Chris Elsden, Mark Selby, Abigail Durrant, Dave Kirk
The Radiohead lyrics shown in Figure 1, from the band's 1997 release, convey goal-driven slogans in a veiled critique of a bland and overregulated modern life. Read again in the era of a quantifed self (QS), smart watches, and the Internet of Things, they provoke reflection on the heart and soul of an increasingly "data-driven life" .
There is understandably great excitement about the promises of these technologies, from patient-led revolutions in healthcare to citizen science. However, the use cases for personal informatics often seem narrow—a whiff of solutionism, grounded in assumptions about model citizens looking to achieve rational insights about their bodies, homes, and lives to be: fitter, happier, and more productive. As the unease that Radiohead evokes may attest, this is not for everyone.
To date this critique has been advanced most strongly in sociological circles, but it has recently gained traction in the HCI community through the notion of "lived informatics" . This considers different styles of use and emphasizes the need to understand how personal informatics are experienced as they become "enmeshed with everyday life" .
Beyond refining behavior-change technologies, this lived perspective should help us recognize that—whatever else they are ostensibly for—personal informatics tools can and will afford all sorts of other experiences around them. In so doing, we might design holistically for a much broader audience and range of values to question what being fitter, or more productive, actually means to people. In this article, we reflect on our own research and engagements with the HCI community to raise three challenges in designing for experiences with data: moving beyond an individual, trajectories and temporalities, and alternative representations of data.
Visions of personal informatics and a quantified self are being sold to us as intensely individual pursuits. However, as data pervades our everyday lives, individual concerns inevitably become entangled with the lives of others—partners, children, colleagues, and employers. As such, we must attend to the social life of data. Such lines of inquiry should break away from rational and individualistic self-reflection to consider more collective and even prosaic uses of data. How will stories be told, identities formed, jokes made, bragging rights earned, and arguments settled through the kinds of records that Fitbits or connected home devices will create?
We should anticipate how data becomes incorporated into the narratives of daily life—how well you slept, when your children got home from school, how much you had to drink last Thursday. Measurement is not a new feature of everyday life. Seemingly practical and benign metrics like clothes sizes and body weight are sometimes used to categorize people, reinforcing potentially harmful dominant norms and values. Yet the envisioned proliferation and sophistication of quantifying tools, rendering so many more human activities as machine-readable, may recast such anxieties in new domains. Existing individualistic models of interaction with personal informatics tools  may inform behavior change but seem ill equipped to grapple with emerging questions of how the meanings of data are socially constructed.
In our own research we have turned to speculative enactments to envisage these emerging social qualities of a quantified self. Metadating  (Figure 2) was a future-focused research and speed-dating event. Participants created hand-drawn "data profiles" about themselves and used these to "date" other participants. The event required participants to judge, represent, and express their identity to others with data, while giving them great freedom and authorial control. Investigating how the participants performed rather than analyzed their data revealed a range of rhetorical strategies. These strategies showed the value of ambiguous and underspecified data, and the ways in which participants drew on data that was illustrative rather than accurate. Crucially, these interactions (and hence the design) of data in a social context differ radically from those suggested by an individualistic and rationalist approach to a data-driven life.
The study of personal informatics frequently concerns the here and now—have I reached my step-count goal for today? How well have I slept this week? How long did this journey take? The focus on pragmatic use privileges action and consequences in the present over slower, evolving interactions over time. However, as these tools increasingly accompany people throughout their lives, we should consider the many different trajectories and temporalities of data.
Evolving meaning of a quantified past. Some records may be instantaneously relevant and then rapidly fade from view, perhaps not even formally recorded. Others may represent significant events or moments—achievements, first times, or foreign travels—that become frequent points of personal reference. Some may lie dormant as ephemera, the by-product of a once familiar routine, until they are chanced upon much later to evoke nostalgia for a particular time or place. Still others may have been captured passively, with little relevance at the time, until circumstances change or the combination with other data casts them in a new light. Importantly, the meaning, relevance, and use of data shifts over time, and we must look for design opportunities beyond a present-focused and analytic relationship to include a longer retrospective and more reflective or performative experience.
Through our interviews with long-term self-trackers, we have characterized the often unintended historical record created through self-tracking as a quantified past . Distinguished by its passive, removed, and "objective" yet egocentric focus, this quantified past is a curious record—one we have yet to design for. Unconsidered in existing models or stages of personal informatics, personal data offers particular accounts of the past, frequently in tension with our own reconstructive memories. Furthermore, a quantifed past may interact with existing long-term records, such as photo collections, medical records, and social media histories. "Smart" journaling apps such as DayOne and Momento treat data such as physical activity, location, and weather as a further index or metadata. A data-driven life therefore not only represents the past in novel ways but also can restructure how we encounter the past.
Prediction and simulation. Self-tracking apps also cast visions of the future—usually ones of self-improvement and progress. Applications and devices may both measure your actions and predict or simulate future possibilities and targets to encourage optimal behavior in the present.
Forecasting like this may be integrated with anticipatory services such as Google Now or Amazon Echo, which seek to identify user needs and actions ahead of time and present helpful advice or information. For example, such a service might flag opportunities to walk a longer route home to meet your pre-set targeted step count. Any such predictive technology naturally raises ethical questions. What data might these suggestions and predictions be based on? What about sponsored or promoted data-driven suggestions? How do we maintain user autonomy and oversight with such systems?
These propositions assume there are many mundane or routine tasks and decisions that people don't want or need to attend to. Yet these prosaic choices of what to eat for lunch or which route to travel home are idiosyncratic, and in their own way are avenues for our self-expression. Here, prediction and recommendation could be in danger of slowly routinizing our experience by entrusting the decision-making processes that orchestrate these experiences to the authority of data. How then should personal informatics systems avoid the "filter bubble" and enable everyday curiosity?
With the principle aim of rational and quasi-scientific self-analysis, many QS tools tend toward representing data numerically in charts or graphs, emphasizing comparison, progress, or reaching set goals. However, such analysis is but one possible mode of interaction with personal data. While objective accounts are valued for their general, authoritative, and categorical perspective, they often do not recognize the complexity of relationships between people, their felt experiences, and the data that describes them.
Careful and flexible interaction design can create more nuanced experiences with data beyond the "scientific exercise." Yet this design challenge is a significant one. How can we design for interactions with data that encourage an appreciation of the world rather than judgment? Or representations that support multiple perspectives rather than reductive explanations? Can social experiences with data avoid elements of competition? How do we allow for the often complex and ambiguous relationships with our digital records?
One way to approach these questions is through the physical manifestation of data. Objects are made "readable" through the language of design and their patina. They have layers of meaning unique to the viewer that productively support more subjective interpretation. The design of digital or software-based representations, however, can tend toward rational, visual information presentation, to be parsed, digested, and programmatically acted upon. Our work such as the Earthquake Shelf  (Figure 3) addresses this relationship, exploring the possibilities of representing data with uncertainty, ambiguity, and complexity.
The Earthquake Shelf monitors live data feeds for earthquakes at a specified location; whenever one strikes, the shelf will shake. Depending on the earthquake's magnitude, objects placed upon the shelf may fall, leaving behind material evidence of a remote event.
For those interacting with the Earthquake Shelf, its tangible rendition provides a more ambiguous description of an earthquake that evokes memories of past events and references personal experience. Rather than using data to describe events and help us know directly about our pasts, this kind of representation avoids the scientific exercise of data analysis and invites people to draw upon their own experiences and ideas to engage with data in more emotional ways.
The Earthquake Shelf is a very particular artifact; can we apply the same design position to other complex human experiences, such as sleep or stress? Simple explanations, insights, and actions are clearly appealing in the bid to train harder, eat better, and sleep well, but they risk oversimplifying the many human experiences with multiple variables and triggers.
Ambiguity in representing data can capture a certain human quality, suggesting that our unique and personal lives can't be entirely reduced to rows of numbers.
While complexity, uncertainty, and ambiguity may run counter to self-knowledge and belief in technological deliverance, they are fundamental elements of the human experience that deserve recognition and consideration in design. Many contemporary means of representing and communicating data fall short in this regard because they fail to question what it is we want to do with our data.
At its most blunt, our critique is that much current engagement with consumer personal informatics is one-dimensional and predicated on an exclusive interest in performance, individualism, effciency, measurement, and rational analysis. To be sure, this overlooks a great range of styles, modes, and alternative opportunities with these tools, but it also potentially limits their appeal and audience. Here, we've proposed three future challenges for the HCI community to address, and we've discussed associated opportunities for the design of technologies focused on embedding data in the social world, the trajectories and temporalities of data, and creating alternative representations of data.
But more fundamentally, we are aiming to raise discussion about what we expect from data and what roles it should play in our lives. Asking how we can use data to have a better experience of and in the world is a rather different question from one that asks how we can gain actionable self-knowledge from data.
While the pursuit of "actionable insights" is the predominant discourse for personal informatics systems, we hope to have reminded readers that this is only half the story. If we are to take seriously a holistic, and lived, focus on self-tracking, then we should recognize there is much to human experience that cannot be reductively or scientifically understood through simple quantification. Lived informatics should not merely recognize that self-tracking takes place over a range of lived activities; in addition, it should question what aspects of lived experience personal informatics can really address, and the implications of a data-driven life for how we experience the world.
There are many questions to which we do not have scientific answers, not because they are deep, impenetrable mysteries, but simply because they are not scientific questions. These include questions about love, art, history, culture, music—all questions, in fact, that relate to the attempt to understand ourselves better. —ray monk, "wittgenstein's forgotten lesson"
Through the three challenges we have mapped out here, we propose a much broader relationship with data than one that recasts human life and expression as machine-like. But it is also data itself that we are keen to liberate—from oft-overhyped expectations and narrow formulations. Despite the vaunted ambitions of a data-driven life and work, there seems to be little opportunity to be artistic or to perform with data, and for it to be used to express, reflect, and appreciate the nuances of everyday life, in the ways we may interact with other media. There is surely more at stake here than being fitter, happier, and more productive.
We believe the role of research and design of lived informatics can be to clarify and communicate the terms upon which people can engage with self-tracking tools. The future challenges we've set out look to both develop emergent issues about the lived reality of a data-driven life and offer a starting point from which the HCI community can reorient toward alternative engagements with such technologies.
We developed many of these thoughts through our CHI 2015 workshop "Beyond Personal Informatics: Designing for Experiences with Data." We thank all of the participants for their thoughtful inspiration and reflections, and in particular our co-organizer, Chris Speed.
1. Wolf, G. The data driven life. The New York Times. Apr. 28, 2010; http://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html
4. Elsden, C., Nissen, B., Garbett, A., Chatting, D., Kirk, D., and Vines, J. Metadating: Exploring the romance and future of personal data. Proc. CHI 2016. ACM, New York, NY, 685–698; http://dx.doi.org/10.1145/2858036.2858173
Chris Elsden is a Ph.D. student at Open Lab. His work concerns field work and design for the everyday experience of a data-driven life. His research addresses technologies of memory, in particular, and the personal management of burgeoning digital possessions. firstname.lastname@example.org
Mark Selby is a research associate at the Centre for Design Informatics, University of Edinburgh. He does research through design to nvestigate new opportunities for combinations of physical and digital material to help people construct new kinds of social and emotional value through technological interaction. email@example.com
Abigail Durrant is Leverhulme Fellow in human-computer interaction at Open Lab Newcastle University. She has a longstanding interest in exploring how digital interactions support expressions of personal, social, and cultural identity. Her approach is design-led and practice-based, using design artifacts and processes to understand and communicate ideas and experiences. firstname.lastname@example.org
David Kirk is a reader in cultural computing in Open Lab, Newcastle University. His work addresses the intersections of the design of interactive computational technologies and philosophical anthropology. He has a longstanding interest in the design of technologies to support human remembering. email@example.com
©2016 ACM 1072-5220/16/09 $15.00
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
The Digital Library is published by the Association for Computing Machinery. Copyright © 2016 ACM, Inc.