Category: Knowledge work

We are imitative creatures. We are happier copying someone else’s approach than inventing solutions based on the actual problem at hand. How often do you encounter situations where change is called for and the first response to any suggestion is “where has this been done before?” For all the talk of innovation, I often wonder how anything new ever happens.

Leaders and change agents have been fond of looking to the military for design ideas to emulate in their organizations. One all too persistent model was best captured by novelist Herman Wouk;

The Navy is a master plan designed by geniuses for execution by idiots. If you are not an idiot, but find yourself in the Navy, you can only operate well by pretending to be one. All the shortcuts and economies and common-sense changes that your native intelligence suggests to you are mistakes. Learn to quash them. Constantly ask yourself, “How would I do this if I were a fool?” Throttle down your mind to a crawl. Then you will never go wrong.

– Herman Wouk, The Caine Mutiny

This is a compelling image and in 1951 it likely represented a pretty accurate description of the Navy. Too many organizational leaders operate as if it were still 1951 and that military was an exemplar of good organizational practice. If you also believe that the military organize to fight the last war, what war are organizations fighting when they copy their designs from that military?

If we insist on copying ideas, perhaps we should at least update our mental models. If you want to draw lessons from the military, do it intelligently. If you look in the right places, the modern military has been more innovative about organization and leadership than other institutions because their consequences can mean the difference between life and death – something just a bit more compelling than making your quarterly budgets.

While business organizations remain fond of command and control, the military has had to cope with the disconnects between the view from headquarters and ground truth. Regardless of what TV and the movies suggest, orders do not flow from the Situation Room to the troops on the ground. Commanders spend their time articulating “commander’s intent,” not on developing detailed orders; they worry about making sure that all concerned understand “why” and what finished looks like first.

If the job at the top is to articulate why, what does that enable at the front lines? It grants those with first hand knowledge of the situation the freedom to exercise initiative and adapt action to the circumstances. It also eliminates “I was just following orders” as an excuse for failure.

This model places much greater responsibilities on the front lines, so you had better staff those lines accordingly. Perhaps put a better way, you must trust that they are capable of what they may be called on to do. Wouk was reminding us that those on the line only behave like idiots if you treat them so.

If you make the transition to starting from commander’s intent and trusting that you’ve put capable people in place, then orders become much simpler to devise. In the volatile world that constitutes today’s environment for most of us, you can offer a default set of orders to cover the unexpected; keep moving, stay in touch, head for higher ground.

Factfulness: Ten Reasons We’re Wrong About the World–and Why Things Are Better Than You Think, Hans Rosling, Anna Rosling Ronnlund, Ola Rosling. 2018

The best data available is useless if you filter it through the wrong mental model; the cleverest model is stupid with bad data. The late Hans Rosling explores this connection by looking at how flawed thinking—bad models—leads to poor judgments despite the availability of quite good data. Rosling’s field is population demographics and public health. He devotes his attention to how and why our intuitions are rooted in dated models. As citizens of an interconnected global economy his arguments matter to all of us. For those of us who worry about how data, analysis, and decisions fit together, Rosling’s insights offer important cautions and guidance.

Rosling’s fundamental argument is that the data, interpreted with the appropriate models, tells us that the world is improving along all of the dimensions that matter. He does not offer this as an excuse to stop working on making the world better, nor is he so naive as to claim that problems don’t exist. What he wants us to do is understand what the data is telling us and use that understanding to design and execute actions that will make a difference.

Take global population and income, for example. Most of us in the West filter the scraps of data we encounter through an old model of “developed” and “developing” world. There are a few of “us” and a lot of “them.” That split is long gone and Rosling offers a better lens to interpret the data. Instead of an over-simplified—and incorrect—model of rich vs. poor, Rosling offers the following richer breakdown, which groups population into four levels of daily income.

What is important about this model is what it tells us about the middle. Level 4 is us; we can afford cars and smart phones and family vacations. Level 1 is the extreme poverty that most of us think of when we invoke the classic “us vs. them” perspective. Levels 2 and 3 represent people who can think about tomorrow and beyond; the car may be used but it runs, the phone is just as smart as yours, and a vacation is in the cards.

Rosling offers good advice about how to compensate for our dated models and the thinking habits that cause us to misinterpret the data. He organizes those bad thinking habits into ten instincts ranging from our preference for bad news to our tendency to think in straight lines to our search for someone to blame. For each of those instincts he demonstrates why they feel so go and lead us so far astray. He offers a wealth of mini-cases and stories to teach us how to tease better insights out of the numbers by being smarter about the models we put to use.

A good number of my consulting colleagues pride themselves on their ability to “torture the data until it confesses.” Rosling shows us that seduction is a much smarter strategy if you want deep insight. He shows how to combine the data with knowledge of the world to derive insights that wouldn’t otherwise be easily found. For example, the data about the rates of vaccination around the world tells us about investment opportunities:

Vaccines must be kept cold all the way from the factory to the arm of the child. They are shipped in refrigerated containers to harbors around the world, where they get loaded into refrigerated trucks. These trucks take them to local health clinics, where they are stored in refrigerators. These logistic distribution paths are called cool chains. For cool chains to work, you need all the basic infrastructure for transport, electricity, education, and health care to be in place. This is exactly the same infrastructure needed to establish new factories. (Here’s a video where Rosling and Gates help visualize the process)

Factfulness is a master class in data interpretation and storytelling with data. Along the way, you’ll unlearn some bad models you’ve been carrying around and replace them with more accurate and effective ones. It doesn’t hurt that Rosling views the world from a systems perspective, is comfortable with “it’s complicated” as a response, and still demands that we take responsibility for our actions.

Rosling passed away in 2017 but his work continues with this book and with his children. When you’re done with the book, plan to spend some more time exploring these ideas at Gapminder.org, where you’ll find more data, some software tools, and more to learn.

 I’ve carried a pocket knife since my days as a stage manager/techie in college. A handful of useful tools in hand beats the perfect tool back in the shop or office. Courtesy of the TSA I have to remember to leave it behind when I fly or surrender it to the gods of security theater but every other day it’s in my pocket. There is, in fact, an entire subculture devoted to discussions of what constitutes an appropriate EDC—Every Day Carry—for various occupations and environments.

I’ve been thinking about what might constitute the equivalent EDC or Swiss Army Knife for the demands of project planning and management. We live in a project based world but fail to equip managers with an appropriate set of essential project management tools.

Like all areas of expertise, project management professionals build their reputations by dealing with more complex and challenging situations. The Project Management Institute certifies project management professionals. PMBOK, the Project Management Book of Knowledge has reached its sixth edition and runs to several hundred pages.

The complexities of large-scale project management push much training and education into the weeds of work breakdown structures, scope creep, critical-path mapping, and more. The message that project management is a job for professionals and the amateur need not apply is painfully clear. But we’re all expected to participate in project planning, and often we must lead projects without benefit of formal training in project management.

Recently, I looked at the need for project design before project management. The essential problem is to use a picture of where you want to end up to lay out a map of how to get there from wherever you are now.

The end is where to begin. Until you can conjure a picture of where you want to go, you have no basis to map the effort it will take to create it. Imagine what you need to deliver in reasonable detail and you can work backwards to the steps that will bring it into being. If you have a clear sense of where you are, you can also identify  the next few steps forward.

Working out the steps that will take you from where you are to where you want to go can be done with two tools and three rules.

Tool #1: A calendar. If you can do it all without looking at one, you aren’t talking about a project.

Tool #2: A messy outline. An outline because it captures the essential features of ordering steps and clustering them. Messy because you can’t and won’t get it right the first time and the neat outlines you were introduced to in middle school interfere with that. (Personally, I’m partial to mind maps over pure outlines, but that is a topic for another time.)

Three rules generate the substance of the outline:

1. Small chunks
2. First things first
3. Like things together

“Small chunks” is a reminder that the only way to eat an elephant is in small bites. There are a variety of heuristics about recognizing what constitutes an appropriate small chunk of a project. Somewhere between a day and a week’s worth of work for one person isn’t a bad starting point. Writing a blog post is a reasonable chunk; launching a new blog isn’t.

Generating an initial list of small chunks is the fuel that feeds an iterative process of putting first things first, grouping like things together, and cycling back to revise the list of small chunks.

The art in project management lies in being clever and insightful about sequencing and clustering activities. Here, we’re focused on the value of thinking through what needs to be done in what order before leaping to the first task that appears. That’s why an outline is a more useful tool at this point than Gantt charts or Microsoft Project. An outline adds enough structure over a simple to do list to be valuable without getting lost in the intricacies of a complex software tool. An outline helps you organize your work, helping you discover similar tasks, deliverables, or resources that can be grouped together in your plans. An outline gives you order and clustering. The calendar links the outline to time. For many projects that will be enough. For the rest, it is the right place to start.

The point of a project plan is not the plan itself, but the structure it brings to running the project. The execution value of widespread project planning capability in the organization is twofold. First, it adds capacity where it is needed: at the grassroots level. Second, it improves the inputs to those situations where sophisticated project-management techniques are appropriate.

“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.

 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?

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.