6 technologies and practices impacting the future of data and analytics in higher education
Now is the time for higher education leaders to plan data and analytics technology solutions and practices that will position their institutions for success, according to a new report from Educause. The Higher Education Computing Association recently released the inaugural edition of data and analysis from its Horizon Report, expanding the annual analysis of trends, technologies and practices impacting higher education to “ an emerging practice area that guides institutional decision-making and strategic planning for the future.”
For the report, a panel of higher education data analytics experts compiled a list of six key technologies and practices that will “significantly impact the future of higher education data and analytics.” “. Panelists categorized each element into six areas: the level of support required from stakeholders; the potential impact on institutional strategic objectives; the potential to support the institution’s digital transformation; expenses required for optimization across the facility; the impact on the size of an institution’s workforce; and required upgrading 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, systems integration, data integrity management, self-service dashboards, data privacy and security. data and consent management, Educause explained, noting that these processes are critical to institutional success, but often rely on cross-institutional committees rather than dedicated staff and resources. “This has resulted in lost opportunities and wasted effort, particularly as staff turnover among committee and task force members creates gaps in institutional knowledge,” the report said. Advances in data management, automated systems, and AI-enhanced processes can help, but only with buy-in from key stakeholders. As a result, data management and governance was ranked high on the list in terms of the level of support required from stakeholders. “Data and analytics leaders should be prepared to help their stakeholders and communities understand the need for and benefits of improved data management and governance,” the report recommends.
2) Unify data sources. “One of the most difficult silos in higher education is separating not people but data,” the report notes. “As complex data ecosystems, higher education institutions contain vast stores of data that are typically disjointed between computing systems that do not talk to each other, reducing institutions’ ability to engage in best practices. holistic data analysis and decision making.” And while unifying these data sources falls under the category of data management and governance, Horizon panelists felt the topic was important enough to be considered a key technology/practice, ranking it at the top of the list for impact on institutional strategic objectives and potential for support. digital transformation.
3) Modern data architecture. This technology/practice refers to the data structures that need to be in place to facilitate sophisticated analytics capabilities such as machine learning and natural language processing, the report explains: “Without a scalable, adaptable data architecture and flexible, data users cannot effectively use modern data analysis capabilities, and the reliability of data analysis is questioned.” Modern data architecture topped the list in two areas: the need for workforce upskilling or retraining, and the institutional spend needed to optimize.