The Higher Education of Data Quality

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

On this episode of OCDQ Radio, we leave the corporate world, where data quality and master data management is mostly focused on the challenges of managing data about customers, products, and revenue, and we get schooled in the higher education of data quality.  In other words, we discuss data quality and master data management in higher education, which is mostly focused on the challenges of managing data about students, courses, and tuition.

Our guest lecturer will be Mark Horseman, who has been working at the University of Saskatchewan for over 10 years and has been on the implementation team of many of the University’s enterprise software solutions.  Mark Horseman now works in Information Strategy and Analytics leveraging his knowledge to assist the University in managing its data quality challenges.

Follow Mark Horseman on Twitter and read his Eccentric Data Quality blog to hear more about the challenges faced by Mark on his quest (yes, it’s a quest) to improve Higher-Education Data Quality.

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Clicking on the link will take you to the episode’s blog post:

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• Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).

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• The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

• Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.