Self-Hacking: Self-Knowledge & Data Literacy

Data That Matters
Location: King's Suite Level: Intermediate
Average rating: ***..
(3.40, 5 ratings)

Backdrop for my talk is the growing trend and community of people tracking an activity or aspect of their lives, whether it’s health or productivity, using various technologies to capture and analyse their data. Also known as Personal Informatics and Quantified Self.

The question I ask is “How do I manage, understand, analyse, re-use and interpret my own personal data without becoming a data expert?” This is an issue of data literacy and I draw parallels with general literacy, which serves as a good model. One way ahead may be through data visualisation. However, current ‘info-graphics’ are a far cry from the power a sophisticated data analysis could give to an individual user. Equally, current data standards and openness (lack thereof) are more of an obstacle than help for individual users.

What if I want to analyse my data differently to the analysis, let’s say, Withings scales app provides, cross-analyse it with my travel data from Tripit to see if my weight is related to change in my diet during travel. Or any other combination of activities I care to track. The possibilities are endless, if only individual users had access to their data and to far more sophisticated tools for data analysis and visualisation.

I will examine the current approached to data visualisation and see which ones, if any, might be going in the right direction and why. This is an open challenge to data geeks and experts everywhere to find a way to make data accessible to those who lack their expertise.

Adriana Lukas

London QS group

A keen advocate of privacy, personal data ownership on the web, my interest in Personal Informatics/Quantified Self and Self-hacking is to voice the same concerns. I would also like to raise the bar on the analytical functionality available to users – data visualisation, database magic, ad hoc and flexible data analysis, all driven by the user and available for any kind of data set they may be generating, passively or actively.


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