Predictive analysis is the art of using past data or occurrences to predict future results.  According to KPMG, this analysis is used by 41% of colleges and universities to determine the makeup of their future admitted students.  Although predictive analysis can give good estimates, it is not a good tool when colleges are looking to grow specific demographics.

“The problem is that colleges have to use their past data to predict future success. And if they haven’t successfully recruited or supported underrepresented students in the past, the model of the “ideal” student will continue to exclude underrepresented students.”  The key here is trying to create an ideal profile for students that are hard to recruit.  

Most colleges emphasize standard test scores such as the SAT or high school GPA to develop a student’s profile.  This is unfair as a student with a low GPA at High School A may be better prepared than a student with a high GPA at High School B.  In addition, standardized test scores have been repeatedly proven to benefit students from certain areas.  

If colleges are truly seeking to expand the net with underrepresented groups, predictive analysis may have to be limited in order to create better ways to recruit.  Developing new strategies to recruit these groups does not mean a lowering of the bar or unfair advantage, it is taking into account the basic issue with current analytics which is, data can only provide so much information.