26 March 2019
Signal vs. Noise: The (Dis)connection between Data, Analytics, and Value
Digital technologies are delivering more and more information to decision makers where they need, when they need it. This success so far is ratcheting up the expectations from investors and executives to deliver financial returns and balance sheet impact from these technologies. Inundated in more data than ever before, leaders are turning to data scientists and techniques such as Machine Learning to unlock new value from all this information, but the potential is limited by the underlying data those methods rely on.
While statistical approaches show promise, there is a steep learning curve still ahead, with questions remaining as to what portion of "Analytics" are actionable by decisions makers. Using examples from predictive analytics case studies, Wood Mac will demonstrate the potential for complex pattern detection across diverse datasets but also highlight the lessons learned so far and what we will need to do as an industry to deliver material value.
Preston leads the Analytics Solutions business at Wood Mackenzie, where we work with our clients to discover new ways to unlock value with data and analytics. Since 2015 he has worked commercializing innovative new products and developing the Wood Mac strategy for transforming its data services into a cloud-based, integrated environment for analysis. Previously he served as a project director and manager within the Consulting team at Wood Mackenzie, advising E&P clients on their strategies for portfolio management, exploration, and unconventionals. Preston has published several thought pieces and presented at conferences on how to evaluate the value and risk between conventional and unconventional resource opportunities, as well as supply chain management for onshore US operations. Before his career in energy consulting, Preston worked on information technology development projects, several of which were awarded patents. Preston holds a BA in Economics from Princeton University and an MBA from the University of Texas. He holds certificates in Finance and Applications of Computer Science from Princeton, and has completed the John Hopkins Specialization for Data Science (via Coursera).