Data Science as a Performance Art

I’ve exhausted my piano practice for today.   At some point mind just said “enough”.   I may have felt a little fatigue in the hands as well.   Whatever the cause, it was enough for a day.

I have not been timing how much time I spend on the piano.   I usually have several sessions throughout the day.  My day has a pretty set schedule of when I’m writing, when I’m eating, when I’m reading, and when I’m practicing.    I’m guessing it is about two hours of practice a day.   That is probably enough because I’m still at a beginners level.  I’m not playing anything that sounds listenable or certainly nothing I’d want to have an audience for.   Mostly, I’m just practicing on relaxing the arms and gaining better control of the sounds, the rhythm, and hopefully the keys.

Despite the slow progress and the frustrations of seemingly not having any innate musical talent, I’m happy to report that I’m keeping myself motivated to keep practicing.   I am probably wrong but I think there may be something to offer by becoming a non-musician pianist, whatever that may mean.   Or if not that, then perhaps something to offer myself as a relatively low cost way of keeping me occupied.

I had a dream of someday being able to play for long periods, playing in a way that brings out the unique voice of the acoustic piano, and playing from memory without any accompaniment.   I doubt if I will ever get there, but it is something to look forward to.   Perhaps it is what keeps me motivated despite the slow progress.

I look over my day as a typical day.   Even though I’m unemployed, I have a fairly productive day of writing, reading, and practicing.  None of this stuff is making money, but it is also not what I’d consider as leisure either.   Even this blog writing, I take it relatively seriously even though it serves little benefit beyond exercising my typing skills.    Unlike the music practice, the blogs do seem to get somewhere in terms of inspecting ideas that previously have not been examined much.

One of those ideas concerns the concepts of how the labor market is different today or how it could be different.   In earlier posts, I alluded to the idea that the nature of the labor market may be influencing the labor participation rate.   That was pure conjecture but I like the thought of thinking what may be lurking under the surface of the basic explanation of the discouraged worker.

Lately, I’ve been watching the construction of a new home opposite my house toward the back.   Because of the long narrow properties, it is a good distance away.  Also my house is set much higher on a hill so I have a somewhat downward angle at the progress.   At various times today I checked their progress laying down the joists for the second floor, then laying the sub-floor, and then erecting the walls with the outer plywood facing already attached.   A whole floor in just one day.  Perhaps two whole floors in two days although the first floor was interrupted by heavy rains when they first started.

I am sure this is not remarkable for carpentry.   The house itself is a middle range typical house one would expect this day in age.  It towers over the neighboring houses that were designed and built in a completely different era.    But it would probably be on the small side when compared to typical suburban subdivisions where there is more land available.   Again, the lots here are long and narrow with a substantial portion declared as protected as part of the Chesapeake Bay watershed.   Thus they are pushing against the limits of what can be done here.

All that said, I’m impressed with the efficiency of their work.   All of the necessary material had already been delivered.  The plans were well documented so the workers could assemble the wood as if it were some snap-together kit.  It was not a kit.  It is real carpentry but done extremely efficiently making it look easy.    And the workers are steadily working throughout their entire work day.

This matches what my idea of a day of work should look like.   When the work starts, there is work to be done, the day is busy, and at the end of the day you can look and see not just real progress but meeting significant milestones.    There are a lot of different tasks involved in today’s effort, but they met a milestone of framing up the entire second floor.   That’s work.

I also recognize it doesn’t happen by chance.   The workers are skilled at what they are doing.  I’m sure there were a mix of different skill levels but there were at least a couple who knew precisely what they were doing.   From the distance, I didn’t see any uncertainty in their movements even when walking on the bare joists.   Also, the architect and builder had everything planned out.  There was no need to stop and figure out some problem.

I call this type of work a performance.   I make the analogy to a stage show or a professional sports game but where the show lasts an entire workday.   In stage shows and professional sports, when the players enter the stage they are expected to perform steadily until they leave the stage.   We don’t expect practice runs, or idle periods of figuring out what to do next, or a break for a lengthy intra-team bull-session about something unrelated.   We expect a performance.    Certainly those carpenters came prepared to perform and they performed very well.

A good performance takes practice and planning.   For the carpenters the practice comes from apprenticeships on other jobs so that they arrive with some aggregate experience.   For performing arts, there are the rehearsals, the practice sessions, the training.   That is done outside of the eyes of the audience.   We see and pay only for the performance.

In the above examples, people are kept busy.  A foreman is directing individual tasks, the show has a program or a script, the game has a clock, opposing teams, and a set of rules.

But working in data science is different.    Specifically during the operational phase of working with data, after all of the systems have been engineered, software developed, etc.   In the absence of some alarm going off or some failure occurring, there isn’t anything to prompt what kind of activity one should doing.    Often, we wait until someone makes a request, or something is raising some kind of alarm condition.   Until that happens, there doesn’t appear to be anything to do.   We fill in the time doing something other than data science.

I keep visualizing that my time on office hours is my time on stage.  It is my opportunity to practice my trade.   Data science is not something that can be simulated elsewhere.   As a result, I deliberately sought out problems with data so that I could flag them to my client to say I found something worthy of spending my time.   With the complex data sets encountered in my last job, I never had any problems finding something that even my agrees it is worthy of my time to investigate.    I would much rather fill in my time practicing my skills than to fill it in with pointless work just to avoid being completely idle.

Over the years, I didn’t even think about it.  I ended up always keeping myself busy sniffing out, tracking down, and working around problems in the data.    I did it because of that prejudice of mine that I have to be performing my expertise when I’m on the job.   But in the end I invented work that others would not find.   There were no rules, no script, no foreman, no rules to dictate what I should be doing.  I created my own work.

In an earlier post I described a taxonomy of data based on the different kinds of suspicions that they introduce.   As I mentioned in that post, I was assembling various thoughts I uncovered in earlier posts as I reminisced about my prior experiences.   During my work experience, I tackled these problems without thinking about them in those terms.   I just found ways to suspect something may not be right about certain data types and then dived into investigating the problems.

As I described above, when I found problems to keep me busy, I had no problem convincing my client that the problem was unanticipated and it merited my full attention.   But, as I hinted at in earlier posts, the aggregate of this labor invited criticism that this system was too burdensome.    There is an expectation that these problems should not be occurring at all.   The data system should be more fully automated consuming new data every day without any need for intervention.

In fact, the system was automated and there would have been no need for intervention if I hadn’t made the effort to find the problems.   It took initiative on my part to hunt for problems, and that initiative was continuously rewarded by finding real problems with the data that could not have been anticipated and did need solutions.   If I hadn’t taken that initiative, the system would not have required so much (if any) such data science labor.  The easiest way to make the project less burdensome would be to eliminate the concept of data science during the operational phase.

A concern I have about data projects in the operational stage is that there is a deliberate motivation to assume that there is nothing wrong with the data.    As long as the data or the systems are not throwing some predefined alarms, there is no need to scrutinize the data looking for problems.   We do not budget for the routine practice of looking for problems, and in fact we may discourage this practice.

Staying on topic of this particular post, I find comfort in working with data because it offers the opportunity of performing my art on a full time basis, coincidentally matching how I want to experience a job.  There are inevitably plenty of problems with the data that can employ a team of data scientists throughout the entire operational life of the project.


One thought on “Data Science as a Performance Art

  1. Pingback: Datum Governance: Distinguishing bots from real world | kenneumeister

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