What is Big Data?
Big is an imprecise and relative term so it makes it hard to pin down a good definition for the term Big Data. According to Snijders et al. (2012), “Big Data is a loosely defined term used to describe data sets so large and complex that they become awkward to work with using standard statistical software” (p. 1). Though one of the best and most concise definitions for big data out there, the definition is one that changes over time. Where in the early 2000’s a data set representing all the learning data for a university could be measured in gigabytes, today that has increased over 1000 fold into many terabytes. The data that constitutes big data can be anything both structured and unstructured but most often it is the latter as tools and business intelligence has tackled many of the scaling problems related to processing structured data.
Some examples of big data and applications include prescriptive analytics in healthcare which allows doctors and pharmacists to flag potential hazardous drug interactions,
computer-aided diagnosis that uses pattern and image recognition to aid in diagnosing rare conditions and early symptoms of common conditions,
https://www.wired.com/insights/2014/08/patient-monitoring-big-data-future-healthcare/
and in projecting the value of professional athletes before they are drafted as depicted in the movie Moneyball.
At the University of British Columbia, there are programs (https://masterdatascience.ubc.ca/), research groups (https://www.grad.ubc.ca/research-cluster/big-data-computational-social-science-research-cluster), and support units (https://learninganalytics.ubc.ca) dedicated to the teaching, exploration, and application of Big Data.