Data Management Challenges in Agile Software Projects: A Systematic Literature Review

Agile software development follows an adaptive and iterative approach. However, the management of data (e.g., development data or product data) can pose significant challenges for projects and agile teams. We aim to identify and characterize key challenges faced in data management within agile projects and to examine potential solutions proposed in the literature. We used a Systematic Literature Review (SLR) to collect and analyse relevant studies. We identified 45 studies related to data management in agile software development. We then manually analysed and mapped data from these studies to categorise different data management aspects and identify challenges and solutions as identified in those studies. Our findings reveal major challenges such as data integration and quality assurance. We found implications of challenges on team members and the product delivery process. We found that teams frequently struggle to integrate heterogeneous data sources, ensuring data reliability and real-time analytics. Additionally, fragmented data collection and a lack of standardized practices can impede team collaboration and project transparency. The studies have also proposed various solutions to address those challenges, including the use of ontologies, diverse data management strategies, automated tools, and the adoption of quality-focused development methods. Solutions also include training to enhance data quality and analysis. This SLR provides in-depth insights and recommendations for practitioners, emphasizing the importance of robust data management strategies. It suggests integrating advanced data management techniques into agile frameworks to enhance decision-making and improve software project outcomes. The study highlights the need for a more focused approach to data management in agile environments, advocating tailored solutions to meet the unique demands of agile software development.