living with data: personal data uses of the quantified self

Living with Data: Personal Data Uses of the Quantified Self Sara M. Watson Candidate Number 562095 Keble College [email protected] 22 July 2013 Word Count: 9,974 Thesis submitted in partial fulfillment of the requirements for the degree of MSc in Social Science of the Internet at the Oxford Internet Institute at the University of Oxford.

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ABSTRACT

Between the internet, social media, sensor-enabled devices, and established industrial transactional

systems, we are living in a world with more data about ourselves than ever before. Public discourse

has largely focused on the opportunities for firms or the risks to individuals as this data

environment expands. These framings do not give individuals enough practical understanding of

how data impacts and integrates into their lives. The Quantified Self community is an advanced-

user community of people who have begun to explore and experiment with novel uses of personal

data. As the Homebrew Computer Club’s hobbyist experimentations paved the way for the

personal computing revolution, the Quantified Self community offers a glimpse of what

engagement with personal data in our everyday lives might soon look like. Through

ethnographically-informed interviews and participant observations, this research explores how self-

trackers derive personal meaning from personal data. I present a lifecycle of personal data use: from

deciding what to track, through collection, analysis, and future uses. I explain how current barriers

to use expose the need for revised policies to support individuals’ personal interest in the use of

their data. By analyzing the metaphors individuals use to explain their personal uses of data, I put

Quantified Self tracking practices in historical context and illuminate the novel affordances that

self-knowledge through data provides. I argue the QS community offers ways of framing and

engaging with personal data in our everyday lives that can help society at large begin to understand

our roles as data selves in a Big Data world.

KEYWORDS: personal data, quantified self, self-tracking, use, everyday life, conceptual

metaphors, data lifecycle, data self, Big Data

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ACKNOWLEDGEMENTS

I am so grateful to my participants for sharing their stories with me. Joshua Kaufmann and

Adriana Lukas planted the seed for this work and invited me into their world. I felt right at home in

the community, thanks to an ethos of collaboration and inclusion fostered by Gary Wolf and

Ernesto Ramirez.

This work would not have been possible without Viktor Mayer-Schönberger’s inspiration

and encouragement. I am grateful for his advice, counsel, and mentorship throughout. I am also

thankful for John Battelle’s expert editorial advice and perspective, as well as his willingness to dive

back into academia. I look forward to working through more of these questions together in the

coming year.

The OII Faculty and my MSc cohort deserve my sincerest thanks for entertaining my

incessant talk of the Quantified Self throughout the year. I am also deeply indebted to the

department for the intellectual and financial support offered by the OII MSc Scholarship. I am

grateful for Jonathan Zittrain’s mentorship and friendship, in leading me to the OII and helping me

find a new home at the Berkman Center.

Thanks are owed to Buster Benson for his 750 Words webapp. So much of my thinking

and drafting was worked out on those pages over the past 51 mornings and 44,751 words. Thanks

are also due to Stan James and Diana Kimball for their coincidentally coordinated nudges.

I am deeply grateful to my parents and friends who graciously waded through drafts of this

work. Last, but certainly not least, I cannot thank my husband enough for his tolerance of my self-

tracking experimentations, his honest critique, his patient editorial feedback, and ongoing support

and encouragement.

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TABLE OF CONTENTS

ABSTRACT ........................................................................................................................................2 ACKNOWLEDGEMENTS ...........................................................................................................3 TABLE OF CONTENTS................................................................................................................4 LIST OF FIGURES ..........................................................................................................................5 1. INTRODUCTION .......................................................................................................................6

FROM BIG DATA TO PERSONAL DATA.....................................................................7 1.1 WHAT IS THE QUANTIFIED SELF? ............................................................................9

THE PRACTICE......................................................................................................................9 THE DATA...............................................................................................................................9 LEADERSHIP AND LABELS ...........................................................................................10 REACH AND DEMOGRAPHICS....................................................................................11 OF MIXED INTERESTS, MOVEMENTS, AND MORES ........................................12 QUANTIFIERS IN CONTEXT.........................................................................................14

1.2 LITERATURE REVIEW ...................................................................................................14 2. METHODS ..................................................................................................................................16

2.1 RESEARCH QUESTIONS................................................................................................16 2.2 RESEARCH DESIGN AND INTERVIEW METHODS...........................................17 2.3 ANALYSIS.............................................................................................................................18 2.4 REFLEXIVE RESEARCH.................................................................................................19 2.5 RESEARCH ETHICS AND ATTRIBUTION ..............................................................20

3. FINDINGS: TYPOLOGIES OF PERSONAL DATA USES...........................................20 3.1 PERSONAL DATA USE LIFECYCLE..........................................................................21 3.2 BARRIERS TO PERSONAL DATA USES ...................................................................25 3.3 METAPHORS AND ANALOGIES FOR DATA USES ............................................27

4. DISCUSSION ..............................................................................................................................31 4.1 KNOWING OURSELVES AS DATA SELVES...........................................................31 4.2 FURTHER WORK ..............................................................................................................33

5. CONCLUSION...........................................................................................................................35 WORKS CITED..............................................................................................................................36 APPENDICES.................................................................................................................................41

APPENDIX A. GLOSSARY OF TOOLS AND APPLICATIONS ................................41 APPENDIX B. PARTICIPANT SNAPSHOTS...................................................................43 APPENDIX C. INFORMED CONSENT FORM..............................................................47

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LIST OF FIGURES

Figure 1. Buster Benson’s 8:36 project, captioned “8:36pm Family portrait hour,” taken 31 May, 2013 via flickr...........................................................................................................................................6

Figure 2. Screenshot from 16 July 2013 of Meetup.com stats for the associated Quantified Self groups......................................................................................................................................................12

Figure 3. Memoto lifelogging experiment discussion at the Europe Conference 11 May 2013, image courtesy Rain Rabbit via flickr. ...........................................................................................................13

Figure 4. Personal Data Use Lifecycle. ........................................................................................................21 Figure 5. Stan James’ Lifeslice aggregate view of hourly webcam portraits...........................................29

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1. INTRODUCTION

Buster Benson asked us all to close our eyes, imagine our deathbeds, and think about the

things in our lives that we would cherish at that moment. These things might be the only things

worth tracking, he suggested. When we opened our eyes, I caught the man seated in front of me

wipe away a stray tear. That is how intimate and personal the Quantified Self can be.

At the end of a two-day conference that celebrated self-tracking of everything from arterial

stiffness to lifelog snapshots taken every 30 seconds, the idea that there might only be a few things

really worth tracking came as an unexpected provocation. When I followed up with Buster I learned

that he draws a clear distinction between self-tracking that can be programmatically automated and

self-tracking that requires some effort. The running tally of unread inbox messages he displays on

his homepage or the semantic analysis that outputs stats on his daily writing habit website 750

Words1 are both scripted and run in the background. They simply expose insights from data

“signals.” But tracking that requires some input on his part needs to be sustainable, and ultimately,

meaningful. The difference, he says, is intention.

In his 8:36 PM project, Buster takes one “uncurated” picture at prompt of his smartphone

alarm, wherever he happens to be. These end up being pictures of the things he wants to remember

on his deathbed: his three-year-old son, his partner, his friends, his travels, the passage of time. He

admits these quotidian moments might be boring to see in data, but they are valuable in life.

Figure 1. Buster Benson’s 8:36 project, captioned “8:36pm Family portrait hour,” taken 31 May, 2013 via flickr.

1 Appendix A offers a glossary of tools and applications mentioned throughout.

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Buster describes himself as a Quantified Self “fanatic and skeptic.” Buster is a developer at

Twitter, but previously worked on a variety of startups like Habit Labs and 43 Things designing

tools to support behavior change and positive habits. He is the most thoughtful and humanistic

kind of technologist; he keeps Github changelogs of his core beliefs and rules to live by (2013b).

He suggests that values, rather than traditions, need to shape evolving norms as technologies

become integral to our lives. He maintains “privacy is a side effect of people not being connected.”

And because he thinks names are given too much power and meaning, he has legally changed his,

twice.

Buster is just one of the many individuals I encountered in the Quantified Self community

who are testing the boundaries of technology and integrating data into their lives. He is both

exemplary and exceptional; his personal data practices expose the mundane, the novel, and even

the extreme of an early adopter’s use of data.

FROM BIG DATA TO PERSONAL DATA

Data is2 the fuel that drove the Information Age, and it now drives the age of Big Data

(Mayer-Schönberger & Cukier, 2013). But for most of us, data has worked behind the scenes,

hidden in corporate servers and in the “cloud.” Now data is entering into our lives, as it refers to

more things about our bodies, our minds, and our behaviors. Between transactional data,

clickstreams, the sensor-enabled Internet of Things, smartphone accelerometers, and location

information, the sources and scales of data that can be tied to an individual are exponentially

proliferating. Firms are excited by the potential for modeling and understanding consumer

behaviors in aggregate, but that data can also tell us something meaningful on a personal scale. Just

as the personal computing revolution introduced computational power into our everyday lives, we

are on the cusp of a personal data revolution that is bringing insights from data out of the industrial

scale and down to the most individual, human scale.

Much of the public and academic discourse about personal data has been dominated by a

focus on the privacy concerns and risks to the individual. I contend this neither accurately

2 “Data” is traditionally a plural noun, but I follow vernacular standard referring to the concept of “data” as a singular mass noun.

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represents the way personal data is created in the world, nor does it empower individuals to make

informed decisions about the uses of personal data in daily practice. Oppositional framings miss out

on the potential positive value for individuals to benefit from insights offered from both the large

aggregate scale (Big Data) and more importantly, from the small scale of immediately relevant data

about ourselves.

Academic discourse sorely lacks empirical understandings of how individuals use and

understand data in practice. Surveys (Fox & Duggan, 2013) offer broad strokes, but do not uncover

novel uses. Expanding on ethnographic work looking at data privacy attitudes and practices, and

identity construction (Turkle, 2005; boyd & Heer, 2006; boyd & Marwick, 2011; boyd,

forthcoming), I wish to address more nuanced questions about personal data use in daily life.

A lack of popular awareness about personal data poses a challenge to studying its role in

the lives of average consumers. So I look to an early-adopter group for indicators of how personal

data is being integrated into everyday life. The Quantified Self (QS) community is both aware of the

systemic shift in the volumes and kinds of data we are generating and also interested its personal

uses. If we wish to anticipate and understand consumers’ broader interests in the uses of their

personal data, we can examine how the Quantified Self community uses personal data today.

Through ethnographically-informed interviews and participant observations, this research

explores how members of the Quantified Self community derive personal meaning from personal

data. I present a lifecycle of personal data use from deciding what to track, through collection,

analysis, and future uses. I expose individuals’ frustrations with barriers that inhibit intended uses of

personal data. I look at how the metaphors people use to describe their data practices put

Quantified Self in historical context and reveal the novel affordances self-knowledge through data

provides. These findings point to what needs to change in current understandings and policies in

order to empower individuals to participate in an emerging data society.

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1.1 WHAT IS THE QUANTIFIED SELF?

The Quantified Self (title case) and by extension the quantified self (lowercase) have come

to mean different things to different people. Before detailing my methods and motivating literature,

I first offer some background on what the Quantified Self looks like today.

THE PRACTICE

The practice of self-tracking and self-quantifying uses apps, sensors, and other tools to

collect empirical data and observations about individuals’ daily lives. Self-quantifiers track a wide

range of things covering both the body and the mind. Basic health and fitness metrics capture

exercise, steps, weight, calorie intake, sleep, caffeine, food sensitivities, to more advanced

biomarkers like blood sugar, variable heart rates, and brainwaves. Tracking also cultivates habits as

enlightened as meditation or as mundane as flossing using apps like Lift or Beeminder. Self-trackers

use calendars, lists, and more advanced activity loggers like RescueTime to collect time-

management and productivity data. They also track more subjective metrics like mood, or mine

semantic content in their email, note-taking systems, or journals.

What motivates self-quantifiers to track? For many, it is a matter of solving a problem, like

snoring (Goldberg, 2012). For others, tracking manages chronic and even terminal conditions and

improves quality of life (Riggare & Addyman, 2013; Clements, 2013). Others track for a sense of

posterity and legacy. Stuart Calimport (2013) is motivated by a desire to extend life and transcend

human limitations. Still others track for data’s sake, without any clear use in mind aside from the

potential future use (Johnson, 2013). In most cases, self-tracking aims to improve, better, or

advance one’s life.

THE DATA

Data in the Quantified Self is both quantified and qualified. Wolf (2010) details the

affordances of numbers: “Numbering things allows tests, comparisons, experiments. Numbers

make problems less resonant emotionally but more tractable intellectually.” Thus stems the group’s

motto, “Self knowledge through numbers.” However, “quantified” is somewhat of a misnomer

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now. Over time, QS data has expanded to include qualitative data that is unstructured, semantic, or

even visual. Data now stands for anything that can be digitized, computed, and at a most

fundamental level, can be reduced to bits. There are still some quantitative purists within the

community who draw a distinction between “self-quantification” referring to numerical data and

“self-tracking” covering other qualitative activities (Augemberg, 2013), but there is also an implicit

tolerance, “to each to his own.” The emphasis lies not in the data or the methods, but in the

personal insights from the data.

Data in the Quantified Self is inextricably tied to the self. It references a physical state, a

mental state, a behavior, a location, an event, an intention, all in context of and in reference to an

individual. This definition brings personal data beyond personally identifiable information (PII) and

into a more phenomenological, holistic experience of the self. Tom Dawson described how his self-

tracking data has evolved since his earliest training logs from 1989: “[The data has] become a lot

more personal I think...I have become a lot more intimate with my data.” Wolf described this as the

move from personal computing to computing for the self, where personal computing comes “all

the way in.”

LEADERSHIP AND LABELS

The Quantified Self was coined as such in 2007 when Wired editors Gary Wolf and Kevin

Kelly sought a way to describe the self-tracking activities they were observing among friends in the

Silicon Valley, using sensors and other personal computing technologies to do dramatically new

things with the data they were collecting. They convened a meeting to share practices and findings

from their self-tracking activities. The term appeared in print in Wired (Wolf, 2009a) and was

popularized in a New York Times magazine feature “The Data-Driven Life” (Wolf, 2010) and a

TEDTalk (Wolf, 2011a). It is worth pointing out that “Quantified Self came to exist because people

were already self-tracking, and some of those people were interested in discussing their self-tracking

experiences with others” (Boesel, 2013).

The Quantified Self (title case) now refers most directly to the community participating

through online forums, conferences, and local in-person Meetup groups around the world. The

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quantified self label (lowercase) has expanded to refer to a wider ecosystem of tools, sensors, apps,

and practices that cover all manner of personal data creation and analysis. It has been used to

describe tools as wide-ranging as Wifi-enabled scales, to forks that sense when you eat too fast, to

chemically-enhanced diapers that signal potential urinary tract infections. The concept applies

anywhere there is data to be collected about ourselves.

Even more broadly, the term describes all manner of exhaust we create in the both the

physical and digital worlds that renders an individual’s behaviors and interests observable and

explicit as data. This includes data such as supermarket club cards, metro transport data, browser

histories, email metadata, and cell-tower triangulation location information.

For the purposes of this case study, I bound my engagement to members of the title case

Quantified Self community, that is, people participating in the Meetups and conferences. But the

implications of this apply to the broadest understanding of the quantified self, as addressing our

role as actors in a data-driven environment.

REACH AND DEMOGRAPHICS

As of this writing, there are now 134 associated QS groups in 103 cities and 34 countries

around the world with 21,619 members subscribed (Meetup.com, 2013). The largest, and most

established groups spawned around tech hub cities like San Francisco, London, New York, Boston

and Amsterdam, but span as far as Memphis, Mexico City, and Mumbai.

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Figure 2. Screenshot from 16 July 2013 of Meetup.com stats for the associated Quantified Self groups.

QS Labs leadership describes the community as “a global collaboration between the

makers and users of self-tracking tools and technologies” (Lange & Ramirez, 2013). Organizers

promote inclusiveness and support the varied interests within the community. To keep the

emphasis on personal stories, they follow a standard question set in their Show and Tell

presentations: “What did you do? How did you do it? What did you learn?”

As might be expected of a technical early-adopter group, attendees of QS Meetups skew

towards white, male, silicon-valley-type developers or entrepreneurs. But the group also attracts

more diverse demographics interested in self-improvement, mindfulness, public health and

healthcare, as well as the broader research community.

OF MIXED INTERESTS, MOVEMENTS, AND MORES

Many individuals in the QS community have both personal and professional interests in

self-tracking and personal data. Professional interests tend to be secondary; the primary interest in

QS is a personal one: using personal data for personal insight. Individuals, therefore, have a

personal stake in the discussions and debates that take place in the community. They are in the

unique position of balancing often conflicting commercial interests and personal interests in data.

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Quantified Self has been described as a movement, though Wolf resists the term for its

political connotations of claim-making and agenda-setting. If it is a movement, it is one “celebrating

a common means, not a common end” (Morozov, 2013). Wolf suggests the community is

positioned to not only adopt new technologies for self-knowledge, but to critique and question the

terms of adoption and to test the difference between “being brave and being reckless with

technology.” Wolf fostered this “movement of questions” by facilitating an experiment during the

Amsterdam conference this spring using a Memoto lifelogging camera. The experiment culminated

in a town hall session discussing how the camera influenced social interactions throughout the

conference, how we felt about seeing our images projected on the screen, and how our normative

expectations around privacy and surveillance were changing with the introduction wearable tracking

technologies.

Figure 3. Memoto lifelogging experiment discussion at the Europe Conference 11 May 2013, image courtesy Rain Rabbit via flickr.

Based on the range of reactions in the Memoto discussion, there is no consensus within the

group about evolving privacy norms. But the Show and Tell format certainly favors sharing,

openness, and even vulnerability. Within a broad range of interests and opinions represented in the

group, there seems to be an emerging set of commonly shared assumptions and values. Based on

my observations, they include:

○ a goal of self improvement or self awareness through tracking ○ an innate sense of curiosity

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○ a higher-order interest in the processes and practice of self-tracking ○ an epistemological belief that data can reveal novel insights about the self ○ a principle that individuals should have access and be able to make use of data that reflects

on or refers to the self ○ a trust that the usefulness of data outweighs the risk of harm or exposure from that data

This list is by no means exhaustive or universal, but does have bearing on my findings about data

uses, limitations, and conceptual framings.

QUANTIFIERS IN CONTEXT

The sensor technologies enabling the Quantified Self have their roots in personal

informatics and wearable computing research coming from Steve Mann and the MIT Media Lab.

Professional athletes have long integrated sensor-based performance monitoring into their training

regimens. The practices of self-reflection and personal archiving draw from the lifeloggers and

diarists like Gordon Bell, Buckminster Fuller, and go as far back as Thomas Jefferson, Benjamin

Franklin, Leo Tolstoy, Samuel Pepys, and Augustine of Hippo. The DIY tool-making ethos in the

QS community has been likened to the Homebrew Computer Club (Wolf, 2008; Donovan, 2013;

Narrato, 2013). Homebrew hobbyists tinkered to make computing more accessible and laid the

groundwork for the personal computer revolution (Freiberger & Swaine, 2000; Markoff, 2006).

Wolf draws out the parallels:

Once upon a time, computers were thought to be useful only for scientists, managers, and planners. But a few people saw things differently: they argued that computers were for all of us...We at the Quantified Self think of data the same way. (2011b)

QS practices are extensions of a long genealogy of technologies of the self (Foucault, 1988;

Bakardjieva & Gaden, 2011). Yet, they also point to novel engagements with data that signal the

start of a personal data revolution.

1.2 LITERATURE REVIEW

Popular media has covered the Quantified Self extensively, but in sensationalized and

reductive accounts. Depictions frame individuals as narcissistic or extreme and make totalizing

statements suggesting individuals “track every detail of their lives” (Hellen, 2013). QS trend articles

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follow a pattern: writers speak with a few key informants and describe their extreme behaviors

(Bowden, 2012; Quart, 2013), or report back personal experiences after testing QS tools for a week

(Hill, 2011; Wolcott, 2013).

As yet, there is not much academic work published addressing the Quantified Self directly.

What is published focuses on self-tracking as applied to personalized medicine and healthcare (Lee

and Dumont, 2010; Lupton, 2013; Swan, 2009, 2012) or citizen science contexts (Swan, 2013).

It also bears mentioning some of the in-process work of colleagues I have come across

over the course of this research. Andrew Butterfield (2012a,b) studied the organizational structures

that support the scaling of global Meetup groups. Whitney Erin Boesel studies mood tracking and

the “missing trackers” (2013) in the socioeconomic makeup of the group. Jenny Davis (2013)

frames QS data creation as an act of “prosumption.” Rodney H. Jones (2013) discusses using data

as a narrative device, “entextualizing” the self. Natasha Dow Schüll (2013) suggests the affordances

of arriving at self-knowledge through tools and apps processing information construct an

“algorithmic” self. Rob Horning (2013) describes a cultural shift towards “postauthenticity” where

the self and identity is constructed through social media and processed algorithms. David J. Philips

(2011, 2012) positions self-tracking in the surveillance literature. Dawn Nafus and Jamie Sherman

(2013) position the individualization of QS practices as a “soft resistance” to the biopolitics of

platforms and Big Data.

To date, the most critical popular theorization comes from Evgeny Morozov (2013) in his

solutionist critique, To Save Everything, Click Here. Morozov takes issue with the idea that human

experience could be understood completely through numbers. Morozov argues that a QS focus on

numbers removes all “narrative imagination” possibility for self-knowledge:

The Quantified Self movement, in its current form, is madly devoted to articulating facts—that’s what numbers are good for—but it still has no way of generating narratives out of them. In fact, it might even block the formation of narratives, as self-trackers gain too much respect for the numbers. (2013)

Morozov identifies the danger in this narrowed understanding of the self, but there is perhaps more

to the story (quite literally). My observations suggest that rather than precluding narrative

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altogether, QS presentations follow an inherently narrative structure, and that data is used as a tool

or a prompt for narrative meaning.

Morozov asks important questions, but his methods are flawed. He draws from secondary

sources, all examples pre-filtered by journalists. Alexis Madrigal (2013) writes in his measured

critique:

Despite the rigorous philosophical underpinnings...there’s something missing: people...His clever use of anecdotes makes it appear as if he’s discussing the way that human beings interact with self-tracking devices, but they are not a serious account of practice...it does not devote any time to the stories the bulk of technology users tell themselves...Without a functioning account of how people actually use self-tracking technologies, it is difficult to know how well their behaviors match up with Morozov’s accounts of their supposed ideology. (2013)

Madrigal also points out Morozov’s “innovation- and product-centered account of the deployment

of technology.” These shortcomings directly motivate my ethnographically-informed research

methods and analytical framing to uncover uses of data within the QS community. In doing so, I

aim to provide a more complete account of the Quantified Self uses of data in everyday life.

2. METHODS

2.1 RESEARCH QUESTIONS

These research questions aim to systematically uncover, describe, and theorize the uses of

personal data in the Quantified Self community. These questions address the various dimensions of

how individuals use personal data in their everyday lives.

○ How do self-quantifiers use data to arrive at self-knowledge? What are the processes of meaning making through personal data?

○ What are current problems, challenges, and limitations of Quantified Self uses of data?

○ How does the Quantified Self community talk about and frame their relationship to personal data?

I treat data as a technological object of study. It is a tool, a technology, and a medium. Data is a

technology in and of itself, not just a byproduct of technology. Data mediates self-knowledge, it becomes a tool

for self-knowledge.

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2.2 RESEARCH DESIGN AND INTERVIEW METHODS

To observe personal data practices in everyday life, I focused on the Quantified Self as a

community of individuals with advanced personal data practices. This case stands as an outlier or

early-adopter group (Yin, 2002). For the purposes of defining a “unit of analysis” (Yin, 2002)

boundary to this case study, I spoke with individuals participating in the Quantified Self community

in some way through the web presence and Meetup groups. I focused observations and interviews

on the London QS community as a convenience sample case within the larger Quantified Self

network, though I did not exclude others I came into contact with as a result of the Europe

Conference and snowball sampling methods. At the most granular level, the case design focused on

individual participants.

The research followed two stages. First, I gathered nonreactive, non-elicited content

(Janetzko, 2008) from the publicly available Quantified Self blogs and forum. I conducted close

reading analysis filtered around the word, “data” in both the forum and blog content to understand

how the community talks about data, coding for patterns and themes in the discourse. These

methods and findings are covered in detail in my Advanced Qualitative Methods paper (Watson,

2013b).3

The outcomes of this first phase fed into my second phase of ethnographically-informed,

interview methods and participant observation, presented here. To prepare for in-depth, semi-

structured interviews, I gathered non-reactive data from blog posts and watched past QS

presentations. I also looked at individuals’ public profiles on Twitter, LinkedIn, Facebook, personal

blogs, etc. to gain a sense of their personal and professional backgrounds. This descriptive

contextual preparation allowed me to spend more time on deeper, more explanatory questions in

interviews and lent “rich rigor” (Tracy, 2010) to the research.

I followed a person-centered interview strategy (Levy & Hollan, 1998), engaging

contributors as both participants and informants. A focus on the individual drew out uniqueness of

each participant’s personal data uses, providing more rich detail than a survey or standardized

interview schedule could have provided.

3 See Appendix C for excerpted methods from this research.

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I conducted twelve in-depth interviews in person or via video Skype where necessary, each

lasting between 45–90 minutes and totaling 18 hours and 20 minutes. With participants’ permission

I recorded interviews and made detailed notes following. I also made observations watching

roughly ten hours of archived presentations (between 25-30 presentations) from Meetups around

the world. I gathered observations and kept field journals in Evernote attending three London

Meetups and the Europe conference over the course of the year.

I relied on key informants who have been involved with the QS community since the early

days. Adriana Lukas and Joshua Kauffman’s expertise and position as gatekeepers and connectors

guided sampling for further interviews. I used purposive sampling (Babbie, 2013) to address a

diversity of data uses. Thematic findings from the first phase of research informed my theoretical

saturation sampling methods (Glaser, 1992), and I cut off interviewing when themes began

repeating. I recognize my sampling methods are both “partisan” and “partial” (Denzin, 2009).

2.3 ANALYSIS

My analysis methods are also qualitative to align with my interview methods. There may be

some irony in studying the Quantified Self with qualitative methods, but qualitative methods are

best suited to address motivations, patterns in practice, and individual narratives. While many self-

quantifiers use quantitative methods of analysis to draw meaning from their data, their

presentations follow a narrative structure that merits a close reading approach. Using qualitative

thematic content analysis on my transcribed interviews uncovered nuance in the distinctions across

tracking practices, which may not have surfaced had I used programmatic or quantitative methods.

I was sought out to represent how individuals are characterizing their own data uses. In

many cases, individuals are well educated and come from scientific or technical backgrounds, so

they have established professional assumptions that inform their practices. In this sense, my analysis

is informed by ethnomethodology (Garfinkel & Rawls, 2008), studying the methodologies of self-

tracking. I present this analysis using participants’ language in keeping with an in vivo method (Berg

& Lune, 2012) of capturing “data in their words” (Watson, 2013b).

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My theoretical framing is informed by domestication and adoption approaches to

understanding how individuals integrate new information and communication technologies (ICTs)

into their everyday lives (Silverstone & Haddon, 1996; Haddon, 2004, 2011; Bakardjieva, 2005).

Here, I focus more on individual use rather than familial or household contexts, in keeping with the

QS focus on the individual. Self-tracking is enabled by always-on devices and ambient sensors, so I

am not exclusively interested in the domestic context of the “household” but rather the

“familiarizing” senses of domestication.

The framing is also inspired by histories of technological adoption that focus not on the

innovation of the tools themselves, but rather on how individuals use these tools in practice

(Edgerton, 2006). Looking at early-adopter uses of personal data might reveal the tensions between

the “unacknowledged assumptions about...personal and domestic concerns at the same time that it

signal[s] profound changes attending…culture at large” (Gitelman, 2006).

Themes across observations and interviews are summarized in my findings, and supported

with examples from individual participants and nonreactive data. I present findings as a montage or

bricolage (Lincoln & Denzin, 2011) of supporting evidence to give a sense of the commonalities and

variations across personal data uses. I favored a thematic presentation of findings over a

biographical, journalistic presentation of participants to give a wider ranging account of practice

within the word limit constraints, but I have provided in-depth profiles of participants in Appendix

B.

2.4 REFLEXIVE RESEARCH

My role as a researcher and participant observer is important to acknowledge (Denzin &

Lincoln, 2011). I built rapport (Dickson-Swift et al., 2007) within the QS community. I have also

participated within the community as a self-tracker, leading a conference breakout session and

sharing my story with the London Meetup in June (Watson; 2013a). Reciprocation is important

within a community that values sharing, feedback, and self-reflection. I was an active participant;

where I had questions that were not addressed, I inserted them into the discussion as a gentle

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provocation (Woolgar, 2004). As research advanced, I tested early findings with informants for

dependability, credibility, and confirmability (Lincoln & Guba, 1985).

My association with the community has evolved over time; this process informs my

analysis. I have been tracking things like diet and exercise since 2009, but I had not attended a

Meetup before this past year: “I never really identified with the movement and the more edgy ‘self-

hacking’ aspects of the trend” (Watson, 2012). As I spent more time with the group and was

exposed to more projects and practices that resonated with my own personal data uses, I began to

situate myself in a more nuanced and inclusive understanding of the Quantified Self.

2.5 RESEARCH ETHICS AND ATTRIBUTION

I have chosen to attribute quotes and ideas to named sources, largely because the

nonreactive content (Janetzko, 2008) is drawn from publicly available blog posts, forums, and

videos from conferences and is therefore easily searchable and identifiable. Attribution also

acknowledges individuals’ contributions to the research. Where I quote personal interviews, my

informed consent form (see Appendix C) asked participants how they wished to be attributed, and

provided the option of anonymity. I refer to participants by first names throughout, to reflect the

familiarity and rapport we built over the course of the research.

3. FINDINGS: TYPOLOGIES OF PERSONAL DATA USES

These findings represent a synthesis of observed patterns across web content analysis,

participant observations, and personal interviews. Each of these findings sections looks at a

different aspect of the personal data uses of the Quantified Self community. First, I explain how

individuals use data at every stage of the personal data use lifecycle, not only in analysis, but also at

the point of collection and even in the choice of what to track. Next, I summarize barriers to data

uses uncovered in the first stage of the research. Last, I summarize the metaphors people use to

describe their relationship to data to position data in a larger historical context of technologies used

for self-knowledge and to elucidate how data differs from precursors.

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3.1 PERSONAL DATA USE LIFECYCLE

How does the Quantified Self community use and intend to use personal data? I have

mapped the uses of data across a lifecycle model to address my first research question of how self-

quantifiers arrive at self-knowledge through data. As became clear in my interviews and

observations, individuals use data in varied ways, and not necessarily covering all stages of use.

This typology also elucidates why I focus on personal data rather than personal

information. Focusing on data reveals uses at the most granular level, that is, in the creation and in

the collection of the data, rather than focusing exclusively on the processes by which data becomes

information. Here I argue data is a technology for self-knowledge, even at the early stages of

decision and collection. This ties individual practices to the Big Data systems that enable and

support these novel digital uses of data. This also brings the focus to the lowest, most elemental

level of abstraction, revealing exactly the affordances of the electronic, digital format.

Inspired by enterprise information lifecycle management schema and Floridi’s (2009, 2010)

information lifecycle descriptions, I present below a pattern of the different stages of personal data

use across QS practices. Unlike traditional lifecycle diagrams which often have a stage dedicated to

“consume and use,” I argue that each stage in the lifecycle represents a use of data in the QS

practice.

Figure 4. Personal Data Use Lifecycle.

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The stages are necessarily sequential, but not all QS practice follows each step all the way

through. For example, one could make changes based on the observed patterns noticed in the

collection stage without having to go through digitally-mediated analysis to enact those changes.

This framework reveals what is new and different about each step as it is enabled or not by

digital affordances. Self-tracking is nothing new, but when the tracking is collected digitally, stored

digitally, analyzed digitally, the role data plays as a digital object becomes more clear. The

potentiality of digital data lies at the heart of the novelty of QS practices that inspired the label in

the first place.

Take the artist’s4 self-tracking in a Word document. In reviewing her data, she is limited by

the format in which the data currently exists. She suggests she might switch to an Excel spreadsheet

to allow her to run an average of her mood highs and lows for the month. This illustrates the

distinctions between first-order digital and second-order digital data. In the Word document, there

is no doubt that the data is captured in a digital format, but it does not set her up to take full

advantage of the digital affordances in further stages like analytical use of that same data. Having

mood ranges in Word only allows for a first-order (analog) analysis of the data.

Deciding what to track is almost as important as the tracking itself. Christopher John

Payne described this as identifying the things that are priorities, that fascinate him, and that are

somehow important to his life or his livelihood. Robin Barooah described it as an elevation of

attention: “The act of deciding to quantify something is this act of elevating a specific

environmental factor into our cognitive meaning process.” This harkens back to Buster’s

provocation that we only track the things that we would want to look back on from our deathbed.

Looking at what people track, and what they do not track, reveals a deliberate focus of attention

and energy, particularly where active tracking is required.

Creating and collecting data varies across QS practices, the largest distinction being

between active and passive tracking. Active tracking requires effort on the part of the tracker to log

and notate the thing they are tracking, either in an app or a spreadsheet or even a Word document

or paper note card (LaGatta, 2013). Here, the tracking is manual. Passive tracking makes use of

4 She prefers to remain anonymous.

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technologies like sensors or background processing on a computer to programmatically create data.

Active collection is inherently an analog process; it requires the conscious action of the individual

tracking to create data. Passive collection is inherently digital, in that individuals can outsource data

creation to sensors or use traces that are already digitally created. Stan James views his computer

like a sensor device; without much effort, it can tell him how many words a day he typed or how

often he switches between programs. Whether actively or passively collected, awareness of tracking

or the act of tracking influences behaviors. Active tracking can take a lot of effort and may seem

extreme to outsiders, but there is some benefit to the process. As Stan describes it, “just making

you think about it forces some reflection.” Some argue the future of consumer self-tracking lies in

passive devices and apps like Human, Saga, and Moves that generate loads of data without much

effort on the part of the individual. The trend is moving towards simplifying and automating the

data collection process to make this more accessible to consumers, but there are tradeoffs in

favoring simplicity over control.

Data is stored and archived. For some this is as analog as note cards in a shoebox

(LaGatta, 2013). Christopher describes his list making and note taking as a process of “encoding”

his memories, outsourcing his memory to the computer. He writes, “One of the most important

lessons I have learned in life is to write things down: if I don’t write my ideas down when I have

them, I generally forget them” (2013). He lists everything from goals, to people he can turn to for

advice, to his favorite “Desert Island Books” in what he calls “My Life Squared.” Neil Bachelor

keeps an ongoing database of his lifelong learning efforts. Whenever he reads an article or a website

he finds particularly useful, he uses a bookmarklet to save and tag that page. Stuart similarly keeps a

running list of memes or ideas that he spends time thinking about to track their impact on his

health and wellbeing.

Christopher uses Spotlight on his Mac to search his notes and lists to recall the

circumstances of the first time he met someone. Mark Carranza (2009) describes his MX project’s

value as providing a “serendipity that’s astounding and pleasurable” of “remembering something

that you thought that you knew.” The same sense of uncovering and recollection occurs with tools

like Timehop that re-present archived data from social networks as “this day in history” (Watson,

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2013a). Christopher also reviews his lists, in printed format: “My ‘self’ is different when I’m typing

at my MacBook Pro vs sitting on a chair with a red pen so I have different thoughts and ideas.”

One of the novel affordances of digital personal data is the ability to integrate data sets to

uncover patterns in the analysis phase. Activity tracking tools are starting to build connecting pipes

between sources like MyFitnessPal for diet and Fitbit for steps and exercise. When Tom notices an

interesting pattern in his performance data, he looks back on his email to say “okay, this is what I

was doing on that day.” But for Tom, this is still a manual, analog process. Adriana describes the

frustration: she would love to see how travel impacts her reading habits, but right now it is hard for

her to do something as basic as taking data from her Kindle and matching it to her calendar.

Integration of disparate datasets is one of the near-future promises of personal data, but data silos

often stand as a barrier.

Analysis is arguably the most important step in processing and drawing meaning out of

data. Time-series visualizations reveal patterns and outliers in the data. For many, making data

legible in this format is adequate to derive meaning. Others look to more advanced tools and

statistical methods to test correlation hypotheses and reveal significance. For example, Neil is

interested in using novel visualizations like weighted shadings to show the distance from the

standard deviation within a data set.

What individuals learn from the data can affect or change behavior. This ties to the “What

you learned” prompt in the standard QS Show and Tell script. Robin notes in his food tracking

practice, once he developed awareness about how certain foods like gluten affected him, he no

longer needed to track because he had learned enough to change his diet. Outcomes from QS uses

of data are almost entirely analog. They affect bodies, mental states, mood, habits, and behaviors.

These changes are enacted in the world of atoms.

Data is not only useful in the present, but could be stored and analyzed for future use. As

Alexandra Carmichael (2008) suggestions, “‘I don’t like to throw anything out because I never

know when I’m going to need it’...I agree when it comes to data. I think it’s wise to collect as much

information as we can and figure out what to do with it later.” For some, creation and future use

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might be the only steps in the lifecycle, where data is collected for data’s sake. Even when there is

no clear present use, there is always a potential future use.

Even with a presumed focus on digital affordances, current QS practices run the gamut

across analog and digital uses of data. This demonstrates the importance of understanding and

engaging with the range of uses at each stage, and classifying them as an analog or digital uses. For

example, journaling is the same activity at the “collection” stage regardless of the medium of

choice, whether in a Moleskin or in a journaling app like Day One. The process of daily reflection is

still an analog one of writing. But the data potentiality of the digital version is created and can be

used further along the cycle. Unlike a Moleskin journal, an individual can query search terms, or run

semantic analysis throughout the entirety of the journal. The digital nature of data affords these

uses.

Understanding the range of both digital and analog uses of data within the early-adopter

community reveals that although these individuals are often technically skilled, they do not

necessarily engage with their data in the most advanced ways. They derive value from the data, even

in analog uses. This has broad implications for normalizing these behaviors and illustrating the

potential value non-technical consumers may still derive from their data, once made available and

legible.

3.2 BARRIERS TO PERSONAL DATA USES

The data concerns of the Quantified Self community illustrate the current challenges and

limitations that confront individuals wishing to make use of their own personal data, particularly

when it is created using proprietary apps that store data in the cloud. This set of findings addresses

my second research question. These findings were initially presented in my first stage of research

and are expanded here.

Some data challenges center around the technical requirements of data, addressing

current limitations on the portability of data from QS applications and tools, and the frustrations

with following commercial tracking standards that do not necessarily match the way individual wish

to track themselves. These concerns are expressed in technical demands, such as requiring a comma

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separated value (.csv) standard for data export. For example, “I want it to be exportable: I can play

with it on my own system” (Wolf, 2009b). These concerns also address the problem of data silos: “I

am not interested in a device that uploads my data to a silo where it is irretrievable, or retrievable

only in summary form” (Nevins, 2012). APIs are of great concern; an entire breakout session

enumerated some of the current problems: “No API, or incomplete APIs that exposes only

aggregate data, and not the actual data that was recorded” (Jain, 2013). If data is kept in walled

gardens that firms and app makers control, then the digital affordances of the analysis stage are

foreclosed. This barrier to current personal use has direct implications for policy provisions

stipulating rights to use and portability in a usable format. This also has commercial and design

implications for companies like Google and Facebook that closely guard their personal data assets.

Other barriers to data uses surface around skills required for deriving meaning from data.

This is often expressed as a frustration with a lack of statistical analysis or programming skills for

uncovering interesting correlations across data sets; it is also expressed as a frustration with existing

analysis and visualization tools. These concerns are starting to be addressed with tools like Wizard,

Fluxstream, and Tictrac, which aim to remove the burden on individuals in drawing conclusions

across diverse data sets. This is an area of great importance as more tools are introduced to the

personal data ecosystem and as consumers of varying skill levels begin to take advantage of the

digital affordances of their collected data.

It is worth noting these data concerns and limitations are so problematic in current practice

that some do not bother to track, based on an ideological position that the individuals’ access and

rights over data are not adequate. Adriana admits she does not track anything anymore because she

cannot track on her terms. But in organizing the QS community, she hopes to change that: “It is

currently the only living, breathing part of the web where people are doing things for themselves.”

These pressure points and barriers to use highlight the need for updating regulation and policy that

centers on individual interests in uses of personal data.

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3.3 METAPHORS AND ANALOGIES FOR DATA USES

The metaphors individuals use to describe their relationship to data reveal conceptual

models used to make sense of novel data use in their lives. These findings address my last research

question about how individuals describe their relationship to personal data. A typology is drawn

from the content analysis of the web and interviews, and also draws heavily on discussion from the

breakout discussion I led at the Europe Conference this May.

There is no shortage of metaphors in the discourse about data. This is due in part to the

challenge of talking about and understanding data in concrete terms:

Bits behave strangely...We have to use physical metaphors to make them understandable...The Internet was designed to handle just bits, not emails or attachments, which are inventions of software engineers. We couldn’t live without those more intuitive concepts, but they are artifices. Underneath, it’s all just bits. (Abelson et al., 2008)

Data is so abstract, so binary, so invisible and mechanical, people come up with familiar media and

technological analogies to make sense of it.

Data has been described as the new “oil,” a natural resource that Big Data firms are in a

position to refine and profit. This metaphor removes the individual and relegates them to an

inanimate producer of minable value. Other metaphors liken personal data to a new “asset class”

(World Economic Forum, 2011) which gives some indication of value, but still has the potential to

alienate the people about which the data refers. Personal data has also been described as an

exhaust, eliciting some polluting image of a byproduct of our transactions. It has also been called a

shadow, indicating vague impressions of our physical embodiment, tied to us, but without

substance. All of these metaphors point to an intangibility and obscurity in data that serves to

separate the data from its human source. These metaphors in the public discourse are dissatisfying,

so I sought to uncover the ways people in the QS community frame their personal uses of data.

Some are mechanistic, others more poetic.

This analysis draws on Lakoff and Johnson’s (1980) notion of “conceptual metaphors,”

based on the idea that human thought processes, and not just language, are metaphorical. When

Robin says that data “creates a construct that I can actually step back from and reflect on,” it reveals

an underlying conceptual model of a mirror. I position pre-existing technologies, media, or tools as

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“source domains” and data as a “target domain.” This analysis is also inspired by Bolter and

Grusin’s (2000) remediation theory that suggests new technologies and media often take on the

familiar characteristics of old interfaces and visual metaphors to help users adjust to novel

behaviors and consumption patterns. The often-cited example is the carryover of the theatrical

proscenium arch as a visual cue for moviegoers to relate to and compare the cinematic experience

to the familiar theatrical precursor. In interface design, this has been described as skeuomorphism.

Analogies reveal how these tools for self-awareness fall into historical patterns of use. Where these

metaphors fall apart, we uncover the novel digital affordances of data.

The macroscope has been used as a metaphor to describe the detail one could collect

using sensors to collect data (Wolf, 2011a-b). This was in scalar opposition to the microscope

looking at the cellular level, and the telescope looking at the universe. But, as Joshua suggests, the

metaphor has been set aside because it implied individuals were also building the tools for self-

examination. The scientific tool metaphors also ran parallel with an early focus on experimental and

scientific-method approaches to self-knowledge that characterized early descriptions of QS practice.

Tom uses a more mechanical analogy to describe his use of data: “I’d call it a burnout

monitor...I’m working extremely hard at the moment, I’m pushing myself. So it’s like I’m making

sure I’m not redlining...It’s really a lot like an RPM in a car so I’m making sure I’m not about to

explode.”

Others take a managerial approach to the role data plays in their lives. For Christopher,

the Tayloristic approach to “managing what you measure” ties directly to his livelihood. Robin

describes this as a “currency of authority and credibility” that enables individuals to speak about

their personal experience through numbers. For others it is about introducing objectivity and

externalizing a subjective experience. Buster argues that data is most useful when it is uncurated in

its ability to tell you things about yourself that you would otherwise ignore or miss. In these cases,

individuals are using data as a check on intuition.

The mirror analogy is useful but fraught. Robin suggests the metaphor is not accurate

because a mirror reflects back everything in the environment, including the things we could not

possibly model at this point given practical and technical limitations. The data is far more grainy,

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bitty than what is shown in a mirror. There is also something self-consciously performative about a

mirror. This is in conflict with desires to uncover objective data about one’s behaviors. Stan’s

webcam script shows him what he looks like when he is not paying attention: “Lifeslice is reflecting

back to me that I am the kind of person who spends a lot of time with my computer in bed.”

Others draw the analogy of a rear-view mirror (Baresi, 2013), based on the collection of historical

time-series data that gives you a sense of past metrics, but not necessarily future-oriented data. And

of course the mirror metaphor brings up the question of narcissism and vanity.

Figure 5. Stan James’ Lifeslice aggregate view of hourly webcam portraits.

Others offer the portrait metaphor, in its interpretive, artistic representational mode. In

explaining the granularity of current QS data, Eugene Granovsky imagines a portrait with bad

lighting; I offer maybe it is not currently a photorealistic portrait but rather an impressionistic one.

Natasha Dow Schüll suggests it might be a pixelated portrait, represented in bits. There are some in

the QS community who experiment with literal self-portraits, like Stan’s webcam project, or Sharla

Sava’s (2012) 365 Days of self-portraits.

The narrative analogy, that is, telling a story or building an autobiography with data,

seems to be an apt metaphor. Buster describes it as a “story that can be picked out of the data.”

The structure of the Show and Tell format for Meetups is inherently narrative. This coincides with

a behavior I have observed in numerous Show and Tell presentations where individuals present a

time-series graph annotate outliers and inflection points in the data with important details from

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their lives. These contextual details are internal and not contained anywhere explicitly in the

presented data. Narrative is internalized, but that the data is used as a prompt for expression. For

example, Robin pointed out peaks and troughs explain how his father’s death impacted his

journaling practice; Buster pointed out where his son was born in rising slope of his unread emails.

Others have described their relationship to data as supporting a practice, much like a yoga

or meditation practice. In some instances, they are literally self-tracking to support meditation, but

others describe this practice of self-tracking as a tool for heightening awareness in specific areas of

their lives. When Stan moved to a new city, he started tracking his “anchor habits” of meditating

and writing 750 Words a day. This ties back to the importance of choosing what to track as a means

of “elevating” attention, intention, and awareness.

Others describe data as a means for “disaggregating” the self. As Joshua put it in our

breakout session:

The Self is very overwhelming, over integrated. Doing QS you can disaggregate various aspects of yourself, work on one aspect, revisit it...I think it takes the incredible burden of having to contemplate the totality of our self-existence to take these small slices and out and say...let’s just look at this.

This disaggregation of the self seems to parallel Martin and Barresi’s (2006) characterization of the

dissolution of a unitary self in favor of hyphenated selves. In this case, only parts of the self can be

quantified, but in that process those parts become knowable and comprehensible.

Each of these analogies and metaphors describe a use of data by comparing it to a similar,

preexisting tool for self-knowledge. The Buddhists use practice to attain greater self-awareness, and

self-narrative has been around through the ages, even in pre-literate times. Mirrors might be the

oldest technology for externalized self-knowledge when Narcissus turned a lake into a tool for

reflection. These analogies draw out a genealogy of self-knowledge technologies self-tracking

practices (Bolter & Grusin, 2000).

This also begins to normalize QS behaviors to suggest the ways self-tracking patterns

continue existing analog patterns and behaviors like journaling, list-making, self-portraiture, etc.

These behaviors then begin to seem not extreme or queer, but a natural extension and evolution of

a process of self-awareness.

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4. DISCUSSION

4.1 KNOWING OURSELVES AS DATA SELVES

Through an account of uses, barriers to use, and conceptual models, I have presented the

ways individuals are using data as a tool to create meaning and to construct a notion of the self. As

Floridi (2011) posits, “ICTs are, among other things, egopoietic technologies or technologies of self

construction, significantly affecting who we are, who we think we are, who we might become, and

who we think we might become.” We can know ourselves in new and different ways when we

engage the novel, digital affordances of data. But what does it really mean to know ourselves

through data?

I began this work with a suspicion that there could be something dehumanizing about

understanding the self only through data, that “self-knowledge through numbers” was inherently

reductive. Wolcott poetically echoes this concern:

Skeptics worry that data harvesters will induce passivity and wan alienation, cocooning compulsive self-trackers inside their feedback loops and subtracting emotion and serendipity from the human equation—the poetry, the ambiguity, the moonbeams in a jar. Thereby reducing life to one long flowchart or, if you’re a more journalistic type, charticle, with death setting the margin tab. (2013)

The worry that self-knowledge through data is reductive aligns with a critique that an empirical,

positivist understanding of the self is incomplete and inadequate. But embedded in that worry is the

presumption that this data becomes the only means for constructing self-knowledge, at the

exclusion of others.

This potential reductive use of QS data has been critiqued as a kind of automated

introspection. Stan pointed out there are certainly members of the QS community who want their

apps to tell them objectively how they are feeling “without doing the hard work of introspection.”

It is perhaps most acute when self-quantifiers daydream about complete automation of the tracking

process.

While reductionist concerns are founded, particularly as an uncritical public begins to adopt

these tools more widely, these critiques oversimplify current uses of personal data for self-

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knowledge in the QS community. One researcher noted the confessional nature of the

presentations at the conference: “these numbers seemed to constitute a language for

communicating experiences that are difficult to convey otherwise” (Zandbergen, 2013).

The more people I spoke to, the more it became clear people in the QS community were

far more critical and nuanced in their approaches to using data as a tool for self-knowledge. As

Madrigal (2013) hypothesizes, “People are pretty good, I think, at integrating what data they get

from the outside world with their own theories of life and experience.” As Robin told me, “The

data means nothing without the self. That’s why the QS movement is not just ‘Big Data.’”

People in the QS community are actively questioning the construction of fact. While the

Latin derivation of “data” “to give” suggests a fact taken as given, the QS community is not

necessarily accepting numbers, words, and images as they stand. They are critiquing the firms who

support the self-tracking process. They are challenging the notion of a “step” or “fuel points” to

which commercial activity trackers assign meaning. The core of the current QS community is

actively engaging with the more philosophical questions posed by the practice. Stan holds an

undergraduate degree in philosophy, Christopher John Payne cited inspiration from Seneca and the

Stoics, and Chris Ellis quoted to me his favorite passage from Heidegger’s Being and Time.

Embedded in this group are some of the practical philosophers of our time. They are navel-gazing

not in the narcissistic sense, but rather the original contemplative and introspective, omphaloskeptical

sense. And most importantly, they are talking through what it means to make data a part of their

everyday lives.

From Plato to Freud, the framing of the self has cycled through many binaries, from the

body/soul, to the material/immaterial, the objective/subjective, the noumenal/phenomenal, the

id/ego. The Quantified Self still contributes to this dualism, its counter a “qualified” self. When we

worry about things in life that could not possibly be captured as data, this dualist opposition

becomes clear. Still, the Quantified Self use of data might move towards a reconciliation of the

objective and subjective (Latour, 1993) view of the self. Data is provides an externalized view of the

self, but may only be truly rich and meaningful to an individual as it is inherently subjective and

contextual. Critics of QS methods often focus on the “unscientific” nature of the practice. As

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Robin points out, these critiques assume the goal of science is to remove the self and that the

problem is that the self is present in the data. Instead, the Quantified Self assumes the self. In this

sense the Quantified Self might reconcile and resuscitate the subjective from an epistemological

paradigm that favors objectivity, facts, and statistics over subjective truths. Perhaps these things can

coexist, and the Quantified Self might preserve the personal context in a world leaning toward Big

Data.

4.2 FURTHER WORK

In outlining the barriers and limitations individuals face that impede uses of personal data,

we see where current policies and architectures do not meet expectations. More broadly, we are

beginning to see how hard it is for individuals to gain access to the data that we know exists about

ourselves (Singer, 2012, 2013). As Mayer-Schönberger & Cukier (2013) warn, “The age of big data

will require new rules to safeguard the sanctity of the individual.” We need empirical approaches

such as the one presented here to understand how expectations are changing alongside technology:

“Without a coherent conception about the nature of a person’s interest in personal data, it is

difficult to design a legal regime to protect this interest appropriately” (Samuelson, 2000). Wherever

data access, portability, and standards pain points lie, we should recognize the need for updates to

policies to address and protect the evolving needs and expectations of individuals.

At first glance, concerns about portability and usable formats of personal data seem to

suggest established property rights framings of privacy (Samuelson, 2010). But these framings do

not align with the technical realities of how data is created and copied and exists as non-rivalrous,

non-exclusive, and alienable (Varian, et al. 2004). Traditional treatments of personal data interests

employ privacy as a negative right, preventing harm or excluding use, rather than supporting an

individual’s positive right to use of the data that refers to one’s person. We are beginning to see

data privacy protections against harm shifting from collection toward uses (Cate & Mayer-

Schönberger, 2012); the positive liberty pairing to this might extend rights of use to individuals.

Though it is beyond the scope and space of this thesis to build a legal argument here, my

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observations suggest a need to update policy towards a positive right of use of personal data to

protect individuals’ interests in their personal data.

Where I have uncovered gaps in skills for deriving meaning from data, we recognize

further opportunity to address those gaps. We all had to learn how to use computers when they

entered our homes, and how to get online when the internet became social. We will have to do the

same as data becomes more personal. If we do not understand what metadata is, how can we form

an opinion on what a reasonable expectation of privacy for metadata should be? Without a working

understanding of how we might use and derive insights from our personal data, there will not be

enough demand to change existing policies and standards.

The QS community is largely technical and thus has access to the tools and expertise to

explore the limits of what is possible. But as we have seen, individuals in the community derive

insights even in basic analog uses of data. In order to take full advantage of the digital affordances

of this data, we have to address this skill gap. We will need to develop new data literacies to adjust

to and participate in the data-driven world. I am hopeful emerging platforms like Singly Data Fabric

and Tictrac will make integration and analysis more accessible, and tools like Immersion will begin

to expose the stories in our data. With its new Graph Search query suggestions, even Facebook is

teaching us to see ourselves as “data subjects” (Watson, 2013c). With these tools in place, there can

be greater pressure put on firms to release individual data in a usable format to feed into these

consumer-friendly management dashboards and interfaces.

An ethical question remains: what happens when our digital self becomes part of a

recursive feedback loop? Without visibility to and active engagement with the digital

representations of ourselves, we have no means to resolve digital feedback loops with our analog,

subjective experiences and intentions. Most consumers exist in this state right now; cookies track

our behaviors, and we are algorithmically judged and acted upon based on those data points. We do

not know exactly why certain friends and not others show up in our Facebook feed, or why ads

seem to creepily follow us around the internet. If consumers continue to operate blindly in this

environment, then we can never hope to have a working knowledge of our digital selves in the

context of Big Data. Self-tracking might give us a means for acknowledging, understanding, and

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engaging with the data that exists about us. We stand to become more empowered and engaged

human beings in a data society.

5. CONCLUSION

As Big Data becomes standard practice and more sensors enter into our homes, cars,

devices, and bodies, data proliferates. This will happen whether or not we are actively engaged in

the creation and uses of data. As such, we are all becoming quantified selves. We have a

responsibility in this emerging data environment to recognize and engage with this fact. If we

ignore this reality, we risk losing our agency. We cannot engage with the means through which

systems, platforms, firms, and even other humans understand us in a digitally-mediated

environment. If we understand the affordances and potential uses data, we can begin to make

conscious choices about how we use data for self-knowledge, and how we participate as data selves

in a Big Data world.

A positive understanding of potential uses and insights of personal data offers a step

towards empowering individuals to engage with their personal data. But we need better metaphors

and frames to familiarize and make sense of our new roles in a changing data society. These

metaphors might come out of the QS community, or they might come from activists, or industry,

or critics writing to make sense of these changes. For now, we need to start working towards what

it means to integrate personal data into our everyday lives, and what it means to be a “data self.” As

Biohacker David Asprey projects, “I don’t know if we’ll be calling any of this the quantified self in

50 years...We might just call it being human” (Kahn, 2013).

36

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APPENDICES

APPENDIX A. GLOSSARY OF TOOLS AND APPLICATIONS

.csv Comma separated values. A non-proprietary, plain text file for storing spreadsheet and database information.

750 Words Webapp built by Buster Benson to encourage writing 750 words per day. Writers are rewarded with badges for streaks. Interface offers statistics on semantic content analysis.

API Application programming interface. A specification that allows applications to talk to one another and share data.

Beeminder Goal and habit tracking application with financial disincentives.

Day One A cloud-based smartphone journaling application.

Fitbit Wireless activity and sleep tracker and related fitness tracking application.

Fluxstream Open source personal data visualization platform.

Github Web-based hosting service for programming projects that supports version control and forking of open source code.

Human Smartphone app that uses GPS and accelerometer data to passively monitor activity and encourage 30 minutes of activity per day.

Immersion Web interface from MIT that securely visualizes gmail metadata, social networks, and history.

Lifeslice Script created by Stan James to capture webcam images, screenshot, and other data every hour schedule.

Lift Web and mobile app designed to track habits, streaks. Supports community encouragement.

Meetup.com Online social networking portal facilitating in-person meetings around the world.

Memoto Small wearable lifelogging camera, funded by Kickstarter, that logs pictures every 30 seconds.

Moves Automatic smart phone activity tracker that classifies walking, cycling, running, etc. from sensor and location data.

MyFitnessPal Calorie counter and food database that integrates with many other health

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tracking applications.

RescueTime Time management and productivity software that logs computer applications and web browsing activity.

Saga Automatic lifelogging activity tracker for smartphones that tracks location and allows annotation with images and social media feeds.

Singly Provides a platform for unifying data streams from apps and devices.

Spotlight System-wide desktop search in Apple OS X operating system.

Tictrac Platform for data integration from health and social data streams.

Wizard Point and click statistics application for Mac.

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APPENDIX B. PARTICIPANT SNAPSHOTS

Given space constraints, I present here brief biographies of my participants detailing their Quantified Self practices and their backgrounds. These are meant to provide context to my thematic findings and to expose the deeply personal and individualized ways that QS practices vary across individuals. Biographies are presented in order of when the interviews were conducted, though conversations continued over multiple interactions at Meetups and the Europe conference in May. Adriana Lukas is the organizer of the London Quantified Self Meetup group. She feels that QS is one of the few places left on the web where people are still “doing things for themselves.” She likens it to the early days of blogging where individual voices were paramount, which she laments has now turned into siloed platforms, replacing blogs with more constrained social media. She thinks we need new mental models and architectures to support an individual-centered internet. She related to me how important mental models can be; her email signature illustrates this: “The network is always stronger than the node...but a network starts with a node.” She has played around with QS tracking devices and apps, but she does not track anything herself right now because she feels too limited in what she can do with the data and she does not have the tools she needs. To start on that path, she is organizing a working group made up of individuals within the London QS community to build open solutions that put the individual at the center of technology. She takes a strong ideological stance on the importance of autonomy of the individual; she contributes to a blog “developing the social individualist meta-context for the future” called Samizdata. Adriana practices yoga and is experimenting with the Intermittent Fasting (IF) diet. Robin Barooah is a developer in the Bay Area, though he spends some of his time in London as well. His opening presentation on mood tracking at the Amsterdam conference along with Jon Cousins set the tone for sharing and vulnerability when he talked openly about depression, the death of his father, and a controlled experiment with MDMA. Robin has tracked a wide range of things, including his diet after he had gained weight adjusting to a new lifestyle moving to the US. He has also shared his experience of weaning himself off coffee. He has also experimented with keeping a shared journal over Google calendar with a friend. Robin has built a number of minimalist applications, including a private location-sharing app LocationSwap that does not require accounts or passwords and keeps no data on his servers. He has also developed Mindful Browsing and Equanimity to track and encourage a daily meditation practice. Robin talks a lot about mindfulness and intention in his QS practice; his personal URL is sublime.org. Once he has learned something from a project, the data is not as important as the awareness or knowledge that it leads to. Robin does not necessarily process data in quantified ways, for example he described how he relied on his brain as a processing, meaning-making engine to recognize a pattern that suggested gluten might be making him tired. He calls this “embodied learning.” He tweeted: “All data are pointers to things that exist within the human mind.” Robin wears his dark curly hair long past his shoulders and often wears a OnePiece jumpsuit. Chris Ellis describes himself as an “ethical entrepreneur,” and is based in London. He is building a future-facing CV to give people the tools they need to become what they desire to be. The landing page copy asks, “A year from now, what will you wish you had started today?” He sees a systemic

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problem in the way recruitment and job placement happens today, focusing on past success rather than potential. He is a self-taught philosopher, and referenced Plato, Merleau-Ponty, and Heidegger throughout our conversations. He is also deeply engaged in Bitcoin and other Crypto currency developments and maintains a newsletter on the subject. He describes his lifestyle as minimalist, citing his computer and his bike as his only essential possessions. Chris tracks physical activity with wearable sensors. He also programmatically keeps track of all the things he reads: “My Evernote Archive currently stands at 5k entries and rising on everything from Quantum Physics to Ancient Athenian Democracy.” Chris is participating in the London Working Group to help build more tools to address some of the data challenges that currently face the QS community. Christopher John Payne is a life coach and entrepreneur, based in Woking, UK. He got started in the direct-mail marketing business and now helps people develop and market their own “information products.” His biggest self-tracking project he calls My Life Squared, which exists as a printed, spiral-bound binder full of lists and statistics about his business and personal accomplishments. His lists cover the total number of books he owns (1,300 nonfiction), to the countries he has visited (22), to his favorite desert island books (Thinking Fast and Slow), to inspirational movies (50 First Dates and Memento). Christopher describes his process as a means of “encoding” his memory, so that he does not forget anything. And everything data point is a potential product, lesson, story, or insight. He is interested in meta-cognition, and also talks about fighting against “lizard brain.” Christopher talked about the importance of narrative and parable in its role in learning and self-realization. He draws inspiration from the Stoics. Christopher’s lists exist on his computer, but it is also important to him to sit down with a cup of tea and a red pen to reflect on and update it on a regular basis. Christopher talked about looking back on My Life Squared if he only had 24 hours to live and felt confident that it would show him he had lived a full and happy life. Approaching every interaction as an opportunity for creating value, Christopher asked to record our conversation to share on his website. Neil Bachelor is a psychometric evaluations assessment consultant specializing in question design. He is also working on productizing Omnifolio.org, a platform for recording lifelong learning. This is an extension of his lifelong learning QS project where he captures things he reads or finds important, and then classifies them using a natural language processing tool that suggests tags based on the content. He thinks tools like this might be better at showing what people know than a CV is capable of conveying. He is interested in novel visualizations, including word clouds that show strength and different classifications of learning, or treemaps that show nested associations. Neil believes in open solutions; where he can, he uses open source software and wants to contribute non-profit solutions. Buster Benson is a developer and has worked on building tools that help people change habits and lead better lives. He now works at Twitter, but previous projects and products include 43 Things, Habit Month, and Peabrain. He also built 750 Words, which encourages writers to build a morning habit of writing at least 750 words (roughly three pages) every day. There are monthly challenges and badges for building streaks of numbers of days in a row. He used to lead the Seattle QS Meetup group before he moved to San Francisco. According to his bio, Buster is also “Singularitarian, humanist, ENFJ, doodler, self-help junkie, romantic-comedy obsessed, self-published novelist.” He has legally changed his name twice: he used to be Buster McLeod, and before that he was Erik Benson. He regularly updates a Github log of his manifesto for living and his core beliefs. Since 1999, Buster has posted “35,005 bits of nonsense” on the internet. As of this writing, Buster has 397 unread emails in his inbox.

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Stan James is a freelance developer based in San Francisco. He was previously based in Colorado where he founded Lijit, built out of his masters thesis on online trust through clickstreams. He has worked on projects like Wordnik and helped the QS Labs team develop a Sparktweet to visualize QS data in tweets (inspired by Tufte’s Sparklines). Stan is interested in the relationship between humans and technology, and runs a podcast The Seventh Kingdom that explores some of these questions. He has an undergraduate degree in philosophy and studied Cognitive Science at the University of Osnabrück, Germany. Stan presented his Lifeslice project at the Amsterdam conference. Lifeslice uses his computer’s webcam to take a snapshot of his face every hour, and also captures a screen grab of what he’s working on, along with his GPS location. Looking at the dataset, he realized how much time he was spending on his computer and has made changes to better match his intentions. He now has programmed his internet to turn off at a certain hour every night. Stan also uses a habit tracking app called Lift, which he started using when he moved to San Francisco, wanting to build “anchor habits” of meditation and writing 750 Words (also using Buster's webapp). His longest running streak right now is 328 days of meditation in a row. Stan is also interested in analog pursuits: he practices calligraphy and posts pictures of his “Word a Day” creations on Instagram. He also sends postcards to friends in different countries on a regular basis (one of which I was lucky enough to receive here in Oxford). Stuart Calimport is a biotech postgraduate researcher. His Human Memome Project looks to identify the ideas and memes that are most healthful in supporting longer life. He asks, “How far will your memes take you.” Stuart keeps a running log of ideas that enter his head, or that he comes across in reading, and then rates them as healthful or unhealthful. He does not want to spend time on unhealthy thoughts or ideas; he wants to live “optimally.” Stuart does not have much use for privacy. He suggests that if we just share all the data, we would favor ethical uses of data and put those who use the data for unethical purposes at a disadvantage. He thinks that ethical uses of data in some way all lead towards prolonging life. Transhumanism is a philosophy that guides his actions; he wants to transcend the things that are not longer useful to humans. Stuart is interested in immortality, and links this back a memory around age five of being dissatisfied with his parents’ explanations of why people have to die. Stuart also tracks his activities using fitness applications like Fitocracy, Nike Plus, and Strava. The Artist I spoke with chose to remain anonymous. She works in mixed media, and said she always has a number of projects going on at any given time. She got into QS because she and her boyfriend shared an interest in self-improvement and productivity. She attended her first meetup in April because her boyfriend had suggested she come along. She expressed disappointment because she felt the discussion focused too much on apps that were all built for iPhones, rather than talking about self-improvement. She prefers to keep her basic Nokia phone. She tracks some things in pen and paper, color-coding between work priorities and simple errands. She keeps a daily log in a Word file, and also tracks her mood for the day. She started off tracking a number 1-10 for her mood, but decided that it was not detailed enough so she started tracking a high and low range for the day. She does not review her data in detail often, but she wants to do a review once she has a year’s worth of data. She also wants to start tracking in an Excel file so she can do more with her mood data. She wants to keep control over her data; she explained that she would not use a cloud backup program because she would not feel comfortable with someone else having access to the data. Tom Dawson is a human performance and sensor researcher and consultant, and founding director of Rescon. He is interested in applying performance tracking to public health. He aims to make tracking simple and effective for populations with language and literacy barriers. His bio

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states: “He firmly believes that performance advances and methodologies at the elite level can effectively be modified and applied to all individuals, enhancing life experience and minimising the morbidity of populations.” Current projects include addressing the impacts of homelessness in cities, and tracking diabetes in veterans. Tom came to a QS Meetup for the first time at the June event after one of his friends told him that his work aligned with the QS trend. Tom also tracks himself, and started keeping pen-and-paper training diaries as early as 1989. Tom is working on an app for self-tracking that includes taking a “selfie” at the moment of logging GPS and other details. He maintains that you can tell a lot about how you are doing from the look on your face. Tom says his tracking has become a lot more personal over time from the early days of his training logs; he never imagined taking a picture of his face before. Tom is interested in making some of his apps available to consumers. He noted that it is important to him they be open and noncommercial, but he also expressed the need to sustain his team and his research at the same time. Tom also keeps pet meerkats. Two other interviews were conducted for this research but they were more product-focused than person-centered so I have not included them here as biographical snapshots.

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APPENDIX C. INFORMED CONSENT FORM

INFORMED CONSENT STATEMENT

Personal Data Interests in the Quantified Self You are invited to participate in a research study to understand the personal data interests of the Quantified Self community. This study is in fulfillment of thesis dissertation requirements of the MSc in the Social Science of the Internet at Oxford Internet Institute. INFORMATION This study involves the following procedures: 1. Analysis of published content from the Quantified Self community in blog and forum posts as well as presentation video archives. 2. Interviews, either in person or via Skype. Interviews will be recorded and transcribed for the purpose of research and analysis. RISKS No risks are foreseen from this research. BENEFIT The benefits of this research are that it will contribute to a scholarly understanding of individuals’ personal data interests. The results of this research could potentially influence future app development, data policy recommendations, and even inform the public with frameworks for thinking about the utility of personal data. ATTRIBUTION AND CONFIDENTIALITY The information you provide for this research will not be treated as confidential unless you request that something you have told the researcher either be kept confidential or not attributed to you. In an effort to respect your contribution to the community and to the research, information you provide will be attributed to you unless you prefer to be quoted anonymously. CONTACT If you have questions at any time about the study or the procedures, you may contact the principal investigator: Sara Marie Watson, Oxford Internet Institute, 1 St Giles, Oxford, OX1 3JS, UK, +44 07583 190169, or by e-mail at [email protected]. The director of the OII, Helen Margetts, can also be contacted by e-mail [email protected] or telephone +44 01865 287210. AUDIO, VIDEO AND IMAGES Audio recordings and/or photographs may be collected during your participation in this research. This information will be used primarily for research purposes, and only researchers working on this project will have access to the original files storing this information. The PI, Ms. Watson, will retain all data when the project has terminated. If you withdraw from this study, the files containing your data will be destroyed. Edited versions of audio and photographic information from this research may be used in instruction, public talks, and publications of this research if you consent to your data being used in this way below. The images and audio will not be used for any additional purposes without your additional permission.

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CONSENT Please circle one response for each question: Yes / No I agree to allow voice recordings of my participation in this research to be used in presentations and publication. Yes / No I agree to allow photographs of me collected during this research to be used in presentations and publication. I wish to be attributed as _________________________________________ [NAME] or ANONYMOUS to quotes or excerpts presented in the research I have read this form and received a copy of it. I have had all my questions answered to my satisfaction. I agree to take part in this study. Printed name: ___________________________________ e-mail: ______________________________ Signature: ______________________________________ Date: _________________