We interviewed one Associate VP and one Executive VP of Georgia Tech, and they wanted to make more informed decisions in managing research activities across Georgia Tech. I summarized specific questions driving the 2 major decisions.
In: How to increase research funds?
Regarding funders outside Georgia Tech:
1. How much, and how quick do funders offer? How has the situation changed over time?
2. Where are different funders’ specific interests, within Georgia Tech?
Out: How to spend research funds?
Regarding researchers within Georgia Tech:
1. How has research performance changed over time?
2. How are different colleges/schools/labs/individuals performing?
3. How are collaborations happen among colleges/schools/labs/individuals?
Other than answering questions in decision-making, users are also interested in exploring the data to raise better questions, as well as using the data as a way of communication to convince other decision makers.
know the data
We would visualize data of research projects initiated within Georgia Tech. The Enterprise Data Warehouse of Georgia Tech contains the funding amount, expected duration, and descriptive text of each individual project, as well as 3 other major categories of data.
1. Time and Status
During a certain period, a project may meet an action like: submitted, declined, awarded, or terminated.At a certain moment, a project may be in a status like: pending, unfunded, ongoing or expired. Further information can be explored regarding: How many days has a project remained pending? How many days has an ongoing project gone through?
By aggregating research projects of a certain action or status, the chronological data could reflect research performance.
2. Funders: Where does money come?
To help drive decisions in managing relationships with funders, I categorized and organized different layers of funding sources for each project.
3. Researcher: Where does money go?
To inform prioritization in research investment, I categorized and organized different layers of researchers for each project, as shown by this example of one individual under School of Interactive Computing, College of Computing.
To gather inspirations for bridging such gaps between user needs and data, I studied some industrial solutions in visualizing research data for universities, as well as approaches of some other universities. For example:
Revenue Flow (University of New Mexico)
Research Awards$ (University of Virginia)