IQ International Webinars:
Data Quality and the Second Law of Thermodynamics
Thermodynamics is the study of systems of particles and their behavior. Beyond studying temperature and understanding heat and cold, It provides us mechanisms to understand the behavior of particles as they move between what physicists call ‘accessible states’. If we begin to look at a single data element as a particle and its various values as accessible states, we can draw many useful parallels between a system of particles and a data set.
As a single example, we look at the entropy or ‘randomness’ of a system and we recall the second law of thermodynamics which states that the entropy of any system will always increase over time unless energy is inserted into the system. With this consideration, it is no wonder that data quality continues to worsen over time.
We will explore this and other analogies and attempt to leverage what we have learned about thermodynamics over centuries of study to better understand the complex behavior of our systems of data today.
28 July 2016
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 IQ International Webinars:
An IQ International 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 IQ International -- the International Association for Information and Data Quality. IQ International seeks to engage experts with experience and expertise in the field of data and information quality.
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