Quantified Self Fourth Person Perspective and Self 2.0

Quantified self trackers¹ are having an increasingly intimate relationship with technology and data flow in mediating their experience of reality. Technology effectively opens up a new perspective (as vaunted by Nietzsche), a fourth person perspective – a new and objective view of the self, possibly on the road to creating the overself (self 2.0). An important and radical aspect of quantified self (QS) activity is its inherent linkage of the former binary of quantified and qualified in three important ways:

1) The QS Act Itself 
The very act of QS’ing fundamentally includes both the collection of objective metrics data and the subjective experience of the impact of these data

2) QS’ing the Qualitative 
QS methods are now being applied to the tracking of (formerly objectively inaccessible) qualitative phenomena such as mood (e.g.; tracking qualitative word descriptors or mapping subjective experience onto quantitative scales)

3) Quant-Qual are part of a Larger Phenomenon 
To understand QS’ing is to see that it is part of a larger more complex process in which the quantified data collection and the qualitative experience of the data are nodes in feedback loops for behavior change. Data, information, understanding, and action are constituent parts of the looping process

¹Quantified self activity: the self-tracking of any kind of biological, physical, behavioral, or environmental information, often with a proactive stance towards action

Big Data Era: Not just More Data but New Kinds of Data

One aspect of 21st century data literacy is realizing that there is not just more data, but also that there are new kinds of data.

There is a significant shift from the model where ‘all data is salient,’ for example, each entry on a calendar is a relevant appointment, to a model of being able to recognize different kinds of data and appropriate actions related to specific data types. The focus level upshifts to the correlation, trend, and anomaly level of big data abstractions rather than on the unitary level of the data flows themselves.

Daily quantified self-tracking data for example may be useful from a longitudinal perspective and might not need to be reviewed unless there is an anomaly. Another example is that the relevant action might be looking for correlations across multiple data streams. There could be potential linkage between coffee consumption, social interaction, and mood per as this Sen.se multiviz project investigates, finding some correlation between social interaction and mood. 

Discussed at greater length in: Swan, M. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw 2012, 1(3), 217-253.

Core 21c Skillset: Data Literacy

A core 21st century skillset is data literacy, meaning the ability to recognize, understand, and manipulate various forms of data. One way is through visualization, using visual techniques to both represent data, and also as an inquiry tool for finding patterns.

Some of the basics of data visualization are being able to distinguish between ordinal (qualitative) and quantitative data, and selecting corresponding plotting techniques. For example, a bar chart may be best for displaying simple quantitative and ordinal values, a scatterplot for multiple quantitative data values, and a shape-based plot chart for multiple ordinal values.

Beyond the basics, the next step is mastering more sophisticated visualization techniques. Some of these build on information visualization pioneer Edward Tufte’s work and include using small multiples (plotting several similar charts to highlight differences in one variable), bullet charts, sparklines, horizon charts, and adding a dynamic element to visualizations.