It’s fall, so that means I’m firmly ensconced in classwork again, this time in two theory courses. Theory is an interesting thing; it’s such a wide-open world, and it’s exciting to plummet into the depths of it, but it doesn’t always translate well to practice. The goal of the program I’m in is to help translate theory into practice – something that’s sometimes easier said than done. That got me to musing a bit about my academic career…
As I will tell anyone willing to listen to me wax poetic about how a person with two degrees in literature ends up becoming a data analyst, data (as a concept) is a narrative. The data you gather are all threads that, when woven together, tell a story.
I’ve spent a lot of time recently cleaning data, which is always a tedious task. Analysts are nothing if not tenacious in this regard, but even we get a bit weary of making sure every little detail is as valid as possible. It’s also a lonely task. Even though I often listen to music when doing things like this, my mind still manages to wander.
I started thinking about data purity. There’s this idea that quantitative data is the purest form of narrative; “the numbers don’t lie,” after all.
I had a conversation recently with someone about this concept, and how so many people, some quantitative analysts included, don’t recognize (sometimes willfully) how easy it is to introduce bias into numbers. Anytime you have humans involved in a process, you have bias. Bias can be introduced through study parameters, during analysis, or in the conclusions drawn and recommendations given. Many people bend data to fit a narrative that’s been pre-ordained.
What I like about qualitative analysis, which is my specialty, is that it doesn’t hide or deny bias. It encourages the researcher to think about and state their biases very clearly. Some methodologies actually use the researcher’s bias as part of the study. It’s impossible to completely put aside your own perspective, so why not channel it?
I’m starting to tackle with a lot of philosophical questions about qualitative analysis, and bias, and constructing studies that are useful operationally but true to what qualitative analysis is at its core. It’s an interesting place to be floating for a while, and gives me a lot to think about as clean, and check, and clean, and recheck the quantitative data I’m slowly polishing into something meaningful.
At the heart of it all, that’s really what all data analysis is about, be it quantitative or qualitative – finding meaning.
And meaning, and what meaning is, can launch a hundred different discussions and poetic manifestations…
I have a graduate degree in Literature, and part of obtaining that degree meant taking poetry seminars. In one of those seminars, we did a section on Gertrude Stein.
I’m not sure what to do with Gertrude Stein. We had to read Tender Buttons, and I recall dreading going to class because I just wasn’t sure what to say. Luckily I had a classmate who did engage well with Stein’s work, and carried us a bit – I recall talking about Stein’s poetic formlessness as a type of resistance.
Here’s a snippet from Tender Buttons:
It’s been nearly 12 years since I read this for the first time, and I still have no idea what to do with it.