As organizations face the challenges of processing exponentially growing data volumes, their reliance on analytics to unlock value from this data has intensified. However, the intricacies of big data, such as its extensive feature sets, pose significant challenges. A crucial step in leveraging this data for insightful analysis is an in-depth understanding of both the data and its domain. Yet, existing literature presents a fragmented picture of what comprises an effective understanding of data and domain, varying significantly in depth and focus. To address this research gap, we conduct a systematic literature review, aiming to delineate the dimensions of data understanding. We identify five dimensions: Foundations, Collection & Selection, Contextualization & Integration, Exploration & Discovery, and Insights. These dimensions collectively form a comprehensive framework for data understanding, providing guidance for organizations seeking meaningful insights from complex datasets. This study synthesizes the current state of knowledge and lays the groundwork for further exploration.

翻译：随着组织面临处理指数级增长数据量的挑战，它们对通过分析从数据中释放价值的依赖日益增强。然而，大数据的复杂性，例如其广泛的特征集，带来了显著挑战。利用这些数据进行深入分析的关键一步是深入理解数据及其领域。然而，现有文献对于什么构成有效的数据与领域理解呈现出一个碎片化的图景，在深度和侧重点上差异显著。为填补这一研究空白，我们进行了一次系统性文献综述，旨在描绘数据理解的维度。我们识别出五个维度：基础、收集与选择、情境化与整合、探索与发现，以及洞察。这些维度共同构成了一个全面的数据理解框架，为寻求从复杂数据集中获取有意义洞察的组织提供了指导。本研究综合了当前的知识状态，并为进一步探索奠定了基础。