Leaders in higher education have an increased desire to harness data to make strategic and cost-effective decisions. With rising pressure to close equity gaps and improve student-success metrics, institutions have shifted over the past decade to leveraging predictive analytics. Done well, analyzing and utilizing student data can significantly transform the way universities operate, boost student performance, enable new ways to engage current and prospective students, realize cost savings and more.
Still, the use of data to drive decision-making remains a fairly abstract concept for many in higher education. Data collection in the absence of strategy, governance, access, data-literacy training, and a supportive campus culture can actually do more harm than good.
We’ve pulled together some tips to help higher education institutions understand how to leverage the data they collect to make wise choices, reduce spending and increase efficiency campus-wide.
Using Data to Improve Recruiting
Data analytics can improve recruitment capabilities by analyzing the best opportunities to boost brand visibility, reduce campaign spending and predict target students and markets.
1. Targeting Geomarkets For Individuals That Are Most Likely To Enroll
Incorporating data analytics in higher education can identify the hottest geomarkets among enrollees. In other words, data can help to show which places most students come from so that the university knows where to target its recruitment efforts. Universities and colleges can analyze the school’s historical data and cross-reference the geomarket list with a marketing approach and campaign with specific, selected messages to the targeted markets. In this way, analytics can help to determine where the best—that is, most likely to enroll—prospects are located and advertise accordingly.
2. Creation Of Invite-Only Events Targeting Students Who Would Be Positively Impacted
Higher education’s goal to increase student attendance at campus events have traditionally been based on conjecture and limited data. However, by utilizing historical and current prospect information, the institution can determine which students are more likely to participate and what phase they are in within the engagement cycle. By using this analytic approach, universities can create invite-only events that comprise the students who would be most positively impacted by their attendance. The data on which such determinations are based would include student interests, areas of study, and so on.
Using Data to Support Faculty and Students
1. Allocation Of Resources For High Demand and Market Sensitive Programs
Through data-driven decision making––which involves modeling and analyzing accurate and relevant data––institutions can identify profitable trends and patterns, then allocate resources to where they are most effective. By aggregating program enrollment data trends, prospective student intent to enroll data, labor statistics and other national market data, institutions can either identify new market sensitive, in demand programs to launch or strategically shift institutional funds to support an existing program that is garnering more student interest.
2. Offering Classes Which Have The Most Impact
For those institutions that are offering class sessions outside of the traditional fall and spring academic calendar format, data analysis can be used to identify high need and high demand courses to offer students to accelerate time to degree completion. Data analytics informs modifications of university curricula to adapt in real-time; universities can now observe the relationship between degree progress and degree completion. When used strategically, data can help universities tailor their class offerings to best meet student demands.
3. Improve Student Experience
Accurate data can enhance the student experience by identifying opportunities to better meet their needs. Does the data indicate a higher call volume to the office of financial aid during a particular time of year? Is there a direct connection with longer wait times? Can the institution allocate additional staff to improve the student experience? What solutions can the institution implement in real time to directly impact the student experience?
4. Intervention Solutions
By coalescing a variety of data points, institutions can identify academic challenges in subsets of the student population and then develop tailored intervention solutions to improve student success. By readily providing comprehensive student data, analysis tools and appropriate support training to faculty, faculty are empowered to make modifications to the curriculum that enhance a students’ experience. Incorporating non-technical elements will result in further insight; the human aspects informed by this data provide a customized, more empathetic, and subjective perspective for students. Together, these elements may identify problem areas causing reduced attendance or academic challenges and what the affected parties have in common.
5. Enhance Students’ Digital Skill Sets
Relying on analytics and incorporating seamless information systems in higher education can generate datasets that result in “real-time” relationships, keeping institutions agile. This also fosters growth in students’ digital hard and soft skills, which include—among other elements—online collaboration and tech savviness. Not only does this prepare students for post-graduate careers, but it also readies them for our ever-evolving focus in a more tech-heavy world.
Speak with Hartman to Learn More about the Value of Data Analytics in Higher Education
There are many benefits to being a data-driven organization, but with so much data available, it can be difficult to determine how to get started. Hartman works with higher education institutions to develop and execute a data strategy that aligns with their strategic goals. Because we don’t sell or resell any technology, we are able to help companies think objectively about how to use technology to drive their business.
Contact Hartman today to learn more about how we can help your institution.
