For many enterprises, the data needed for predictive modeling applications comes at a price. A common question then for managers and data scientists building predictive systems is how much is a particular set of data worth? Despite a rich body of research that quantifies the impact of adding additional data to a predictive modeling application, there is almost nothing that offers managers a tool for understanding how to evaluate incremental data in economic terms.
In this talk we will present an overview of techniques that managers and data scientists may use to value their data investments from an ROI perspective. We will review the methodology
with the goal of understanding the concepts from a more intuitive level, so that both practitioners and managers can walk away better equipped to apply these tools in their own work. We will also illustrate the methods with two working case studies.
Brian Dalessandro is the director of data science at SparkBeyond, a research and consulting platform that accelerates discoveries and insights. Brian is also an active professor for NYU’s Center for Data Science graduate degree program. Previously, Brian built and led data science programs for several NYC tech startups, including Zocdoc and Dstillery. A veteran data scientist and leader with over 15 years of experience developing machine learning-driven practices and products, Brian holds several patents and has published dozens of peer-reviewed articles on the subjects of causal inference, large-scale machine learning, and data science ethics. When not doing data science, Brian likes to cook, create adventures with his family, and surf in the frigid north Atlantic waters.