Anchoring data quality dimensions in ontological foundations
Anchoring data quality dimensions in ontological foundations
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DOI：
10.1145/240455.240479
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发表时间：
1996-11-01
影响因子：
22.7
通讯作者：
Wang, RY
中科院分区：
文献类型：
--
作者：
Wand, Y;Wang, RY
ACM 1996年11月39期，第11期87期的通信在现有客户中的潜在客户；可以对它们进行分析，以发现昂贵的公司习惯；它们可以被操纵，以预测未来的趋势。只有一个问题：那些巨大的数据库可能充满了垃圾……在一个人们正在转向全面质量管理的世界里，数据是关键领域之一。产品的质量取决于产品的设计和生产过程。同样，数据的质量取决于生成数据所涉及的设计和生产过程。为了设计出更好的质量，首先必须了解质量意味着什么，以及如何衡量质量。如文献中所述，数据质量是一个多维概念。经常提到的维度是准确性、完整性、一致性和及时性。这些维度的选择主要基于直觉[4]、行业经验[10]或文献回顾[13]。然而，文献回顾[25]表明，关于数据质量维度并没有普遍的共识。大多数数据质量研究都将准确性视为一个关键维度。尽管这个词有一种直观的吸引力，但对于它的确切含义，没有一个普遍接受的定义。例如，Kriebel[13]将准确性描述为“输出信息的正确性”。巴鲁和帕泽[4]将准确性描述为“记录值与实际值一致”。因此，这一术语似乎被视为等同于正确。然而，使用一个术语来定义另一个术语并不能达到明确定义这两个术语的目的。简而言之，尽管经常使用某些术语来表示数据质量，但并不存在一组严格定义的数据质量维度。
COMMUNICATIONS OF THE ACM November 1996/Vol. 39, No. 11 87 prospects among existing customers; they can be analyzed to unearth costly corporate habits; they can be manipulated to divine future trends. Just one problem: Those huge databases may be full of junk.... In a world where people are moving to total quality management, one of the critical areas is data.” The quality of a product depends on the process by which the product is designed and produced. Likewise, the quality of data depends on the design and production processes involved in generating the data. To design for better quality, it is necessary first to understand what quality means and how it is measured. Data quality, as presented in the literature, is a multidimensional concept. Frequently mentioned dimensions are accuracy, completeness, consistency, and timeliness. The choice of these dimensions is primarily based on intuitive understanding [4], industrial experience [10], or literature review [13]. However, a literature review [25] shows that there is no general agreement on data quality dimensions.Consider accuracy which most data quality studies include as a key dimension. Although the term has an intuitive appeal, there is no commonly accepted definition of what it means exactly. For example, Kriebel [13] characterizes accuracy as “the correctness of the output information.” Ballou & Pazer [4] describe accuracy as “the recorded value is in conformity with the actual value.” Thus, it appears the term is viewed as equivalent to correctness. However, using one term to define the other does not serve the purpose of clearly defining either. In short, despite the frequent use of certain terms to indicate data quality, there does not exist a rigorously defined set of data quality dimensions.