There has been a great deal of interest within the CHI community in technologies that ‘collect relevant information for the purpose of self-reflection and self- monitoring’. They have been the subject of 5 previous CHI workshops – about personal informatics (PI), ‘Quantified Self’ or self-tracking. The driving concern for these workshops, and many existing PI tools, is to offer people rational ‘self-knowledge’, motivate behaviour change and monitor health as a means to improve wellbeing. However, as living a quantified and ‘data-driven life’ becomes increasingly possible, as our lives become part of the Internet of Things, we argue that we should think more broadly about a multitude of interactions and experiences with data. In particular, we suggest that while a focus on behaviour change and health is well motivated, designing solely for a rational relationship with data, potentially occludes many other rich experiences.

We propose an interactive workshop to look beyond personal informatics. Through panel discussion and a speculative design approach, we hope to broaden and remap a design space, considering more holistically, the situated experience of a data-driven life.

We adopt an experience-centred design perspective, addressing a call to consider ‘Lived Informatics’, and the ‘felt life’ of data ‘ as it becomes ‘enmeshed with everyday life’. As a starting point, the workshop will address the following themes or boundaries, which we suggest have so far been seldom crossed or recognised in existing PI research.

1) Moving beyond the individual
As data proliferates in our everyday lives, we question how it will move beyond an individual concern and become inevitably enmeshed with the lives, and data, of others – e.g. partners, children, colleagues, and employers.

2) Designing for dynamic trajectories of data
We contend that the value of personal data can evolve, gain or lose meaning, beyond a first impression or initial use. As personal informatics tools become embedded and networked, and personal circumstances change, what alternative trajectories of data could design support, beyond Li et al.’s five-stage model?

3) Representing and interpreting data in new ways
With the principle aim of rational self-analysis, we note that many QS tools tend towards representing data numerically, in charts or graphs, with an emphasis on comparison, progress or reaching set goals. What new experiences and interpretations might be offered by alternative representations and enactments data?