Triangulating and Visualizing Big Data

We always had data. But then came big data. And now we need to connect big data. The Chronicle of Higher Education did a special report on "Big Data's Mass Appeal."  The Sloan Consortium devoted a whole journal issue to it.

In trigonometry and geometry, triangulation is the process of determining the location of a point by measuring angles to it from known points at either end of a fixed baseline. (While trilateration is measuring distances to the point directly.) It is also used for surveying and triangulation methods were used for accurate large-scale land surveying until the rise of GPS and global navigation satellite systems in the 1980s.

It has a somewhat more figurative usage in business and education when we use it in "surveying" data. As is usually the case, the business world was on it faster than education. The big companies have lots of data that is untouched in organizational and software "silos." Triangulation means connecting these silos.

This is what led to "data-driven decision making" back in the 1980s. Now, the buzz term is "big data" which uses software to do analytics. In education, we are still early on in being able to do this and produce useful results for administrative functions. We are even more basic in our use of it for useful instructional analytics.

Businesses love being able to take data about customer experiences and see trends emerge as as customers conduct normal business. These are not surveys or focus groups. It is any number of everyday operations, and it often triangulates customer activities in social networks including Twitter trends, Facebook likes and other interactions.

How educational institutions will use their data and what angles they will use to survey that data are questions still unclear now. Two areas of concern in education that are using big data (see links below) are improving course success and completion rates.

One site that I have been looking at is the Visualization and Behavior Group at IBM because I like their perspective that data visualization should make data analytics accessible to anyone, not just the experts. They see using social software for communication as the new norm, not a trend.


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