Name
Abstract | ||
|---|---|---|
Corporate data of poor quality can have a negative impact on the performance of business processes and thereby the success of companies. Similar to machine tools corporate data show signs of wear (imaging a moving customer with a new address, for example) and have to be monitored continuously for quality defects. Effective quality control of corporate data requires metrics that monitor potential data defects with the most significant impact on the performance of a company's processes. However, due to company specific success factors and IT landscapes, it is hardly possible to provide generic metrics that can be implemented without any adjustment. This paper presents a method for the identification of business oriented data quality metrics. The presented approach takes into account company specific requirements from both a business and an IT perspective. The method's design and evaluation process is discussed in the context of three real-world cases. |
Year | Venue | Keywords |
|---|---|---|
2009 | ICIQ | quality control,business process,machine tool,data quality |
Field | DocType | Citations |
Artifact-centric business process model,Information management,Data quality,Systems engineering,Quality policy,Success factors,Business process,Data governance,Process management,Machine tool,Business | Conference | 7 |
PageRank | References | Authors |
0.47 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Otto | 1 | 384 | 40.02 |
| Kai M. Hüner | 2 | 111 | 6.96 |
| Hubert Österle | 3 | 474 | 57.36 |