I’m thrilled. If I can hold a 4-minute pace for the rest of the race, I’m going to be close to a personal best. I think about the time and think about my body. Do I feel good? Am I hurting more than usual?
Figure 0.0 Garmin Vivoactive 3 running interface.
Source: Author.
When I look at the display, I’m generally looking at three things. I’m always paying attention to the average pace. It is the main indicator for how I’m doing overall. It needs to stay below 4:00. Since I start fast, this number is almost always creeping up after the first kilometre. In order to keep under 4:00 minutes per kilometre, some kind of body management is needed and this is where the other measures come in. I use the current pace and current heart rate to make adjustments as I run. If my ‘current pace’ gets too slow, I obviously need to speed up. If my heart rate gets too high, I know I can’t maintain the current pace and need to slow down. It’s a balancing act. The last measure, total distance, I look at less frequently. It gives me a sense of where I’m at in terms of overall duration (even though it measures distance). For short runs, I generally only pay attention to it during the final kilometre as a kind of last-ditch effort to motivate my body: 500 metres to go! 300! 100! And so on.
These are fairly straightforward measures, but they have come to shape much of my experience of running. They are why I’m willing to hold my arm in the air as a somewhat absurd offering to the GPS gods, and why I get anxious, even desperate, when the gods are not forthcoming with a signal. Simple as these measures are, when combined and placed into an active context they have become key structuring coordinates for my running activities. They structure what I’m thinking about, how I read the unfolding runscapes, how I gauge my performance and how I motivate myself. Despite their simplicity, they generate dynamics that are decidedly less so.
After the race, I can access many more data through an accompanying phone app: data on calories burned; elevation (gain, loss, minimum and maximum); pace, speed and timing (including averages, moving averages, bests, maxes and totals); heart rate (average, maximum and ‘zone’); running dynamics (cadence and stride length); lap times; and a detailed map of the route with each kilometre marked. Many of these measures are visualized in such a way that I can follow the continual variances over the course of the run. I can also compare this particular run to any other previous run, and data from this run feed into a much larger data profile. I can see how often I run, how often I do other forms of exercise, and many other things. For example, the watch also tracks steps, floors (elevation), sleep, calories burned and ‘stress levels’ on an ongoing basis. All these other data matter for any number of reasons, and they certainly matter for the companies aiming to turn data into money.1 However, when I look at these data on my phone after a run, whether it’s to scrutinize, evaluate or just casually explore, it is not the same as when I’m looking at data while running. The context has changed significantly and thus so too have the epistemic and operational qualities of the data. In fact, even the exact same numbers appearing on an interface – such as 3:40 – may differ significantly in terms of their epistemic character.2 What data are and can be is not separable from the arrangements and situations within which they participate.
When I’m looking at data while running, the data are deeply woven into the unfolding of the activity itself. These data are constitutive of the experience of running; they are designed for being in the moment and for participating in the transition from one moment to the next, until the run is concluded. These are data for being in motion and they are themselves in motion. Their feedback loops are so tightly bound they appear to move with us and us with them.
There are many ways of being with data. An outcome of our societies becoming ‘datafied’3 and routinely producing data in quantities previously unfathomable is a proliferation of instances where we find ourselves being with data. There are, of course, any number of research and business opportunities made possible by such datafication, just as there are accompanying sets of political and ethical questions. But focusing on such opportunities or on critiquing them often occurs with little attention to how people experience and encounter data. Indeed, one of the very criticisms of data and the algorithms that are sustained by them is that they are hidden, inaccessible or black-boxed.4 This risks overlooking the more ordinary situations where data are presented to us; where they have entered everyday life with little fanfare but not without significance.
Casual weekend running is one of these situations, one of these ways of being with data. It is not simply that running has become ‘datafied’ – that is, turned into a bunch of data points and made amenable for metrification and analysis5 – though this is a necessary component. Rather, post-datafication, running itself is transformed – at least for those making use of the respective devices. Running now contains a data element; it has become running-with-data. And while there are many ways of being with data, the way of being with data that I describe above is quite specific. It involves a visual display, with a limited number of measures and indicators that change in response to my own activities and other things beyond my control that bear on the situation. The data and measures are preselected for relevance out of many possible alternatives and they are designed to be looked at while in the act of doing something. This, I suggest, is a very specific way of being with data. As far as the history of data goes, it is a fairly recent invention.6 This book is an attempt to understand this way of relating to data. Where does it come from? How does it affect how we think? How we encounter and perceive time and space? How we conduct ourselves individually and as part of larger organizations? That is, this book seeks to understand this way of being with data on cultural grounds, as something with a history, something that shapes subjectivity (how we think), modes of being together (how we organize) and experiences of space and time.
My entry point into this data relation and the subsequent questions it opens up is through the format of the dashboard. This format, developed over a few centuries, can help shed light on the way of being with data that I experience when I’m running, and many other places besides. I hope to show that this particular relation, of being with data formatted through a dashboard, is widespread and worthy of sustained attention. Both the concept of ‘format’ and the format of dashboard will therefore play a central role in how the ideas and empirical material that follow are organized. Indeed, my opening gambit is that data and format must be thought together. As much as this is a book about data and ways of being with data, it is a book about the format of dashboards.
Dashboard
Dashboards are generic digital artefacts that facilitate a specific relation to data. Dashboards and dashboard-like interfaces are everywhere, used all the time, by scientists, civil servants, economists, managers, factory workers; and by pretty much anyone who has a phone, tablet or personal computer. They are used for business, leisure and the murky in-betweens. Despite efforts by industry to sell them as part of broader visions of our data-driven future, it’s hard to get too excited by dashboards, comprised as they are of charts, graphs, tables, gauges and ‘key performance indicators’.
You may already know what a dashboard is. If you hail from the overdeveloped regions or are otherwise connected to the digital world, you’ve almost certainly seen one and there’s a very good chance you’ve used one. Perhaps, like me, you have a line manager who uses them to oversee the performance of you and your coworkers, figuring you and them as lines on a graph or pieces of a pie chart. That is, perhaps dashboards know you even if you’re not properly acquainted with them. If you’re lucky, like me, your seniors will tell you about your dashboarded existence and you can look at it together; reflecting on how dashboards quantify the complexities of work; how they arrange and value your activities and outputs. Perhaps you’ve laughed
