Understanding Data Quality

1. Understanding Data Quality

2. Data quality dimensions in theliterature • include dimensions such as accuracy, reliability, importance, consistency, precision, timeliness, understandability, conciseness and usefulness • Wand and Wang (1996: p92)

3. 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.

4. 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.

5. Summary of Philosophical Position and Important Definitions

6. 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