UNDERSTANDING DATA QUALITY 1

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