Being with Data. Nathaniel TkaczЧитать онлайн книгу.
the weekly ceremonial congratulations and recognitions that are part of parkrun convention: such and such has made it to 100 runs, such and such regular runners just got married, ‘any first timers?’, ‘anyone from out of town?’ (a few raised hands; applause), ‘please consider volunteering …’ (silence). While this is going on there’s a slight surge at the front and a jostling for position. Latecomers are ducking under the guide ropes and slotting into any remaining spaces. It remains civil, but a subtle defending of one’s territory is needed to ensure you don’t get nudged backwards. I’m still holding my arm in the air, feeling sheepish and a little desperate, but then I feel a buzz on my wrist. Finally! I see the watch interface flash green. The GPS is active. The man with the loudspeaker approaches the starting line. Along with almost all the runners around me, I hold my finger on the start button of my watch. ‘Ready … Go.’ I am close to the front and cross the start line a few seconds after his call, pressing the start button as I do.
As far as running strategies go, mine is the worst. It’s basically an anti-strategy; precisely what you’re not supposed to do. I go out fast, taking full advantage of the brief period before fatigue sets in and my breathing shortens, and much faster than I can possibly hold for any length of time. I never look at my watch for the first kilometre. At the 1-km mark I feel a buzz on my wrist. I know roughly when and where it’s coming because I’ve been doing this race most Saturday mornings for the summer. The buzz comes just past a bend at the far side of the park. I glance at the watch dashboard interface: first kilometre pace: 3:40 min/km. There’s no way I can keep this up, and I never do. This first kilometre gives me the initial thrill of running with the race leaders and stops me from getting caught up in the sea of runners, but it usually means I spend the last few k’s gradually overtaken by those able to maintain a steadier pace. My heart rate is already high, well into the 170s (beats per minute). Software on my phone will tell me later that I’m already in ‘Zone 5’, the highest of its Heart Rate Zones. In total, my heart will spend 96 per cent of the race in this zone. The remaining 4 per cent, spread across the other zones, occurs right at the start of the race. I fleetingly pass through each one in the time it takes my heart to catch up to the rest of my body.
From now on, I’m glancing at the watch every minute or so. I have the running dashboard interface configured to display four measures: total distance, average pace, current pace and heart rate (figure 0.0). I’m aiming to finish in under 20 minutes. Having managed this feat earlier in the summer, it has become a benchmark. It means I need to average under 4:00 minutes per kilometre. Since I go out fast, I generally know I’m going to be under for the first kilometre. Today, I’m 20 seconds under. The numbers ‘3:40’ (min/km) cast a new reality upon the situation. Although it’s not outwardly visible, inside 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