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Good Measurement and Bad Mimicry

Theodore Roosevelt captured of fundamental truth in the human condition when he shared the following:

Comparison is the thief of joy.

But comparison isn’t the problem, of course. Envy is. The act of comparison is just an information-gathering exercise. Our response to that information is where the joy-stealing really happens.

This effect is heightened when comparison is facilitated through the precision of metrics. When people can compare numbers, be it their GPA or net worth or friend count, it’s like comparing Win-Loss records in a sports league. Lower numbers, relative to the competition, are just that. Lower numbers. The numbers themselves say nothing.

It’s us who decide if higher numbers = winners and lower numbers = losers. We decide this based on the game we want to play.

Case in point: a few months ago, I examined my relationship with LinkedIn. I realized I didn’t like the game it wanted me to play. Engaging with that platform led me to an infatuation with followers, connections, “likes”, and all manner of ugly insecurity brought on by the bad kind of comparative thinking.

Worst still, I started to adopt the language and mimic some of the behavior. This is really embarrassing in hindsight. I started writing in that sort of empty careerist patois that is more half-hearted and hollow than the small talk at a convention booth. Lots of exclamation points! And shallow positivity!

I decided to stay on the platform but only so that I could play a different game. For example, I decided to maintain no more than 499 “connections” at any given time. It felt like a cute retaliation against the platform’s implied validation of having “500+” next to your name.

#Forever499 sounds weird. And trite. But I’m sticking to it.

The idea here is that metrics fuel comparison. This comparison usually sparks envy. That envy usually leads to mimicry. That mimicry typically causes you to behave in ways that reinforce the game that everyone is playing. That reinforcement often leads to all sorts of additional behaviors, like paying for premium services (be a LinkedInsider!) and peer recruitment via network effects (Norm Wright invited you to join LinkedIn).

As the game continues, the only people who win are the ones who designed it in the first place. You can see this with MLM over the long run.

It all begins with a single metric that suggests a certain game based on a bad kind of comparative thinking.

Which means there is a good kind of comparative thinking. These metrics, these comparative tools, can bolster your identity. They can make you an outlier of your own choosing. They can clarify the strategy you want to hold.

The One Metric That Matters

The authors of Lean Analytics are champions of the idea that you should find the One Metric That Matters (OMTM) and use it as your proverbial North Star for every decision. This is a fantastic way to practice strategy. It is also the way we can really practice what Stephen Covey advised so many years ago when he said:

The main thing is to keep the main thing the main thing.

It feels so tautologically tautological!

The OMTM differs for every endeavor. But in the business world, certain fundamental measures can be used across all manner of industries. As the authors write:

There are few metrics—like growth rate, visitor engagement, pricing targets, customer acquisition, virality, mailing list effectiveness, uptime, and time on site—that apply to most (if not all) business models.

I enjoy thinking about the cross-disciplinary value of these metrics. It fuels comparison in a good way. What is a proper customer acquisition metric in business consulting versus SAAS? What is the proper growth rate for a technology company versus an industrial manufacturer?

Maybe it’s all academic but the difference in each numerical value amplifies the differences and trade-offs in each business model. Amazon’s profit margin sounds horrible if you compare it to Boeing but that’s because they are fundamentally different companies operating different models. The metric explains how.

But when you compare the same companies in the same models, you get draw fun, alluring conclusions. So Amazon’s margins compared to, say, Target and WalMart, show the deep underpinnings of each retailer’s strategy and helps explain their comparable performance at any given time. We read too much into these things but, again, such analysis of Target’s margins helps us tell stories about today’s winners (Amazon and Wal-Mart), losers (Target), and tomorrow’s forecast (Amazon wins).

The Other Metrics That Don’t Matter (as much)

The question at the heart of that particular comparisons is the following: does Target care?

Maybe Target fully accepts its shrinking profit margin because it’s playing a different game. Or maybe it can’t fix the shrinking margin so it has no choice but to play a different game. In either case, if the metric matters most, they will pin their strategy on it. If that metric doesn’t matter most, then something else does. And that something else, that OMTM for Target, is the game their playing. It is their identity. It is their strategy.

The folks at Harvard Business Review have written many, many times on how the critical aspect of strategy is not what you will do. Great strategy is about the other half of that coin: what you won’t do. A willful use of the word “no” helps you maintain coherence.

The authors of Lean Analytics know this well. So when it comes to all the various measures that any business can use, they wisely advise that the OMTM be something so perfectly-designed as to naturally lead you to embrace it. Even when people cast worries about your profit margins or some other metric.

The OMTM needs to be so good, so pure to your mission and identity, that it practically creates an existential dependency.

For example, consider this story of Facebook’s early OMTM: growth. At that point in time, nothing else mattered. Was it infatuation? Maybe. Should other things have mattered more? I don’t know. All I know is that a business can’t be coherent and sensible until something is the focus. Growth was Facebook’s.

And what makes for a proper OMTM that really deserves that focus? The authors identify five traits of such a measure. An OMTM must be:

  1. Simple.
  2. Immediate.
  3. Actionable.
  4. Comparable.
  5. Fundamental.

Here’s an illustration of this OMTM framework in the restaurant business. The authors spoke with the owner of the high-end Michelin-starred Solare Ristorante where the OMTM for their business is the ratio between per-diner revenues and staffing costs. (emphasis added):

[The owner] explained when staffing costs exceed 30% of gross revenues, that’s bad, because it means that you’re either spending too much on staff or not deriving enough revenue per customer.

The ratio works because it’s:

Simple: it’s a single number.

Immediate: you can generate it every night.

Actionable: you can change staffing, or encourage upselling.

Comparable: You can track it over time and compare to other restaurants in your category.

Fundamental: it reflects two basic facets of the restaurant business model.

Strategically, the tactics used to keep that number down and keep the business strong all cascade from this one beautiful, sensible OMTM. What works for Solare Ristorante is unique to that specific restaurant. They maintain a balance that suits their vision of what a great restaurant, to them, needs to be. The only comparison here is with themselves. They’re running their own race.

The owner didn’t go look at Applebees’ ratio. He didn’t first scope out McDonald’s either. And even if he examined a true competitor’s ratio, he didn’t try to copy it. Because he’s playing his own game.

Mimicry Can Lead To Individuality

There is mimicry in some of this, for sure. After all, I doubt Solare Ristorante invented their specific OMTM. They copied it from elsewhere. And Facebook isn’t the first or last company to be maniacal about growth. But what matters here is that each individual (you and I) can use these metrics as an initial comparison in productive ways.

Comparison doesn’t have to steal our joy. It can spark it.

When we see other people’s numbers, be it follower counts or “likes” or profit margins or revenue-staffing ratios, we can make a choice. We can decide to attach value to those numbers or we can decide to value some other number instead.

The choice doesn’t matter so long as you make the deliberate decision yourself. What you choose to value is just that: your choice. For your social media, your business strategy, your identity. Until you are capable of making that choice, you’re probably just going to mimic what others do. That’s okay for a time. Mimicry helps you get started. It helps you find what matters.

Just don’t get lost there. Be a willful participant in the game. Whichever one you choose. Then make your own.

The Power of Entrepreneurial Empathy

The act of creating a new business venture is akin to the act of writing a novel. It feels very personal; your heart and soul are wrapped into the work. It’s all about achieving a vision. Fulfilling a certain need.

Only, it’s not your need that matters. It’s theirs. The customers. The audience. The readers.

This is the common theme in most creative acts. When creative work is successful, we find a serendipitous alignment between the thing that is offered (the product, service, or new novel) and the needs from the broader market. For novels, that can mean a new book just-so-happened to fulfill the needs of 10,000 readers who were looking for that very thing. For a business, it could be as few as a 10 clients. Or a single contract.

Obviously, this is just a small fraction of the millions of people waiting for someone to deliver a solution to a given problem. So opportunity is everywhere.

It begs the question: why do novels fail? Why do businesses go bankrupt? Some of it appears to come down to rigid self-involvement. We love our own work, build things in our own image, and hope that others will love it, too. We cling to a specific identity even when there aren’t a lot of people who want it.

You can see this more easily with novelists. So many authors fail to remember that no one cares if they love the books they’ve written. To succeed in any conventional sense, others have to love the book, too. Great art isn’t beholden to the whims of a market. But publishing is.

Again, I see it with some products and services, too. Particularly those new ventures that have a firm, deep-seated, multi-layered identity of who they are before they’ve acquired a single customer. These groups occasionally succeed. But not without a pivot or three.

This necessary alignment between what is offered and what is needed is an inescapable gravity problem. And I don’t know about you, but everything I like and want and need, as an individual, appears to be so niche, so quirky, that I can’t trust myself to rely on mere instinct alone. I need some good analysis to help me understand the market’s broader needs. I need some better understanding of other people.

How can we develop that understanding?

The Great Ideas Starts With Empathy

Here’s a classic line from Zig Ziglar that always sticks with me:

You will get all you want in life, if you help enough other people get what they want.

Given all the data generated by our modern practices, it’s tempting to think that we know what people want. After all, we have their browsing history, demographic data, spending patterns, psychographic profiles, and viewing behavior. Yet, for those of us who don’t operate massive tech platforms, this data is seldom as accurate as we’d like to think. And rarely predictive.

Indeed, for all the love of big data, accuracy continues to prevail over scale. Even the tech giants and their vast platforms can’t see it all. This is where the hard work comes in.

To know what people want, you probably have to talk to them. I know. It’s terrible. But not as bad as it may seem.

Empathy Starts With Interviews

In the book Lean Analytics, authors Alistair Croll and Benjamin Yoskovitz share fantastic insights on exploratory methods that can help us. Their approach offers the highest level of accuracy that I think we can find. It starts with a clear sense of a problem that you want to solve and a series of interviews with people to test the viability of a solution.

These interviews are critical. Which is funny, I think, since a book on analytics would presumably give you all manner of ratios and measurements from some trove of data that allow you to treat the whole thing like a math problem. It just further proves the old adage that you can only find value by getting out of the office.

And when you’re out there, you have to look for one specific measure above all. As the authors write:

Ultimately, the One Metric That Matters here is pain—specifically, your interviewee’s pain as it pertains to the problems you’ve shared with them.

Thankfully, this is a fun thing to talk about. And it is a whole lot easier than talking about solutions. No one cares about your solutions just as surely as no one cares about my unpublished novels. But everyone loves to talk about their pain. So invite them to do so. As it pertains to the problem you want to solve.

This is what empathy is all about. It is anchored on what others feel instead of what they think. Far too often, we get advice that we should just have a great problem. Or a great solution. A google search on both fronts will give you delightfully-contradicting advice. So I’ll spell this out more plainly:

A great problem isn’t enough. A great solution isn’t enough. Don’t fall in love with either until you find people searching, in pain, for what you want to offer.

Ask any charity organization and they’ll hopefully affirm this. They’ll tell you that there are plenty of problems and plenty of solutions that no one wants to fund. Why? Because there is no pain. Thus, there is little sense of intrinsic value.

So how do you measure pain? The medical profession has tons of literature on the topic and yet results are still inconsistent and subjective. Thankfully, breakthroughs are emerging that may lead to true objectivity. But for the rest of us who don’t have fMRI machines, what are we to do?

Structure your interviews. After all, this is analysis. So it needs a consistent method that produces real qualitative data at the end.

To get the best data possible, use a consistent method of delivery. Explain the problem in the same way with each person. Try not to lead them with suggestive phrases like “Don’t you think … “ and “Isn’t it obvious that ….” Take a lot of notes. Calibrate their sentiments. “On a scale of one to ten, how important is ….”

Just don’t go overboard. Because this is tricky. You want to be analytical in these conversations but you don’t want to be clinical. Your interviewees are not patients. So the main objective isn’t pure accuracy in their response, but rather a consistent, comprehensive view.

What questions do you ask? What insights do you look for? Our authors suggest that you explore people’s thoughts along the following themes:

  1. Their level of interest in the problem.
  2. Their level of effort already expended to solve the problem.
  3. Their level of engagement in the conversation.
  4. Their willingness for a follow-up meeting.
  5. Their willingness to refer you to others.
  6. Their unsolicited willingness to ask about solutions and even pay for them

Ask structured questions along these themes. Write down the answers. Afterward, score each response on a point scale that offers some level of granularity. Add varying weight to each theme.

For example, our authors weigh Questions 1 and 2 the highest. Favorable responses can earn up to 10 points whereas Question 6 can only yield 3 points. Why? Because the authors find that finding the pain point is more important at this stage than finding the business opportunity.

But this is philosophical. So if you think Questions 4 – 6 are more important, weigh them accordingly. The key, of course, is to do this rigorously and consistently across a broad set of interviewees. This is how you get the signal.

Bring Me Your Grievances

How do you find interviewees? By finding people who deal with the problem you’re looking to solve. It’s that easy. Hopefully, this includes people you know who then lead you to people you don’t know. So long as you aren’t trying to sell anyone (yet), this shouldn’t be too difficult.

Because ultimately, this is just a conversation. I think it’s important to keep that intent clear. It’s easy to misconstrue these interviews as some form of market analysis. Some might say its an exploration of “product/market fit.” But again, that’s misguided.

I think it’s easier and more honest to imagine these interviews as a form of constructive complaining. Help people complain about the problem, the ways it really bothers them, and use that inform to test and inform your solution afterward.

If and when you find a problem that is of high interest, that people are already trying to solve, you’ll find a fabulous opportunity. The empathy is easy here. You don’t have to feel their need. You can see it plainly.

I think of it in terms of duct tape. When you find people trying to make things work with improvised solutions, like when I use duct tape to fix something, you’ve found the pain point. And the empathy. Because we’ve all been there.

After the interviews, take a look at the problem. If it has a high score, meaning that most interviewees really feel the pain and want a solution they can’t provide themselves, you have a great opportunity to give them what they want.

If the scores don’t show that, either keep searching or take the insights you have and think about a different problem to address. I’m quite sure the exercise will point in many new, interesting directions. Thus the search continues.

Angry Customers = Happy Prospects

In closing, Bill Gates once said you learn the most from your angriest customers. This is true. But people don’t have to be your customer for you to learn from their anger. Every point of their discomfort is a highlighted need just waiting to be addressed.

Coincidentally, this is the stuff of tactical empathy that was covered in the study of Chris Voss’s fantastic book Never Split The Difference (book review here). It works fantastically for negotiation. If you’re curious, here’s an article on the concept.

I think it works just as well for entrepreneurship. To understand a customer’s pain is to prove the severity of their problem. And when the problem is severe enough, the solution doesn’t have to be perfect. Just better. With enough customers looking for something that is “just better”, you have “just enough” opportunity to serve.

This method seems a lot better than just developing a business plan in the dark, armed with mere factoids, generalized reports, and vague intuitions. We can power of lot of work with our own love for the idea or the problem we want to solve. But like so many unread novels, it’s not going to work if no one is looking for it.

Go Ahead. Move The Goalposts.

There is one particular logical fallacy that children use to great effect whenever they’re in an argument. It always makes for a good laugh. To illustrate, imagine a scene on the playground: an adolescent looks up at the sky and says, “It won’t rain today.”

A schoolmate says, “It will, too.”

“No it won’t.”

“Yes it will.”

Time passes and, later in the day, both kids observe the soft patter of raindrops falling from the sky. The victor of that little debate says, “See, I told you it would rain.”

To which the other kid says, “I meant real rain. Like a thunderstorm. This isn’t rain. This is just sprinkles. I knew it was going to sprinkle today. But it’s not raining.”

This showcases the act of moving the goalposts. The original intent of the argument shifted rather crudely, in light of new evidence, to somehow preserve the sanctity of the claim. Children aren’t the only who do this, of course. It happens to us childish adults, too, whenever we maintain a stubborn attachment to our own faulty statements.

Why do we maintain this attachment? Because we hate to lose.

Moving Goalposts – Literally

It happens elsewhere, too. In sports, people have occasionally been known to actually, physically, move the goalposts. A favorite example is the hockey goaltender David Leggio. He did this on multiple occasions. Here’s video of the first instance in a minor league game between the Bridgeport Sound Tigers and the Springfield Falcons in 2014: video link.

The situation involved a turnover that lead to a 2-on-0 matchup, meaning two offensive players attacking the goal with only Leggio to protect it. This is probably the hardest situation a goaltender can face. So one could argue that Leggio’s tactic was ingenious. Rather than protect the goal, he just move it off the pegs! Here. Now nobody can score.

He did this on multiple occasions. In multiple countries. It eventually led to a rule change that still carries his name.

The reason to do this seems obvious. In one sense, this was a clever exploitation of the rules. All in the name of preserving his team’s chances. But it violates every moral sense of fairness and ignores the true spirit of the game. This is equivalent to a cardinal sin in such a deeply traditional sport as hockey.

And he also did it, I think, because he was scared. Even when its explained as some altruistic thing (I’m doing everything I can to help my team win!), it still feels cowardly.

So yeah, this kind of behavior is wrong. For all the right reasons.  

But we take this attitude a little too far. Physical goals, as constructed on sports fields, and literal definitions, as used in conversational debates, should be fixed. Certainly. But our figurative goals that we use to achieve a “win” can, and should, be made of looser stuff.

The Triumph of Hope Over Experience

“Aim high”, right? This is the common refrain of most startup founders and a key feature of the 60-70% rule in the OKRs system at Google. To borrow from Google’s own Larry Page, the idea of aiming high is as follows:

“If you set a crazy, ambitious goal and miss it, you’ll still achieve something remarkable.”

So moonshots all the way! Aim for a really crazy goal.

One of my favorite images of all time.

This works for some people. It doesn’t work for others. And I think a lot of the difficulty comes back to our preconditioned attitude to games and fallacies and literal, physical goals on an actual field of play. When we miss a goal of any kind, especially the ambitious ones we might tell others about, we feel loss and embarrassment. We feel like a failure.

Plus, a lot of this “aim high” stuff is really just another form of what’s called the planning fallacy. Or what might also be called The Triumph Of Hope Over Experience. In a vast array of situations, this fallacy appears once we replace our most probable outcome with the one we most desire.

So when someone says “I’m going to lose 20 pounds,” they are stating a goal. But this goal is more accurately described as a statement of hope. A low probability hope at that. It is not a statement of fact.

The real problem emerges when this statement of hope becomes the sole condition for success. Imagine if this aspirant “only” loses 15 pounds or even 19.5 pounds instead of 20. More often than not, they will feel disappointed. As if such a change weren’t an incredible achievement.

Specific Metric + General Desire

It happens to us all. Particularly with these very specific goals. And as someone who writes a lot about the deep power of specific-yet-simple goals, I understand how a single number can spark enormous change. In the case of the weight-loss effort, that single number—20 pounds—is someone’s Mount Everest. It can trigger summit fever really fast.  

So what should we do? Stop using specific goals? SMART goals? Stop aiming high? Allow ourselves to slip into the ease of unambitious contentment?

Let’s consider the underlying motivations. This can be easily done with the Five Whys exercise. In the weight-loss situation, why does a person want to lose weight? To be attractive? Why does losing weight make them attractive? Can they be attractive by other means?

You get the idea. This exploration might seem nauseating but it gets to the reality of desire. There are two major aspects of desire that we should all recognize. First is what’s known as the intrinsic desires we crave, such as feeling attractive. Everyone wants this, likely as not. It isn’t the goal, per se, but it is the general desire.

The goal, especially the specific numerical goal, is the instrumental desire that presumably gets you to the intrinsic desire you pursue. This is important because the act of losing 20 pounds is simply the instrument by which the weight-loss aspirant presumably gets what they really want: that feeling they wish to have of themselves.

When we fixate on the instrumental desire, we tend to ignore the intrinsic one. This is something covered in William Irvine’s excellent book On Desire. The book review is here.  

But when we keep our intrinsic desire top-of-mind, embracing its general nature amidst the specificity of an instrumental goal, we can find a fantastic level of clarity and flexibility that makes our efforts more resilient. This mixture of a specific measure co-existing with a clear-yet-general motivation is where the great opportunity exists to do things that are not only impressive but actually enjoyable.

Moving The Metric

This gets to something I deeply appreciate from the book Lean Analytics. You’d think an analytics book would champion the use of rigid, hyper-specific goals that pull performance to such great heights. And that is certainly an idea in the book. But our authors, Alistair Croll and Benjamin Yoskovitz, are more nuanced than that. Which is why the book is so outstanding.

They make an important distinction in the goals we create. There are late goals, meaning goals that we form after a lengthy period of performance (e.g., sales goals based on a one-year track record), but there are early goals, too. Early goals are not necessarily relative to a specific phase of a business. Early goals can come at any time you try something new. Especially new endeavors that have no priors on which to calibrate your goals. As examined in our coverage of Bayes rule, estimation is hard without priors.  

So what do we do when setting goals and metrics in an early/new effort? Consider our authors’ wisdom:

When picking a goal early on, you’re drawing a line in the sand, not carving it in stone. You’re chasing a moving target because you really don’t know how to define success.

That last part is so vital. We think we know how to define success but we really don’t. We’re dancing with uncertainty. And whether or not we realize it, the goals we’re trying to chase are going to move. If not by our hand, based on what we feel, then by mere reality itself and the limits we uncover.

So adjust. Within reason. As our authors advise:

Adjusting your goals and how you define your key metrics is acceptable provided that you’re being honest with yourself, recognizing the change this means for your business, and not just lowering expectations so that you can keep going in spite of the evidence.

That last part about how we stubbornly “keep going in spite of evidence” has to do with the delusional nature of vanity metrics. When we chase such numbers, as explained in yesterday’s post about empty metrics, we do so to keep up appearances. Hence the use of the term “vanity.” And vanity is often why we feel compelled to never move our goalposts in the first place. I told everyone I was going to lose 20 pounds. But I only lost 19. I’m a failure.

It only feels that way when other people are watching and we’ve mistaken what the real effort was all about.


There is a fine line that must be kept, however, when it comes to moving our goals. In a previous writing, I shared the wonderful insights of a Hall of Fame football coach who once said:

When you set small, visible goals and people achieve them they start to get into their heads that they can succeed. They break the habit of losing and begin to get into the habit of winning. — Bill Parcells.

The key here is that the small goals seldom remain as small goalposts. They are responsive. They grow. They turn into ever-larger goalposts over time. They ratchet up.

To borrow the phrase from our Lean Analytics authors, these responsive goals are the “lines in the sand” that move further and further out once our teams cross them.

This article is written, however, to illustrate that these lines can not only be drawn further away as the team gets closer. These lines can also be drawn closer in. In other words, sometimes the best thing to do is shrink the change.

Or lower the metric.

But only to a point. Here’s where the caveat from our authors comes into play:

Lower the bar if necessary. But not for the sake of getting over it.

So lower the metric for the sake of changing behavior. Do it for the sake of your intrinsic goal. It’s okay. Really. Because whatever you’re striving for, I’m pretty sure it ain’t hockey.

So move the goalposts.

Just remember there are still rules and a “spirit of the game” to which we must stay true. What is the spirit of the game? For better or worse, you are the only one who can define that. When you have that definition, these goals, these measures, these analytics, can work for you. Wherever and however you decide to place them.

Photo by Jimmy Ofisia on Unsplash

Blind Values, Empty Metrics

Nothing compares to great data. Nothing does more to help me clarify situations, develop solutions, or make changes. And when data is woven into an honest narrative, it makes for some of the most beautiful, compelling expressions of thought we can find. It reminds me of a great line from Steven Pinker in his seminal work, Better Angels of Our Nature:

Narratives without statistics are blind, but statistics without narratives are empty

Weaving narratives comes natural to most of us. But statistics has to be learned.

This is a great way to level up. So I explore a lot of books of data. But it isn’t easy for generalists like me. Most resources are textbooks and instruction manuals that teach K-Means and logistic regression and all that jazz. Some of which is intuitive. Some of which isn’t. Developing these skills can feel great. But then come the questions:

How do I apply this to my work?

What, exactly, must we look for in our situation?

What questions should I pose?

What data should I measure?

In other words, despite all my love for the practice, I occasionally fail to understand how these methods help us run a better operation.

New Techniques For Old Questions

At the core, data is the resource we use to answer the age-old, constant conundrum: what do we change and what do we maintain? We ask ourselves this question all the time. And if that weren’t enough, we often change our answers as we change our goals.

Consider it from another age-old, constant question: what are we going to eat for dinner?

If you have a busy schedule, you probably find yourself modifying your weekly dinner plans from home-cooked three-course events to quick and easy dishes made in 30 minutes or less. Rachael Ray built a whole franchise on this. All in an effort to simplify because hectic schedules require us to solve for a single variable: time.

This works for a while. Then it gets boring. Eventually, you find your food is somewhat bland or it’s all frozen, unhealthy stuff. Simplifying saves time but diminishes other factors. Our tastes change, we adapt, and we’re unsatisfied again. So we change once more, bringing healthier foods and different recipes into the mix in an effort to now optimize on three variables: time, freshness, and taste.

Which, coincidentally, tends to add more time in the kitchen. Which is what you wanted to avoid in the first place. So a cycle is born. Simplification to optimization to simplification, etc.

Personally speaking, my approach to weekly cooking has changed drastically through the years. All along this continuum. It’s maddening and I’m about ready to just sign up for Soylent instead.

Jokes aside, data is the critical resource throughout this exercise. ROI is determined by the time and money invested in the kitchen divided by the satisfaction at the dinner table. When are these things just right? Honestly, no one really knows.

Because the data is very fuzzy. We measure by feeling. We go with our gut. Literally. And this is perfectly understandable in our personal kitchens and dinner table experiences. But going with our gut in the workplace feels a little less professional.

There’s a better way.

A Lean Pursuit

Again, the bedrock questions that we all face in any regular, goal-oriented work include:

What do we change?

What do we maintain?

And we respond with answers that either simplify or optimize but rarely do both.

How do we know which path is best?

I think the best method for answering these granular decisions is the deliberate, iterative cycles of the Lean process. The process and rationale itself is covered in my review of Eric Reis’s terrific work, The Lean Startup. That book is the right place to start.

But if statistics textbooks leave you wondering how to bring new analytical techniques to work (when, exactly, do I need polynomial regression?), Reis’s Lean Startup can leave you wondering how to apply data to the process.

The next resource to bridge that gap is Alistair Croll and Benjamin Yoskovitz’s Lean Analytics. At times, it is a wide-ranging treatise that offers soft illustration on a given topic. At other times, it is a deep, focused examination of fundamental concepts. And throughout, it is the book that shows me what analytics is really for. Specifically when it comes to actual metrics.

The One Purpose of Metrics

Early in the work, our authors state something so obvious it can easily forgotten:

A good metric changes the way you behave. This is by far the most important criterion for a metric.

Again, that feels obvious until you think about the number of times we are all seduced by the numbers that do not change the way we behave (e.g., “likes” on social media). The distinction here is between vanity metrics and actionable metrics. As our authors explain:

Vanity metrics make you feel good but don’t change how you act. Actionable metrics change your behavior by helping you pick a course of action.

We know that some of these metrics are quantitative. Some, of course, are qualitative. When we go back to the weeknight dinner example, the quantitative metric is time spent in the kitchen. It is an objective measure. The qualitative metric is our level of satisfaction with the meal. This is deeply subjective and open to broad interpretation. Especially when I try to make Thai food.

I think what matters is that both types of metrics show what we value. In fact, actionable metrics are just a more precise definition of what’s important to us. It may be the only way to really define what’s important to us in ways people can understand.

After all, what do we mean when we say we “value” the customer? There is a philosophical component to this idea but a measurable definition is needed if we want to consistently demonstrate the ideal. It starts with the easy correlation to satisfaction ratings. A target satisfaction rate of 95% goes a long way to explaining how much you value the customer.

But there are other metrics, too. How much of the annual budget is invested in customer service? What does that yield in Customer Retention? Or Total Customer Cost?

These questions are a means for clearly articulating your philosophy beyond empty platitudes. Maybe “customer retention” matters. Maybe it doesn’t. It depends on your intrinsic values.

This work shouldn’t be too formulaic or deeply complex. It shouldn’t be without measure, either. To gently adapt the Steven Pinker line:

Values without metrics are blind, but metrics without values are empty.

So great metrics lead to action. As it is aligned to your values as a service or business or kitchen cook.

An Extra Benefit Of Metrics

And ultimately, it should lead to simplification, too. Or rather, focus. Because the one bad thing about metrics is that you can have a lot of them. In Lean Analytics, the authors give a basic overview of all the different measures that you can find in any major technology sector. There are hundreds. And that just barely scratches the surface.

You can’t have them all. The authors recognize that a collection of many equally-weighted actionable metrics typically leads us to having no action. That’s not exactly the “Lean” way.

Optimization, and school valedictorians, would say otherwise. We can get an A+ in everything! But that doesn’t work until you’ve simplified to a single metric above all of it. In the school valedictorian’s case, that single metric is GPA.

What is it for the business? The service? The weekly meal plan? I’m not sure. But I want to know. It’s amazing what can happen when we get it right. It’s terrifying what happens when we get it wrong.

In closing, I don’t want to measure everything in my life, per se. I just want to define what matters most. A solid measure can help me do that. And thus help me simplify.

Photo by Stephen Dawson on Unsplash

The Final Book To Read To Become A Great Manager

How important is management, as a profession, to the overall success of an enterprise? Pretty important, I think. Especially as you scale upward. This means we need to train people to specialize in this work, right? After all, we train specialists in many other walks of life. Engineers, scientists, and artists. Managers surely need the same specialization, too.

Maybe. This is where the argument for an MBA emerges. And frankly, I wish I had one. I’m deeply fascinated by everything that is taught in those programs. But Jack Welch once said something that I’m afraid is unerringly true and reported in Matthew Stewart’s The Management Myth:

Jack Welch, who holds a PhD in chemical engineering, recently advised students at MIT’s Sloan school to concentrate on networking. Everything else can be learned on the job.

And later in the book, Stewart also argues:

Other economically successful countries (Japan, Germany, Singapore, China) make do with few MBAs and no major business schools of their own.

Why? Because Welch is probably right. Everything can be learned on the job. Great managerial skill comes from experience.

To bolster that argument, consider the following coincidence: the three books I consider to be the best books on management are all written by people who, like Jack Welch, never stepped foot in an MBA program. Andy Grove had a PhD in chemical engineering (just like Jack Welch). Ben Horowitz has an MS in computer science. And Matthew Stewart, the author of the featured book, has a PhD in Philosophy.

Here, together at last, are those top three books:

#1. High Output Management – and the book review

#2. The Hard Thing About Hard Things – and the book review

#3. The Management Myth

This final book is my personal favorite of the three. Largely because it is a perfectly-tempered counterfactual to the very persuasive ideas that Grove and Horowitz offer. Without Stewart, I would have a very hard time not becoming a died-in-the-wool absolutist about OKRs and Horowitz’s “ones and twos” framework among other things. So Stewart helps me maintain a healthy, well-rounded skepticism.

Or so I’d like to think. But I might get a little too existential when I read him. I certainly build a big, ripe, healthy dose of contemplation. It leads me to question everything. This week’s articles show that in a small way. They’re good work and I’m proud of them. But some of the thoughts have a bleak tinge:

Monday: Love Your Data At A Distance

Tuesday: Management Should Be Messy

Wednesday: A Higher Form of Procrastination

Thursday: Stories We Tell Ourselves

There is a great deal more to learn from Stewart’s terrific writing. I’ll cover a few things here and hope that you’ll read his book as soon as you’re done with Grove and Horowitz. As pairings go, Stewart is a definite palette cleanser full of philosophical overtones and a warm, hopeful finish.

Fishing For Whales

I have a deep love for books that introduce their broad principles and ideas through personal experiences. It is a common theme among all three works I’ve selected: our authors live the work, share it in great stories, and use those stories and fact patterns to illustrate why their concepts matter. It’s a storyteller’s way of “showing the math.” So these aren’t textbooks. These are structured autobiographies. There’s a lot of heart in that sort of thing.  

Grove and Horowitz tell stories of their time as corporate CEOs. Stewart shares the story of life as a management consultant. It is utterly fascinating. His work allowed him to study the common patterns of many clients, many organizations, and come to great conclusions. That, after all, is the entire point of being a consultant. It sounds lovely. As he writes:

Management consulting, in its best moments, is a recognition of the quantitative nature of our reality—of the fact, too easily overlooked by innumerate liberal arts grads, that a hard look at the numbers can explain much of the structure of the world around us.

I love that bit about “a hard look at the numbers.” We’ll focus more on this in later parts of the review. And in next week’s book, too.

But there’s a troubling aspect to consulting that I’ve heard about before and seen firsthand:

Management hired us to fulfill their dreams, not to quash them.

Which is to say that management consulting can often be a higher expression of confirmation bias. This gets to the self-perpetuating properties of the guru industry, as covered in yesterday’s article. And it also gets at the fundamental aspect of consulting that no one likes to talk about: sales.

Let’s say I wanted to run the Boston Marathon this year. I can run 10 miles right now. Slowly. And I’m not in my twenties anymore. Which is to say that it is highly unlikely that I can qualify for the event. I can’t run any marathon right now, let alone in the time required for Boston.

Nonetheless, imagine I begin the search for a trainer. I tell each candidate what I want to do. Invariably, one of these trainers will say, “Sure, I can help you.” Even though it’s kinda impossible.

Why would they offer their services? Because that’s what they do. It’s what a consultant does, too. The consulting market is built on hopes and promises and ready-made solutions that can be easily misconstrued as “easy buttons.”

Stewart describes this whole business development process as The Whale Hunt. And while I ordinarily prefer to shy away from long quotations, I have to copy/paste this in its entirety. It’s just that good. Stewart is deepy entertaining when he shares these straightforward tactics in a slightly cynical, sarcastic way:

Phases of the Whale Hunt:

1. Marketing (Luring): hold a conference, fly experts in from around the world, or offer to do a quick, painless “diagnostic” at steep discounts with a money-back guarantee. Whatever it takes.

2. Diagnostic: Scare the pants off them. Crater their self-esteem. Give a “trick”; a quick and easy analysis that will produce predictably horrifying results—predictable for you, horrifying for them. Consultants spend years honing this. Then offer to give them their self-esteem back in exchange for your treat! Choose the implementation plan that results in the largest volume of fees.

3. Implementation: The key to establishing an enduring presence is to colonize key functions in the client’s central nervous system. A good place to start is the planning function. Send existing staff on long, impossible errands and steal their office space. Make it impossible for the client to think without you.

4. Follow-ons: You’re already expanding deep into the organization like a cancer; look for subsidiaries and other departments to replicate.

5. The Break-up: at some point the client either wises up or just gets tired of your smell. And by then you’re fed up with making reports that vanish into the bureaucratic ether. So end gracefully.

This honest, humorous, genuine, accurate portrayal is a real gift. It took courage for Stewart to write this. It isn’t something that any active consultant would want to broadcast and it might even offend a few of them for its deep-cutting truths.

To be fair, I don’t think all consultants really see their work and their tactics this way. But a few probably do. Particularly after they’ve done the work as long as Stewart did. Time heals all naivete.

Where Is The Value?

Given all the coverage this week, you might think that Stewart’s book is a long screed against all the things he disliked about his job. That would be a sad mischaracterization. In truth, Stewart develops a very good, very useful perspective on all that is right with management.

In many instances, his claims harmonize nicely with the insights from Grove and Horowitz. Together, these three are something of an expert panel that you can consult a’la the Delphi Method and find real signal amidst the noise.

For example … consider Stewart’s thoughts on organizational design:

Individuals acting in good faith and with adequate knowledge may still have reason and desire to exploit their fellows and they will do so unless constrained within a system wherein those tendencies are adequately checked and balanced. This theory makes for the Constitution, great novels, and great managers.

Stewart’s thoughts may appear to have little to do with org charts but the system of checks and balances that he mentions is one and the same. Which is to say that a natural level of tension and bureaucracy must be inherent in a healthy system. This goes perfectly with Andy Grove’s talk about the hybrid organizational structure he developed at Intel. For more, consider this article.

The idea of healthy tension also emerges in Horowitz’s book when he discusses the trench warfare between various divisions of his company. He created a clever solution to that problem that should be considered much more regularly. For more, here’s the article of team-switching.

Additionally, Stewart champions the value of rationalist, data-driven methods. They have their place, just as Grove and Horowitz argue. They are necessary tools. Managers who lack any formal training in these methods have a massive hole in their game.

That doesn’t mean you need to go get a degree in data science. Stewart offers more accurate, pragmatic, balanced advice. So do you want to be an effective manager? Start by being a well-rounded, educated person and then …

Add a three-week mini-MBA to hone spreadsheet skills, review basic financial analysis techniques, and master some of the business jargon and graduates will be ready to take over the world.

This is the heart of Stewart’s argument when it comes to making a great management practice. And aside from some specialized knowledge that gets your foot in the door (e.g., Horowitz’s CS degree which got him hired at Netscape or Grove’s PhD for Intel), the rest is exactly what Jack Welch said: networking.

But I think Stewart would agree that a few additional tools have real merit. Horowitz and Grove point those out in their books. OKRs and LEAN methods of data-driven decision-making are critical. Can they be learned on the job? Yes. But why wait?

For LEAN, you can start with Eric Reis’s fantastic book, The Lean Startup. Here’s my review.

OKR’s, meanwhile, are very straightforward and I have a few sources in this article.

The Benevolent Dictator

Ultimately, I think Stewart’s work leads me to the same place I find others pointing. When it comes to the best method of organization, cooperation, systems operation, and leadership, nothing beats the model of a benevolent dictator. It is the most efficient and, by virtue of benevolence, the most progressive. So what makes a great manager? What makes management worthwhile? People who are kind, honest, smart, decisive, analytical, philosophical, open-minded, humble, confident, and loose-yet-firm in their thinking.  

No one has all of that.

So for all us imperfect humans, what matters most among everything on that list? Stewart argues for the side of ethics and morality. Even when it comes at a cost to the enterprise.

Because, ultimately, that kind of cost is small in comparison to the long-run costs of immoral, unethical action. Any reasonably-educated person knows this. Which is why any reasonably-educated individual can be a manager.

The key, I think, is that any person who wants to be an effective manager must recognize that their ethical, moral leanings are just table stakes. That gets you in the game. To then be a great manager, you must pursue your own growth rapidly and ravenously. Your saw, as Stephen Covey writes, must be continuously, constantly sharpened.

This is the one thing that Jack Welch didn’t quite capture (apparently) in his advice about MBA degrees. Andy Grove didn’t really touch on it, either. Outside of his emphasis on magaer-ked training, that is. But Ben Horowitz captured it beautifully in his work. He refers to it as “The Struggle.” I love that. To learn more, here’s an article on that idea.

This so-called struggle is in pursuit of a specific ideal that Stewart captures perfectly in his book. Please bear in mind that this comes after tremendous exploration and research and observation on his part. Every word he writes has a significant amount of horsepower behind it. This final passage, though long, has become my own personal target and I think it can be yours, too.

It feels obvious when you read it. It also feels impossible. But it’s strangely-universal. Wouldn’t any of us want this regardless of our profession?

A good manager is someone with a facility for analysis and an even greater talent for synthesis; someone who has an eye both for the details and for the one big thing that really matters; someone who is able to reflect on facts in a disinterested way, who is always dissatisfied with pat answers and the conventional wisdom, who therefore takes a certain pleasure in knowledge itself; someone with a wide knowledge of the world and an even better knowledge of the way people work; someone who knows how to treat people with respect; someone with honesty, integrity, trustworthiness, and the other things that make up character; someone, in short, who understands oneself and the world around us well enough to know how to make it better.

This is perfect description of the benevolent dictator. Or the philosopher king. Or a good person.

Which is to say that the path to being a good manager is really just the path to being a good person. I don’t think there is another profession in the world that so deeply aligns the best personal and professional aspirations in such a way.

Will good managers be wealthy? Not really.

Will they be famous? I doubt it.

Will they be powerful? Perhaps.

But wealth, fame, and power are ancillary. Because the good manager first pursues those timeless virtues that Matthew Stewart describes above. The rest are secondary effects.   

This rhymes with what I think Grove and Horowitz embodies in their personal stories. This rhymes with what I see in the great managers I’ve known and served. This can hopefully rhyme with what you see in yourself.

Matthew Stewart’s work shows why it’s so important. His work also shows why it’s easy to forget. So this book is a vital reminder. One we should all read more than once. You can find a copy at Amazon through this link.

Stories We Tell Ourselves

A writer far greater than myself once said we are pattern-seeking animals. But that’s not the story I want to tell; it’s not the story you want to hear, either. The notion that we simply seek out patterns, even when they don’t exist, makes for a very unsettling reality. Before you know it, we’re questioning everything and the world suddenly feels like nothing more than gauzy ephemera.

Indeed, despite another great writer’s warnings, we actually prefer to be Fooled By Randomness. This is where we find truth in Warren Buffett’s fabulous analogy about the nature of success and the stories we tell about it.This comes from an essay written in 1984. The best source is here.

In short, imagine a national coin-flipping contest where 225 million people compete by meeting the nearest person, calling the coin flip, and either winning a dollar by calling the flip correctly or losing a dollar if they get it wrong. Over and over again, the contest continues as winners are sorted from the losers in a winner-take-all contest.

Project this out to 20 flips and you’ll find 215 people—out of a starting field of 225,000,000—who have called the coin correctly twenty straight times. With every consecutive win, each of these people have now earned over a $1,000,000 in the competition. And not only are they wealthy now; they’ve never been wrong.

Well, as you can imagine, these people start to believe there must be some magic to this. They were clearly imbued with a level of superhuman clairvoyance that no one can match. People flock to them, wanting to see and touch this mystical power. These lucky winners write books, hold workshops, and talk very emphatically on TV shows about what it really takes to be successful.

All because random chance.

But don’t tell them that. Or their followers. The story matters far more than the truth.

Gurus, Pundits, and Other Shamans

I have spent nearly every day of the past eight months building a formal knowledge base from all the best books I’ve read. This curated heap of information is not even remotely close to completion and already I’ve amassed over a thousand pages of text. Models, principles, lessons, patterns, anecdotes, and more.


Because I think there are real, proven bits of knowledge that anyone can use to help them achieve their goals. These techniques and tactics don’t always work for me. But they still work.

When they don’t work, it’s important to understand why. This is a big reason I’ve tried to read broadly across a few fields. When I find something like Matthew Stewart’s The Management Myth, it serves as the strong counterfactual against the preachings and sermons and rituals that are rightfully (and convincingly) purported by people who have won a lot of coin flips

In Stewart’s case, he attacks every writer who says management is a sacred mythical art. He says it’s really more of a myth.

I don’t know who is right. So I read it all. And I formalize the literal paradoxes that emerge from every point so that I can maybe develop something better in the end.

But again … why?

I guess I don’t want us to be fooled. I don’t want us to resort to dogma and orthodoxy and tired old practices that are pushed forward with tradition and confirmation bias.

These things are self-perpetuating in the annals of Management Folklore. And management books are, indeed, folklore. It’s stories we tell ourselves. As Stewart explains in the following passage of his book:

When management theorists cite company after company that has succeeded by building its resources and competencies, they aren’t supplying evidence for a theory. They are merely expressing joy at seeing their preconceived interpretative framework reflected back to them.

I am a fan of Jim Collins but look no further than his seminal book Good To Great. Collins is an honest scholar. He uses data and proper case study to explain his theories. The case studies he selected for his book were excellent examples that reflected the framework he established.

And some of those companies are gone now. Circuit City, for example. So is Collins’ completely disproven? Of course not. But he is not wholly proven, either. To his infinite credit, Collins has acknowledged all this.

But again, does that mean his carefully-researched framework is flawed? Probably not. Or yes. Actually it depends. Like every other story, the strength of its narrative is predicated on the willingness of the reader to suspend disbelief.

Do you believe that Tom Sawyer really existed?

Or that Alice really did stumble into Wonderland?

If so, you might also believe in the power of all those BHAG’s and flywheels from Collins’ book. And if you do, who am I to question those completely harmless, well-founded beliefs?

The Slippery Bedrock of Management Theory

But we shouldn’t pick on Collins. Again, he’s an honest scholar. There are others to choose from. All of them, in fact. Take the classic management text In Search for Excellence by the celebrated author Tom Peters. His book makes absolute perfect sense to me. It delivers the sort of cognitive ease that leads to immediate persuasion.

But just because it makes sense doesn’t mean it’s true. There’s only one way to prove the truth. As Stewart writes:

The first obvious flaw in the method of The Search for Excellence is that it provides for no credible control group. Might there be companies that applied the lessons and failed?

Yes. I’m certain of it. It’s a veritable Fermi’s Paradox. The probablistic argument proves itself with common sense. There have been so many failed companies over time that surely one of them involved people who read Peters’ book, followed the tenets, and still failed.

Not that anyone cares. Not that anyone wants proof. Because what Peters provided wasn’t the winning recipe for making the next great company. Instead, he provided the validation for what many people (particularly non-CEOs) want to believe is (or should be) the winning recipe.

After all, who reads these books? People like me. I am not a CEO. So why should I care? Because this is for validation. To hear someone else think like me, in ways that are more articulate, through books that are provided from NYC publishing houses, makes me more certain about my point of view.

But don’t take my work for it. Consider what Stewart writes regarding Peters, Collins, and other such gurus:

The guru itself is the pack. True to their calling as mass entertainers, they are followers rather than leaders. Their choices pander to rather than create the mood, aspirations, and conventional wisdom of the moment.

Pandering. Affirming. Extolling. Echoing. Whether it’s a management guru or a self-help guru or a diet guru or any other such champion of nonfalsifiable “truth”, the value of the work comes from its ability to reflect the values of the audience.

Do the things they say reflect your values? Does it create a beautiful harmony with what you want to be true? If so, you’ll think these gurus are brilliant. If not, you’ll think these gurus are horrible. Even if they are right. This is what Ignaz Semmelweis discovered when he literally saved mothers in hospitals. Despite his great work, he was reviled and exiled. Because if you want to be a guru, a persuasive and celebrated soul, you can’t merely be right. You have to be right in the right way. As a member of the tribe.

Or else you’ll suffer Ignaz’s terrible fate.

So again, it’s not about being right with the theories of management. It’s about being believed. Stewart explains it as follows:

The theories offered by gurus can explain everything and predict nothing because they aren’t theories at all. Like the more elaborate conceptual frameworks of the strategy discipline, they are in fact bundles of nonfalsifiable truisms.

So what are we to do?

Leaders First. Managers Second.

I don’t know. My guess is that we start with humility. We knock out the vaunted pedestal that management usually rests upon. Management is not precious. It is not pretty. It is not a science. It is barely an art. It is not something smart people should do in order to prove they are smart. Frankly, it is not very worthwhile.

Most days, anyway.

But then some days it is. Some days it is profoundly worthwhile. This usually happens when either (a) you get the chance to oversee truly meaningful work, or (b) you get to do things that genuinely help your staff in the long-run.

Not the short-run. That’s the stuff of Michael Scott from The Office. That’s Hawaiian T-Shirt Day and “31 pieces of flair.”

And even then … even when you have one of those two great things going for you … it still might not be worthwhile. Why? Because you won’t have the chance to be yourself. To say what you want to say, lead how you want to lead, help as you want to help.

Because there are stories to maintain. Narratives to prop up. Prevailing cultures to adhere to and uphold.

Is all that necessary? Only if you believe in it. And if you don’t believe in it, but others do, then it’s still necessary.

For a little while.

Yes, for a short period of time, it’s necessary to go with the cultures and narratives you are a part of. But if you don’t believe in any of that, you can make a different story. You can champion a new narrative. You can be a leader.

This is what we should do with all this existential dread I’m digging up. We should become leaders. Humble explorers hacking through this jungle, learning as we go, pushing forward best we can, covering new territory and reporting our findings with crude maps we send back to the civilized world.

We should make our own frameworks, in other words. We should become our own gurus. The self-authored kind that leaders always seem to portray.

In other words, we should move past management and onward to leadership. In some form or facet. This requires a big change.

A leader is fundamentally different than a manager. A leader writes the story instead of following the script. And such leaders are the sort of thing everyone, including your own manager, needs. There’s no myth there. It doesn’t require a job title, either.

That’s not to say that you should be some flinty-eyed contrarian who fights your own organization at every turn. No leader does that. But a leader does develop an influential, successful, proven method that is unique. And ever-evolving. Regardless of station.

That’s the idea anyway. And I freely admit that it’s still a story. But when you strip away the management myths and stories we let other people tell us, I think you find that this is the story we’re all trying to write.

Image from The Internet Archive’s Book Images photostream

A Higher Form of Procrastination

Steve Jobs hated Powerpoint. I understand why. He said it was the surefire sign that someone didn’t know what they were talking about. This seems apparent when you consider the poor soul who just reads words off the screen. But it’s also apparent when the presenter fails to shift their thinking once you take the discussion off-script.

It’s weird at first. Because if you did a Powerpoint, doesn’t that make you an expert? Why can’t you answer the tangential questions? The indirect challenges?

The inflexibility originates from one of two sources. Either (a) the presenter really does not know the material enough to speak outside the original thesis, or (b) the presenter is caught up in the pressure of public speaking.

It’s hard to know which of these factors are really to blame in a given circumstance. So Jobs made things a little easier. He eliminated the second factor entirely. He made sure that his people didn’t use Powerpoints or assume a “public speaking” posture in initial meetings. No performance art. No time goofing with graphics and slide themes and those corny animations. Come to the meeting with your thoughts secure and your knowledge sound and show that you know the material and the direction you want to take.

I like this a lot. Jeff Bezos does too. He also banned Powerpoint.

Powerpoint is still necessary for many things, of course. It is a fantastic tool for illustrating concepts, visualizing ideas, and communicating with large audiences. And once people really know their material, this is the tool to share that knowledge broadly. You just have to know the material first. Until then, time spent on a slide deck is time not spent on mastering the content.

Some may say the act of developing the Powerpoint is what helps them master the content. That may be true. But I doubt that’s the case for most of us. For most, that time is just a sneaky form of procrastination. It feels productive because it’s an exercise in figuring out what to say. But it’s still procrastination because if you don’t know what to say, you literally don’t know what you’re talking about. So get back to learning.

This is but one form of sneaky procrastination. There are many more. And we should be more mindful of one of the biggest forms of all.

Bad Productivity

There’s nothing worse than spending a lot of time on the wrong thing. It is a frightening prospect. It is also why I’m a big believer in the LEAN approach of planning and management. That iterative cycle of Build-Measure-Learn provides good signal for what has value so that I keep producing that value with every next cycle.

As production methods go, this is bedrock stuff.

This is also what strategy is all about: making sure you spend your time making the right thing.

I’ve featured two books on strategy so far. The first is the foundational work of Richard Rumelt, Good Strategy, Bad Strategy, and the second is a great extension on the theme that narrows the focus towards business: Lafley and Martin’s Playing To Win. The reviews are here and here.

In short, strategy is all about finding a problem, developing a clear sense of how you will address it, and then putting forward the actions to do so. There is a distinctive narrative arc to the whole thing that fits nicely with the traditional structure of a high school essay. Everything starts with the argument (i.e., thesis) and moves on to supporting evidence and a conclusion of next steps to continue.

Within the argument, we have the problem statement. Rumelt calls it the “diagnosis” and I think that is the better term. So if you work at Apple and you have to present some ideas on what to do next with, say, wearables, you need a clear diagnosis of the problems and needs today. For example:

There isn’t enough incentive for people to purchase our entire suite of products. We need to create deeper cross-device functionality for unique experiences. Starting with wearables. If we can enhance the functionality of our watches and airpods by adding the capability for gesture-based commands to Apple TV and Macbooks, we can make a more intimate experience that transcends individual devices.

I wrote that in less than five minutes. I do not own a single Apple device. But, as ideas go, this probably makes sense. The rest of the strategy is explaining why, how, and what to do next in the Build-Measure-Learn cycle to see if this broader “thesis” is true.

My point is that strategy isn’t complex. It only feels complex. Just like essay writing feels complex. The complexity in these things come from a lack of framework (e.g., the narrative arc of an introduction, body, and conclusion). For strategy, Rumelt’s book establishes this framework as “the kernel” of a diagnosis, guiding policy, and coherent action. As frameworks go, this is absolutely perfect.

This all leads to a very important and very reliable test against bad productivity. Until you can naturally articulate what you want to do, in a complete story, there is a very good chance that you are working on the wrong things.

Because you don’t have a firm, reliable sense of what the right things are. You just have pieces of the puzzle: maybe you have a good sense of the problem. Or a fixation on a solution. Or a set of guiding policies on how you “should” do things with no firm sense of what things you should do.

Again, it’s like writing an essay—what’s the point, why do you argue that, and what should we do about it?

Strategy truly is that simple. And that necessary. When done right, it is a great test against bad productivity.

This explains why strategy consulting is estimated to be a $43 billion dollar industry.

It is necessary. It is also difficult. Simple, yes, but we shouldn’t conflate what is simple with what is easy. Just like writing an essay is simple but difficult.

That difficulty leads us back to the idea of sneaky procrastination. Because another reason why this is a $43 billion dollar industry is that strategy work has a devastating power to pull whole organizations into a non-productive trance. It is a higher form of procrastination. A sneakier form.

Plans For Plans

Everyone gets anxious now and then. Everyone suffers some level of uncertainty. You could be the CEO or the front-line staff, the parent or child, the teacher or student. In every walk of life, we all face the question: What am I going to do? And anxiety results when the answer is: I don’t know.

Or perhaps you’re already doing something. Then the question is: Am I doing the right thing? And the anxiety results when the answer, again, is: I don’t know.

No matter the scale or context, these moments of uncertainty naturally compel us all to do some strategizing. It’s not always a formal thing. More often than not, this is all in our heads. Of course, there are instances where it just stays in our heads.

That’s overthinking. And here’s something you can do to stop that.

But in the workplace, especially within the large organizations, these very natural questions are occasionally processed in very formal ways through the strategy and planning sessions that consultants typically provide.

There are few things more potentially wasteful than these sessions. Or better still: the meeting beforehand where you plan those sessions. Those are the conversations about planning the planning process.

“Let’s talk about our plan for how we will plan.”

Sounds fun! And it is!

But my guess is that most of these meetings result in 10% useful information, regardless of the time spent.

It never feels this way. But it turns out to be devastatingly true.

The Inadvertent Conspiracy

There is an unspoken, perhaps even unconscious, motivation for the big strategic off-site session or the regular planning meetings. It goes something like this:

If we put together a big explanation of what we will do, it will seem like we’ve really done something. Which means we won’t have to do anything else.

Again, few people deliberately think that in a formal sense. But it is still a very real motivation. Just as surely as my constant search for the next diet plan is my way of saying I’m researching, which means I’m kinda being healthy, and that makes my bad diet okay for now because I’m still working on it.

If you ever want to really procrastinate like a genuine professional, do some planning. Make some strategies. Ponder the trade-offs and opportunity costs of a variety of options and tell everyone you’re just kind of “in-between things right now” and “waiting for the right thing to come along” and “exploring your options” and “keeping the conversation alive.”

This is the right way to not do stuff. Because sometimes, it’s a legitimate reason to not do stuff. And few people can tell when it is legitimate strategy versus inadvertent procrastination brought on by indecisiveness.

This hard-to-spot distinction is a big reason why strategy consulting in a multi-billion dollar industry. It provides succor to our anxieties. It is akin to talk therapy, a structured means of verbally exploring why we feel so unsure, so anxious, so torn on what to do with ourselves or our business or our workplace. It leads to clear definitions of problems. And occasionally solutions. And occasionally action.  

Emphasis on the word “occasionally”.

Great Procrastination

There are worse ways to spend one’s time. So I don’t want to seem cynical here. There is real value in all this work. But only when we remember that it doesn’t have to be complex. Strategy is simple. And it doesn’t have to be lengthy. It’s difficult but it mustn’t take too much time.

In the talk therapy world, much has been written on the dangers of perpetual patient sessions and the co-dependencies it creates. The New York Times has an excellent article on this: In Therapy Forever? Enough Already.

Really? Enough already? Maybe. But what is “enough?” How do you define that threshold? The article’s title is great but misleading. One thing is true, just as the writer suggests:

… the longer therapy lasts, the less likely it is to be effective.

Furthermore, just one session with a therapist is often enough to create a jolt of energy, newfound paradigms, discoveries, etc. Until, of course, we regress to the mean. So we should maintain a regular practice of therapy, I think, but not on a constant basis and certainly not with the same person over and over again. “Enough” therapy doesn’t mean just one session. It doesn’t mean endless sessions either. The point in-between is really helpful to define.

Similar balance is probably needed in our strategic planning work. Whether it’s in the name of self-improvement, a’la Tim Ferriss’s “fear-setting” exercise, which he recommends monthly, or quarterly off-sites of the sort recommended in Patrick Lencioni’s book Death By Meetings.   

In either event, it’s regular but not daily. Or weekly. And maybe not monthly, either.

And when you find the right interval, be sure to avoid what Matthew Stewart writes about in his beautiful book, The Management Myth (emphasis added):

[Strategic planning] did little for us because it merely restated a simple problem but did nothing to change the underlying dynamics that had created the problem. Indeed, by studying the problem in such a clever way, it served the purpose of many so-called therapies: it allowed us to pretend we were doing something about it when we weren’t.

In his case, the strategic planning involved the use of the BCG matrix. Also known as the “product portfolio matrix” or growth-share matrix. There are a lot of articles on this. It’s a very intuitive model.

And it’s a great way to feel like you’re doing something. It’s constructive worry. It’s tactical introspection.

And in Matthew Stewart’s book, it turns out to be a great way to build an entire consulting business where he helped other people feel like they were doing something, too.


There is good strategy and bad strategy. A quick venture on the internet will show many strategic planning models and frameworks like the BCG matrix that can help you restate a problem. Just as Stewart describes in the passage above.

It’s fun work. It’s great procrastination. It’s bad strategy. And until you can really talk about the full picture, in an arc of the sort that Richard Rumelt describes as the kernel, you can’t really say you’ve done strategy. You’ve just done some talk therapy. That’s not bad. So long as it isn’t in excess.

And thankfully, once you’ve done that work (a’la Rumelt’s kernel), you don’t need to do anything else as far as planning and strategy goes. You can start working.

Because at that point, you know what you’re talking about. You might be proven wrong but you know what you’re talking about. You know what you’re doing. No powerpoint required.

Photo by Anthony Young on Unsplash

Management Should Be Messy

There are many celebrated adages in the business world. Old chestnuts like, “The customer is always right.” Or adjacent notions like “It takes money to make money.” And when it comes to the management side of business, we often find people espouse the corporate ideal:

“Our most important resource is our people.”  

There is something noble about that sentiment. Those who embody it have a hallowed spot in the pantheon of leadership. But they probably have a less-lofty spot in the pantheon of capitalism.

Indeed, when it comes to the titans of industry, everyone can do a google search right now and find something less-inspiring about the way employees have been treated by the likes of Bezos, Jobs, Welch, Walton, Gates, Ellison, Ford, Rockefeller, Carnegie, etc. So it seems like we have a divide. Do you want to be a brilliant, dominating capitalist or a warm, charismatic leader? Right brain or left brain? Execution by command-and-control or aspire-and-inspire?

No recognizable leader has been completely one-sided in this dichotomy. Some appear to have come close, though. For example, I don’t think 1990’s Bill Gates had the charisma to conjure a Steve Jobs-ian reality distortion field. Not that he needed it. He had the blunt force leverage of a pre-installed operating system to ensure his company’s success.

Yet, even if he wasn’t charismatic and resonant, there was an earnest humanity to Bill Gates that doesn’t get attention when people cover his legacy as a business leader. There’s a reason he hated firing people. He still had a heart.

You probably weren’t supposed to broadcast that in 1990s business culture. Or even today. Save it for the philanthropy stuff instead.

The Head and Heart

The humanities are poorly represented in business and management literature. Not for lack of trying, though. Writers in this niche just tend to be less eloquent at tackling anything outside of “return on investment” and “shareholder value”. The technical elements of price wars, moats, 10x returns, hockey stick growth, and market penetration are shorthand signals for credibility. I use the lingo. I know what I’m talking about. I can run the machinery of business.

It’s rational. It’s scientific. Or it gives the impression anyway.

Everything else, including business ethics, just boils down to a catch-all patois of “Do the right thing”. Or “Don’t be evil.”

Or again, “Our people are our greatest resource.”

Crack open a book on leadership and you’ll find a lot of derivative stuff. It’s all been said before. And it will be said again. Because it gets restated by employees all the time.

When reviewing the book, Work Rules!, by Laszlo Bock, I remember the frustration Bock captured when he looked at the final results of a massive survey project called Project Oxygen. The effort was designed to determine the necessary qualities that Googlers wanted to see in their managers. It included such deep, provocative, wholly novel ideas like …

Be a good coach.

And …

Empower the team and do not micromanage.

Really? That’s it? Bock could have lifted these nuggets from any book at a local store and saved the company a lot of survey fatigue. He even wrote as much:.

We now had a prescription for building great managers, but it was a list of, quite frankly, pretty dull, noncontroversial statements.

I agree. Pretty dull. But only when I see this from the pretend-perspective of a social scientist. When I’m in that frame of mind, I believe the rational, scientific, data-driven methods of business are supposed to help us extract greater, newer truths. None of that old (i.e., conventional) stuff!

Can’t rational, empirical methods deliver better insights on how we make our employees happy? Beyond the whole “empowerment” thing?

Probably. Just as surely as algorithmic index fund management has led to the methods Netflix and others now use to develop their decisions. These methods can always cross-pollinate and spin off into many hybrids. But a horticulturalist will tell you that hybrids can be difficult to reproduce. What’s true in nature is true in management.

In other words, while a more empirical method of data-driven management might raise the bar for a particular organization, as it happens with Bock’s descriptions of Google, the methods can’t be easily transferred as a copy/paste. The secret is still in the sauce, I’m afraid.

So let’s all stop thinking that we can “solve” The Workplace. The findings of Project Oxygen, obvious though they may be (e.g., Be a good communicator—listen and share information), shouldn’t be seen as the same boring, hoary chestnuts on how to be a manager. This isn’t a disappointing finding.

These instead of elegant truths we hold as self-evident. A beautiful expression of humanity.

If seen from a scientific point of view, there’s frustration in the lack of new discovery.

If seen from the philosopher’s point of view, there’s joy in the perpetual validation.

Sloppy Messy Management

This reminds me of a line from Matthew Stewart’s outstanding book, The Management Myth:

The insistence on driving the stake of science into the muddy ground of philosophy does worse than beget textbooks.

Indeed. It begets some warped systems. Or it can. Stewart writes this while setting up the basic framework for two camps of management practice.

Camp #1 is the rationalist, industrious, scientific Taylorism that I featured from Andy Grove and Ben Horowitz. They don’t fully occupy that camp but you get the idea.

Camp #2 is the humanist, emotional, psychological approach that I featured from … no one in particular.

This is deliberate, I’m afraid. There just isn’t a great book out there from an accomplished leader who drives purely from a humanist approach.

This isn’t an indictment on the lack of such leaders. It’s about the lack of such books. The more-humanist leaders are very much present, however. The best examples that come to mind include the legendary Herb Kelleher, former CEO of Southwest. Then there’s Tony Hsieh of Zappos. And Jason Fried of Basecamp. And many more unsung heros that I just don’t know because they don’t get invited to a lot of podcasts.

Anyway, there are great humanist leaders. None of whom have a single “great” book that I think captures their practice in the context I want. Fried comes very close with the fantastic Rework but it’s more platitudinous and has very little narrative. Again, it’s a great book but not precisely what I want here.  

So there’s these two camps … rationalist and humanist … and they occasionally blend. The aforementioned Laszlo Bock has a fascinating effort in this blended space right now. When we examine the outputs of these efforts, what do we find?

Something that concerns me a little whenever I reread The Management Myth.

Despite the best of intentions, efforts to blend measure-and-manage principles of data-driven “scientific” management with humanist philosophies of leadership produces the astringent aroma of a sterilized utopia.

I don’t want to get cynical but this is the stuff of a Black Mirror episode. If we connect data and people and real-time feedback loops, we will … what? We will presumably achieve this data-rich state of technological perfection where seamless UI, AR, and perpetual sentiment analysis allows us to make sure that nothing bad ever happens, the workplace is never boring, everyone is above-average on their performance reviews, and there are no days off because no one wants to leave. Right?

I’m straw-manning the idea here but what Matthew Stewart flags in his writing is really important. Whether it’s what we see in the Smart City ideas or the mechanized components of direct democracy a’la Citizenville or the stuff that Bock is doing to create what I’ll call “The Well-Tempered Workplace,” the underlying vision is inherently utopian when taken to its full conclusion.

Which is fine! But Stewart does the necessary-but-dull job of pointing out:

The great problem with U-types is that their visions of eternal sunshine usually involve a form of tyranny.

The term “U-types” refers to utopian visionaries. And again, I’m really trying to ride a fine line here. I don’t accuse anyone of being weirdly utopian. Or tyrannical. But the mixture of rationalist and humanist methods, especially in measure-and-manage practices, can lead people to thinking this will solve all our problems.

I should know. I’ve taken this jump to conclusions several times. Unintentionally. I just get excited.  

I’m not alone. This tendency drives a lot of what Erin Griffiths reported in her NYT article. Especially when you get the propaganda of burnout culture. I don’t think it’s a conspiracy. Chasing the perfect workplace, the management nirvana of a deeply-engaging worker experience, has unintended consequences.   

No matter how refined and data-driven the managerial approach may be, we see the limitations. However gorgeous the aesthetic, we still deal with people. This is something we’ve done since the dawn of time. So maybe the old ways are the best ways?

Inescapably Crude

I remember my surprise when I saw a friend’s x-rays from a back surgery. For all the advances in medical science, and all the vast knowledge that is required to become an orthopedic surgeon, the x-rays showed that the best practices in orthopedic surgery still comes down to screws, rods, and hammers. It’s sawbones carpentry in the end. Smarter, more effective, less invasive, but inescapably crude.

So I’ll close with the following from Stewart,

At bottom, the core truth is that management is all about people.

I kinda wish it weren’t. Sometimes. Because this is what makes it hard. Unpredictable. Messy. And crude. There are emotions. Dealing with those emotions requires the stuff of books I’ve featured like Difficult Conversations and Never Split The Difference and Influencer and Words That Work and Desire and Transitions and more. With more to come.

The most honest, necessary parts of management happen to be the hardest. And those aspects happen to involve the oldest problems of human cooperation. Those old problems don’t need new solutions. The right methods are already here. The right methods are practically timeless.
They are also thoroughly exhausting, boring, melodramatic, and (gasp) inefficient. I tend to forget that. I tend to be romanced by new things that might make it easier. A unified field theory of management. But reality and Matthew Stewart has a funny way of reminding me that such things are mirages at best and deliberate swindles at worst. Embrace the messiness. Oh the humanity.

Love Your Data At A Distance

I have a deep love for data. It has an incredible ability to unmask the hidden truths of reality. So many discoveries come from the observations recorded in data and shared with the world.

Consider its power to advance public health, as it happened with the world’s first epidemiological analysis in 1854. Then there’s the commercial value, of course. Tech giants aren’t the only ones to recognize this but they’re the easiest to point out given the potential monopoly effects, as written by The Economist.

So yes, I love data for many good reasons. But it’s important to keep data in the friend zone. Otherwise, it creates some really deranged behavior.

After all, what makes a good public school? Is it the test scores? Really? The crushing pressure to meet academic test standards wrenches the life out of learning. Not because data-driven education is a failure. No, it is a debilitating success.

At scale, test scores are a reliable signal of student ability. These scores contribute to a systematic approach. But as the teachers in Atlanta showed, it’s a system that can easily be gamed.

And even when the scores come about honestly, the kids still aren’t necessarily engaged. All the same, we shouldn’t throw out test scores. We shouldn’t stop offering standardized tests.

We should just keep that data in the friend zone.

It’s useful. It’s appreciated. And at scale, these test scores help us do that thing that every manager loves to do: control performance.

We Are All Managers

Regardless of your job title, you are a manager. Because there is some aspect of your life that you wish to improve. There is also more data to help you than ever before. Consider our efforts for weight loss. Correlate these efforts with the explosive growth of the wearable industry and you find that everyone not only wants to lose weight and be healthy but they also want the data to show them how.

Why? Again, to control performance.

For many people, these wearables become deeply influential. The data defines the quality of their lived experience. Was it a good day? Am I becoming the best version of myself? Let’s consult the Fitbit and see.

This is a completely healthy idea if you have the right goal in mind. The data then follows as a reinforcement mechanism. It helps you define and then, again, control performance.

Just remember that we cannot easily measure what matters.

We often make what we can measure matter instead.

This is how the 10,000 step goal became ubiquitous. Millions of people have this as their daily goal. Why? How did we become so enamored with the 10k? There are two reasons.

First, it is a nice round number that lends itself to cognitive ease (that phenomenon that makes us also think a chicken’s body temperature is 144 degrees). This nice round number informed the Japanese marketing campaign that introduced the whole concept in the 1960’s. See the history on manpo-kei for more.

Second, the 10k step goal is born from the limits of instrumentation. Wearables originally began with the pedometer. This technology goes as far back as the 1700s. But sooner still, the classic digital pedometer launched in 1985 and became the best method for enthusiasts to manage and control their physical performance.

As devices go, there simply wasn’t anything else that was easily available for mass production. Heart rate monitors couldn’t be affordably minitiatorized. So pedometers it was.

If necessity is the mother of invention, constraint is the step-father.

Fitbits soon followed. Along with Nike/Apple cross-brand devices. These originally clipped to your garments. Then they went around your wrist. Regardless, the mass adoption and the easy programmable tracking of 10,000 steps led to its growth as a ubiquitous idea.

Which, to be clear, is lovely. It just isn’t the most effective means of achieving the “fit” part of the “fitbit”. If you want fitness, and subsequent weight loss, try three 10-minute sessions of vigorous activity.

Because step counts aren’t the best measure. Heart rate is. That’s what matters. And now that heart rate monitors are relatively affordable, we can shift our focus. Until some better metric comes along.

The point, of course, is that we made what we could measure more important than that which actually mattered more. Which is something every manager does from time to time. So we’re all managers. Congratulations.

We’re all people, too. We can’t discount the power of a compelling idea and a device to helps to sell it. In an alternate universe, if someone would have created an affordable breathing monitor and someone else had conjured a public health campaign around healthy people taking 30,000 breaths a day, I think we could have seen the same result. Breathe your way to 30k!

To quote Kurt Vonnegut, So it goes.

… All Others Bring Data

If that isn’t enough, consider the astonishing effect of our data fixation during the Vietnam War. This article from MIT, coupled with Ken Burns’ 18-hour documentary, is quite sobering. Mostly because I could easily have seen myself acting the same way as McNamara. I could have said the same words he spoke in this speech highlighted by the article:

It is true enough that not every conceivable complex human situation can be fully reduced to the lines on a graph, or to percentage points on a chart, or to figures on a balance sheet. But all reality can be reasoned about. And not to quantify what can be quantified is only to be content with something less than the full range of reason.

This is true. McNamara is right. But this idea must be curtailed because it is an invasive vine that spreads over the garden and chokes out everything.  

And like a vine, it doesn’t spread overnight. And it doesn’t happen without success. There is a certain bureaucratic momentum that takes over once the first body count report shows a real marker of success. The hyper-rational systems of data-driven management hinges on explore/exploit. You explore all methods until one shows signs of success.

Then the exploitation mode kicks into hyperdrive and you get, well, the stuff that happened during Vietnam. Stuff that wartime generals would later call “blatant lies.” With numbers that were “grossly exaggerated by many units primarily because of the incredible interest shown by people like McNamara.” Again, see the MIT article for more.

It’s just like the teachers changing the answers on a standardized test.

This all gets to a critical idea provided by the former management consultant Matthew Stewart:

In an imperfectly knowable world, there is a latent irrationality in all metrics. For any given metric, there will always arise instances when maximizing the metric is at odds with advancing the goals that metric was designed to service.

Consider the irony that a war in Vietnam, which was launched to restore peace, defined its success on body count. I don’t think it is a stretch to say that McNamara’s logic centered on the idea that increasing the body count would restore the peace. The metric (violent death) is truly at odds with the goal (peace).

And teachers changing test answers? That, and the underlying metric, is truly at odds with the goal of educating children.

But it is these same data-driven methods that I’ve championed in two favorite books on management and a book on organizational development. Those books include Andy Grove’s High Output Management, Ben Horowitz’s The Hard Thing About Hard Things, and Laszlo Bock’s Work Rules!

I consider Grove and Horowitz’s books to be two of my Top Three best books ever written on management. The book reviews are here and here. I consider Bock’s book to be the best in organizational development. The book review is here.

I stand by those. But only because I have Matthew Stewart’s masterpiece to keep me balanced. Steward wrote his book, The Management Myth, in 2009 and it immediately became a challenge to all the dominant thinking that continues to this day. Rightfully so.

It stands as a bulwark against the dangerous behaviors that occur when data-driven management becomes mechanized. The data is fact-driven. Which is why we love it. But the management activity that follows must be value-driven. Otherwise, what’s the point? Otherwise, we get what Stewart writes below:

The confusion of facts and values (or rather, the attempt to find pseudotechnical solutions to moral and political problems) is the most cardinal sin of management theory today.

We get a cardinal sin.

Should we measure? Yes. Even when done imperfectly, it’s crucial to have for the reasons McNamara himself said. Just remember: it is always done imperfectly. And the commensurate reasoning that it provides, as important as it is, cannot replace the values we hold dear.

Love you data. Get all the data you can. But keep it in the friend zone. Keep it at a distance.

The Best Book for Becoming Decisive

Algorithms To Live By

By Brian Christian and Tom Griffiths

Rating: 10/10

Best Line #1: Life is full of problems that are, quite simply, hard. And the mistakes made by people often say more about the intrinsic difficulties of the problem than about the fallibility of human brains.

Best Line #2: … being aware of complexity can help us pick our problems: if we have control over which situations we confront, we should choose the ones that are tractable. But we don’t only pick the problems we pose to ourselves. We also pick the problems we pose each other, whether it’s the way we design a city or ask a question.

Who Made Who?

We invented computers. Computers invented us. The modern form of us, that is. Our behavior with these machines is that of a symbiote. We are deeply dependent upon computers for all facets of life.

“That’s not true,” you may say. “I’m not dependent on these things.”

Which is precisely what a symbiote would say. Because a symbiotic relationship seldom occurs overnight and rarely happens by our choosing. It emerges slowly, unseen over time. Graduated expansion is the way of a smart parasite. And when it comes to good parasites, whose symbiosis helps us both, we rarely understand the dependency that emerges.

For example, we don’t think we’re dependent on bacteria. But we are. In fact, we have more bacteria in our body than we have cells. Without them, I guess we’d be dead?

On the technology front, the journalist Kashmir Hill did a fantastic experiment earlier this year. She tried to experience life without The Big Five tech companies. That’s Amazon, Facebook, Google, Microsoft, and Apple. She cut them out, one per week, and tried to continue functioning in the modern world without them. It was hard. It was illuminating, too.

So again, when it comes to modern life and its relationship with computers, we must ask the question first posed by the AC/DC in 1985: who made who?

Computer Science: An Expression of Humanity

I don’t want to get existential here but a theme kept recurring as I read Algorithms To Live By. Every algorithm or heuristic featured in the book was a clear, formal expression of human cognition. Obviously. No alien species bestowed these kernels upon us from some far corner of the galaxy. All the same, these formal expressions were often discovered with the aid of computer science or, otherwise, optimized through computer science.  

And while physics and chemistry are supremely important, and math is really helpful, too, there is something intrinsically more human about computer science that allows it to bridge the other half of our brains. This is the central thesis of the book, of course. It’s right there in the title.

There won’t be a book called Physics Proofs To Live By. Nor will there be a book called Self-Help Through The Periodic Table. Physics isn’t a tool. Chemistry isn’t a tool. These things exist whether we use them or not. But computer science? That is a tool. Without us, it doesn’t exist. Thus it is designed by us. Thus it is an expression of our needs and our thinking.

Specifically, it is an expression of our need to solve problems in a reliable fashion. Without vagueness. Much of the humanities and practically all of philosophy centers on the soulful exploration of values and truths through a rather subjective lens. Reality is perceptual and all that. The authors quote a famous Bertrand Russell line that echoes this idea:

All human knowledge is uncertain, inexact, and partial.

But some of parts are more uncertain, inexact, and partial than others. And I’m convinced that computer science is one of the best methods for moving all of our knowledge closer to the preferred realm of certainty, exactness, and impartiality.  

Obviously, it can’t stand alone. It is still just a tool. But what a fabulous tool it is. Especially when it comes to the use of algorithms.

I have written a lot about how Daniel Kahneman’s System 1 and System 2 thinking should reinforce one another. This book shows how. It’s the algorithm.

Our intuitions are formed by first experiencing vague, unfamiliar situations through System 2. This system translates those experiences into “truths” or algorithms that then build our future System 1 reactions.

Yesterday’s article showed how this is a largely good thing. A small amount of data, thanks to Bayes’ Rule, can help us make smart System 1 choices. But we can do better. Single models built on small amounts of data are deeply inferior to multiple models built on more data. This is why our authors provide multiple algorithms and point to multiple sources of data.

Some of which were written about this week. Here’s all the articles inspired by the book:  

Monday: How To Fix Overthinking

Tuesday: Multitask Like Your Computer

Wednesday: The Big Sort

Thursday: Bayes, Crime, and “Big Enough” Data

For this review, I’ll highlight three more fascinating concepts. As always, I do my best to give a useful glimpse into the ideas in order to persuade you to buy the book. And as with every book I choose to feature here, it comes after many other books of a similar theme. Out of the bulk of them, this one happens to be the best book for learning how to make better decisions more efficiently.

So want to be more decisive? Absorb this book.


This concept, found in second chapter, is my personal favorite of all the featured algorithms. Judging by a few reviews on Amazon, I’m not alone. This simple model is a perfect two-word encapsulation for the dilemmas we all face. It’s best captured in that great song by The Clash.  

Should I stay (and exploit) or should I go (and explore)?

Specific to algorithms, our authors establish the concept as follows:

Exploration is gathering information and exploitation is using the information.

Easy enough. And presumably, these two acts should be kept in some sort of balance so that we make informed decisions without suffering from the analysis paralysis covered in Monday’s article. Consider this through the example of the crowded parking garage.

It’s a busy day; there are a lot of people and a lot of cars in the garage. You have a lot of shopping to do in the mall above this garage and a parking space closer to the store entrance would really be nice. But again, there are a lot of cars. How long will you spend searching for that perfect parking space? How long, in other words, will you “explore” before you choose a space to “exploit” and thus park the vehicle?

This question is at the core of what’s known as the one-armed bandit problem. I love this problem but I love it’s name even more. Borrowed from the colloquial title of a slot machine (the slot machine’s single lever being the “arm”), this problem is all about the optimized search for a desired outcome.

We all know to stick with a slot machine when it is paying out. You “exploit” that machine for all it has. When it stops paying out, you eventually reach a point of diminishing returns and move on to the next. But how soon?

The answer is wrapped deeply in a number of factors including the specific timeframe (how long will you play?), the discount rate of future time (we typically care more about payoffs now instead of later), and the track record of prior results. Our authors explain this far better than I can in two paragraphs but it leads to brilliant invention called The Gittens Index.

This index essentially formalizes the common notion that you should stick with a winning track record until the moment you’re just below “break even.” If you’re playing the slot machine 10 times and your record at the end of 10 rounds is 4 payouts to 6 losses, you should “explore” another machine.

Makes sense. But there are assumption baked into this index that, when modified, change everything. If the potential payout is really valuable to you, and time isn’t an issue, then the numbers in this index become far more tolerant of losses. The index could show that you shouldn’t leave until you find yourself suffering a win-loss rate of 3-7.  

Everything in the index hinges on what you value more: the short game or the long. Time is the real determinant. And while the index itself is a bit rigid in its application, it is nonetheless a fantastic illustration. The authors use it well to explain additional explore/exploit methods like Win-Stay/Lose-Shift, A/B testing, adaptive medical trials, and so much more.   

The point? The Gittins Index and other such measures are a formal definition for preference and patience.

In a casino, if you have one specific slot machine that you really love, you’ll play it for a long time. Even when it isn’t paying out in large quantities. Because you value the small payouts today over the large payout tomorrow. You love that machine. You love watching its lights flicker and its rolls spin. The future has less value to you because the present is quite satisfying already. The payout is already there.

If you discount the future at significant rates, because you find the present to be far more valuable, you will exploit your current situation for a long period of time. And why not? You have a good thing going.

But if you value the future highly, so much so that you feel like you’re wasting time with something that isn’t paying out at the desired rate, you will stop the exploiting. You will abandon the remaining chances in order to explore instead. You’ll seek new information, new experiences, because the future holds more promise elsewhere.

This is one reason why I lived in all four times zones from 2010 – 2017. It wasn’t deliberate, per se, but it was a result of intense exploration. I had been trying to find the right thing, the big payout, here and now. Once that happens, the exploitation (i.e., settling down) can occur.  

So when it comes to geography, my Gittins Index is a bit skewed towards exploration. I need a lot of immediate wins or else I’ll move on because the present will be less promising than the future elsewhere. At this point in time.

But a day will come where age and experience will change my view.

Our authors show, for example, the ways that people, after age 60, gleefully abandon a lot of those exploratory tendencies once they’ve honed in on the relationships and places they want to enjoy for the remainder of their lives. Most younger folks will still explore, of course, because the present time has less value than the future and its promises.

Younger folks might still play a long game of another kind. In my case, creative endeavors like these humble writings are all about exploitation. My Gittins Index is all about the present moment, the love of the work even without a massive payoff. We stay in an “exploit” mode when we work on what we love. The payoff in the present moment is enough.

This gets at the eternal question for all things. Should I stay or should I go? Buy or sell? Breakup or get married?

The answers change but the formula remains be the same. This chapter gives clarity to the variables that should matter to you: timeframe, track record of current wins-losses (i.e., good experiences vs bad ones), expected probabilities of future payouts, and expected value of future payouts. Is the grass greener on the other side? These variables will tell you.

It begs the question … how many times have we made decisions to stay or leave, buy or sell, without thinking clearly about all these factors?  

A couple readings of the chapter can show you how those variables work in a variety of ways. It isn’t actual math, per se, but it’s close. Or it should be.

Stress, Stress, Stress, Decompress

Chapter Ten is about networking. Specifically, the management of an information network such as our beloved internet. These networks can get congested just as surely as our road networks. They seldom come to a complete stop but they can slow down quite a bit.

As they do, certain algorithms help to manage the flow. One deeply interesting example is Additive Increase, Multiplicative Decrease (AIMD). As explained by our authors, this algorithm treats a congested network as follows:

AIMD takes the form of someone saying, “A little more, a little more, a little more, whoa, too much, cut way back, okay a little more, a little more…”

Here’s an illustration to help with the idea:

AIMD graph with gradual peaks that hit a threshold and trigger an immediate decrease, hence the “sawtooth” pattern.

This illustration shows an interesting behavior: as congestion rises ever-closer to a breaking point, a certain threshold is met and an intervention occurs. This intervention creates a quick correction, throttling the network, removing a lot of the “riders” off the proverbial road, and basically clearing the travel lanes so the travels can steadily flood it again.

It’s a constant cycle and a curious behavior: steady-climb, fast-fall.

Our authors match this pattern with several interesting behaviors in nature and make a very interesting leap into management practices and the Peter Principle. I’ll save those ideas for the book. But I want to reflect on how this behavior is already active in our own lives. Or once was.

Every week, the daily grind steadily adds stress and exhaustion to our central nervous system. Then, when it’s TGIF, we abandon the workplace, leave the daily grind, escape for a couple days of immediate relaxation. This recharge allows us to cycle back to the grind on Monday. The rapid deceleration into relax mode is necessary. It cleans out the junk and low priority messes and frees the mind. Quite literally.   

Or it used to. These days, we work far too much and this recovery cycle has been badly disrupted. So the stress and exhaustion climbs and exceeds our thresholds without any immediate “multiplicative decrease”. If we decrease at all, it’s very slowly and in small amounts that never really free our subconscious. We carry the stress everywhere.

Eventually the whole system will just come to a halt.  

Burnout ensues.


And if it isn’t burnout, then it’s thrashing. This is a concept from Chapter 5 that refers to the slow, laggy, chugging performance that a computer will exhibit when burdened with too many tasks. In this situation, there may not be a total crash of the machine (a’la burnout) but there will be something that is probably worse: paralysis.

When you see a computer thrashing, what you’re actually seeing is a machine overwhelmed with so many tasks that it fails to handle any of them. It’s that constant “buffering” loop that spins on a video that just … won’t … play.

Sound familiar? I’ve seen entire organizations suffer this at the executive level. It is often brought about by congestion (too much information coming all at once). Or it’s caused by the perpetual switching between too many projects, context-shifting so much that everything blends together. It is also brought about by the launch of too many new initiatives without the cancellation of other pre-existing commitments. Working memory is flooded. It’s just busyness all around. Thrashing ensues.

It gets to the old expression: “We’re so busy we can’t get anything done.”

In such a circumstance, the network congestion typically leads to the aforementioned AIMD response. And this is true for the organization, too. Only the AIMD response is usually manifested in the firing of an executive or three. That’s the severe intervention that sparks the immediate drop in pressure.

Does it always have to come to that?

Of course not. Our authors describe methods of interrupt coalescing. Think of it as a method for eliminating or delaying distractions. Put simply, it means slowing things down, responding only when absolutely necessary while maintaining most of the focus on basic tasks. This slowing down creates recovery and actually causes the computer to speed back up. Because as Chess Grandmaster Josh Waitzkin is known for saying,

Slow is smooth and smooth is fast.


I failed Geometry. In the 11th Grade. I never took trigonometry or calculus. I only learned about statistics in graduate school. Intellectually-speaking, I’m a late bloomer. I’ve had to work on myself quite a bit to catch up on the more analytical aspects of human thought. Better late than never, right?

Indeed. For me, anyway. Many other people still think it’s all pointless.

I shared that thought once upon a time. Since then, years of striving unsuccessfully have compelled me to explore these analytical and rational components with more fervor. I still can’t tell you what a differential equation is but I’m getting closer. Because I want to. Because I see the deep usefulness of these things in ways I never could have before. Thanks to my lived experiences and failures along the way.  

Our authors have written a book that help us see this utility before we screw things up. Like great teachers, they’ve come with a work that is stimulating, engaging, educational, and profoundly useful. It’s the sort of thing I want to do here with this service. So their work is deeply inspiring.

There is a lot of cross-pollination to be had between what John McPhee calls “the literature of fact” and what the sciences might call the “exploration of fact”. Our writers know this well and Algorithms To Live By is one of the best efforts I’ve seen for bringing science into the story of our daily lives. There are deep lessons here.

How will we live? The same way we always have: through algorithms. Either the mediocre ones we already have, thanks to our natural intuitions, or by the better ones science can provide. If you want the better ones, try this book. Here’s a link to it on Amazon.

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