数据质量评估：方法与技术的理论结构化概述
Data Quality Assessments: A Theoretically Structured Overview of Approaches and Methods
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中文总结 AI 辅助
本研究针对数据质量评估文献空白，引入含评估逻辑、驱动因素两维度的分类体系，衍生出4种高级方法及15种具体方法，为学术研究与实践应用提供理论结构化概述。
中文摘要 AI 辅助
数据质量对实践与学术研究均至关重要，描述性统计、人工智能及高级分析领域亦是如此。本研究针对现有文献中存在的明显空白，提供了全面且基于理论的、涵盖数据质量评估方法与技术及其核心特征和相互关系的概述。为此，引入了一种广泛的分类体系，采用两个理论维度：评估逻辑（形式化对非形式化）和评估驱动因素（规范对数据）。该分类体系衍生出四种高级数据质量评估方法及15种具体方法。研究结果对学术界具有重要意义，因其提供了基于理论的数据质量评估方式的概述与定义；对实践领域同样重要，可帮助专业人员在使用这些方法时做出明智决策，例如将其作为审计或全面数据质量评估策略的一部分。
英文摘要
The quality of data is crucial for both practice and academia, and this holds for descriptive statistics, AI and advanced analytics alike. This study addresses an apparent gap in the literature and as such presents a full and theoretically grounded overview of data quality assessment approaches and methods, as well as their defining characteristics and inter-relationships. For this purpose a broad typology is introduced that employs two theoretical dimensions, namely the evaluation logic (formal versus informal) and the assessment driver (norms versus data). This yields four high-level approaches and 15 methods for assessing data quality. The research results are relevant for academia, as they provide a theory-based overview and definition of the ways that data quality can be evaluated. The study is also relevant for practice because it allows professionals to make informed decisions on using these methods, e.g. as part of an audit or broad data quality assessment strategy.
发表机构
- HEINEKEN International（喜力国际）
机构由 AI 辅助整理，请以论文原文为准。
