Qualified Selves: Co-Creating Meaning Post-Big Data

Individuals are increasingly reliant on digital applications and services to store photos, documents, notes and other valued personal data. They are also accustomed to - and tacitly accept as a hidden cost of using otherwise 'free' services - these applications amassing activity data and metadata from which companies derive significant business value. For example, Facebook makes much of its £22bn yearly revenue by being able to precisely target advertisements to users by deciphering their unique preferences from their likes, tags, contacts, updates, photos, travel patterns etc (some accessed through permissions to Facebook via other apps) [BBC]. There is a highly lucrative, if shadowy, trafficking in users' data: data brokering companies such as Acxiom and Epsilon compile thorough dossiers on people's physical and mental health conditions, sexual orientation, personal vices, and vulnerabilities to aid companies in identifying likely consumers [CBS,SCH]. Meanwhile, there are no corresponding tools accessible for individuals to learn about themselves through their personal data.

Within recent years, there is a growing literacy around data as a medium for generating information and key insights. This is represented in the Quantified Self movement (see, e.g.: http://feltron.com), with individuals self-tracking their patterns of behaviour, physiological responses, productivity, correspondences etc with a view toward enabling personal reflection and gaining greater self-knowledge [LI]. Wearable activity trackers have been appropriated by some for self-diagnostic purposes: e.g. finding correlations between activities and symptoms to make informed changes to improve personal wellbeing [ROO]. There is untapped potential in applying this sensibility toward broader and deeper personal sense-making by drawing connections between the full diversity of one's personal data currently siloed in various services and applications - from the wide array of web services, to mobile applications, wearable and home IoT devices.
Personal Information Management (PIM) is a growing ICT sector with an estimated market worth of £16.5bn [NES]. Focusing on four major activities - keeping, finding, organizing and maintaining - PIM offers valuable insights into how to develop and sustain practices for effectively managing one's own data [KLI].

A particular challenge in developing PIM solutions is the individuality of lay data management techniques and strategies, which map onto people's individual strengths and familiar, established practices; in short, individuals thrive when they are able to develop strategies that work for them and for the particular goals they have defined. Given that many services ostensibly offer information management to users (albeit with pre-set UX constraints), an especially interesting frontier for extending PIM research lies in lifting data out from the applications that are currently managing them to support individualised, goal oriented collection and management of personal data - and further, offering techniques for managing between diverse data types (e.g. the minutia of metadata, narrative/textual data, photographic data, activity data, etc).

This project will fill several important gaps in understandings of personal sense-making, including: 1) in contrast to commercial ends for extracting, collecting and analysing people's personal data, understanding what kinds of self-knowledge would offer significant value to individuals, and how bridging personal data between applications and services might uniquely afford these personal insights; and 2) understanding how people can derive meaning from mixed data types and across applications, unbounded by the goal orientations of the individual applications or services they use to capture their personal data.

The research expects to make significant impact across a number of communities. Emerging technologies such as those related to the Internet-of-Things, smart cities and wearables are radically accelerating the amount of data that is produced and consumed on a global scale. To fully engage the general public in use of this data (and create more meaningful and informed collective decision making) it is critical that we create better methods of humanising this data, making it intelligible and meaningful for everyone. This project will contribute to this big challenge for the ICT community in three key ways: (1) We will address the non-trivial question of what does it mean to derive meaning from data? This is a major challenge for this community and one that defies simplistic quantitative metrics. Through ethnographic investigations and extended engagement with extreme and typical users of personal data we aim to contribute real insights into how people might engage in rich dialogues with data. (2) We will provide insights about how people can better make sense of large qualitative personal datasets by co-creating and testing tools and techniques. Critically, we anticipate that rich solutions to this problem may require simultaneous innovation relating to externalising data (e.g. visualisation, machine learning) and internalising data (e.g. storytelling, mnemonics) and this will be tested throughout the project. (3) We will develop new open-source technologies and techniques that may provide valuable insights for related application domains for emerging technologies.

Academic Impact:
The project contributes to academic impact in building research capacity in the area of Personal Information Management (PIM) and research toward the Co-Creation of Data Science tools. This is achieved by providing an approach to understanding how extreme users of data find meaning in the applications that gather, sort and represent personal activities. This research provides a unique opportunity in a Post-Big Data society to understand the methods required to allow data science to better support publics and individuals in making sense of data that is often obfuscated in the siloed databases of apps. Academic impact on researchers would be achieved through publications, capacity building workshops and exhibitions of prototypes.

Public Impact:
Public impact is achieved through the development of a unique set of workshops and participatory activities that lead to an understanding of how to co-create across and toward managing data for personal use. The co-created tool kits from these sessions will offer usable platforms for publics to begin developing an ownership for the data that they have been gathering since they joined applications such as (for example) Facebook, Evernote and Strava. Public impact will be achieved through a series of high profile exhibitions.

Policy Impact:
The Policy impact of this project lies in informing existing models toward a data driven society through methods of co-creation and developing viable tools to allow policy makers to encourage publics to take control of data. By creating methods and tools that empower the public to design with personal data, the project cultivates a civic approach to data science. Supported by an advisory board, white papers will provide policy makers with access to the research findings whilst the use of workshops and exhibition will offer platforms for learning and co-creating the next generation of policies toward a data driven society for personal information management.