6 Technologies and Practices Impacting the Future of Data and Analytics in Higher Ed
The time is ripe for higher education leaders to plan for data and analytics technology solutions and practices that will position their institutions for success, according to a new report from Educause. The higher ed IT association recently released the inaugural Data and Analytics Edition of its Horizon Report, expanding the annual analysis of the trends, technologies and practices impacting higher education into "an emerging area of practice that is driving institutional decision-making and strategic planning for the future."
For the report, a panel of higher ed data analytics experts compiled a list of six key technologies and practices that will have "a significant impact on the future of higher education data and analytics." Panelists ranked each item in six areas: the level of support needed from stakeholders; the potential impact on institutional strategic goals; the potential to support digital transformation at the institution; required spend for optimization across the institution; the impact on the size of an institution's workforce; and required upskilling or reskilling of the current workforce.
The six technologies and practices with the greatest overall impact are:
1) Data management and governance. This category encompasses processes such as workflow automation, access management, system integration, data integrity management, self-service dashboards, data privacy and security, and consent management, Educause explained, noting that such processes are central to institutional success, yet often rely on cross-institutional committees rather than dedicated personnel and resources. "This has resulted in lost opportunities and wasted effort, particularly as staff turnover among committee and working group members creates gaps in institutional knowledge," the report said. Advancements in data management, automated systems and AI-enhanced processes can help, but only with buy-in from key stakeholders. Accordingly, data management and governance was ranked highest on the list in terms of the level of support needed from stakeholders. "Data and analytics leaders should be ready to help their stakeholders and communities understand the need for and the benefits of improving data management and governance," the report recommended.
2) Unifying data sources. "One of the most challenging silos in higher education separates not people but data," the report noted. "As complex data ecosystems, higher education institutions contain vast data stores that are typically disjointed across computing systems that don't talk to each other, diminishing institutions' ability to engage in holistic data analysis and decision-making practices." And while unifying those data sources falls under the category of data management and governance, Horizon panelists felt the topic was important enough to stand alone as a key technology/practice, ranking it highest on the list for both institutional strategic goal impact and potential for supporting digital transformation.
3) Modern data architecture. This technology/practice refers to the data structures that must be set up to facilitate sophisticated analytics capabilities such as machine learning and natural language processing, the report explained: "Without a scalable, adaptable, and flexible data architecture, data users cannot effectively use modern data analysis capabilities, and the trustworthiness of data analytics comes into question." Modern data architecture ranked the highest on the list in two areas: the need for workforce upskilling or reskilling, and institutional spending required for optimization.
4) Data literacy training. "Though the volume and types of data collected by institutions have increased significantly in recent years, many institutions have not seen parallel advancements in end users' abilities to interpret and use findings," the report found. Professional development in data literacy is critical to help stakeholders more effectively use data to inform decision-making, and can be executed at relatively low cost and with little impact on the size of an institution's workforce, in the estimation of Horizon panelists. "Investment in data literacy training is beneficial for all levels of institutional stakeholders in higher education, from board members to administrators, faculty, and staff," the report asserted, and even for students, who "benefit from data literacy training as key stakeholders in higher education data analytics and as future members of the larger workforce."
5) DEI for data and analytics. "Data and analytics professionals are increasing their focus on diversity, equity, and inclusion (DEI) in the ways they collect, manage, and analyze data," as well as leveraging data and analytics to drive equity, support strategic DEI goals and measure progress toward those goals, the report said. Panelists also emphasized the importance of questioning the status quo: "Collectively, the field is reexamining who makes choices about what data get collected, how they are collected, what they are used for, and what implicit biases are baked into every step." Best practices continue to change, and "changes in analytics practices must be supported by institutional leaders."
6) Assessing and improving institutional data and analytics capabilities. This technology/practice highlights the need for data and analytics professionals to assess and improve their own capabilities in response to increased expectations for high-quality, impactful analytical insights, the report explained. Panelists ranked it among the highest on the list for institutional strategic goal impact. "Assessing and improving institutions' analytics capabilities will require institutional leaders and analytics professionals to determine whether they are effectively using their immense stores of data to tell meaningful stories," the report emphasized. "And with better processes in place, institutional leaders can begin thinking about what role data analytics can play in the future. Cross-institutional collaboration will be safer, more practical, and more advantageous. Ultimately, as data analytics processes improve, analytics officials can expect to drive better outcomes for students, faculty, and staff."
The full report, including real-world examples of each key technology and practice, an analysis of 15 overall trends, four futurist scenarios and implications for specific institutional roles, is available on the Educause site.