Quantify Everything: A Dream of a Feminist Data Future

I’m writing this essay in day seven of an eight-day sprint.

I’m not immersed in software development, but caregiving. My husband is out of town for work, and I’ve been at home with our two year-old daughter. She goes to preschool part-time; I’m fortunate to have the resources to pay for it, and for additional care for her from babysitters when I need more time to work or take care of myself. But when my partner and co-parent is gone for days or weeks at a time, the intensity of caregiving weighs on me like nothing else. Caregiving is, of course, joyful at times, with giggles and smiles and affection. But it’s also a total grind: paying attention, playing with her, keeping her clean, fed and clothed, put to bed on time and out the door on time, and arranging for her care when I’ve got other commitments.

I still don’t know really what makes caregiving so hard. There’s no quantification of it, no unit of measure, aside from the wages that you would pay someone else to do it, which are usually very low in comparison to other work. And of course, wages make little sense as a metric for efforts that are expected to be done for free, or are divided unequally and distributed in ways that are largely invisible. In moments of anger, I tell my husband that I’m tired of working for free for his boss.

By training, I’m an information scientist. What drew me to the field was a fascination with classifications and categories, units of measure and orders of knowledge. I’d devoted myself professionally to examining biases in the way that things are organized and structured. But nothing prepared me for these moments when I realize that my own life has started to veer outside of the grid of what is valued and made visible by data and quantification.

Quantified Self and the Politics of Measurement

If you take the rhetoric at face value, the Quantified Self movement is poised somewhere between what’s been traditionally considered the metrics of human life, such as health assessments by doctors and other professionally-dispensed information, and radical reconsiderations of them. Quantified Self, its adherents argue, is not a brand like Kleenex. Researchers like Dawn Nafus have observed that it is a “big tent.”

The current late-capitalist take on Quantified Self proposes that if you, a consumer, submit to an untested battery of somewhat proprietary metrics, you yourself can have an all-around better life. QSers are largely male, middle class to wealthy, and doing it voluntarily. I think of Sandra Harding, who points out matter of-factly that “defining what is in need of scientific explanation only from the perspective of bourgeois white men’s experience leads to partial and even perverse understandings” of things.

Thus, even if there are not hard and fast rules to Quantified Self, there are definitely distinctive practices, a particular method and phenomenology at play. It’s a utopian, techno-libertarian, entrepreneurial vision of sensor devices playing happily with machine-learning techniques, of developing perfect metrics, and application to human bodies in order to streamline the rough edges of the physical experience. But before we can talk about data-driven lives, we need to talk about what is being measured and why, about who is being measured and why.

Tracking Care

For those of us who have significant caregiving responsibilities, there’s a little absurdity to the consumer industry of self-tracking. After all, as a caregiver you have a responsibility to perform as a human data tracker. Whether you are taking care of a child, an elderly or sick or disabled person, or just a professionally busy person, you track their movements, their diet, their routines and schedules, their needs and wants. What’s more, as your body and your time are taxed by this, you usually experience some reproduction of labor in your life: data labor at home converges with data labor in the workplace.

For a movement that promises “self knowledge through numbers”, there’s little emphasis placed on what those numbers might reflect outside of their immediate circumstances. In Quantified Self’s vision, data points are rarely problematized. My local QS chapter in Portland is currently hosting a sleep-tracking challenge. However, if I suggested this other Moms and Dads at the playground, I’d be greeted with either laughter, sobs, or simply, “are you fucking serious?”

In QS, as in other areas of contemporary life, human-relationship data points are rarely emphasized, or fully acknowledged. Tracking milestones in relationships and communities, like birthdays, anniversaries, holidays and rituals, is often a feminine-branded behavior. While your boss isn’t necessarily expected to remember that next week is your birthday, or your partner’s, their assistant probably does have this type of expectation. How often is what gets branded “nagging”, either maternal or spousal, just a ritual in data gathering?

“Are you hungry?”

“Do you need to go to the bathroom?”

Data Becomes a Man

“Data” has historically been a neglected byproduct of action and interaction, and looking after it has been less a priority than an accident. That data has taken on masculine and technologically essential attributes in recent years is a testament to how quickly and pervasively market semantics can work. For centuries, collecting, caretaking, curating and analyzing data has been the domain of women’s work—look at the histories of librarianship, nursing and programming.

Yet to look through speaker lists for conferences like O’Reilly Strata, you’d be hard-pressed to remember that. The job title of “data scientist” has been invented with this maneuver in mind: a masculine and prestige-boosting rebranding of a type of work that asserts the value of a commodity and elevates the work to a respected status:science.

If data analysis in business is a science, in domestic life, it is a working practice – perhaps even a common art. Everyday data consumption is, I would argue, the most common and largest proportional working data relationship in existence. Like caring, cooking and cleaning, the task of managing data is something you’ll see at work in every household. It starts with money and food, the most banal avenues for data-working. Before she turned two, my daughter mimicked both swiping a credit card and checking the nutritional information panel on a granola bar.

I think about More Work for Mother, Ruth Schwartz Cowan’s 1983 classic on the tradeoffs of home-industrialization. Cowan argues that the nineteenth and twentieth-century introduction of new technological systems for food preparation, clothing, and other forms of housework had the veneer of easing labor and creating comfort. However, they also created an effect of cultural obfuscation by both gendering housework and by delineating it from other forms of industrial labor. Cowan argues that housework is “more characteristic of our society than market work,” as it is the “the first form of work we experience as infants” and “the form of work that the largest proportion of us (to wit, almost all women) identify as the work that will be the principal definition of our adulthood.”

Sensing Caregiving

Because caregiving is given to such a marginalized position in intellectual history, there is little we actually know about it through formal, or even informal channels. We have discourses to measure other parts of our lives, like our jobs or education or even the milestones of family formations. There’s not a recognized conceptual vocabulary for caregiving, no official forms of recordkeeping or documentation of it.

I trace my fascination with documentation with being from an immigrant family: when your life is uprooted, you lose not only your surroundings, but so much of your history. In my family’s history, I know that my abuela left Havana for Miami in 1961 with my father and uncle, and that my abuelo stayed in Cuba for another four years. I don’t know what her life was like in terms of caregiving, though, what it was like to emigrate with two children and live without your partner for years, and then finally be reunited. I wonder, sometimes, if I would be better prepared for the challenge of taking care of my daughter with my husband away so often if I had some kind of record from that time in my abuela’s life, some way of knowing what she experienced in the day-to-day. Sometimes I think that the reason that I know so little about this is because it was so hard, and that recording it or quantifying it would be triggering and painful.

As I’ve taken on caregiving responsibilities as an adult, I’ve witnessed my parents and their peers labor in taking care of older relatives; my father was the primary caregiver for my abuela, who suffered from dementia that stripped away her pride and strength before dying last year. The loneliness and fear I saw in him might have mirrored what she had felt during those years in Miami. I also realize now how I might very well face a similar situation when my parents are older.

For me, there is little solace in quantifying aspects of one’s life that are given little recognition, or fall into areas so fraught with tough choices, struggle, and emotional toll. For the hours during the week that my daughter is at preschool, would I feel anything but simple gratitude (and a twinge of guilt) if I saw how much effort her teacher had expanded? But then again, I sometimes wonder what will she know about this time when she’s an adult. I want her to know that she was loved and cared for, and that it was hard for me. Would being able to see, in physical measurement over time, the extent of that effort, make me feel any less sad and anxious about it all?

The Myth of the Universal Metrics

Much of the promise of the Quantified Self movement is in the discovery and adoption of near-perfect, near-universal metrics. If we can develop the perfect measurement for an object and its functions, nothing can be out of order, and we all can achieve a sort of equal footing. This is a dangerous line of thinking, and one that’s been problematized since Rousseau.

The Quantified Self movement searches for universal points and scores and payoffs, but doesn’t acknowledge the systems behind how those are valued, who chooses them, what they mean, and who they leave out — often the already overlooked and marginalized, like caregivers and other low-wage workers.

Imagine, workers doing all sorts of labor engaging with their data traces in ways that make their work safer and their efforts better recognized. Rather than seeking to perfect measures and standards of that work through statistical working-over, can we envision workers taking their own data to management to improve working conditions? I want Quantified Self to be a messy space, one where users willingly choose the aspects of their lives they are proudest of, and most troubled by, and allow them to track, and engage with their narratives over time on their own terms.

I wonder if we can ever reach a point where sensor technology and data-mining can be accessible and successful, flexible enough to be genuinely empowering, allowing users to control their own narratives. Is it improbable to dream of a feminist data future?