Published on 2017/03/13

Making Sense of the Numbers: Using Data to Support Students Across Their Lifecycle

The EvoLLLution | Making Sense of the Numbers: Using Data to Support Students Across Their Lifecycle
Democratizing data means putting it in the hands of decision makers at every level of the institution, creating the potential for more data-driven work and intervention at every stage of the student lifecycle.

Businesses of all stripes—from online stores to banks to health service providers—have been leveraging Big Data and analytics in their decision-making and customer-facing processes for decades. Higher education institutions have only in the past few years begun to pick up on this trend, but given the challenges institutions are facing with budget limitations and competition, it’s critical for colleges and universities to make data more central to their process. Getting to the point of actually leveraging this data, however, requires buy-in from leaders across the institution. In this interview, Michael Kabbaz shares his thoughts on the benefits Big Data brings to the table when it comes to supporting students at all stages of their lifecycle and reflects on the impact data visualizations can have in making this kind of information actionable.

The EvoLLLution (Evo): Why has Big Data become so popular in higher education circles today?

Michael Kabbaz (MK): The growth in the field of data analytics has led to great advancements across other disciplines and industries, and we have now found their applicability in higher education. It’s coming at the same time as a serious questioning of the value of a higher education degree, which has intensified in recent years. This has led to increased expectations from the external market. There’s pressure from the media—we’re all accustomed to seeing the annual New York Times article of the student who is $100,000 in debt from a prestigious institution and can’t find a job—funding bodies, and students themselves. In short, return on investment has become a driving factor that has really pressed institutions to be much more involved in the conversation about Big Data and analytics.

Enrollment management has really moved towards this concept of leveraging data from the entire student lifecycle to impact an institution. From admissions to graduations, from the types of students we recruit to how we enroll them and how we support them—everything we do can be improved with data.

Evo: What are a few of the most important benefits of Big Data that senior institutional administrators should know about?

MK: I think a lot of the fear that comes out of Big Data and predictive analytics is that it actually takes away the personal side, which is why many of us got into this work. But in fact it’s the reverse. What senior administrators should understand is that data can be a powerful tool for better serving our students. The reality is certain students need more assistance and support than others. Data can help us sort through the students who most need our help so we can better target our time and our resources to maximize our impact on the student experience.

Data and predictive analytics can also be powerful tools for breaking down silos. For a long time higher education has been very decentralized—there’s the bursar’s office, financial aid, admissions and more—and when you start to leverage data points from each one, you can connect the dots and get a clearer picture of the whole student lifecycle. Data and predictive analytics can identify students who are having trouble paying their bills, track students who have grade drops in certain areas or who are missing courses they need to graduate.

You can’t track 20,000 students at everything they do, but by better leveraging data analytics and predictive analytics you can intervene sooner. They help you to fully understand the student, allowing you to better support them.

Evo: On the flip-side, what are the limits of Big Data and analytics that senior leaders need to keep in mind?

MK: We’re inundated by data more than we ever have been before, and some days it feels like too much data. But one of the things that I think is absolutely vital for higher education administrators is to make sure that, as you’re looking at the data, you’re also looking at the qualitative stories behind it. Having access to more data and more information than ever before doesn’t negate the importance of the qualitative side. The qualitative side should inform how the data is used, what actions are taken and what policies are impacted.

Evo: How important are data visualizations and dashboards to being able to translate these high-level or complex ideas into something that’s easily digestible?

MK: Data visualizations are very important. Most institutions have a lot of expertise terms of producing the data, but the real power comes from being able to analyze and discuss the impact of it. The visualization of data is just as important as the data itself because it makes it understandable and actionable.

Further, sharing analysis broadly is the transparency piece to this. As we democratize data, we put it in the hands of decision makers at all levels of the organization. Visualization and the ability to analyze and understand data at all levels is a critical part of this.

Evo: Is there anything you’d like to add about what senior leaders need to understand about the use and value of data to their operations?

MK: There are some institutional leaders that I would call skeptics, who fear that the data can actually harm the student. But I have learned that data is a companion to the knowledge and expertise of faculty and staff. For example, we’re starting to collect LMS data for our online courses looking at how students engage with the platform and the assignments. We’ve found that engagement with the assignments connects to engagement with the class as a whole, and ultimately with the broader university. If the faculty member in the classroom knows how students are engaging with the material, isn’t that a powerful tool to engage those students further?

The idea that I think we have to work on is getting people to understand that data really has the ability to enhance and it doesn’t preclude the personal side of higher education.

This interview has been edited for length and clarity.

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Key Takeaways

  • Data analytics have allowed colleges and universities to respond to external pressures about students’ ROI by facilitating better student engagement across their lifecycle and allowing for more quantitative descriptions of outcomes.
  • Every division across the institution tracks and collects data points relevant to their particular work, but by breaking down silos, institutions can support students through every stage of their lifecycle with better information.
  • All quantitative data must be tempered by qualitative stories and understanding that help to contextualize what the numbers are saying.
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