Turning Data into Value with Advanced Analytics

Editor's Note: Countless companies fail to implement data management and advanced analytics properly -- and that's understandable, given the changing data landscape, its complexity, the rapidly increasing amount of data, and the accompanying integration challenges. In this piece from Data Management at Scale, Principal Architect, Piethein Strenholt, provides principles, observations, best practices, and patterns to overcome these challenges. Advanced analytics focuses on projecting future trends, events, and behaviors. It is the most complex form of value creation because it requires statistical models for newer technologies, such as machine learning and artificial intelligence. While it is getting easier to train and develop accurate models, deploying them into production -- especially at scale -- is a major challenge.