Importance of Shared Language in Big Data/Analytics Adoption

One of the necessary, yet overlooked, steps to the success of initiatives involving folks from across campus is the development of shared/common language to minimize misunderstandings and provide clarity. One of the first campus-wide projects sponsored by the new CIO Matt Hall at UC Santa Barbara when he came on-board two years ago was a series of day-long sessions for the 400 IT community members. The aim of these sessions called “IT Foundations” is to establish shared vocabulary and understanding of how the campus governance structure, IT infrastructure, and the general campus IT direction. Based on feedback, participants found the experience and the information valuable towards their understanding of the current campus IT layout and the vision of the CIO.

As the campus moves to adopt big data and analytics, I once again realize the importance of developing shared language for initiatives related to these technologies to move forward. The most significant barriers to adoption have not been technical in nature but rather the lack of understanding of the applications of these technologies especially as they involved ethics, privacy, and potentially unintended negative consequences.Specifically, the use of predictive analytics (using algorithms) for academic advising may lead to certain student populations (first-gens, etc.) or students who fit certain parameters to be inappropriately excluded from certain programs or opportunities. Certainly, the concerns about the use of predictive analytics are valid, but adoption of big data and analytics for other campus functions should not be stopped given that the specific concerns related to predictive analytics may not apply. It is for this reason that it’s important for campus administrators, technologists, and other folks involved to have a common understanding of big data and analytics as related to higher education.

A framework to understand big data and analytics in higher education was introduced in the book “Big Data and Learning Analytics in Higher Education: Current Theory and Practice” by Ben Kei Daniel. The framework by Daniel and Butson (2013) classifies the different analytics and their uses in higher education. I translated their descriptions into the graphic below.

In addition, Daniel and Butson (2013) also classified the scope of analytics as shown below.

Gartner also developed the analytics ascendancy model below to highlight the different types of analytics with respect to their values and difficulty.

credit: http://dataanalyticsandvisualization.com/negocios/modelo-analitico-ascendente/

The frameworks introduced above should be good starting points in campus conversations as they provide shared language and understanding of big data and analytics towards actions to benefit the students and the institutions in general.

Can you recommend other approaches to introduce big data and analytics in higher education? Can you share applications of these technologies in higher education beyond marketing/communication (web analytics) and instructions inside the classroom?

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