Application of the Scrum methodology in a data analysis team

Abstract

The Fourth Industrial Revolution has precipitated substantial advancements in technologies originally developed during the Third Industrial Revolution, markedly enhancing data analysis capabilities. These advancements have resulted in a dramatic increase in the volume, velocity, and quality of data generated, thereby strengthening organizational decision-making processes. For teams engaged in data analysis, the adoption of an appropriate management methodology is pivotal to optimizing performance, as it directly influences both autonomy and analytical efficiency. This study aims to investigate how management practices can be enhanced within a data analysis team operating in a multinational beverage corporation, specifically one that handles Big Data, to achieve superior performance outcomes. To this end, the Agile Scrum methodology was selected due to its iterative, adaptive nature, which is well-suited to dynamic analytical environments. The methodology was implemented through a four-step process: training the team in Scrum principles, integrating Scrum practices into daily operations, conducting regular assessments to evaluate adaptation, benefits, and areas for improvement, and standardizing the optimized workflow. Weekly evaluations were conducted to ensure continuous improvement. Over the four-week implementation period, the introduction of Scrum resulted in a 45% increase in task completion, with 35% of tasks being autonomously selected by team members. Furthermore, in the most recent month, the team independently selected seven tasks, in contrast to the previous month when all tasks were assigned by the team leader. Notably, the average task completion time was reduced from eight days to five days, demonstrating the method’s impact on efficiency.