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.