The increasing adoption of commercial-off-the-shelf infrastructure components and the rising integration of sensors into assets have led to a notable proliferation of operational data in industrial systems. As a result, a significant portion of investment and risk management decisions now heavily rely on the provenance and quality of heterogeneous data, sourced both internally and externally from specific industrial systems. This paper presents a review that covers three critical aspects of data quality: first, ensuring data quality through deliberate design; second, understanding the dynamic interplay between data and its users within sociotechnical systems; and third, attributing ongoing value to data resources as their roles evolve. These aspects are examined through a lens encompassing both traditional and the state-of-the-art theoretical frameworks for defining data quality. In addition, we incorporate insights from contemporary empirical research and highlight relevant industry standards and best practice guidelines. The synthesised insights serve as a practical foundation and reference for researchers and industry professionals alike, enabling them to refine and advance their understanding of data quality within the landscape of data-driven industries.

中文翻译：

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在数据驱动的行业环境中了解数据质量：来自基本面的见解


商用现成基础设施组件的日益普及以及传感器与资产的日益集成，导致工业系统中运营数据的显著激增。因此，现在很大一部分投资和风险管理决策严重依赖于异构数据的来源和质量，这些数据来自特定工业系统的内部和外部。本文提出了一项涵盖数据质量三个关键方面的综述：第一，通过深思熟虑的设计确保数据质量;其次，了解社会技术系统中数据与其用户之间的动态相互作用;第三，随着数据资源角色的发展，赋予数据资源持续的价值。这些方面通过一个包含定义数据质量的传统和最先进的理论框架的镜头进行研究。此外，我们还整合了来自当代实证研究的见解，并强调了相关的行业标准和最佳实践指南。综合见解为研究人员和行业专业人士提供了实用基础和参考，使他们能够在数据驱动型行业的环境中完善和推进对数据质量的理解。