Enrollment management at colleges and universities is always evolving. It evolves because industry changes, strategic priorities change, needs of institutions change and students change. What’s more—unless they’re stabilized—enrollment numbers can change dramatically, which significantly changes the enrollment management operation. Simply put, the times change and so must enrollment management change as well.
The three main themes of enrollment management pretty much stay constant: enrollment, persistence, and brand awareness. Big data can help institutions determine opportunities within these themes and highlight what should be the focus. From there, resources can be aligned with priorities and goals and the work can begin.
Specifically, I see this happening in the following ways:
Focus:
For a long time and at many institutions, enrollment management has focused on recruitment and enrollment. Increasing enrollment, especially in light of a reduced budget, is critical not only to progress, but to survival. Higher education is, after all, a business. To keep our doors open, we must have students to serve. Many have expanded their focus from enrollment to student persistence. Big Data pushes us strongly in this direction as it allows us to identify those who are likely to persist.
Scale:
Large scale change is difficult to design and difficult to implement. Small scale, focused efforts are not. An initiative that is simple and requires few resources can be implemented quickly because (1) it is easily understood by stakeholders, which creates buy-in and (2) implementation just isn’t as messy.
These small-scale changes, especially when combined with similar efforts can bring about big change. The nature of the changes themselves can be more varied. In place of a complicated completion strategy for example, a simple retention tactic can be achieved. This can move enrollment management from a large-scale framework to small operational tasks that can be incorporated into the everyday work of staff and faculty.
Speed:
It can be very easy to lose yourself in Big Data. The never-ending splicing and dicing of data can take you down a rabbit hole miles away from your initial research question. The flip side to that, however, is making discoveries faster. For many of us, what we’ve learned about our students and their behavior has come from working directly with them for many years. In our experience, our minds have been our computers identifying variables, outcomes and finally, patterns. Compared to computer-generated analytics, our process of analysis is much more lengthy and maybe less scientific. With computer-generated analytics, discoveries can come quick and take us swiftly to action.
Response:
With discoveries coming quickly, the need to change course may be amplified. Pivoting from one issue to another, from one strategy to another, or from one student population to another is likely to occur based on what we learn in the process of analytics and interventions.
Leveraging Data to Drive Student Success and Outcomes
Analytics are being leveraged throughout the entire student lifecycle to support student success and institutional outcomes. From the recruitment of prospective students to retention of existing students and to alumni building, there is a use for Big Data and analytics to improve what institutions do and what students experience.
To dive in deeper, let’s talk about the goal we all share: student success. To reach a goal, a plan is required for “a goal without a plan is just a wish.” (Antoine de Saint-Exupéry). Taking time—but not so much time that your target changes—to build a plan should ease the journey to success. Analytics create discussion around the specifics of the goal, are used to focus and make decisions and, from there, are used to inform strategy.
The information learned for data analysis helps educators understand the students they are serving today and identify the actions and behaviors associated with success. Through this work, and very much in theory, a model for success is created. The model is then applied to students for whom Big Data has shown to have a lower rate of success. Unique interventions can be created based on the risk factors of students.
Offering high-touch, personalized service to students is ideal. When it comes to building relationships and helping students succeed, nothing compares. But when student headcount is large and/or budget is restricted or reduced, delivering a high-touch experience to every student is just not possible. Not only can it be a challenge to staff appropriately, too much experimentation will cost the institution time and money. Using Big Data and analytics to quickly focus and find the students most in need allows us to work quickly and efficiently.
But what about other institutional goals/outcomes such as increased enrollment, increased revenue, improved brand in the marketplace, or an improved student body? Analytics can be leveraged to meet those goals as well by helping institutions align their resources with their goals. Evaluating return on investment (ROI) through analytics allows higher education leaders to make educated, data-driven decisions.
Primed with a goal and a plan, institutions will determine where they will focus and where they will devote resources. They will determine what risk to reduce or mitigate, how to increase student success and what new innovation to embark upon. Whether the plan is an enrollment management plan or a strategic plan, analytics can be used for the following purposes:
- Improve the incoming student body
- Identify at risk students and tackle risk with targeted, real-time interventions
- Improve the student experience in ways such as eliminating or reducing barriers
- Improve proactive and personalized service to students
- Create and implement personalized, adaptive learning and competency-based learning
- Leverage data in decisions and processes
Maximizing the Value of the Data
It is relatively easy to understand how Big Data and analytics can help institutions positively impact student success. If one puts on their consumer hat and looks to their daily life, the impact of Big Data can be felt when we surf the web, make purchases online, engage with people and movements on social media, and when we apply for life insurance. And it is relatively easy (budgets aside) to outsource the data science part of Big Data to a vendor. Do not underestimate the flashy, user-friendly interface you will get in return, because once you get in there and play with the data (and that is exactly what most of us need to do to understand the beast before us) the appeal of the flash and snazzy look-and-feel will fade away. And that is because the data is BIG. It is vast. And it is everywhere including email, social media, web traffic, rosters, student life, customer feedback, surveys, assessment, you name it. Even if the vendor can predict likelihood of success, understanding why and then replicating that with other students, is quite a journey. Several years into Big Data and analytics, the institution I work for is still on this journey and that is because of the significant challenges we faced.
Getting the data to the vendor was our first challenge. That took a little over a year. Once the data was there and our teams began to play in the system, the questions came flooding in: How does this system work? How/when will I use it? Where was this data coming from? What did it mean?
Mapping out data points from information stored across our numerous systems to data points in this new system were not obvious. Then came the big question: Do we trust this data? When it comes to data, it doesn’t matter if it comes from in-house or a third-party vendor; it’s hard to trust. Perhaps this is due to a cultural shift from hands on/experiential learning to looking at system-tracked student behaviors. The process is definitely not as personal as many of us are custom to in our daily work at our campuses. As an aside, at this point change management becomes really useful. Using change management strategies is a good way to introduce something new and build engagement and ownership among those who you need to be stakeholders.
Once you trust the data—or abandon the idea of trust in favor of just rolling up your sleeves and diving in—then you allow the mind to be taken over by the possibilities, which brings about another challenge: how far do you slice and dice the data? How much do you want/need to know before you actually make a move and say, reach out to students? Action is paramount. Action is a learning opportunity. If we give ourselves to the data and the tools and just start talking to students, our fears and doubts will ease. Because this is where higher education professionals are skilled, knowledgeable and confident: working directly with students.
So now more and more people in the institution are comfortable with the new tools and are diving into the data. This is great! Right? Maybe? How much action is being taken by all the well-intentioned staff and faculty? How many calls, emails and texts are being made to students? Good deeds, all of them, but a communication overload for the student that could cause them to take pause and wonder, “Where is all this coming from?” Knowing so much about someone and predicting their next steps (or misstep) can be, well, a little creepy. Avoid the Big Brother analogy and develop a communication plan and governance structure early on the project.
With Big Data being applicable to many areas of institutional management, one significant challenge may be deciding what to prioritize. Big Data requires technological analysis that for many institutions means purchasing a platform, framework and/or software. Some products offer à la carte options, which means a different solution for each problem. The more an institution wants, the more it will have to spend—both in money and time—to become fully operational.
In the words of rap group Public Enemy, “Don’t believe the hype.” The buzz around Big Data is not necessarily hype, but it is important to remember that when working with Big Data we are looking at correlation, the relationship between variables. With so many variables being analyzed simultaneously, it is impossible to determine the cause. When decisions are made with surface-level data, organizations take risks and create false hope for quick wins. Institutions are likely to find out that what moves the needle on enrollment and success is not a silver bullet but many small meaningful changes. Accepting this can be a challenge for some.
The Growing Possibilities: Leveraging Big Data In New Areas of the Institution
I can’t think of any industry that would be immune to the transformative powers of data, and this holds true for all areas of institutions of higher education. Student success, in a broad sense, is an institution-wide endeavor, which means the entire organization has the potential to be transformed by Big Data and analytics. From issues with parking on campus to working with at-risk students to the acquisition of knowledge to career development and beyond, Big Data can help identify root correlations and spark meaningful action.
My biggest hope for transformation, aside from student success, is Human Resources (HR). A leader in HR once said she was going to transform HR and went on to describe something completely different from the current state of HR. She envisioned an HR department that enabled colleges to be nimble, to quickly respond to the market, to empower employees to make a difference and provide the tools to succeed. I was stunned, excited and totally on board. Unfortunately, she left the institution before any sort of transformation occurred. Today, I still believe in that vision and wonder how Big Data and analytics can make that transformation a reality. HR is a critical component to institutional and student success. A great hire who is supported, empowered, and provided with meaningful training (preferably in an adaptive format) will help create the future of higher education and keep us progressing.
Another area of institutional management that I hope to see transformed by Big Data is operations. Much of the work I do is in support of staff who directly support students. Quality assurance, process improvement, and optimizing productivity through tools, technology and organization structure are the things that I hope Big Data can transform. If that’s not possible, I may just have to go back and get my MBA.
Finally, there’s innovation. Whether that be new ventures or improved ventures, Big Data can make a difference. Higher ed is a hard industry to disrupt. Our industry is comprised of institutions steeped in tradition, regulation and ego. Of course not everyone in higher ed believes a transformation is necessary. But, I would go out on a limb and say that if transformation was a must (and it is), higher ed professionals would prefer that it be at our hands and on our terms. If we could take our beloved but limited anecdotal data and scientifically support our stories with data, we could improve what we do and pave the way to new discoveries. I truly believe that if we can harness Big Data, provide the space to get dirty with it, tame our grand illusions of wild success right out the gate and commit to learning and refining as we go, we will develop a sound manner in which to use Big Data and we will transform higher education.
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