Prioritizing the right things can help you create a strong foundation as your institution builds a successful analytics model

3 questions to ask when embracing an analytics model


Prioritizing the right things can help you create a strong foundation as your institution builds a successful analytics model

If you’ve attended an edtech conference at any point since 2012, then you’ve likely seen some variant of Gartner’s analytics maturity model.  While some fair criticism of the model exists, there are good reasons for its ubiquity.  It successfully maps out the growth trajectory institutions face in their efforts to move successively through four types of analytics–Descriptive, Diagnostic, Predictive, and Prescriptive–in a way that is intellectually intuitive.  The model regularly makes the rounds in Twitter and LinkedIn feeds because, like a good TED Talk, it takes the abstract concepts we interact with daily and wraps them in a tidy, understandable package.

Here’s the logic of the model: As an institution strives for increasingly sophisticated levels of analytics, the value of the information produced similarly increases.  If executed well, an institution steadily ascends from a place of hindsight, through insight, and into foresight – knowing what happened in the past and why it happened, what will likely happen in the future and how to influence this likelihood for the better. 

As both a higher ed and edtech professional, I’ve worked with dozens of college and university leaders who state this analytics maturity process as an aspirational goal, especially with matters pertaining to enrollment, retention, and persistence. They dream of leveraging their data to know who in their student body they failed to retain, what factors and experiences these students had in common, and how they can predict and prescribe policies and practices that minimize risk for future students. In an era of declining enrollments across all of higher ed, predictive and prescriptive actions are a pragmatic necessity for institutions that are increasingly reliant on tuition dollars to simply stay open. 

All the pieces would seem to be in place for widespread analytic success, right? A well-known model exists and can be readily applied to real-world problems facing higher ed.  Why is it, then, that so many institutions struggle to achieve even the first level of Gartner’s model–Descriptive Analytics–let alone the higher, more “valuable” levels?  

eSchool Media Contributors