Listen to Laura Sebastian-Coleman, author of the book Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework, and I discuss bringing together a better understanding of what is represented in data, and how it is represented, with the expectations for use in order to improve the overall quality of data. Our discussion also includes avoiding two common mistakes made when starting a data quality project, and defining five dimensions of data quality.
Laura Sebastian-Coleman has worked on data quality in large health care data warehouses since 2003. She has implemented data quality metrics and reporting, launched and facilitated a data quality community, contributed to data consumer training programs, and has led efforts to establish data standards and to manage metadata. In 2009, she led a group of analysts in developing the original Data Quality Assessment Framework (DQAF), which is the basis for her book.
Laura Sebastian-Coleman has delivered papers at MIT’s Information Quality Conferences and at conferences sponsored by the International Association for Information and Data Quality (IAIDQ) and the Data Governance Organization (DGO). She holds IQCP (Information Quality Certified Professional) designation from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and a Ph.D. in English Literature from the University of Rochester.
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