Years ago, I secured a job interview at a bank (courtesy of a friend) in which I had to explain how a degree in music composition could possibly qualify me for a position in data entry. My analogy was simple: I played keyboard instruments, I explained, and the process of playing the piano and reading music was really quite similar to the process of reading data and typing it into a computer. The interviewer was intrigued and admitted to having never thought of that correlation. A follow up typing test demonstrated that I could type nearly 100 words a minute (impressive but hardly anything compared to playing a Bach fugue) which landed me the job that same day.
Although my background at the time was not in business, I soon found that I had a real flair for loan analysis. My department head concurred, and pretty soon I was helping the loan analysts with their work. The thought process reminded me of musical analysis, really, except that I was using numbers now instead of notes.
A few years later, I found that musical training again served me well, this time during law school and, later, when studying math and engineering. Again, the thought process required in all of these fields was often similar, albeit with various substitutions made, either notes for words, notes for numbers, or notes for lines of code. I didn’t give much more thought to it, though, until I read Bill Franks’ book, Taming the Big Data Tidal Wave: Find Opportunities in Huge Data Streams with Advanced Analytics (Wiley, 2012). In this book, Mr. Franks makes the claim that the best data analysts are not merely trained in math or computer science but have musical or other artistic training, as well.
I began to reflect on my own history. I, too, have come to believe that musicians do indeed possess a foundation to be good analysts, programmers, researchers, and scientists (assuming that those who struggle with math can conquer their fear which, unfortunately, tends to keep many creative people out of the STEM fields). Here’s why:
DATA EXTRACTION AND INTERPRETATION
The first step in any data analytics project is the Extract-Transform-Load (ETL) process in which oftentimes meaningless reams of data are extracted, transformed, modified and then loaded, eventually transformed by the analyst into something meaningful and useful. Musicians also undergo this process every time they play a piece of music. The dots and lines that make up a piece of sheet music appear meaningless to those without training. It is the musician who then takes those notes and either reads or sings them to convert the meaningless into the meaningful; i.e., to extract and transform the data points into a mental rendering of what the actual music sounds like. Conductors and composers take another step, transposing parts, analyzing how two or four or ten lines sound together, deciding from which line the melody must be drawn, and how the orchestration as a whole combines to produce a meaningful end product. Data analysts do the same: they derive meaning and value by looking at the data and running it through a similar mental or computer-driven process of extracting value, transforming it into something meaningful and then arriving at the final product.
Professional musicians are experts at finding patterns. Bass players, in particular, who often have repetitive parts (dee-dum, dee-dum, dee-dum or oom-pah-pah, oom-pah-pah, oom-pah-pah) can size up their part within a few seconds of reading the music. Pianists faced with long, complex passages search immediately for repetitive patterns—the “easy parts.” All musicians also listen for harmonic patterns with certain expectations; for example, a I-IV-V harmonic progression should resolve back to a I (as will most listeners expect, even if not trained). Data analysts, researchers and scientists do the same, looking for patterns in an effort to extract conclusions or predict outcomes and, by using software code, to even write regular expressions (i.e., a sequence of characters that define a search pattern) using tools like RegexPal, a Java Script regular expression tester.
ANTICIPATION AND PREDICTION
Most musicians, when reading music for the first time, undergo a process of anticipating what the composer is going to do next. When performers or ensembles are asked to sight read their parts (i.e., play them through for the first time without any chance to practice or hear it ahead of time), an important skill needed is the ability to read beyond what one is currently playing, so as to see what’s coming before actually playing it. Anticipation is important here, as is the ability to predict what’s coming in an effort to secure proper breath control needed, hand or finger position on keys, volume control, and a myriad of other necessary things in advance—all within a split second, usually.
This ability works well when serving clients, something I especially noticed when practicing law. A good lawyer must also anticipate things that can go wrong so as to write them into contracts or settlement agreements. A good lawyer must anticipate what a client is going to need down the road, even if the client hasn’t realized or thought of it yet. This requires anticipating and predicting the market, the political situation, current and future legal trends, the competition, and so on. The same applies, naturally, to research pursuits, data analysis, and business.
This one is almost a no brainer. Creative people are known for their innovative solutions and approaches to problem solving and ability to think outside the box. Problem solving skills are a must for engineers, for example, and problem solving skills often require creative approaches and ways of looking at challenges. This is so important, in fact, that in my Introduction to Engineering course at the Fulton Schools of Engineering at Arizona State University, an entire section was devoted to creativity and building creative skills, ways to think differently, and methods for activating some dormant brain cells. Everyone is creative to one degree or another. Unfortunately, unless students study or pursue hobbies in the creative arts, by high school, not much attention is paid anymore in the curriculum to fostering creativity. This does a disservice to employers down the road. For those who eschew creative pursuits for a purely math and science emphasis, they often find themselves severely challenged later in their careers when a creative approach is required. Too many new graduates are coming out of school today with an ability to plug-and-play or imitate from an example but lacking skills in how to actually innovate or solve problems on their own..
Musicians who play in ensembles or sing in choirs become very good at working within a team structure, working well with others, blending, compensating for others, picking up the slack on occasion, paying attention, being able to change at a moment’s notice depending on performance needs and situations, and taking direction well. What employer does not relish these skills in an employee? For analysts, researchers, engineers, and scientists, the work is at times solitary, much in the way that individual musicians spend time alone practicing their parts, but usually involves some kind of collaboration with others down the line. In other words, an ensemble performance between departments, between company and client, liaisons with government, vendors, third-party stakeholders and the like is often required. For the musically-trained, this is par for the course. Whether one is trying to figure out how to merge disparate sets of data or understand the relationship between them—or blend their voices and instruments with dozens of others, plus listening to their own part while listening to the whole of the ensemble, all while watching the conductor—musically-trained analysts, researchers, engineers, scientists, and even lawyers come already equipped with this ability.
As one HR executive noted, “I wouldn’t make musical training a job requirement, but if I can find a candidate who has it, along with the other skills that we’re looking for, he or she will definitely make it to the top of my list.”