Analytics to Power: Measuring the Impact of Student Success Initiatives
I am sitting in the Civitas session watching. This is the company that handles the data for our system, and one of their co-presenters is Valencia CC: a community college system extolled for its innovations.
The people filing in are admin. The suits. The low talks and loud laughter. These do not have the smell, the feel, or the sound of academics. Again, I am a newcomer, looking in. But I am waiting to see how this group leverages the information at their disposal.
Civitas’s rep is giving a quick background, orientation–informative and useful–filled with bullet points.
- We are relatively a young company.
- We have been in business four years.
- Higher education is late to the game with predictive analytics. (He mentioned gambling–the leaders.)
- Colleges struggle with “letting the data out.”
- Academics has a culture of protectiveness–all of which inhibits this work.
- We must unlock the pods of data to hear the stories this data tell.
His understanding of the silos in higher ed establishes a clear ethos–more so than the “NASCAR Slide” with logos of the colleges they service.
In the course of the introduction, he touches on getting the data into the hands of faculty but quickly slides to IR.
In some ways, this like a scene from a WWII movie, think Thin Red Line. The lieutenants on the front lines are the ones moving, maneuvering troops. The admin is calling in reports and listening to reports. (He just used the phrase “front line.”) They are responding after the fact.
This group has a clear grasp on the sweeping range of data available at academia. But they are looking at it from the top down–as if they have to rely on the upper layers of management for information–gathering, disseminating, implementing.
Upper levels of admin can set the policy; the tools are there to allow the “front lines” to use the data.
The tools are there to tell the stories in a way that the lieutenants can make front line, real time decisions. The current top down academic models, though, slow the movement of information, pushing the decision away from the present. Officers at the front have to wait for the orders to be called in on data that is a week old, a month old, a semester old, a year old.