The Role of Analytics and Big Data in Shaping the Future of Projects Management

Anderson, J. S., & Williams, S. K. (2019). Turning Data into Better Decision Making: Asking Questions, Collecting and Analyzing Data in a Personal Analytics Project. Decision Sciences Journal of Innovative Education, 17(2), 126-145.

The project aims to create managers that utilize and appreciate information from data analytics and use it in their decision-making process. To achieve that goal, the researchers had the students prepare questions in their respective fields of interest. They embarked on an 8-week data collection exercise that sought to answer the questions in the fields such as health, fitness and academics. They used regression, data visualization and descriptive analytics techniques to answer the posed questions. The project discovered that the initially expected correlations were not always supported by the data (Anderson and Williams, 2019).

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Haynes, C. C. & Arbor, A. (2020, August). The Role of Self-Regulated Learning in the Design, Implementation, and Evaluation of Learning Analytics Dashboards. In Proceedings of the Seventh ACM Conference on Learning@ Scale, (pp. 297-300). Association for Computing Machinery.

The study seeks to understand students’ actual use and monitoring practices using trace data. The research reports on implementing, design, and evaluating dashboard views known as My Learning Analytics (MyLA). The study used 885 students from 10 courses. The researchers then obtained and analyzed trace data of students’ interaction with MyLA to note the frequency that each user used each view. According to Haynes and Arbor (2020), the low self-regulated learners had less faith in MyLA after using it and had the least use of grade distribution view.

Zeng, X., Garg, S., Barika, M., Zomaya, A. Y., Wang, L., Villari, M., ... & Ranjan, R. (2020). SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study. ACM Computing Surveys (CSUR), 53(3), 1-40.

It is a systematic literature review of the art of SLA-specific management for Cloud-hosted BDAAs. The contemporary approaches and challenges are surveyed, and a research taxonomy is proposed. The proposal is meant for the formulation of systematic literature review results. A new conceptualization of the SLA model is defined, and a proposal of the multi-dimensional categorization scheme, whose basis is the application of SLA metrics.

Araz, O. M., Choi, T. M., Olson, D., & Salman, F. S. (2020). Data analytics for operational risk management. Decision Sciences. 00(0), 1-4.

The article discusses the use of data to safeguard the global supply chain and improve its resilience. The article seeks to answer the question as to how organizations can make use of data analytics to improve their Operation Risk Management (ORM) practices. The authors develop three types of literature classification and proposals, such as the three-stage framework applicable to data analytics for operation risk analysis.

Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Role of Analytics for Operational Risk Management in the Era of Big Data. Decision Sciences. 0(0), 1-27.

It is a review of recent literature on ORM in regards to data analytics. The paper focuses on current ORM trends concerning natural and human-made disasters (Araz et al., 2020). The primary articles are Operations Management (OM) journals that include empirical and analytical outlets. It discusses how ORM has been facilitated by data analytics tools and methods plus the data monitoring needs. The article proposes a three-phased implementation process for data-driven ORM.