Assessing Information Quality: Use Cases for the Data Stewardship Maturity Matrix

Information Quality (IQ) is an important characteristic of a data repository. Being recognized for providing "good" or "high" quality information enables trust to be built between the data repository and its communities, and therefore, fosters collaborations and potentially improves the utility of its data holdings. However, currently, a common standard or framework does not exist to allow IQ to be assessed consistently across different data repositories. There are several aspects that need to be considered when evaluating IQ. In particular, the data stewardship practices applied to datasets during the curation process can have significant impact on the accessibility, usability, understandability, and integrity of the datasets over time. The Data Stewardship Maturity Matrix (DSMM) provides a framework for the evaluation of a dataset's quality based on nine distinct categories. For each of the categories, the DSMM provides criteria that can be used to apply a 5-level rating to an individual dataset, ranging from Ad Hoc to Optimal. This presentation introduces the overview of the DSMM and the recommended process for using DSMM to evaluate the quality of a dataset. The presentation will also provide the key findings after applying the DSMM to several datasets, including those from the Advanced Cooperative Arctic Data and Information Service, the National Center for Atmospheric Research, and the Long Term Ecological Research's Santa Barbara Coastal site. The presentation concludes by summarizing the crucial lessons learned and the potential benefits when a data repository uses the DSMM to assess and convey the quality of its datasets.