Brian Rohrback is the President and CEO of Infometrix, Inc., the first company to form around the field of chemometrics. Under his guidance, the company has built over 300 commercial software and firmware implementations of multivariate analysis for process instruments, lab-based systems, and field deployment. Dr. Rohrback's expertise is in the integration of multivariate data processing for a variety of analytical instrument methods and has more than 60 publications cover topics in chemistry, informatics, analytical instrument systems, process control, petroleum exploration, and chemometrics. He was awarded the International Society of Automation's highest honor for Technology Achievement in 2016.
The term Big Data implies a systematic approach to extracting the information content from multiple, byte-dense raw and meta data sources. Effective extraction of this information leads to improvements in decision making at all levels of the petroleum and petrochemical industries. To accomplish anything in the Big Data space, we need to combine traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to better understanding of data mining, neurocomputing, and machine learning. In order for industry to release the improvements that this form of AI promises, we need to address the issues of data silos, security concerns, and the danger that we might be only partly-right in the answers we attain. Guidance from our knowledge of process systems and the underlying chemistry is critical to success in the Big Data world.