Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address these challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. These solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success.

翻译：背景：管理软件产品及其开发相关数据对软件项目和敏捷开发团队构成重大挑战，包括整合多源数据以及在持续变更和适应过程中确保数据质量。目标：本文系统探讨敏捷项目中的数据管理挑战与潜在解决方案，旨在为研究人员和实践者提供关于数据管理挑战与解决方案的见解。方法：我们采用混合研究方法，包括通过系统性文献综述（SLR）了解研究现状，随后开展实践者调查以反映实践现状。SLR回顾了45项研究，识别并分类了数据管理维度及其相关挑战与解决方案。实践者调查收集了32位深度参与数据管理的行业实践者的实际经验与解决方案，以补充SLR的研究发现。结果：研究发现实践中存在的主要数据管理挑战包括：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。针对这些挑战，研究识别出自动化工具、去中心化数据管理实践和基于本体的方法等解决方案。这些方案通过提供适应敏捷项目需求的灵活框架，能够增强数据集成、提升数据质量并支持实时决策。结论：本研究明确了敏捷开发中数据管理的重要挑战与可行解决方案。研究结果为实践者和研究人员提供了实践启示，强调开发有效的数据管理实践与工具以应对这些挑战，从而提升项目成功率。