Presentation on theme: "UNDERSTANDING DATA QUALITY 1. Data quality dimensions in the literature include dimensions such as accuracy, reliability, importance, consistency, precision,"— Presentation transcript:
Data quality dimensions in the literature include dimensions such as accuracy, reliability, importance, consistency, precision, timeliness, understandability, conciseness and usefulness Wand and Wang (1996: p92) 2
Kahn et al. (1997) developed a data quality framework based on product and service quality theory, in the context of delivering quality information to information consumers. Four levels of information quality were defined: sound information, useful information, usable information, and effective information. The framework was used to define a process model to help organisations plan to improve data quality. 3
A more formal approach to data quality is provided in the framework of Wand and Wang (1996) who use Bunge’s ontology to define data quality dimensions. They formally define five intrinsic data quality problems: incomplete, meaningless, ambiguous, redundant, incorrect. 4
Summary of Philosophical Position and Important Definitions 5
Data quality could be emphasize on these levels: Physical - Empirical - Syntactic - concerned with the structure of data Semantic - concerns with the meaning of data Pragmatic - concerns with the usage of data (usability and usefulness) Social - concerns with the shared understanding of the meaning of the data/information generated from the data Concern with physical and physical media for communications of data 6
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