University of Maryland

Analyzing data in order to determine a collective vision that the University of Maryland’s Athletic Department should follow for the subsequent year in order to satisfy different university stakeholders.

Project Context

This was an one-week academic sprint project for a Customer Analytics course where we were tasked to unfold a narrative that is backed up by rigorous data analysis. The goal of this project was to use storytelling effectively to a management audience that may not understand technical jargon, explain data analytic concepts in a simple manner, and to convince them that our data analysis methods make logical sense.

Problem Framing

Using data gathered from a survey conducted with 168 different stakeholders across the University of Maryland, we needed to cluster the data to determine key themes and to determine key audience groups we needed to consider in the university body when encompassing diverse perceptions of success for the Athletic Department.

Key Persona Groups

Using the survey findings, there were 4 key discrete groups of individuals that have various perspectives that the Athletic Department should consider when setting the university’s athletic vision for the following year.

Recommendation

Based on the data findings, we recommend the next steps that the University of Maryland’s Athletic Department should take is to conduct a further deep dive into building a more accurate demographic profile in order to select the best communication channel to reach the variety of demographics and then create a tailored targeted marketing campaign towards each group.

Reflection

Having received the top grade on this storytelling presentation with a score of 96%, I felt proud that I was able to leverage my design background in order to tell a story of data in this business course. From my design courses and project experiences, I have learned the valuableness of being able to communicate a design and tell a story about the process and solution to an audience to drive support. Likewise to data analytics, I feel that data is only meaningful when I can turn it into information to create actionable next steps.