FSU iSchool Professor Dr. Besiki Stvilia was awarded a $92,922 grant from The Institute of Museum and Library Services for a collaborative research project on data quality assurance practices with Texas A&M University (TAMU). Stvilia partnered with Dr. Dong Joon Lee, an Associate Professor in the Mays Business School at Texas A&M.

Stvilia and Lee share ties through the FSU iSchool doctoral program and the previous research they have collaborated on. For this specific project, they will use the IMLS grant to evaluate current data quality assurance (DQA) practices with the goal of making data sets more reusable. According to Stvilia, “data quality assurance involves defining what data quality means in a particular context and how to measure it, identifying targets and priorities for data quality interventions, and then designing specific data quality intervention strategies and actions.”

“Users need useful and usable data, not just big data … I’ve noticed, however, that often data repositories and curators’ understanding of the concept of data quality is fragmentary and lacks a strong grounding in the data and information quality literature and theory,” Stvilia said. “That limits the generalizability and reusability of their data quality models and quality assurance workflows.”

With the increasing popularity of data-driven science, the need for reliable data is also increasing. Stvilia and Lee will share the study’s findings with data curators to help them design and evaluate their DQA workflows. FSU will focus on the data collection and analysis part of the study, while TAMU will analyze “the existing data quality standards and metadata vocabularies.”

Data quality and digital data and information management have been long-term research interests for both Stvilia and Lee. In 2016, IMLS awarded Stvilia a $49,950 grant for a project on research information management systems, which Lee was also a Co-PI on.

“We have developed significant expertise in these areas,” Stvilia said. “Hence, we felt that we could make a useful contribution to a better understanding of data quality and quality assurance in institutional and research data repositories.”

With the IMLS grant, Lee and Stvilia hope to establish a shared knowledge base supported by both empirical evidence and data quality theory and literature.