In December 2021, the Institute of Electrical and Electronics Engineers (IEEE) held an International Conference on Big Data. IEEE is the world’s largest technical professional organization dedicated to the advancement of technology, and inspires a global community through its publications, conferences, technology standards, and professional and educational activities. This includes events like the Robotics in Disaster Aid Relief showcase hosted on our campus last month, and the publication and presentation of research performed by Capitol Tech’s own professors and students.
Dr. Sondria Miller, Dissertation Chair, Business Analytics and Decision Science and Adjunct Professor of Doctoral Programs at Capitol Tech, along with Gregory Smith, Capitol Tech Business Analytics and Decision Science Ph.D. candidate, were featured at the Big Data event and presented their IEEE published paper, “Impact of organizational factors on big data analytics adoptions in U.S. public sector organizations.” In their research, they outline issues surrounding big data analytics (BDA) when adopted by employment sectors. They argue that while BDA is seen to increase productivity within organizations, there is a substantial cost associated with adopting this practice – a cost felt not only by the organization, but the taxpayer as well. Within the public sector, these loses are especially exacerbated as the perceived waste of funds is scrutinized much more than seen in private sectors by stakeholder counterparts like political overseers, taxpayers, and the general public. This, in turn, is seen to negatively affect agency decisions to invest in and use BDA, as any risk of failure may seemingly outweigh the potential benefits of this system.
Big data and how these systems can be implemented is a hot topic in today’s business world. According to Forbes (2021), using BDA can help companies and the government “work smarter, not harder.” This is because big data can be taken from many different avenues and sources, then fused into one cohesive model to provide usable data. But this, of course, requires an upfront investment of not only money, but time, employee training, model and algorithm building, and software compatibility, to name a few key considerations. Company and agency leaders must also understand these investment costs and know their end goal with what BDA can offer them, as well as understand how to deal with the huge amounts of data that will be received. This can be an overwhelming undertaking, especially when under certain restraints or scrutiny, which can deter organizations from even thinking about the option. But investing in BDA should not be overlooked, as it can be critical to streamlining operations, providing cybersecurity, improving public management, preventing major losses, and increasing public trust.
Miller and Smith’s research differs from that which is currently available because it focuses more on the organizational flaws of BDA adoption versus the technical successes of the system. It also highlights the important differences between public and private sectors in terms of BDA adoption, citing that the “operational realities” are quite varied. Expanding on this perspective could help public sector employers and managers make more informed decisions for their agencies based on relevant research, which reduces uncertainty and perceived risks.
An extensive, systemic literature review was performed within this study to address the main question of “how do organizational context elements impact BDA adoption outcomes in U.S. public sector organizations?” This research question was then broken down into three primary areas of review: Public value, organizational context factors, and organizational culture. Each of these elements address the construct of an organization, in terms of its capability of physical success in BDA implementation (i.e. does BDA further its objectives?), characteristics of the organization that could affect BDA outcomes (i.e. size, management, human capital, etc.), and the cultural perceptions of BDA (i.e. expectations and assumptions of the system).
A conceptual framework was then developed to give structure to the research, which was a parallel, nested mixed-methods approach of qualitative case study and quantitative survey. The case study focuses on the organization within a time-bounded process, while the survey presents data about the organizational culture. Together, these study elements can be triangulated into mixed data and translated into results. Currently, this paper serves as a preemptive summary of their collaborative project and study design. Final research findings are expected to be available in Spring 2022.
Collaborating on research publications are just a few of the many opportunities afforded to students at Capitol Technology University. Our professors not only teach, but also work in their field of study as professionals and can offer the best perspective and education on what the industry is really like and what employers are looking for in their employees. This approach ensures that our graduates are the most prepared for their careers upon program completion.
To read the full paper by Dr. Sondria Miller and Gregory Smith, click here.
If you are interested in learning more about our Business Analytics and Data Science program, click here.
For more information on our other degree programs taught by world-leading experts, click here.
Migdal, M. (2021, August 23). How big data empowers organizations to work smarter, not harder. Forbes. Retrieved from https://www.forbes.com/sites/forbestechcouncil/2021/08/23/how-big-data-empowers-organizations-to-work-smarter-not-harder/?sh=36c8ee18532f
Miller, S., & Smith, G. (2022). Impact of organizational factors on big data analytics adoptions in U.S. public sector organizations. 2021 IEEE International Conference on Big Data. DOI: 10.1109/BigData52589.2021.9671758