- Using indicators to identify problems with data quality (e.g., essential data quality checks that should be routinely employed to monitor data quality);
- Tools (e.g., visualizations, dashboards) developed to audit data quality;
- Techniques for anticipating changes to data quality when data sources or data elements change over time;
- Procedures for resolving data quality challenges.
10 August 2010
Call for Proposals for Session Addressing Data Quality at the 2010 Annual Conference
The Scientific Program Committee is seeking proposals for a special conference session addressing data quality. The purpose of the session is to highlight methodologies and “best practices” used to monitor data quality. Examples include: