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UNDERSTANDING DATA
QUALITY
Data quality dimensions in the
literature
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include dimensions such as accuracy, reliability,
importance, consistency, precision, timeliness,
understandability, conciseness and usefulness
Wand and Wang (1996: p92)
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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.
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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.
Summary of Philosophical Position and
Important Definitions
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Data quality could be emphasize on
these levels:
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Physical Concern with physical and physical
media for communications of data
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