Melanie Swan: Big Data and the Quantified Self

Melanie Swan is visiting National Consumer Research Center in Helsinki. Swan is a Quantified Self and Big Data Research Principle at MS Futures Group, Palo Alto, California. Minna Ruckenstein is hosting the visit and served as the chair of the invited talk by Swan. The title of Melanie Swan's talk was "Big Data and the Quantified Self".

According to Swan, a key contemporary trend emerging in big data science is the quantified self (QS). The quantified self refers to the activity in which individuals are engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as individuals or in groups. She covered a number of QS projects and tools including Personal Analytics COmpanion (PACO) and MIT Body Track as well as closely related developments such as internet of things that contributes to the explosion of big data.

Swan discussed various topics in Personal Health 'Omics' and reminded that the fastest growing area in the big data area is human biology-related data. One possibility emerging from the developments is shifting from reactive to active. An opportunity is QS Data Commons where Github has emerged as the de factor platform. Mental performance optimization (mood management apps, etc.) and quality of life development belong to the current QS frontier. As a means for behavior change, Swan discussed Shikake that are sensors and actuators embedded in physical objects to trigger a physical or psychological behavior change.

Big data opens up new methodological opportunities. A traditional example is Google in building services based on unsupervised machine learning modeling of vast text collections rather than relying on traditional artificial intelligence approach. Swan mentioned a number of contemporary topics including:

• foundational characterization (longitudinal baseline measures of internal and external daily rhythms, normal deviation patterns, contingency adjustments, anomaly and emergent phenomena)
• new kinds of pattern recognition
• multidisciplinary models (turbulence, topology, chaos, complexity, etc.)

An interesting these was building exosenses for the qualified self. This leads to extending our senses in new ways to perceive data as sensation. Exosenses serve as quantified intermediates.

In the second part of her talk, Swan concentrated on the social aspects of QS, i.e, collecting and analyzing group data. Underlying trends include growing and aging world population and urbanization.

Towards the end of the presentation, Swan discussed limitations and risks related to big data. An interesting concept that came up is sousveillance which is the opposite of surveillance. In French, surveillance means "watching from above" whereas sousveillance means "watching from below." It seems that sousveillance and general transparency could be a useful counter force or antidote against totalitarianism and big brother activities. Some other means are needed, though, to diminish widely spread categorical thinking that can be a source for many kinds of societal problems, whether top-down or bottom-up. Far too often people base their decisions and actions on too clear cut interpretations. This prevents from reaching good solutions through evolutionary processes. Therefore, analysis of big data needs to deal with the level of interpretation and its complexities including contextuality and subjectivity.