2016 - Regional Seminar - Salt Lake City
Applied Data Science, Big Data and The PI System – Teaching the Next Generation of Engineers the Skills of Today
Industrial applications of big data and IOT are established and well known. However, studies have shown that data utilization is low in many organizations, which greatly limits the scope and sustainability of big data initiatives. To compound the problem organizations are exposed to a variety of data types: relational, signal, spatial, and unstructured data. Each data type requires different approaches to “mine” information and gain business insight. Traditional process, mining, and industrial engineers are being called upon to be data scientists early on in their careers. Low levels of data utilization will continue unless changes are made on how engineers are schooled and trained. Industry and academia must work closer together than before to address this problem. The University of Utah’s Mining Engineering Department is working on a model to teach engineering applications of big data. Courses like these require close collaboration between industry and academic intuitions. An approach is presented showing how University of Utah has partnered with mining companies and OSIsoft to create a big data class.
University of Utah
W. Pratt Rogers
Pratt is an Assistant Professor of Mining Engineering at The University of Utah. He is deeply interested in the use of data to improve organizations. He has extensive consulting and research experience in the mining industry related to business intelligence, mining technology, operational excellence, and safety system optimization.