To me, Robert Pirsig’s bestseller Zen and the Art of Motorcycle Maintenance (1974) was definitely one of the more engaging reads I’ve come across. The aim of this article is not to give a synopsis on the Metaphysics of Quality, but to distill some insights that might aid data scientists when getting discouraged with their project.

Zen and the Art of Motorcycle Maintenance

For those who have not read it — or those in need of a quick recap — the book interweaves the philosophical life journey of the author with the fabric of an All-American motorcycle road trip. It provides a thesis on the concept of quality, unifying the romantic and the classical perceptions of the world. As an illustration of those two viewpoints, the titular act of motorcycle maintenance serves as an extensive anecdote throughout the book. The romantic soul is primarily interested with how the motorcycle drives, looks and feels. A mechanical failure is nothing but a source of frustration. The classical soul, on the other hand, is concerned with the inner mechanisms of the machine, wants to know exactly what each part does. For them, a breakdown might actually pose an interesting challenge.

It is not hard to see the analogies with programming and data science. When faced with a malfunctioning piece of software or nonsensible results, many users will throw up their hands in despair. It takes a certain fortitude and analytical mindset to dive into the underlying data sets or error messages. The classical soul is at work here.

However, even one with a strictly classical view on the world is not immune to discouragement. Who never wants to toss their laptop in a dark corner after yet another incomprehensible error message, who stays motivated after running five different machine learning algorithms in vain?

There is no magic pill or deep philosophical insights that keeps you motivated at all time. Nonetheless, the more awareness you have on the causes that halt you in your tracks, the more likely you are to resolve the issue and get rolling again. Pirsig comprehensively outlines common reasons why people get stuck and lose motivation. If it worked on motorcycles in the seventies, why wouldn’t it work in the modern era of data science?

Gumption traps

I have never attempted to disassemble a motorcycle into a thousand pieces and try to reconstruct it afterwards, but apparently it is no easy feat. Pirsig was not a professional mechanic either; for him it was a major learning experience. Like any human being, he frequently got lost, stuck, demotivated, bored, frustrated. He categorized all these feelings under the common denominator of gumption traps, and provided concrete suggestions on how to power through them.

Gumption is not necessarily a word we use in everyday life, so a definition might be in order. There are multiple (including terms such as shrewdness and initiative), but ‘common sense or resourcefulness’ seems to capture its meaning fairly well. The gumption trap, in turn, is an event or mindset that results in losing interest or confidence to complete a project. The ‘trap’ part refers to the self-reinforcing feedback loop that kicks in. If you get discouraged, you will put less effort into the project, leading to disappointing outcomes, even more discouragement, resulting in… You get the picture.

The traps may be divided into external factors (setbacks) and internal ones (hang-ups). The latter is further decomposed into value traps, truth traps, and muscle traps. Pirsig discusses both common causes and solutions to all of them. Here, I try to translate them to specific applications in the domain of data science.

Pirsig mainly attributes external setbacks to a lack of knowledge. Not just any lack of knowledge; it is what strategy consultants refer to as ‘unknown unknowns’. It is hard enough to fill knowledge gaps that you are aware of, yet being ignorant that such gaps exist is arguably worse. In the latter case, setbacks may seem like acts of the gods, because you are simply unaware of the obstacles you face. It’s like navigating a reef without a map.

In data science projects, the lack of knowledge and understanding is often substantial. This is not a reflection on one’s intellectual capabilities: real-world projects are generally messy and poorly defined, being shaped along the way.

This is where the ‘scientist’ in ‘data scientist’ should spring to life. Pirsig’s general advice is to be slow and meticulous moving forward. Don’t jump directly into coding, but plan ahead. Take the time to get a proper grasp on the project, the expectations, the potential obstacles. Frequently stop to reflect and analyse.

Concretely, the following steps may be of use to mitigate setbacks:

Naturally, there will always be unanticipated obstacles. Plans are never perfect, and some external factors will remain out of your control. Setbacks will inevitably arise, yet a careful and systematic approach will preserve focus on the project and reduce the risk of being thrown off your game. Remember that data science is rarely a straight road: drive slowly.

Hang-ups

Unlike setbacks, hang-ups stem from internal factors. Projects do not only get stuck due to external circumstances or unforeseen obstacles, but also due to insufficient information or the wrong problem angle. When working with data or code, it is easy to get completely caught up into a tiny aspect of the problem; a statistical outlier, a bug, a visual detail. Often the best fix is a quick break, reassessing whether you truly face a problem within the grand scheme of things. If it is, you might want to go back to the drawing board to tackle the problem step by step.

Pirsig divides hang-ups into three categories: value traps, truth traps, and muscle traps. The definitions and examples follow below.

Value traps

Value traps are hang-ups that occur within the mind. People are not machines: they want their work to be meaningful, to see their hypotheses confirmed, to book successes. When spending a lot of effort on a certain solution direction, it is hard to re-evaluate your frame of mind (i.e., your values). However, data science is a dynamic process, and one must be flexible in dealing with new facts and information. Also keep in mind: the solution you are invested in, might not be the best solution from the business perspective.

The typical answer to combat value traps is to take a step back when experiencing any of the points below, re-assess the problem and facts at hand, gather more information when needed. Often, getting out of your state of hyperfocus is all that is needed.

Truth traps

Truth traps relate to either misunderstanding feedback received by the environment or a mismatch between questions and answers. As such, the ‘truth’ gets distorted, often causing frustration because there is seemingly no path to a solution.

“Everything should be made as simple as possible, but no simpler.”

Muscle traps

Data science requires many skills, yet physical prowess is typically not listed among them. As you will see shortly, it has nothing to do with your bench press or 10 mile records. In general, muscle traps encompass the interaction between you, your computer and your work environment.

Final words

I realize that most of the solutions posed in this article seem obvious, trivial even. As humans, we are fallible though. We work for months with a docking station that does not charge. We get frustrated by the same error message over and over, without ever look up what it actually tells us. We keep overclocking our laptop day and night, although it is obvious we need a cluster for the job. We endlessly keep tweaking the neural network we spent so much time on, ignoring the red flags telling us it simply doesn’t work.

Pirsig’s core message is not to stop for a coffee or replace your mouse. His argument is to take a slow, conscious and deliberate approach to your project, to frequently zoom out and assess the big picture. As a data scientist, a clearly defined and well-informed action plan is essential for success. When feeling stuck or frustrated, a slow and systematic approach is needed to troubleshoot the problem and address the underlying obstacle.

Everyone will experience the gumption traps listed in this article from time to time. We don’t always have the right information or skills at hand, obstacles will pop up, and yes, even mundane feelings like boredom will kick in. That’s ok. As long as you have awareness of the traps you might walk into, and have a plan to get out of them, you are already halfway reaching your goals.

So, next time you feel frustrated with your project and are ready to throw in the towel, turn off the screen and make yourself a double espresso. It will make you a better data scientist.

Takeaways