Who is Better at Self Upgrading: Engineers or MBAs?
By: Dave Asprey
For years, I’ve used self-measurement techniques to guide my process of upgrading myself physically and mentally. I was honored to speak about it at the first Quantified Self Conference last month.
At first, self upgrading was easier, because I’m a geek who worked with managing and troubleshooting complex high scalability systems, like the Internet and cloud computing for most of my career. Concepts like event correlation and root cause analysis are easier to learn when you’re dealing with systems where you know the rules, because they were designed entirely by people. On the other hand, humans are very complex systems that almost certainly weren’t designed by people, making troubleshooting more difficult and event correlation more important. We don’t know all the rules yet. We don’t yet know how people work from a hardware or software perspective, and some commonly held theories like, “a calorie is a calorie and you need to burn as many as you eat per day or you’ll get fat” are simply wrong, but still are used to design flawed systems for hacking people.
My self-upgrades got even easier when I went over to the geek dark side by getting an a Ivy-league Wharton MBA (actually, the real conversion to the Dark Side came when I joined a VC, but that’s another story…) Wharton is a quantitative school. I never in a million years imagined that I’d be asked to do integrals in a leadership class, but somehow they find a way. The one-sentence Wharton MBA mantra is this: If you want to grow your business, track it and do things that make your metrics improve.
In fact, one of the most influential and un-credited quantified self forefathers was one of my professors, Stew Friedman, author of Total Leadership. If you haven’t read it, you need to. Borrowing on his years of being a top 100 exec at Ford, Stew created a program to quantify the amount of energy and effort that you put into different domains in your life, like family, career, health, friends, spirituality, and to compare it to the return you got. Going through the quantifying exercises in his classes as he was writing the book was very illuminating for me, as it gave me tools to see where I was wasting effort. As you’d expect, there’s a quote from Stew in the The 4-Hour Work-week.
The MBA mindset is very Quantified Self friendly (maybe more so than a pure engineering mindset). The reason is that engineers use fairly static underlying assumptions and rules about how things work in order to perform engineering tasks. Quality assurance, the part of engineering that tracks defects, is the lowest level of the totem pole in every engineering organization I’ve worked with. After all, who wants to look for bugs when you can instead create something entirely new? Certainly not me when I’m functioning as a geek.
From a self-measuring perspective, one could argue that engineers are better at instrumenting things, which is true. They’re also generally better trained in math and statistics; however, when it comes to systems where we don’t know all the rules, or even what to measure, engineering becomes much harder. Science gets more interesting, but engineering is a different discipline than science. Engineering is using what we think we know to design something that will solve a problem. Science is the art of figuring out what we don’t know and then discovering it. (By now, all my engineering and scientist friends should be pissed off at these gross generalizations…)
In most businesses, no one has a clue what’s really going on. You can track things like cash flows, returns, queue lengths, and countless other variables; but no matter how much you track, there is always more data you could gather. From that perspective, a business is more like a biological entity. The real skill you learn in a quant-focused MBA program is how to decide which metrics to track in a poorly understood, poorly instrumented, dynamically changing system while working in a chaotic, unpredictable environment. (You know – a business). After all, if you track and optimize the wrong metrics (metrics that don’t tie to the outcome you seek) you’ll go out of business.
The same is true for those of us using metrics to track physical and mental self-improvement. An overabundance of data, or bad data, can waste enormous amounts of time by generating analysis paralysis or simply guiding you to make bad decisions. False assumptions about underlying biological mechanisms will also lead you to poor choices that will stunt your performance.