Running the numbers on the journey to insight

I’m a product of the case method approach to an MBA. After two years of analyzing three cases a day, I then spent time as a case writer. One of the first questions you would always face was “have you run the numbers?”

Figuring out which numbers you should run and what the heck “running the numbers” was supposed to mean was all part of the learning process.

Vaclav Smil’s most recent book, How the World Really Works: The Science Behind How We Got Here and Where We’re Going provides an excellent example of the power of this strategy. It also offers a flavor of the experience I encountered too often when I faced a professor without running the numbers first. Here’s Smil’s motivation for this book:

The gap between wishful thinking and reality is vast, but in a democratic society no contest of ideas and proposals can proceed in rational ways without all sides sharing at least a modicum of relevant information about the real world, rather than trotting out their biases and advancing claims disconnected from physical possibilities.

Smil lays out his case for the relevant information about the real world that we ought to share. He starts with energy and food. Hard to get much more fundamental than that. If you’ve got eight billion people on the planet, that’s going to call for a lot of food to produce and distribute. That production and distribution depends on energy and most of that energy comes from fossil fuels. Fossil fuels that won’t be easily displaced by renewable sources at the scale implied by a population of eight billion.

This is a theme that Smil continues to hammer on; that you have to look at systems and scale in sync. He drives that home in a series of chapters examining his candidates for the four material systems that underpin our current economic environment; steel, cement, plastics, and ammonia. Not exactly the “software is eating the world” message that we’ve become accustomed to.

Smil stops short of offering specific policy recommendations. His desire is to see policy debates grounded in a better understanding of the relevant underlying systems and their scale. He hints at options that he deems plausible;

Solutions, adjustments, and adaptations are available. Affluent countries could reduce their average per capita energy use by large margins and still retain a comfortable quality of life. Widespread diffusion of simple technical fixes ranging from mandated triple windows to designs of more durable vehicles would have significant cumulative effects. The halving of food waste and changing the composition of global meat consumption would reduce carbon emissions without degrading the quality of food supply. Remarkably, these measures are absent, or rank low, in typical recitals of coming low-carbon “revolutions” that rely on as-yet-unavailable mass-scale electricity storage or on the promise of unrealistically massive carbon capture and its permanent storage underground.

The reality is that any sufficiently effective steps will be decidedly non-magical, gradual, and costly.

This is a book that should be widely read. What it needs is a companion volume that delves into the human systems side of how we might go about tying the politics of large scale system change with a grounded acceptance of the facts on the ground.


McGee’s Musings turns 21

I’m pretty sure that I would never have predicted that I would be writing this post from Portugal. Certainly not as an open-ended decision to relocate here.

When I started this experiment, I was beginning my time on the faculty at Kellogg. I did end up victim of the contingent faculty phenomenon and just recently wrapped up teaching at Loyola University in Chicago. It was a good gig, but the adjunct faculty game mostly suffers from all the problems that others have documented. The only saving grace for me is that this occurred at the tail end of my professional career and I was less affected by the downsides of adjunct work.

I expect to continue posting some of what I read and think about here. I am giving serious thought to shifting from a basic blog structure to more of a digital garden. We’ll see how that thought grows and evolves from a passing thought into an intention and, I hope, changes that become visible to the broader world.

Becoming a More Reflective Practitioner

It’s been nearly six months since I last posted here. That post was a look at dealing with baggage in the context of knowledge work. So I think the following image is fitting to account for the gap.

multiple bags of luggage in airport arrival area
Arriving at Lisbon Airport

That’s me in the Lisbon airport on September 1st with fourteen bags of most of our worldly possessions. A few hours later, Charlotte and I would begin unpacking them in Nazaré, Portugal, our new home for the next chapter in our story.

While the move has been in the works since the early days of Covid, most of the execution has been packed into these last six months. We sold the house, sold the car, rented an apartment, bought a car, closed accounts, opened accounts, said goodbye to friends, started making new friends, and started learning Portuguese (a constant reminder of my general lack of decent study habits and a likely source of future blog posts about learning challenges in my dotage).

The closing graf in that earlier post was

If your value depends on making sense out of the collision between the present situation and what has come before you have to manage your understanding of both.

There’s an intuition there that I am still trying to unpack.

Donald Schön’s notion of reflective practice is central to understanding knowledge work. Experience is the raw material that fuels practice. Your practice improves as you process experience and make adaptations and changes. Schön’s argument was that the more intentionally and mindfully you process and make sense of experience, the better your practice gets.

There’s a scale problem here. Maybe more than one.

The first is the basic problem of information overload that faces all knowledge workers. The second scale problem is that more of us are knowledge workers. We’ll table that problem for another day.

Richard Saul Wurman wrote about information overload in the late 1980s in Information Anxiety as did Vannevar Bush in 1945 in “As We May Think”. Moreover, it’s an exponential growth problem that has nearly always exceeded the capacity of available coping strategies (willful ignorance doesn’t count as a coping strategy no matter how often it is chosen).

The spate of new PKM tools are the latest attempt to address this scale problem. It’s encouraging to see some recommended strategies for deploying the tools. What I would like to see is even more case studies of how we are all learning to leverage available tools.

I was about to add that I’ve made a mental note to work on case studies of my own practice. But, in point of fact, I’ve just finished capturing that “mental” note as an actual note. Stay tuned.

One aspect of the information overload problem I want to investigate is the metaphor of the blank sheet of paper. Sometimes it’s explicit. More often it’s hidden in the assumptions of new tools and new practices. Everyone wants to pretend that you start clean because it simplifies their problem. Dealing with whatever mess of prior systems and practices you’ve cobbled together over the years is your problem.

Which it is.

But it really helps to be aware that it is your problem and that it exists. Taking some time to see what is already on your sheet of paper is the first step to becoming a more effective reflective practitioner.

Choosing to Learn New Tricks

You can’t teach an old dog new tricks

This cliche has been on my mind lately, particularly as an old dog myself. How much of learning new tricks as an old dog starts with figuring out how to deal with history; with the cumulative baggage of what has come before?

This is in the context of the new wave of attention to personal knowledge management. It’s also in the context of an email I got this week from a consultant at Gartner Group reminding me of a column I wrote in 2005 on Why You Need a Personal Knowledge-Management Strategy

Time was when most of the value/baggage in history was “encoded” in your experience. Grey hair was a marker that you knew things and your cumulative experience expressed itself as informed judgment. Even if you had the files it wasn’t worth trying to mine them for value.

Today we have the opposite problem. All the history is there hanging like a weight over your head. You now need a strategy and practices for leveraging that history without letting it paralyze you. This is the dilemma of building a second brain marketing promises. It’s the digital equivalent of the professor’s office buried in stacks of paper and yards of shelves.

But most of us aren’t professors and haven’t been at this long enough. We haven’t accumulated enough digital stuff to trip over the looming problem. In analog days, only the most compulsive types were troubled by the steady accumulation of material. In a digital world, you have to actively fight the accumulation. Declarations of email bankruptcy make for good copy but miss the point. Warnings to beware of the collector’s fallacy also miss the point.

If your value depends on making sense out of the collision between the present situation and what has come before you have to manage your understanding of both.

Scope the Problem First

Some of my colleagues have objected to my disparaging connecting the dots thinking. They think I’ve caricatured the strategy to make my point. They’re probably right.

What I am really criticizing is a particular form of laziness. The kind of laziness that always wants to cut to the chase, to skip to the last chapter where we discover who the killer was, that just wants the answer.

We all want to skip over the boring parts. We get in trouble when the important insights are hiding in the boring parts.

There’s the classic joke about the hikers who encounter a bear in the woods. One stops to put on running shoes. The second hiker scoffs that “you can’t outrun a bear”. The first hiker answers “I don’t have to outrun the bear, I just have to outrun you.”

There are rarely simple solutions to complex problems. Few would argue that point. What’s harder to address is the pressure to ignore the complexity in a problem when there’s a tempting simple solution at hand. How much of the sales copy and sales pressure that we encounter consists of efforts to label the problem at hand as one that matches the solution being offered?

Curiously, this tendency cuts both ways. My initial conversations with prospective clients often begin with the client telling me what the answer is. “We need a mobile app”, “we need to move to the cloud”, “we need a CRM system”. The specific answers evolve over time, but the pattern persists.

“What problem are you trying to solve?” is the starting point I strive to move the conversation to. Some cynics will interpret it as an attempt to extract more fees from a simple situation. But, it’s essential if you want to get an answer that ties to the right questions.

Don’t Forget to Play: Solving for Pattern as an Infinite Game

I keep criticizing connecting the dots thinking as overly narrow. It’s just now occurred to me that my fundamental objection is that connecting the dots is a finite game when we need to be playing an infinite game.

Finite vs. infinite games is a distinction philosopher James Carse makes in his book Finite and Infinite Games. A finite game is a game with a clear set of rules and boundaries that you play to win. An infinite game, on the other hand, is a game you play with the primary goal of continuing to play. Carse makes a compelling case that infinite games are far more interesting and that we should be looking for opportunities to transform whatever game we are playing from finite to infinite. If you haven’t read it, go find yourself a copy now and read that.

Converting a connecting the dots situation into a solving for pattern exercise is a perfect example of converting a finite game to an infinite game. Rather than seeking the picture cleverly hidden in a series of disconnected dots, solving for pattern opens up the game to the exploration of multiple possibilities and options.

There’s an old joke about baseball umpires discussing calls after a game;

Umpire #1 – I call them as I see them
Umpire #2 – I call them as they are
Umpire #3 – They aren’t anything until I call them

The point about infinite games and about solving for pattern is that you are playing the game and making up the rules as you play.

Learning To Solve For Pattern

Isaac Newton is credited with the observation that “If I have seen further it is by standing on the shoulders of giants” (which he actually appears to have borrowed from 12th Century philosopher Bernard of Chartres. see (Standing on the Shoulders of Giants). Computer scientist Richard Hamming was a bit less gracious, and perhaps more relevant, when he pointed out that “In computing, we mostly stand on each other’s feet.”

Both men were clear, however, that progress builds on the work that came before. This is an axiom in academic disciplines. Becoming an effective academic starts with long apprenticeships centered on immersion in the context of a field. You don’t get to play until you can demonstrate that you are familiar not just with the forms but the substance of what has come before.

Familiarity with context and history has not been so axiomatic outside of ivy-covered halls. For much the industrial revolution, the story has been about what is new, different, and innovative. Men of action were rooted in the now and facing tomorrow.

As more economic activity depends on those who think for a living, that equation has been changing. The old canard “those who can, do; those who can’t, teach” has lost much of its currency. At the very least, we need to stop standing on one another’s feet.

Claiming that your new product or service is new and different is easy to do if you don’t bother to look around you. Too many careers have been built on confident assertions without evidence. I suspect that the continuing popularity of connecting the dots arguments is partly anchored in the hope that confidence will out.

Solving for pattern, on the other hand, starts with accepting that you don’t, and can’t, know everything. While there won’t be a specific picture hiding in the data, there will be fragments and familiar elements that can be discerned, however incompletely. Your job is not to match what you see against a standard, it is to construct a plausible story and make sense out of what you think you are seeing. There is no passive role to adopt.

Stop Trying to Connect the Dots

Connecting the dots is a fun children’s game. It’s a lousy strategy for making sense of complex phenomena.

As a game, it’s fun to see a picture emerge as you drag your pencil from dot to dot. When it’s used as a metaphor for sensemaking in the real world, there are a set of hidden assumptions that are particularly pernicious because they are hidden. In the game, you start from a picture, select and number a collection of dots, and then erase the rest. You simplify a complex image into a subset of data points and a prescribed sequence for connecting the dots.

This is not a reversible process. You cannot collect a set of dots, connect them as you choose, and then claim that the image was sitting there all along. Nor can you criticize an analyst for failing to collect or connect the necessary dots in advance. It’s lazy language and lazy thinking.

While lazy thinking, it’s also rampant. Cast your mind back to your university days. It’s a safe bet that you had a course syllabus with extensive verbiage about academic integrity and the perils of plagiarism. And expectations for preparing terms papers laden with correctly formatted references to the literature you consulted as you did your research. Fail to include the proper incantations in your submitted work and you were at risk of dire punishment.

All of which encourages connecting the dots thinking and discourages developing the more robust skills of solving for pattern that you are going to need to tackle the open-ended and complex problems you will encounter.



Forcing Functions To Understand Complex Work

From time to time, I get invited to speak to groups of executives. I’ve even gotten paid to do so and been invited back for repeat performances. What I haven’t done is develop an inventory of stock speeches. That can be an effective and lucrative path for those who opt for it. But, that path is premised on having a message you want to share widely. It’s more about preaching and I am more interested in teaching.

Here’s an example from about a dozen years ago that might tease out the distinction. A software vendor I’d worked with asked if I would keynote their annual users group meeting. They were (and are) a player in the corporate knowledge management space. As a former Chief Knowledge Officer for a growing management consulting firm, I was trying to work out why certain efforts had failed and others succeeded only modestly.

I treated the invitation as a forcing function; an opportunity to work out some thinking with an audience who might have insights to share. Consider it a segment in the longer process of solving for pattern.

A persistent problem in corporate knowledge management efforts was getting knowledge workers to share their work more broadly. Various incentive and control systems didn’t appear to have much effect. The premise behind these solutions was that people were reluctant to share their work for reasons unknown.

I used this event to explore an alternate hypothesis; sharing knowledge work was hard to do because technology made the doing of knowledge work invisible. Work that had been visible in the movement and flow of paper or the flipcharts and whiteboards in conference rooms had disappeared behind the keyboards and screens of workstations, personal computers, and laptops. Solving the sharing problem was not about incentives, it was about visibility. Make knowledge work observable and sharing becomes more natural.


Honor the Work

John Cusack’s first movie The Sure Thing is one of my favorites. In one scene, Cusack’s college roommate is explaining his success bedding a string of random coeds; “It’s all about sincerity; once you can fake that the rest is easy.” Cusack’s character arc is all about his inherent sincerity and learning to let it through in place of the games he thinks he should play.

It’s a constant challenge to stop looking for shortcuts and do the work. This is my fundamental objection to the language of “connecting the dots” It perpetuates the mythology of shortcuts. It puts the focus on the final step, the reveal.

“Solving for pattern” shifts the emphasis productively. Wallowing in the phenomena to identify things that may or may not turn into a dot. Trying out different combinations, connections, and clusters to see whether they point to something interesting. Formulating and testing hypotheses that might explain what you are observing.

I grew up wanting to be a scientist; preferably a rocket scientist based on the dominance of science fiction in my reading habits. As I found myself drawn more and more to the human side of the equation, I questioned that dream. Things were too messy to fit into the narrow conception of science I had started with. I was confusing the pretty pictures with the underlying discipline of the process.