Data quality is critical in supporting data driven research and decision making. Poor data quality has a significant cost to both data providers and data consumers, yet it is not easy to manage. Data quality requires a high level strategy, practical procedures, tools, standards and people coming together to have sustainable data quality management for an organisation. This proposed Australian Data Quality Interest Group will provide a forum for Australian data providers, repository operators and data consumers to discuss challenges and strategies for meeting data quality standards and procedures, and also to connect to international data quality communities for exchanging experiences and best practices.

This survey will gather information on where we (interest group member organisations) are up to in managing data quality and identify principal challenges and pain points. The survey results will help the Interest Group identify common challenges, share practices and experience, and plan future activities.

Bear in mind that data quality may have many aspects,  and when answering each survey question, please cover as many aspects as possible. For example, the ESIP Information Quality Cluster covers four perspectives of data quality, namely:

* scientific quality (accuracy, precision, uncertainly, validity and suitability),

* data product quality (how well the scientific quality is assessed and documented, completeness of metadata and documentation, provenance and context),

* stewardship quality (how well data are being managed, preserved, and cared for by an archive or repository),

* service quality (how easy is it for users to find, get, understand, trust, and use data)