Data Stars

The Data Star program was started at Rapid Insight to recognize leaders within organizations.  Data Stars are people who are making a difference, creating results and making an impact.  Becoming a Data Star isn’t about just utilizing a software program, it’s about looking at things just a little differently and asking questions that lead to discoveries and sometimes even more questions.  We are proud to be able to support the Data Stars with a set of tools to help them create success.

Michael Dean

Dean1Michael is the Associate Dean and COO of Mercer University School of Law.  At a time when Law schools are under tremendous pressures of declining enrollment, Michael has utilized predictive analytics to take a proactive position by utilizing historical information to make predictions about individual applicant matriculation probabilities. The models are used to make decisions to drive the optimal class.

He continues to push for the use of informed decision making with quality data both at Mercer and in other projects he is working on. 

Learn more about Michael's use of Predictive Analytics.

Jennifer MacCormack


Jennifer is the Associate Director of Development Research at the University of Washington in Seattle.  She uncovered an additional 60% of high value planned giving donors, previously unknown to the university.

She used Rapid Insight software to quickly and affordably build a predictive model as a proof of concept.  Looks like that's proof positive!

Learn how the university brought Predictive Modeling In-House.

Michael Johnson

Mike is the Director of Institutional Research at Dickinson College.  Like any institution, retention is something that is a focus for Dickinson. Mike wanted to make sure that they could identify first year students who would be at potential risk for dropping out of school.  This would allow Dickinson to get them the necessary resources and programs the students would need to improve their second semester performance and stay enrolled in school.

Mike utilized the Rapid Insight product suite to help identify that the bottom 10% of underperforming  students in terms of actual vs. predicted GPA are over 2 times less likely to be retained.

Dickinson has now been utilizing predictive modeling for over four years, seeing concrete results, thanks to the work of Mike and his team.

Learn how Dickinson utilized Predictive Modeling for Enrollment Management

Lynne Myers

Lynne is the Financial Aid Director for The College of the Holy Cross.  For her institution, accurately assessing projected financial commitment during the enrollment process is critical.

Lynne wasn't satisfied with the narrow scope of predictive models that outside firms had built for her so she started researching how they could build their own models to get better information.  In no time, her team was building models that were showing a marked improvement in the quality and accuracy of the resulting assessments.  They were learning new things along the way and discovered variables that were previously not known to be important as key indicators predicting enrollment.

Because of their work, Lynne and her team in the Financial Aid Office are now able to accurately predict what the school's financial aid obligation will be for any given enrollment cycle.

Learn how Holy Cross utilized Predictive Modeling 

Loralyn Taylor

Loralyn is the Director of Institutional Rearch at Paul Smith's College.  She and her team wanted to focus on improving student success by incorporating predictive modeling to help identify high risk students at admission so they could provide them the proper support and resources up front rather than waiting for problems to happen.  

As they worked through this process, they found discrepancies in data sets which added to their challenges.  Through their use of Rapid Insight to help prepare their data and to help them implement predictive modeling, they have saw an ROI of over $2,000,000 in just the first two years.

Learn more about Paul Smith's College's Data Reporting and Analysis Project