Industry Perspective: Centralizing Data in Higher Education 

Data is a powerful guiding tool in higher education, and it becomes stronger when it captures the entire student journey. Unfortunately, institutions’ data is often siloed across departments, which prevents them from having a “whole picture” approach to all of the strategies they have in play, from enrollment to student retention. For instance, CRM data alone might be enough to help admissions understand what’s in the pipeline, but CRM data is only one part of the picture; it’s the marketing data that helps explain why the pipeline looks like it does. Data silos prevent connections which, in turn, limits the insights that are possible. 

So, who should care about data? And how can it be used to achieve better outcomes, like increased enrollment? 

Dan Antonson, director of Marketing Technology & Analytics at Collegis, gives his perspective and answers key questions about why collecting good data – and centralizing that data – is important. 

What do you think is the greatest challenge schools face when dealing with their data?   

Data silos often reflect the organizational structures in education. If admissions, IT and marketing (or any other function, really) aren’t collaborating in the same technology ecosystem, it will often create fractures in the data that result in the silos we’re all too familiar with. I’d encourage any leader in education dealing with data challenges to start solving the problem by inventorying where and how the teams are collaborating and looking for opportunities to create consistency. 

What are some examples of how connected data can drive better decisions and outcomes?

Data often becomes more powerful when it’s integrated and connected with other datasets. 

For example, Google Analytics data is certainly valuable by itself; just understanding which pages are popular, how people move through a site or what content someone typically views before applying can be super insightful and useful to help make the experience better for the prospective student. But when Google Analytics is integrated or connected with a CRM system, it unlocks so many more opportunities for deeper insights about the journey because now it can enable new answers around “what do applicants look at on the site AFTER an application is submitted?” or “what programs are being compared before a student applies?” or “what marketing sources drive actual enrollments (not just inquiries or applications)?” 

Integrating and connecting data systems often allows for new types of questions to be answered, which, in turn, creates new forms of value.   

What is the one thing you wish schools would understand about their data?  

Many modern technology platforms tout a “360-degree view of the student.” Whether it’s Salesforce, a customer data platform or marketing automation tools, everyone talks a similar strategy and outcome. But what those systems lack is an inherent ability to support multiple data models; the data model you need for a pipeline report is different from a marketing yield/attribution report, which is different from a forecasting/projection exercise.  

We see too many schools getting frustrated when they can’t answer certain questions because they’re limited by the data model that is built into their CRM or their web analytics tool. There is not one data model to rule them all (I’m looking at you, EDA), and the sooner schools start to realize that they need systems and tools that support flexible data models, the further they will get with driving value with data. 

In addition to finding best fit students and personalizing their enrollment experience, how else can data be leveraged to stabilize or increase enrollment? 

So much of enrollment management is a simple “prioritization problem.” Whether the goal is to grow enrollment or improve outcomes, it’s a matter of figuring out where to focus resources to make the biggest impact. For instance, enrollment pipeline data might help identify backlogs, which can help admissions teams identify prospective students to reach out to. Web analytics data can be used to identify the most popular pages, which can be used to inform site roadmaps. Marketing data can be used to identify efficient marketing channels that could use more investment. The easiest way to generate value from data is to simply use it to identify focus areas that can make a difference. 

Who should care about data? Does it just belong to reporting and analytics?   

Everyone should care about data. Data is a lot like any resource, whether it be water or steel. Data is an enabler, and just like the water you use for cooking, you need data that is clean, safe, reliable and, most importantly, available when you need it. 

I think higher ed, generally speaking, has too often thought about data as a one-dimensional resource; data is often just thought of as an ingredient for reporting. I think this misses the big opportunity because data can be used in so many different ways beyond a simple report. Data can be used for alerting via anomaly detection; it can help us understand what’s changed. Data can be used to reach new audiences. By sharing data with platforms like Google and Facebook, they can help an institution talk to new prospects. Data can help us test new processes and messages; conversion rate optimization is a great example of a way to test what works and what doesn’t. Higher education needs to consider data as the ingredient needed for data-driven applications; reporting is just one of many use-cases. 

Get the Most of Your Data with Collegis

A foundation of integrated, centralized data is critical to overall institutional health. Collegis builds this infrastructure for higher ed partners to achieve full-funnel visibility and attribution-level tracking so decisions can be driven by data.