Data Quality Management: An Overview of Methods and Challenges.

Data quality is a problem studied in many different research disciplines like computer science, statistics and economics. More often than not, these different disciplines come with different perspectives and emphasis. This paper provides a state-of-the-art of data quality management across these disciplines and organizes techniques on two levels: the macro-level and the micro-level. At the macro-level, emphasis lies on the assessment and improvement of processes that affect data quality. Opposed to that, the micro-level has a strong focus of the current database and aims at detection and repair of specific artefacts or errors. We sketch the general methodology for both of these views on the management of data quality and list common methodologies. Finally, we provide a number of open problems and challenges that provide interesting research paths for the future.