MONITORING DATA PRODUCT QUALITY

The importance of data quality has been considered for many years and is well recognized among practitioners and researchers. A great deal of work has been done and most of the work to date fall under two main categories. One group of scientists has focused on mathematical and statistical model to work at the database layer to introduce constrain based mechanism to prevent data quality problems. Another group has focused on the management of the process of data generation. While the body of knowledge in the area is vast, the practical application of these approaches is still limited. One particular area which is still rarely considered in improving data quality is the development cycle of information system. Recognising this limitation and aiming to provide a practical-orient approach, we take a process centric view, and focus on preventing deficiencies during the IS design. In this paper we propose a process centric framework for data quality monitoring

AN APPROACH TO MONITORING DATA QUALTIY - PRODUCT ORIENTED APPROACH -

The data asset is increasingly becoming one of the top factors in securing organization success. Recognizing the importance of the quality of data, practitioners and researchers have considered for many years ways to improve data quality. Scientists have worked on mathematical and statistical model to introduce constrain based mechanism to prevent data quality problems. Management of the process of data generation has also attracted many researchers. The practical application of most of the proposed approaches is still very limited. Improving data quality with in the development cycle of information system is rarely integrated. Neither process mapping nor data modeling provides sufficient provision to define the required quality that data must conform to. Furthermore, ongoing monitoring of data for quality conformance is not possible without developing cost and time prohibitive data monitoring system. Recognising this limitation and aiming to provide a practical-orient approach, we propose a process centric information system design incorporating data product quality and conformance. In this paper we consider the benefit of a process centric framework for ongoing data quality monitoring

A rule based approach to data certification - applying DQXML for system independent data certification

Many researchers and practitioners have been attracted to improve data quality due to its monumental importance as a key success factor. Mathematical and statistical models have been deployed to information systems to introduce constrain and transaction based mechanisms to prevent data quality related problems. Entire management of the process and roles involved in data generation has also been scrutinized. Vast amount of knowledge base progressed in this area are mostly limited from practical perspective. Quality related meta data is absent from most information systems. Neither process mapping nor data modelling provides sufficient provision to measure quality or certification of data in the information systems. Furthermore, on-going monitoring of data for quality conformance through a separate process is expensive and time consuming. Recognising this limitation and aiming to provide a practical-orient comprehensive approach, I propose a process centric quality focused solution incorporating data product quality, conformance monitoring and certification. I base my work on DQXML developed by Ismael Caballero and deploy rigour of design science to construct InfoGuard. InfoGuard consists of DQXML incorporating quality meta data and an independent data quality monitor that provides certification of data through a rule based process centric framework for on-going data quality monitoring