There are these two young fish swimming along, and they happen to meet an older fish swimming the other way, who nods at them and says, “Morning, boys, how's the water?” And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes, “What the hell is water?” – David Foster Wallace, This is Water
Perhaps you’ve never heard of Frederick Winslow Taylor or the scientific management method known as Taylorism, but it’s increasingly become “the water” that makes up our culture. Taylorism is all about breaking down tasks into measurable pieces and tying incentives to those measurable tasks. In Taylorism “what gets measured gets done”. It seeks efficiency and productivity. I first learned about Taylorism as a business undergrad and in the ensuing decades I’ve become a devotee without even realizing it. I’m constantly looking to optimize aspects of both my work and personal life. The idea of optimization has seemed so obvious and invisible - like air, like water – I wasn’t aware it was driving my thinking, let alone asking if there is any downside.
In his book Data-ism: the revolution transforming decision making, consumer behaviour and almost everything else, Steve Lohr unpacks the philosophical point of view that points to data as the way to run the world. There’s so much in this book that resonates with me but perhaps my biggest takeaway was the idea of data as a construct of socio-economic and political forces. “Counting is political….the selection of what to count reflects the values and biases of the people doing the counting.” (Lohr, p 91). When we think about biases in data, it’s not just that the data itself that might be flawed but the determination of what to gather in the first place and how it should be categorized.
Deciding what data to collect, what to count, was on my mind this weekend as I was attending a live performance of The Allusionist – a podcast about language. Host Helen Zaltzman was sharing a story about going to her bank to change the name on her bank card. She wanted to go from Miss Helen Zaltzman to Ms Helen Zaltzman to reflect her change in marital status while retaining her own last name. She wasn’t Mrs Helen Zaltzman (“like I’m married to my dad – gross!”). In fact, if she could just simply be Helen Zaltzman, no title, she would have preferred that option. Yet, whoever decided what to count, had decided that wasn’t an option. That bank had determined it was important to have this category of “title”. So down the list of titles they scrolled – from the obvious, Mr, Miss and Mrs, to Dr., Rev. and Hon., to Bishop, Gen. and Brigadier…until somewhere, near the bottom, they landed on…Ms!
It’s moments like these that illuminate the need to categorize data and the inflexibility of databases (and institutions). We’ve all experienced this to some degree when we’re filling in a form with required fields. Having structured, standardized, data is necessary for pulling it back out of the system as information in a report or as inputs to other systems such as artificial intelligence algorithms. But who gets to decide on these categories and how they will be used?
Should we be categorizing everything?
I wonder about the interplay between how our data reflects us and how we reflect our data. How does being labelled a Miss, a Mrs or a Ms reflect back on our sense of self? In walking us through the etymology of these titles, Zaltzman shares that these are all abbreviations of Mistress, which at some point was a martial-neutral title, similar to Master. Unfortunately, the word Mistress also took on other connotations in addition to spawning this host of martial-specific titles. More on that here if you’re interested.
We live in data feedback loops that classify and measure. We see it in everything from the “likes” on our social media posts, to how much time we’ve spent on our devices to the number of steps we’ve taken in a day. This is just the beginning. There is so much we can measure to know ourselves better so that we can live a more optimized life. There are apps to track what you’re reading, where you’ve been, the duration and quality of your sleep, how much water you’re drinking or exercise you’re getting or how you’re measuring up to any one of your life goals. We can be happier, healthier and get more done. We can be productive and efficient. What’s wrong with that? If there is a downside to all of this, how we determine where to draw the line? What do these particular measurements – the specific categories contained in an app and its data – reflect? What are the ethical considerations when each piece of data makes a statement beyond just its content?
These are things we need to examine for ourselves. At the same time, there are societal level decisions that need to be made about all of this and more people deserve to part of that conversation. First we need to know that there is “water” – basic realities that often go overlooked. Things like the title on your bank card. After a host of errors, misprints and reissues, a human was finally able to override the database and issue a card as simply, Helen Zaltzman.
Lohr, Steve. Data-Ism: the Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. HarperCollins Publishers, 2015.