Higher education has reached the connected age, and it is experiencing rapid changes in the US. Currently, the US holds position 16th based on the highest national college completion rate ranking globally. Consequently, President Barack Obama had challenged the US to attain the first position by the year 2020. Higher education, however, experiences myriads of issues concerning student success, costs, and access. Stakeholders must address these challenges within the next few years for the US to make significant improvements in the national completion rates. These multiple challenges need deeper analysis and comprehension of available data on higher education to guide decision-making and policy formulation for effective responses. The purpose of this study is to demonstrate how analytics can be used to support critical higher education outcomes.
Analytics has been defined as the “use of data, statistical analysis and explanatory and predictive models to gain insights and act on complex issues” (Grajek, 2013). There are specific functional areas in higher education that analytics and prediction can be applied to support outcomes. Analytics, for instance, can be applied in critical areas such as finance and budgeting, enrollment, instructional and student progress management among others (Mattingly, Rice, & Berge, 2012).
Higher education stakeholders are keen on adopting analytics to enhance achievements and scholarship. Institutions of higher learning have thrived on thorough analysis and evidence-based outcomes to support their progress. Today, however, evidence suggests that higher education has slowly responded to the use of analytics for decision-making in strategic areas relative to other sectors (Grajek, 2013). On this note, higher education must change its practices, focus on emerging technologies and adopt best business practices to promote scholarship. By considering the ongoing changes in innovation and technologies, only best practices would ensure that higher education in the US remains competitive, promotes scholarship, and still manages to maintain a culture of performance excellence. In this regard, the focus should be on ensuring affordability, effective use of data, and formulating new policies to promote data culture and data-driven decision-making.
Available evidence demonstrates that institutions of higher learning that have adopted analytics in their operations have realized improved results (Grajek, 2013). They have good data culture and ask relevant questions during decision-making processes. This evidence is also found in non-governmental business practices such as online companies like Amazon and airline companies among others (McAfee & Brynjolfsson, 2012). In the case of higher education, institutions would have to go through their huge volumes of data to extract useful information and focus on collaborative analysis to restructure certain activities or improve student performances, attraction, and retention with the ultimate goal of maximizing opportunities and gains while reducing bottlenecks and costs.
Institutions of higher learning should build interdisciplinary teams from different departments to work on analytics. These teams should concentrate on critical areas that require improvement. The IT department must be initiative-taking to offer the necessary data for analytics.
It is noteworthy that information alone will not make decisions. Instead, executives are responsible for decision-making, and therefore once useful insights have been generated from analytics, they should develop actionable strategies based on predictive analytics, track outcomes, and make changes accordingly.
Effective data analytics would promote data culture and guide higher education on strategic objectives that require continuous improvement. Successful management of data and IT alongside supportive leadership, talent management, changes in culture and decision-making would ensure that higher education overcomes bottlenecks in the application of data analytics for its advantages.
References
Grajek, S. (2013). Top-Ten IT Issues, 2013: Welcome to the Connected Age. EDUCAUSE Review, 48(3).
Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal, 4(3), 236-247.
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review. Web.