This document discusses the concept of late binding in data warehousing and its importance for analytic agility. Late binding means delaying the binding of data to business rules and vocabularies for as long as possible. This allows data to be analyzed and related in different ways over time as needs change. The document outlines eight levels of analytic adoption in healthcare and how data binding needs evolve as organizations progress to more advanced analytics like predictive modeling and personalized medicine. It emphasizes the need for late binding approaches given the lack of comprehensive and persistent agreement on rules and vocabularies in healthcare.