance through the day-to-day challenges of competing and surviving.

Organization as performance art or theatrical production offers a middle ground between simplistic and over-engineered. It appeals to me personally given a long history staging and producing. It’s my hypothesis that most of us have enough nodding familiarity with the theater to take advantage of the metaphor and model without so much knowledge as to let the little details interfere with the deeper value.

The goal of theater is to produce an experience for an audience. That experience must always be grounded in the practical art of the possible. This gives us something to work with.

Let’s work backwards from the performance. We have the players on the stage and an audience with expectations about what they are about to experience. If that is all we have, then we are in the realm of storytelling. Storytelling demands both the tellers of the tale and the creator of the tale itself. Our playwright starts with an idea and crafts a script to bring that idea to life and connect it to all of the other stories and ideas the audience will bring to the experience. We now have a story, its author, storytellers, and an audience with their expectations.

Theater takes us a step farther and asks us think about production values that contribute to and enhance the experience we hope to create. Stage and sets and lighting and sound can all be drawn into service of the story. Each calls for different expertise to design, create, and execute. We now have multiple experts who must collaborate and we have processes to be managed. Each must contribute to the experience being created. More importantly, those contributions must all be coordinated and integrated into the intended experience.

This feels like a potentially fruitful line of inquiry. It seems to align well with an environment that depends on creativity and innovation as much as or more than simple execution. How deeply should it be developed?

Going behind the screen: mental models and more effective software leverage

I’ve been writing at a keyboard now for five decades. As it’s Mother’s Day, it is fitting that my mother was the one who encouraged me to learn to type. Early in that process, I was also encouraged to learn to think at the keyboard and skip the handwritten drafts. That was made easier by my inability to read my own handwriting after a few hours.

I first started text editing as a programmer writing Fortran on a Xerox SDS Sigma computer. I started writing consulting reports on Wang Word Processors. When PCs hit the market, I made my way through a variety of word processors including WordStar, WordPerfect, and Microsoft Word. I also experimented with an eclectic mix of other writing tools such as ThinkTank, More, Grandview, Ecco Pro, OmniOutliner, and MindManager. Today, I do the bulk of my long-form writing using Scrivener on a Mac together with a suite of other tools.

The point is not that I am a sucker for bright, shiny, objects—I am—or that I am still in search of the “one, true tool.” This parade of tools over years and multiple technology platforms leads me to the observation that we would be wise to spend much more attention to our mental models of software and the thinking processes they support.

That’s a problem because we are much more comfortable with the concrete than the abstract. You pick up a shovel or a hammer and what you can do is pretty clear. Sit down at a typewriter with a stack of paper and, again, you can muddle through on your own. Replace the typewriter and paper with a keyboard and a blank screen and life grows more complicated.

Fortunately, we are clever apes and as good disciples of Yogi Berra we “can observe a lot just by watching.” The field of user interface design exists to smooth the path to making our abstract tools concrete enough to learn.
UI design falls short, however, by focusing principally at the point where our senses—sight, sound, and touch—meet the surface of our abstract software tools. It’s as if we taught people how to read words and sentences but never taught them how to understand and follow arguments. We recognize that a book is a kind of interface between the mind of the author and the mind of the reader. A book’s interface can be done well or done badly, but the ultimate test is whether we find a window into the thoughts and reasoning of another.

We understand that there is something deeper than that words on the page. Our goal is to get behind the words and into the thinking of the author. This same goal exists in software; we need to go behind interface we see on the screen to grasp the programmer’s thinking.

We’ll come back to working with words in a moment. First, let’s look at the spreadsheet. I remember seeing Visicalc for the first time—one year too late to get me through first year Finance. What was visible on the screen mirrored the paper spreadsheets I used to prepare financial analyses and budgets. The things I understood from paper were there on the screen; things that I wished I could do with paper were now easy in software. They were already possible and available by way of much more complex and inscrutable software tools but Visicalc’s interface created a link between my mind and Dan Bricklin’s that opened up the possibilities of the software. I was able to get behind the screen and that gave me new power. That same mental model can also be a hindrance if it ends up limiting your perception of new possibilities.

Word processors also represent an interface between writers and the programmer’s model of how writing works. That model can be more difficult to discern. If writer and programmer have compatible models, the tools can make the process smoother. If the models are at odds, then the writer will struggle and not understand why.

Consider the first stand-alone word processors like the Wang. These were expensive, single function machines. The target market was organizations with separate departments dedicated to the production of documents; insurance policies, user manuals, formal reports, and the like. The users were clerical staff—generally women—whose job was to transform hand written drafts into finished product. The software was built to support that business process and the process was reflected in the design and operation of the software. Functions and features of the software supported revising copy, enforcing formatting standards, and other requirements of a business.

The economics that drove the personal computer revolution changed the potential market for software. While Wang targeted organizations and word processing as an organizational function, software programmers could now target individual writers. This led to a proliferation of word processing tools in the 1980s and 1990s reflecting multiple models of the writing process. For example, should the process of creating a draft be separate from the process of laying out the text on the page? Should the instructions for laying out the text be embedded in the text of the document or stored separately? Is a long-form product such as a book a single computer file, a collection of multiple file, or something else?

Those decisions influence the writer’s process. If your process meshes with the programmer’s, then life is good. If they clash, the tool will get in the way of good work.

If you don’t recognize the issue, then your success or failure with a specific tool can feel capricious. If you select a tool without thinking about this fit, then you might blame yourself for problems and limitations that are caused by using a tool that clashes with your process.

Suppose we recognize that this issue of mental models exists. How do we take advantage of that perspective to become more effective in leveraging available tools? A starting point is to reflect on your existing work practices and look for the models you may be using. Are there patterns in the way you approach the work? Do you start a writing project by collecting a group of interesting examples? Do you develop an explicit hypothesis and search out matching evidence? Do you dive into a period of research into what others have written and look for holes?

Can you draw a picture of your process? Identify the assumptions driving your process? Map your software tools against your process?

These are the kinds of questions that designers ask and answer about organizational processes. When we work inside organizations, we accept the processes as a given. In today’s environment for knowledge work, we have the capacity to operate effectively at a level that can match what organizations managed not that long ago. Given that level of potential productivity and effectiveness, we now need to apply the same level of explicit thought and design to our personal work practices.

Design projects before worrying about managing them

I’m gearing up to teach project management again over the summer. There’s a notion lurking in the back of my mind that warrants development.

We would do a better job at managing projects if we spent more time designing them first.

We turn something into a project when we encounter a problem that we haven’t tackled before and can’t see exactly how to solve in one step. The mistake we make is to spend too much time thinking about the word “management” at the expense of the word “project.”

Suppose our problem is to improve the way we bring new hires on board in a rapidly growing organization. There’s an obligatory amount of HR and administrative paperwork to complete, but the goal is to integrate new people into the organization. Do you send them off immediately to new assignments? Do you design a formal training program? There’s a host of questions and a host of possible ideas so you now have a potential project.

Given the pressures in organization to “get on with it” the temptation is to grab a half-formed idea, sketch a a plausible plan, guess at a budget, pick a deadline, and start. If we have some project management practices and discipline, we’ll flesh out the plan and develop something that looks like a work breakdown structure and a timeline.

What we don’t give sufficient attention to is the design thinking that might transform this project idea into something that might add meaningful and unexpected value to the organization. We don’t give that attention because we don’t view a project as something worthy of design.

What would it mean to design a project? Two ideas come to mind. The first would be to generate ways to increase the value of the effort. In our on-boarding example, one objective was to offer new employees practice in preparing client presentations. Rather than develop a generic presentation skills module, the project team came up with a better idea. We asked teams in the field for research and competitive intelligence tasks on their to do lists and transformed those into assignments for the on-boarding program. This allowed us to accomplish the presentation skills training, contribute to actual client work, and build bridges between new employees and their future colleagues.

A second design thinking aspect would be a more deliberate focus on generating multiple concepts and approaches that would evolve into a work breakdown structure. My conjecture is that more projects are closer to one-off efforts than to executing the nth iteration of a programming or consulting project. Project management personalities are tempted to standardize and control prematurely. But standard work plans and templates aren’t relevant until there are enough projects of a certain category to warrant the investment. Doing this kind of design work calls for expanding the toolset beyond the conventional project management toolkit that is focused on tracking and monitoring the sequence of tasks.

This is a notion in development. It isn’t fully baked but my instinct is that it is worth fleshing out. Some questions I’m curious to get reactions to include:

Is making a distinction between project design and project execution worth the trouble?
How do standard project management tools—Gantt charts, project management software such as Microsoft Project or Primavera, or spreadsheets—interfere with early stage idea generation and creativity?
How might you/do you expand the toolset to support better design thinking?

Review – Scrum: The Art of Doing Twice the Work in Half the Time

Scrum: The Art of Doing Twice the Work in Half the Time. Jeff Sutherland and JJ Sutherland

There’s a rule of thumb in software development circles that the best programmers can be ten times as productive as average programmers. This is the underlying argument for why organizations seek to find and hire the best people. There is research to support this disparity in productivity. There is similar research on the relative productivity of teams. There, the range in productivity between the best and the rest is closer to two orders of magnitude; as much as a 1,000 times more productive.

Jeff Sutherland makes the case that the practices that collectively make up “Scrum” are one strategy for realizing those kinds of payoffs.

Sutherland is a former fighter pilot, a software developer, one of the original signatories of the Agile Manifesto, and the inventor of Scrum. This is his story of how Scrum came to be, what it is, and why it’s worthwhile. The particular value of the book is its focus on why Scrum is designed the way it is and why that matters.

Scrum’s origins and primary applications have been in the realm of software development. Sutherland builds an argument that Scrum’s principles and methods apply more broadly. Although he doesn’t make this argument directly, this wider applicability flows from evolutionary changes in the the organizational environment. Changes in the pace and complexity of organizational work simultaneously make conventional approaches less effective and Scrum more so.

Scrum is a collection of simple ideas; it’s a point of view about effective problem solving more than a formalized methodology. That’s important to keep in mind because like too many solid ideas, the essence can get lost in the broader rush to capitalize on those ideas. There appear to be an unlimited supply of training courses, consultants, and the usual paraphernalia of a trendy business idea; you’re better off spending time reading and thinking about what Sutherland has to say first. That may be all you need if you’re then willing to make the effort to put those ideas into practice.

Sutherland traces the roots of Scrum to the thinking underlying the Toyota Production System. He also draws interesting links to John Boyd’s work on strategy embodied in the OODA Loop and to the martial arts. Scrum is built on shortening the feedback between plans and action. It is a systematic way of feeling your way forward and adapting to the terrain as you travel over it.

Sutherland draws a sharp contrast with more traditional management techniques such as Waterfall project management approaches and their well-worn trappings such as Gantt charts and voluminous unread and unreadable requirements documents.

Understanding the managerial appeal and limitations of these trappings is key to grasping the contrasting benefits of Scrum. Waterfalls and Gantt charts appeal to managers because they promise certainty and control. They can’t deliver on that promise in today’s environment. In the software development world, they never could and in today’s general organizational environment they also come up short.

Understanding that appeal and why it is misplaced clarifies the strengths of Scrum. There was a time when managers came from the ranks of the managed. They had done the work they were now responsible for overseeing and were, therefore, qualified to provide the direction and feedback needed to pick a path and follow it. Management was primarily about execution and not about innovation.

The illusion in waterfall and other planning exercises is that what we are doing next is a repeat of what we have done before. If we have built 100 houses, we can be confident of what it will take to build the 101st. If we are building a new road or a new bridge, then what we have learned from the previous roads and bridges we’ve built can provide a fairly precise estimate for the next.

This breaks down, however, when we are building in new terrain or experimenting with new designs. The insights and experience of those who’ve built in the past don’t transfer cleanly to this more dynamic environment. The world of software has always been new territory and we are always experimenting. The terrain is always in flux even when the technology is temporarily stable. Now, it is those who are doing the work who are best positioned to plan and manage as we move into new territories and terrain.

Scrum comes into play when we are moving into territory where there are no roads and are no maps. If you are moving into new territory you can only plan as far ahead as you can see. There are no maps to follow. Sutherland puts it thus:

Scrum embraces uncertainty and creativity. It places a structure around the learning process, enabling teams to assess both what they’ve created and, just as important, how they created it. The Scrum framework harnesses how teams actually work and gives them the tools to self-organize and rapidly improve both speed and quality of work.

There’s a terminology and a set of techniques that make up Scrum. Sutherland covers the basics of such notions as scrum masters, product owners, backlog, sprints, retrospectives, communication saturation, continuous improvement, and stand up meetings. But he’s no fan of turning these into dogma.

Scrum runs the risk of being viewed as no more than the latest management fad. Sutherland is a true believer and has evidence to support his belief. There are lots of true believers but only a few are willing to bring substantive evidence to back up that belief. That earns Sutherland the right to offer his own closing argument:

What Scrum does is alter the very way you think about time. After engaging for a while in Sprints and Stand-ups, you stop seeing time as a linear arrow into the future but, rather, as something that is fundamentally cyclical. Each Sprint is an opportunity to do something totally new; each day, a chance to improve. Scrum encourages a holistic worldview. The person who commits to it will value each moment as a returning cycle of breath and life.

The heart of Scrum is rhythm. Rhythm is deeply important to human beings. Its beat is heard in the thrumming of our blood and rooted in some of the deepest recesses of our brains. We’re pattern seekers, driven to seek out rhythm in all aspects of our lives.

What Scrum does is create a different kind of pattern. It accepts that we’re habit-driven creatures, seekers of rhythm, somewhat predictable, but also somewhat magical and capable of greatness.

When I created Scrum, I thought, What if I can take human patterns and make them positive rather than negative? What if I can design a virtuous, self-reinforcing cycle that encourages the best parts of ourselves and diminishes the worst? In giving Scrum a daily and weekly rhythm, I guess what I was striving for was to offer people the chance to like the person they see in the mirror.

Learning from cases; getting smarter by design

One of my enduring memories from my first days in business school is a video interview of a second year student offering advice on surviving the case method. While I didn’t fully appreciate it at the time, it was totally appropriate that his advice was a case study in its own right.

He began with a story of the business challenge of “sexing chickens.” In the chicken business, it turns out that it is important to separate roosters and hens when both are no more than puffs of feathers. The clues are subtle and not reducible to a checklist. Novice chicken sexers can’t be taught to do their job but they can learn.

Prospective chicken sexers go through an apprenticeship. They sit down with a batch of chicks, pick one up, flip it over, inspect, and guess. Sitting next to our prospect is a veteran chicken sexer. The veteran’s job is to give the prospect feedback; either a “yes” for a correct guess or a smack in the head for a mistake. After a hundred or so guesses, the prospect’s error rate will drop close to zero. Neither the prospect or the veteran will be able to offer an explicit theory of what they are doing, yet they are effective.

Learning by the case method is a similar process of guessing and rapid, memorable, feedback. As an aside, recognize that this also describes the essence of machine learning. It is experiential learning at its purest.

The craft in designing case method learning lies in selecting and sequencing cases so that the lessons can be delivered more rapidly and reliably than the random accumulation of experience permits. The assumption here is that there is an order that can be exploited to guide action. There must be an underlying pattern that one can solve for.

If you subscribe to the value of the case method as a learning strategy, you are making a claim that there is a balance between theory and practice to be managed. It is a claim that the particulars matter; that experience or theory can only go so far in crafting a response. That you have a responsibility to design a response that acknowledges that every situation is a mix of old and new, predictable and unpredictable.

There’s a notion here that I am struggling with that has to do with the rate of change. I think that case-based learning potentially makes this issue more visible.

In a slow-changing world, experience matters greatly. Recognize how this situation maps to what we’ve seen and responded to before and right action is clear. As the rate of change increases, the value of experience changes. Prior experiences suggest actions and responses that can serve as the basis for designing a modified response that blends old and new.

Pushing the rate of change still higher means that effective response demands more design and less “here’s what we’ve done before.” What does that imply for learning effectively?

My hypothesis is that we need to make the experiential learning process visible, explicit, and deliberate. In a conventional case-based learning environment, there is a separation between those who are learning and those who are facilitating the learning—which is not the same thing as teaching. The goal is for those learning to build a robust, internal, theory of action. The facilitators have strong ideas about what that theory should look like and cases are sequenced to force the learners to develop an internal theory that matches the target theory.

What’s happening in higher-paced environments is that our learners and facilitators are becoming harder to distinguish. In a sense, we are back to a world of pure learning from experience. What changes is that as learners we now must be responsible for building our theories dynamically.

While this can still be described as a form of reflective practice, that reflection now must operate at several levels of abstraction. We can’t rely simply on organic processes to slowly and unpredictably get smarter over time. We need to get smarter on purpose and by design.

The Siren Call of Proven Systems

Seductive Sirens Our admiration for the assembly line is so deep that we are suckers for the promise of “proven systems” regardless of their feasibility. We so treasure predictability and control that the promise seduces no matter how many times it is broken.

I started my career at what ultimately morphed into Accenture. That tenure coincided with the development of one of the early systems development methodologies—Method/1. The methodology was an attempt to make the process of understanding, designing, and implementing a technology solution to an organization’s business problem something that was manageable and repeatable. Method/1 made Accenture a lot of money and can be seen as a distant ancestor of a host of systems for building systems; agile, scrum, rational unified, waterfall, extreme programming—the ingenuity of marketers in packaging and labeling ideas is endless.

All are variations on a theme. They embody a desire for control in a turbulent world. If Ford could manufacture identical cars and McDonald’s could guarantee the consistency of fries and shakes from coast to coast, then we ought to be able to turn out information systems with similar confidence in quality and predictability.

There seems to be an uptick in promises of proven systems in multiple settings; not simply in the arena of design and development. Given their appeal, it’s critical to recognize the limits of these promises.

The first limit is the dangerously thin data from which these proven systems are built. One of the first ideas pounded into your head when you are compelled to study statistics and research methods is that “data” is not the plural form of “anecdote.” Yet most proven systems are based on a handful of prior examples that happened to work.

Look at the history of Method/1. The late accounting firm Arthur Andersen & Co.—which birthed Accenture—automated the payroll department of a GE manufacturing plant in the 1960s. That first project failed and Andersen rebuilt the system at a loss to make good. Andersen’s sales pitch to their second client boiled down to we’ve learned what mistakes to avoid while our competitors have yet to make them. Several projects later, Andersen consultants documented what they had done and packaged their ad hoc approach into a standard project plan for internal use called the “client binders.” As an accounting firm, Andersen was accustomed to protecting client identities and to thinking of audit processes as something common and repeatable for all clients. Consequently, the client binders were devoid of client specifics and mirrored the look and feel of standardized, repeatable audit processes.

In spite of their limitations and the thin data they were built on, the client binders gave Andersen’s consultants a better starting point for new projects and helped Andersen extend and consolidate their lead in the market. They worked best in the hands of experienced consultants who could use these materials to organize and support productive conversations with clients and prospects about how to structure and manage new efforts.

Then the methodology zealots took over. The experts took the crib notes that were valuable to experts and rewrote them into recipes for reasonably smart people with limited experience to follow. Repeatable, industrial, processes promise good economics to those who invent them and acceptable quality to those who seek the outputs.

The fundamental problem is that today’s knowledge work doesn’t consist of repeatable, industrial, processes no matter how much we wish they did or how often we claim they do. I’ve written before about the problems this strategy presents (Repeatable Processes and Magic Boxes).

Where does that leave us?

Be suspicious of claims about proven systems. Look for the demands for creative leaps and flashes of insight hiding within seemingly innocuous steps. Look for potentially endless cycles of analysis with no stopping rule. Find the magic box. Be wary of maps that don’t show where the roads end and the dragons hide.

Searching for the secret class

permanent whitewater There was a time when I wanted to get my hands on the syllabus for the “secret class;” the secret being how to navigate the real world outside the classroom. Think of me as a male version of Hermione Granger; annoyingly book smart and otherwise pretty clueless. Classes were easy; life not so much.

Most of us spend enough time between classes in hallways and playgrounds to soak up the necessary experiences to get a clue. Through a peculiar set of circumstances and events, I was late in encountering and absorbing these experiences. My wife is on record that she would have crossed the street to avoid that earlier me. My therapist assures me that these lessons are learnable as long as I put my heart in circuit between my brain and my mouth.

Because this arena was foreign to me, I had to study it much as an anthropologist might; observing, cataloging, and making sense of what I see. The biggest risk for an anthropologist is to “go native”; to leave the edge and to immerse themselves in the action. Life demands immersion.

I make no claims about life in general. Life within organizations, however, is a place I understand. Thinking about secret classes and learning in organizational settings turns out to be a fruitful path. Really smart people, like Chris Argyris and Donald Schon among others, have thought a great deal about the things people need to learn within organizations. They talk about skills and perspectives that are effectively secrets and acquired by way of experience rather than classrooms.

Experience alone is rarely sufficient to impart these lessons; managers and executives become effective by way of reflective practice. They must process and digest experience to transform it into effective managerial practice. Classic examples of this deliberate reflection are Chester Barnard’s The Functions of the Executive and Alfred Sloan’s My Years with General Motors.

These are valuable and insightful analyses of complex organizations and executive work. They also highlight critical ways that reflective practice must evolve. It’s a common trope that organizations operate in an increasingly fast and complex environment. It’s common because examples to demonstrate it surround us. In the early days of my career, we were still converting paper-based business processes to electronic. Today, we’re knitting together digital processes spanning multiple organizations and continents.

That plants us in a world where the volumes of digital data threaten to collapse into a wall of noise and this wall feels more like the leading edge of a tsunami rather than a fixed landmark somewhere “over there.” Simply coping with the onslaught consumes our attention and drives too many of us and our organizations into reactive mode. We become driven by events. Planning feels like a luxury and the notion of reflecting seems an unrealistic, academic, dream. Peter Vaill makes a compelling argument that we now live in a world of permanent whitewater and have to learn to operate accordingly.

We have a conundrum then. A changing world demands changed responses, yet the pace of change leaves no time for the reflection needed to transform experience into new practice. What are the elements of a strategy that might even our odds? How do we make reflective practice work in the organizational environment that now exists?

Peter Vaill offers a clue in the title of the book where he talks of permanent whitewater, Learning as a Way of Being. Learning is not something neatly distinguishable from practice; we pursue learning while doing. Elsewhere, I’ve discussed the notion of observable work as a pre-condition for making that kind of learning possible. Then, there are various techniques, such as after action reviews that should be in any knowledge worker’s toolkit.

There is no “secret class.” Even it one existed, it wouldn’t make sense to take it. Instead, we structure experience so that learning is a continuing parallel element of doing the work.

Effective Executives Are Design Thinkers

The Effective Executive: The Definitive Guide to Getting the Right Things Done (Harperbusiness Essentials) (Peter F. Drucker and Jim Collins)

There are certain smart, articulate, thinkers that provide anchors for my thinking; Peter Drucker is high on that list. Somewhere around the time I was figuring out that organizations caught and held my attention, I also stumbled upon Drucker.

Not too long ago, I added a copy of The Effective Executive to my Kindle and chose to revisit Drucker’s observations about effectiveness. I’ve been uncomfortable about the surge in attention around efficiency and productivity of knowledge workers and thought Drucker might have relevant insight.

He does.

Drucker was credited with coining the term knowledge worker and the bulk of his work focused on why they mattered and what to do about it. The Effective Executive was originally published in 1966 and was reissued in a 50th anniversary edition. It’s a bit scary to contemplate the level of insight available for the taking.

Organizations consist of two kinds of people; those who follow scripts and those who write them. For a long time—and certainly in 1966—the overwhelming majority of headcount fell into the first category. Drucker’s attention was always drawn to those who wrote the scripts—executives. What’s changed since then is that scripts have become the responsibility of software. Organizations are gradually—and not so gradually—eliminating people who follow scripts.

That makes Drucker’s observations and advice about the script writers orders of magnitude more important than it was then. Drucker argued that “working on the right things is what makes knowledge work effective.” Doing the right thing is more important than doing things right, regardless of how much of the visible activity in organizations seems to be about doing things right. The Effective Executive is Drucker’s extended examination of how to systematically go about doing the right thing.

Drucker’s worldview was not tainted by today’s slavish devotion to shareholder value. In Drucker’s world, organizations exist to create and serve customers; there is no possibility of value creation, much less maximization, until a customer exists. And customers exist outside the organization. This creates a fundamental challenge for executives, which Drucker characterizes as follows:

Every executive, whether his organization is a business or a research laboratory, a government agency, a large university, or the air force, sees the inside—the organization—as close and immediate reality. He sees the outside only through thick and distorting lenses, if at all. What goes on outside is usually not even known firsthand. It is received through an organizational filter of reports, that is, in an already predigested and highly abstract form that imposes organizational criteria of relevance on the outside reality.

He goes on to say that

The fundamental problem is the reality around the executive. Unless he changes it by deliberate action, the flow of events will determine what he is concerned with and what he does.

Regardless of the changing dynamics of organization and environment, executive effectiveness consists of judgments whether a particular situation fits within the parameters of existing organizational scripts, will yield to one-time improvisation, or calls for developing a new script.

What this implies, and what Drucker argues, is that

effective decision is always a judgment based on “dissenting opinions” rather than on “consensus on the facts.” And [effective executives] know that to make many decisions fast means to make the wrong decisions. What is needed are few, but fundamental, decisions. What is needed is the right strategy rather than razzle-dazzle tactics.

Several things flow from that. Most importantly, effective decisions require meaningful chunks of time. Here, Drucker’s thinking connects closely with Cal Newport’s more recent discussion of deep work.

As is so often the case with Drucker, his insights get picked up and repackaged. Drucker’s analysis of effectiveness means that executives are engaged in design thinking. More importantly, it is design thinking that strives to synthesize the analysis and insights of multiple specialized perspectives.

There is much more in this short book. It bears close reading and regular re-reading if you aspire to do meaningful executive work. To wrap this up, I want to examine one aspect of this design thinking perspective that appears to run counter to rhetoric of many strategy efforts and many consulting proposals and reports. Let’s look at Drucker’s own words once again,

To get the facts first is impossible. There are no facts unless one has a criterion of relevance. Events by themselves are not facts.

The only rigorous method, the only one that enables us to test an opinion against reality, is based on the clear recognition that opinions come first—and that this is the way it should be. Then no one can fail to see that we start out with untested hypotheses—in decision-making as in science the only starting point. We know what to do with hypotheses—one does not argue them; one tests them. One finds out which hypotheses are tenable, and therefore worthy of serious consideration, and which are eliminated by the first test against observable experience.

The effective decision-maker, therefore, organizes disagreement. This protects him against being taken in by the plausible but false or incomplete. It gives him the alternatives so that he can choose and make a decision, but also so that he is not lost in the fog when his decision proves deficient or wrong in execution. And it forces the imagination

If finding the time and the money to pursue an MBA is currently difficult, consider adding The Effective Executive to your reading stack and put it near the top.

Don’t connect the dots; solve for pattern

Last time, we looked at the limits of a rhetorical strategy (”connecting the dots”) as a tool for planning. There are too many dots forming too many possible pictures, yet we want to take advantage of what we know has gone before to set a way forward. There is a better way that resides in “solving for pattern” a phrase I first encountered in an essay by Wendell Berry.

During my first weeks of business school, marketing mystified me. I had a decent background in accounting and project management; those classes I could make sense of. In marketing, I was lost during the discussion. When the professor laid out the analysis at the end I was no more enlightened than I had been eighty minutes earlier.

I concluded that the fatal flaw of the case method was its failure to provide neat pictures I could lay over the dots scattered throughout the case. Shortly before the midterm exam, a second-year student offered a review session with a roadmap for how to make sense of any marketing problem. I now had the picture that I could lay over the dots and connect them myself. The faculty was very distressed by this review session. We thought it was because their secrets had been revealed.

Perhaps.

I still made a mess of the midterm.

It was much later, after I worked as a case writer, when I began to grasp that the faculty was pursuing a deeper agenda. The case method is not about learning how to connect the dots; it’s about learning how to solve for pattern. That second-year student’s crib sheet was no help for dealing with a supply chain problem masquerading as a marketing problem.

Elements of Solving for Pattern

Organizations are stuffed with people ready to solve problems once they’ve been labeled. What is rare—and more valued—is the ability to frame and structure problems so that they can be solved. There are no simple pictures hiding in the environment waiting to be revealed and pointing to pre-determined responses. The proliferation of dots are signals in the environment worth attending to; ignoring them is not wise. If we’re not looking for dots to connect and trigger responses, what are we to do? Solving for pattern is a problem framing and response design process that better fits the world we occupy.

Wendell Berry introduced the term in an effort to contrast industrial farming strategies with family farming strategies. The strength of the strategies Berry advocated came not from swapping corporate managers for family ownership but from treating farms as complex systems rather than faux factories. As a trivial example, Berry points to replacing chemical fertilizers on crops with the manure produced by the milk cows feeding off those same crops. Connecting those two isolated systems into a larger dynamic system produces better outputs, reduces costs, and has other positive effects that ripple through other components of the overall farm system.

Much of solving for pattern is a change of perspective. It starts by viewing the current situation as a dynamic equilibrium producing outcomes that are a mix of desirable and undesirable results. The goal is not to fix a component in a bigger machine but to push the system into a new stable equilibrium with a better balance of desirable and undesirable outcomes.

This strategy of solving for pattern melds pattern recognition, systems thinking, design skill, and change management practices to effect these rebalancing efforts. Pattern recognition and systems thinking are the tools we need to understand where we are and the trajectory we are on. That understanding feeds into the design and change management efforts needed to envision and shift from the pattern we have to the pattern we want.

In his essay, Berry was exploring the complexities of managing the miniature ecosystem of a working farm. Whether you call them problems, opportunities, or messes, the situations that arise on farms —and organizations—never come with labels or instructions attached. Nor are they neatly isolated from whatever else may be going on. Tackling those situations effectively starts with choosing a better point of view.