IDQ Webinars: with Dave Becker from The MITRE Corporation
Flexible & Generic DQ Metrics, Measurements & Assessments
In order to improve the quality of your data it is important to first establish a baseline measurement of exactly what the quality is, and whether that level of quality is adequate for your purposes. As you proceed with your data quality improvements, you can then compare interim measured levels of quality achieved against the baseline to determine if you are making progress toward your improvement objectives. This briefing discusses a flexible and generic approach to construct DQ metrics and target thresholds, and then use them in the measurement and assessment of the quality levels of DQ improvement efforts. The approach utilizes techniques that build on the identification of DQ subjects and business rule violations. For a given data quality (DQ) subject, there are many dimensions that are appropriate to consider. Each dimension must have a separate metric. There are also multiple potential users to consider, and each usage context may require individual quality thresholds of usefulness.
About the Author
David K. Becker is a Principal Information Systems Engineer with the MITRE Corporation working for the USAF at Wright-Patterson AFB located in Dayton, OH. He is currently engaged in projects involving enterprise architecture, information quality, data strategy, data management and program acquisition. David has over 30 years of experience in software development and information technology. While working as an employee, consultant, and senior technical officer for several public, private and academic organizations, he has had a broad range of assignments, including senior level IT and business consulting, technical leadership and management, project management, product research & development, seminar and workshop development, college level computer science course development and instruction, industrial liaison, international standards development, systems administration, and systems analysis, design and implementation. David’s particular areas of strength include business, application, data and technology architectures, systems dynamics, project management, quality management, statistical process control, information search and retrieval, and artificial intelligence.
About the Author
Robin Rappaport is the Data Quality Team Leader responsible for delivery of the Data Quality Initiative for Research Databases at the Internal Revenue Service (IRS). Her work and that of her team contributed to the IRS being awarded a Computerworld Honor and a Government Computer News (GCN) Gala Award. She has over 25 years of experience as a Data Quality practitioner. Her undergraduate degree was in Economics with Computer Science. Her graduate work was in Operations Research with a concentration in Mathematical Modeling in Information Systems. She has worked in both private (6 years) and public sectors (since June 1990). Her positions include Computer Programmer, Systems Analyst, and Operations Research Analyst.
In addition to IQ International, the International Association for Information & Data Quality, she is a member of the Institute for Operations Research and Management Science (INFORMS). She was Chairman, Individual Membership for the Washington, D.C. chapter from 1987- 1990. She was elected Secretary and served from 1990 - 1991.
Contact Robin by email at robin [dot] rappaport [AT] iaidq [dot] org