Starting this conversation with endpoints like data analytics and Big Data is a bad strategy, because this always results in solutions in search of questions. Instead, other factors such as the campus culture, and resources (both in terms of personnel and financial) necessary for supporting and analyzing the information both play a crucial role in developing and executing a strategy. There’s a wide and diverse range of technologies available for storing and analyzing these data-the roadblock is not availability. Similarly, we can discern a lot based on email logs or network usage, which many institutions do not want to engage in because of privacy. This includes data such as LMS activity and course-related multimedia usage, where practically every click can be tracked and stored for further analysis. However, there are terabytes of other data to which we have access that we do not track or attempt to store-mostly for privacy reasons. Increasingly, with the proliferation of ancillary systems that we use to support many of the administrative functions of the institution, data warehouses that attempt to integrate most of those data have shifted to become the institutional data source. Lacking that, traditionally, we have considered the data stored in ERP systems as institutional data. It is fair to say that the interpretation and communication are much bigger issues in smaller institutions than larger ones.įirst off, Big Data itself is relative and perhaps it is time that we seek a definition, even if it is loose.
The interpretation of data is a particularly dangerous business and it is tightly coupled to the other landmine: communicating the discovery. The reality is that Big Data has been the norm in many areas of science for a long time, but now it has become the norm everywhere! The big question is, has Big Data become the norm in small higher ed institutions?ĭata analytics is widely defined as “the discovery, interpretation, and communication of meaningful patterns in data.” No one can argue about the value of this until you drill deep down into it. This surprising move was explained by the title of a blog post by Gartner’s Nick Heudecker: “ Big Data Isn’t Obsolete. The adoption of data analytics and Big Data in higher education has led to an improved staff experience, greater operational efficiency and effectiveness, and the ability to improve the student experience.īig Data, a highly subjective term, entered the Gartner Hype Cycle in 2013, and rather than moving through the various stages of that cycle, it fell off the cycle in 2015.
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