Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. Challenges include integrating data from diverse sources and ensuring data quality in light of continuous change and adaptation. To this end, we aimed to systematically explore data management challenges and potential solutions in agile projects. We employed a mixed-methods approach, utilizing a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. In the SLR, we reviewed 45 studies in which we identified and categorized data management aspects and the associated challenges and solutions. In the practitioner survey, we captured practical experiences and solutions from 32 industry experts to complement the findings from the SLR. Our findings reveal major data management challenges reported in both the SLR and practitioner survey, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. Based on our findings, we present implications for practitioners and researchers, which include the necessity of developing clear data management policies, training on data management tools, and adopting new data management strategies that enhance agility, improve product quality, and facilitate better project outcomes.

翻译：管理软件产品及其开发过程中产生的数据对软件项目和敏捷开发团队构成了重大挑战。这些挑战包括整合来自不同来源的数据，以及在持续变更和适应的背景下确保数据质量。为此，我们旨在系统性地探索敏捷项目中的数据管理挑战及潜在解决方案。我们采用了混合方法，首先通过系统性文献综述来了解研究现状，随后通过实践者调查来反映实践现状。在系统性文献综述中，我们回顾了45项研究，识别并分类了数据管理的各个方面及其相关的挑战与解决方案。在实践者调查中，我们收集了32位行业专家的实践经验与解决方案，以补充系统性文献综述的发现。我们的研究结果揭示了在系统性文献综述和实践者调查中均报告的主要数据管理挑战，例如管理数据集成流程、捕获多样化数据、自动化数据收集以及满足实时分析需求。基于这些发现，我们提出了对实践者和研究人员的启示，包括制定清晰的数据管理政策、开展数据管理工具培训以及采用能够增强敏捷性、提高产品质量并促进更好项目成果的新数据管理策略的必要性。