TDNet Discover

The purpose of this action research study is to improve the efficiency of the Jira ticket workflow and workload balance for the data engineering team at the Buyers Edge Platform (BEP). As digital transformation accelerates, the data engineering team faces increasing pressure to manage interdependent pipelines and high-velocity data ingestion. However, existing workflow governance structures often lag behind technical capabilities, leading to inconsistent ticket assignment, process bottlenecks, and technostress among engineers. Grounded in SocioTechnical Systems (STS) theory, this study will investigate the misalignment between the technical subsystem (Jira configuration) and the social subsystem (engineering team dynamics). This study will employ a mixed-methods action research design, using a census of historical Jira metadata to establish a quantitative baseline for cycle time and throughput. On the qualitative side, semi-structured interviews and focus groups will explore the lived experience of fairness and cognitive load. A purposive sample of 12-15 participants, including data engineers, team leads, and managers, will be recruited to co-design a new workflow framework. The intervention will introduce relative estimation (Story Points) and standardize service-level agreements (SLAs) to replace the ad hoc assignment method. Anticipated outcomes include a measurable reduction in cycle-time variability, a decrease in ticket reopen rates, and an improved perception of workload fairness. The findings will be disseminated through an internal DataOps playbook and executive presentations, offering a scalable model for socio-technical alignment in distributed technical teams.