IDQ Webinars: with Gian Di Loreto
How to integrate Data Quality as part of your Master Data Management Solution
Data Quality is a problem that everyone has, and that by now most of us understand. Gone are the days when we had to explain to decision makers what data quality is and why it’s important. However, we still have no out of the box solution to our data quality problems. Progress is fleeting and incremental at best.
Master Data Management (MDM), on the other hand, is a field in which great strides have been made. Standard vocabulary and metrics exist, enterprise software solutions, whether standalone, or bundled with database engines exist and continue to improve. What is interesting to the data quality practitioner is that data quality usually manifests itself as a small subset of features in an MDM tool. Rather than using what we’ve learned about MDM to attack the ever-present issue of data quality, we have minimized the importance of data quality within our discussions of MDM.
This webinar will attempt to reconcile these two practices. Ultimately, we believe there is a solution to the data quality problem and there are aspects of MDM that can lead us to it. We will focus of Golden Record Management and tie the concept of a golden record to the concept of a ‘subject’ in a data quality project. We will also discuss the data enrichment techniques applied in MDM to slowly changing master data and learn how we might extend these techniques to large sets of transactional data.
Ultimately, we realize that in order to reach new heights we must not be afraid to stand on the shoulders of those whose work has preceded ours. A great deal of thought, time, and energy been invested in Master Data Management; let’s see if we can leverage this work and arm ourselves with the knowledge and solutions represented by MDM in our battle with data quality.
About the Author
Gian Di Loreto is one of the USA's leading authorities on human resource data quality and is currently Senior Consultant at Profisee Group.
Gian holds a Ph.D. in particle physics from Michigan State University. He began his career as an experimental physicist at Chicago's world-renowned Fermi National Accelerator Laboratory (Fermilab), where he spent several years performing statistical analyses and identifying errors in massive databases generated by proton/anti-proton collisions. After his tenure at Fermilab, he leveraged the analytical skills he developed in a scientific context as a software developer for a firm that reconciled and corrected data generated by General Motors' pension funds.
He has worked at Loreto Services & Technologies, a consulting and IT outsourcing firm whose mission is to help companies understand what is in their databases, identify errors and discrepancies, integrate disparate databases and, in the process, eliminate historical and ongoing mission critical data errors using proven, scientific and statistical techniques.
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
About IDQ Webinars:
An IDQ Webinar is a moderated webinar open to all, designed to serve as discussion platform for the data and information quality community. The webinars are supported and organized by the International Association for Information and Data Quality. IAIDQ seeks to engage experts with experience and expertise in the field of data and information quality.