Data-driven decision making: implementing analytics to transform academic culture
The role of an academic is to work on four key areas—research, teaching, service, and outreach. As any faculty progresses through the chain of academic life, the emphasis on each of these areas most likely depends on the factors including the institution, department, and college s/he is working within. At the heart of the pursuit of success in academic life is the attainment of excellence in each of these areas—but how do we measure that? Research abounds on ways that universities measure success, particularly in research productivity and pedagogical innovation and excellence. In Krishen et al. (2019), a knowledge creation framework is proposed which can enable intersectionality and inclusion in academia; this concept was discussed in a recent review from Journal of Marketing Analytics as well (Baker 2019). As discussed in business research, analytics can be used as an objective and metrics-based tool for data reduction and understanding (Petrescu and Krishen 2017; Verhoef et al. 2016; Wedel and Kannan 2016). Analytics and metrics are an efficient way to obtain insights, to monitor, and optimize performance, as well as to maintain competitiveness (Krush et al. 2016; Wilson 2010).
The bottom-up approach provided in Fig. 1 shows three outcomes: those are performance and productivity (merit-based allocation), demographics (intersectionality and inclusion), and creativity and motivation (transformational culture). Performance and productivity, or merit-based allocation, requires key performance indicators, or targeted analytics (Chapman et al. 2018). To be able to measure performance based on merit, organizations must implement on-going analytics-based data collection and data quality systems (Becker et al. 2018; Ryazanova and McNamara 2016). As a result of this type of resource allocation, the demographics can become more intersectional and inclusive because fairness is increased through transparent, clearly stated, and objective measures. The last box, creativity, and motivation encompasses two key ideas: (1) transformational leadership in combination with diversity leads to higher creativity (Wang et al. 2016) and (2) motivation can be contagious (Krishen 2013) and follows from a carefully implemented organizational culture which serves as a crucial driver of decisions (Lee and Raschke 2018; Lee et al. 2016). A qualified department chair has the potential to implement bottom-up analytics-based decisions and institute resource allocations which are congruous with performance (Aggarwal et al. 2008; Honeycutt et al. 2010).
The use of analytics in evaluating academic success and effectiveness provides the benefit of a comprehensive performance picture that includes productivity, demographics, as well as creativity and motivation aspects. Analytics can also help with organizing all evaluation elements related to research, teaching, service, and outreach, and with obtaining metrics from objective and subjective sources, as well as qualitative and quantitative data. In combination, universities and departments can use both top-down (the previous decisions and culture) and bottom-up (data-driven analytics) to make more transparent and fair decisions.
- Krishen, A.S., E.A. Robleto, J. Meza, and J. Santana. 2019. From Homophily to Reality: Proposing the Intersectional, Diverse, and Inclusive Knowledge Creation Framework. In Marketing and Humanity: Discourses in the Real World, ed. A.S. Krishen and O. Berezan, 98–117. Newcastle upon Tyne: Cambridge Scholars Publishing.Google Scholar
- Sharma, D., N.D. Albers-Miller, L.E. Pelton, and R.D. Straughan. 2006. The Impact of Image Management, Self-Justification, and Escalation of Commitment on Knowledge Development in the Marketing Discipline. Journal of Marketing Education 28 (2): 161–171. https://doi.org/10.1177/0273475306288659.CrossRefGoogle Scholar