This is Part 2 of 2 from my recent discussion with Tom Redman. In this episode, Tom and I discuss data quality and big data, including if data quality matters less in larger data sets, if statistical outliers represent business insights or data quality issues, statistical sampling errors versus measurement calibration errors, mistaking signal for noise (i.e., good data for bad data), and whether or not the principles and practices of true “data scientists” will truly be embraced by an organization’s business leaders.
Dr. Thomas C. Redman (the “Data Doc”) is an innovator, advisor, and teacher. He was first to extend quality principles to data and information in the late 80s. Since then he has crystallized a body of tools, techniques, roadmaps and organizational insights that help organizations make order-of-magnitude improvements.
More recently Tom has developed keen insights into the nature of data and formulated the first comprehensive approach to “putting data to work.” Taken together, these enable organizations to treat data as assets of virtually unlimited potential.
Tom has personally helped dozens of leaders and organizations better understand data and data quality and start their data programs. He is a sought-after lecturer and the author of dozens of papers and four books. The most recent, Data Driven: Profiting from Your Most Important Business Asset (Harvard Business Press, 2008) was a Library Journal best buy of 2008.
Prior to forming Navesink Consulting Group in 1996, Tom conceived the Data Quality Lab at AT&T Bell Laboratories in 1987 and led it until 1995. Tom holds a Ph.D. in statistics from Florida State University. He holds two patents.
Data Quality and Big Data
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Related OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
- Data Driven — Guest Tom Redman (aka the “Data Doc”) discusses concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.
- Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.
- So Long 2011, and Thanks for All the . . . — The OCDQ Radio 2011 Year in Review, featuring Jarrett Goldfedder, who discusses Big Data, Nicola Askham, who discusses Data Governance, and Daragh O Brien, who discusses Data Privacy.
- Big Data and Big Analytics — Guests Jill Dyché and Dan Soceanu discuss big trends in Business Intelligence, including Cloud, Collaboration, and Big Data, the last of which lead to a discussion about Big Analytics.
- Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
- Decision Management Systems — Guest James Taylor discusses concepts from his book: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics