Abstract
Modern information systems supply operative and analytic/statistical data for users. The system design and the usage must be done in such a way that high quality of the stored data is assured. This implies the necessity of fixing quality objectives, defining its characteristics, choosing appropriate measures and measurement techniques and, finally, of embedding this into a step by step procedure for data quality assurance. We start by examples of bad business data, discuss a data quality control methodology and its workflow, offer a first insight into the corresponding metadata model, and demonstrate DaRT – a data quality reporting tool on top of Oracle’s Warehouse Builder (OWB).
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Notes
- 1.
The (synthetic) database of (fictive) Service GmbH used was kindly made accessible by A. Schlaucher, Oracle Deutschland GmbH.
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Lenz, HJ., Borowski, E. (2012). Business Data Quality Control: A Step by Step Procedure. In: Lenz, HJ., Schmid, W., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 10. Frontiers in Statistical Quality Control, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2846-7_25
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