That advantage may help identify the best students or customers for a given institution, decrease costs or increase service. Often times, organizations start by looking for opportunities to increase operational efficiency, which, in turn, may decrease costs and provide much-needed investments into strategic priorities. What does operational efficiency look like for your institution? How do you know where efficiency opportunities exist?
Every institution has hundreds or maybe thousands of data sets about their operations. The enterprise resources planning (ERP) system contains demographics, enrollments, grades, financials, staffing levels, etc. The learner management system (LMS) is full of data about how students access and utilize instructional material. The card access control system tells a story about how students, faculty and staff move through your campus. The electronic workflow system holds the key to understanding how actions get processed. The content management system (CMS) gives hints as to which communication style works most effectively with various audiences. All of these systems hold a piece of the puzzle that could help you identify opportunities for improved efficiency.
How are you using this data today? How do you access and digest it? Can you quickly and easily answer basic questions about staffing, enrollments, facility utilization, learning outcomes and student body demographics?
If you can’t easily describe your current state then it will be almost impossible to identify areas for improvement. At the conferences, I heard many conversations about the need to use predictive analytics in order to:
- Monitor admissions targets
- Manage financial aid
- Understand program ROI
- Identify at risk students
- Manage performance against goals
- Plan for future course enrollments
Before tackling any of these topics, each college or university needs to take a close look at their data to determine if it makes sense. Are the systems recording data in an accurate and consistent manner? If not, perhaps business process re-engineering is needed to develop that accurate and consistent process (thus begins operational efficiency). Secondly, when accessing the data is there a common set of definitions that will be applied to data elements across the institution? Typically, these definitions are stored in a data dictionary. This provides important level-setting information that is crucial to the success of any reporting or analysis. This is where you will decide things like “what qualifies as a part-time versus full-time student,” “how to count student enrollments in a semester if you have multiple term starts within a semester,” or “what does successful program completion look like.” You can’t analyze something that you can’t describe and the only way for enterprise-wide reporting and analytics to work well is to have everyone working form the same data and definitions. Thirdly, the right questions must be asked to yield actionable results.
It takes time to understand and trust the data, formulate the right questions, and finally, analyze the data so informed decisions can be made. There is a very natural progression to the kind of BI that can really assist in the quest for operational efficiency. It isn’t unusual to spend a couple of years just getting through the basic steps. So, if you are on the quest for operational efficiency, now is the time to start leveraging your data.
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