Insight Sprints

Bring us your problems.

We welcome participation from professionals from the business community. To bring us your big data difficulties, contact Sanjay Srivastava at ssrivastava@gsu.edu to explore how your company can partner with the institute and find solutions.

These unique, collaborative engagements provide organizations with the opportunity to gain insight into a big data challenge.

Organizations with exploratory big data projects bring their staff and data together with institute faculty and students to engage in a 3- to 4-week focused effort to understand what is in their data and what can be done with it in the institute’s big data lab.

Students work in teams on data sprints to find solutions to these real business problems involving data management and applications. The students tackle each project with a company staff member and devise possible solutions.

The objective is to see if specific questions can be answered using the data or if the data may be helpful in other ways.

In fact, our inaugural cohort of MSA 2015 students already has worked with a large hotel group to improve revenue estimates for bookings.

Watch a video of an Insight Sprint in action, plus student and employer commentary.

Past Insight Sprints

2017

Georgia-Pacific
Georgia-Pacific challenged Robinson students to use images in operations to determine whether use of image recognition can detect fraud and monitor activity. Students matched same-day inbound/outbound truck images and explored the use of image data in logistics.
SunTrust Banks
Robinson students were asked by SunTrust Banks to explore what website behavior, by a customer, leads to a sale and whether the bank can tailor individual interaction in real-time. During the project, students measured the impact of “visitor engagement” that increased the probability that a customer would acquire a new product.
United Healthcare
Robinson students were challenged by United Healthcare to mine data to determine who is cheating the healthcare system and what an abnormal pattern of claims is. Using the big data lab in the Institute for Insight, students explored which physicians were deviating from their regular and acceptable billing behavior.

2016

SunTrust Banks
Using Robinson’s big data lab, students used text-mining to predict client attrition for SunTrust Banks. Investigating “unstructured” texts such as underwriter’s notes, client acquisition or risk review, and sales manager’s notes from servicing clients, students provided approaches for supporting SunTrust’s goal.
Watch a student-produced video about it 
American Red Cross
To address the ongoing demand and need for blood, students set out to determine whether the American Red Cross could identify those likely to be a repeat donor and those likely to be a high value donor. Students analyzed demographic, geographic and behavioral profiles for donors and offered insights on drivers of donor loss and retention.
Starr Companies
Students were challenged to use Starr Companies’ data on customer attributes in an existing line of business to determine what external data is useful and for what purpose in the property-casualty business. Using Robinson’s big data lab, students conducted data analysis and mining on the existing book of business to find correlations and patterns.