Live and simulation-based training systems are data-rich environments with a wealth of human-performance measurements. Knowledge Management Systems (KMS) that fuse multiple data sources to predict current proficiency and future training needs are essential to managing localized and longitudinal learning goals for individuals, teams, and teams of team. This is the foundation for adaptive proficiency-based training that improves training efficiency and effectiveness. A KMS can take many forms but fall into three distinct categories of technology and capability. This paper illustrates three primary categories of managing learning that apply to the majority of KMSs available today. Recent advances in training technologies along with lessons learned from implementation in the simulation and live operations domains have made it possible for the Air Force Research Laboratory (AFRL) engineering and research teams to execute advanced capture, processing, and visualization of training research data. This paper delves into some challenges that exist to measuring, tracking, warehousing, and tracking proficiencies across human performance data sets within the U.S. Air Force (USAF) tactical domains. These challenges include tailoring assessments by domain, strategies for handling the ever-increasing complexity of training in live, virtual, and constructive (LVC) environments, and the need for operationally-relevant and scientifically-based methodologies and practices when measuring and tracking training. This paper draws upon lessons learned from implementation of an enterprise Knowledge Management System and looks at the way forward in terms of performance measurement, data storage, and proficiency prediction.