Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities of your organization. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.

Peter Aiken

Founding Director, Data Blueprint

Peter Aiken is an acknowledged Data Management (DM) authority. As a practicing data consultant, professor, author, and researcher, he has studied DM for more than 30 years. International recognition has come from assisting more than 150 organizations in 30 countries, including some of the world's most important. He is a dynamic presence at events and author of 10 books and multiple publications, including his latest on Data Strategy. Peter also hosts the longest running webinar series dedicated to DM (hosted by dataversity.net). In 1999, he founded Data Blueprint, a consulting firm that helps organizations leverage data for profit, improvement, competitive advantage, and operational efficiencies.  He is also Associate Professor of Information Systems at Virginia Commonwealth University (VCU), past President of the International Data Management Association (DAMA-I), and Associate Director of the MIT International Society of Chief Data Officers.