Enterprise Analytics and Advanced Research


In higher education today, institutions are racing to convert institutional insights into meaningful results. Successful institutions are infusing enterprise-wide analytics to drive smarter decisions, enable faster actions and optimize outcomes.

At VCU, Enterprise Analytics is referred as the skills, practices, applications and technologies for ongoing quantitative, qualitative, algorithmic, and visual exploration and investigation of institutional data to gain insight and understanding, drive institutional planning, and determine the best or near-optimal courses of action using techniques such as data visualization, statistical methods, computational algorithms, optimization, and applied mathematics within the context of intuitive institutional comprehension.

Our main goal is to enhance and leverage institutional capabilities to amplify our ability to create value from data and analytics to support integrated institutional decision making, research, planning and assessment.

Our Approach

At VCU, Enterprise Analytics is seen as a continuum (see below) that consists of three mutually exclusive and collectively all-encompassing stages: descriptive, predictive and prescriptive analytics. These stages are defined by the types of questions they answer.


Descriptive Analytics stage answers questions such as: What has happened in our organization? Why has it happened? What is happening? What do we know about our students, faculty and staff and their behavior, as well as institutional peers etc.?

This is the area where Business Intelligence, Reporting, and Advance Data Visualizations are primarily located. 80% of the all information inquiries of an organization in any given time are descriptive in nature therefore successful architecture and execution of descriptive analytics stage is essential for the success of Enterprise Analytics.
Predictive Analytics stage answers questions such as: what is likely to happen? What is likely to be true about our students, faculty and staff?

Techniques that are used in this stage to meet information requests are forecasting, regression analysis, data mining and simulation models, etc.  

With the advancement of technology forecasting models are being incorporated into Business intelligence which actually results Descriptive Analytics to expand and cover almost 90 % of all information needs in any given time.
Prescriptive Analytics stage answers questions such as: What should we do? What is the best course of action given what we know and what we think will happen?  

Examples of this are optimization, mathematical programming heuristic algorithms etc. The effectiveness of the enterprise analytics continuum depends on the foundational work built by each stage for the other. Without solid descriptive analytics, most predictive models are completely irrelevant, and without solid descriptive and predictive models, most prescriptive models are irrelevant.