Many institutions remain unable to answer a common set of questions critical to their sustainability. While pressures and challenges persist, and well-intentioned investments in BI and analytics initiatives fall short, higher education has a growing need for effective institutional analytics.
When working with MUA and Kenyan Univervisity At UMUC I learned the importance of integrating data across all stages of the student lifecycle, increasing visibility into the connections between revenue, student outcomes, and expenses. Without this visibility, institutional blind spots prevent leaders from planning effectively and, more importantly, identifying risks and intervening early. Many institutions have reached a point where they can’t afford not to invest in BI tools. Common themes that are top of mind, yet unanswerable, for many college and university leaders include:
- Are we recruiting the students who are most successful at our institution?
- What financial aid package should we offer to our incoming class?
- Are we meeting our access and diversity goals
- How do we optimize our admissions yield rate while reducing spend on recruitment?
Financial Aid and Discounting
- Are we getting the most out of our institutional grant aid?
- Do we discount tuition for the right students?
- What impact do financial aid changes have on enrollment and bad debt?
- What effect does tuition have on access and diversity?
- Are there obstacle courses that result in much higher non-persistence rates?
- What student attributes contribute the most to persistence and retention?
- Are there significant opportunities to improve our first year to second year retention rates?
- Do faculty and students engage in a way that will yield the most positive outcome?
- What are the degree completion patterns, and how can we improve time to degree?
- How can we separate students into subpopulations to serve them better?
- Will we meet our tuition revenue and enrollment targets?
- Which degree programs are driving demand, degree production, and margin?
- Are we maximizing the use of facilities and average class size across all campuses?
The challenge for many institutions isn’t that they haven’t already tried to answer these questions. In some cases, institutions have become fatigued from trying to develop an analytics capability in-house. In other cases, they may have invested in BI and analytics solutions to help address a challenge at a specific stage in the student lifecycle only to realize they are missing key information from another area of the institution. We’ve also met with institutions that have had tremendous success in data integration but lack the resources to support data analysis, storytelling, and data science needs.
Focus on the Fundamentals
Institutions must take certain fundamental steps before they can answer these hard questions. They must also build their level of maturity to reach a state of advanced analytics. We at EDUMAAT experienced this firsthand over five years in a university setting. In building this capability, institutions need to solve the technology, talent, and time equation to build an advanced analytics capability in-house to support stakeholders and provide greater visibility into the critical connections between enrollment, student outcomes, and institutional financials.
- Step 1 is the data integration layer. Centralizing key data from across the institution is the foundation for any institution-wide analytics initiative.
- Step 2 is modeling that data and building tools that provide easy access to that data for users across the university. This accelerates the adoption of descriptive statistics and diagnostic analysis to serve as the basis for strategic discussions across the institution. It also enables what-if and self-service capabilities to empower users across the community to mine and visualize their data.
- Step 3 is investing in data science and data storytelling expertise to help ask and answer the pressing questions and provide guidance on how to take action.
- Step 4 is prioritizing the questions to answer so that they align with the institution’s strategic goals. Start small, show value, and grassroots success will go viral.
Once these steps are in place, institutions can begin to introduce high-value analysis.
Technology Alone Isn’t the Answer
While technology serves as the foundation for actionable data and sophisticated analyses, it will never facilitate a cultural evolution. Departments evolve at varying paces, and institutional inertia is still one of the greatest hurdles facing implementation of effective BI and analytics programs. One may ask, “How does an institution reach a point where data is critical both within and across traditional departments?”
I can’t overemphasize the importance of building relationships across departments. My experience has shown that the best technology and dashboards in the world, alone, will never achieve the adoption and impact desired. Facilitating dialogue and decision making around the data is critical. Independent, unbiased experts are needed to drive the conversations that will yield insights leading to action. Only through a combination of technology and services can an institution effectively leverage data to inform decisions, affect business outcomes, drive policy, and guide the university’s path forward.
Call to Action
I will close with this call to action for my peers and colleagues in data leadership roles: Whether you are a chief data officer, an institutional researcher, or the data lead for your department, you are in the best position to challenge the status quo and show your institution the “art of the possible.” You understand the data, the tools and technology, and the challenges facing your university or college. Each of us has the ability to help ask and answer the most pressing questions at our institutions, spark conversations, and facilitate healthy debates that lead to data-based action. Seize the opportunity to make a significant impact.
To do that LET’S FIRST DISCUSS……