The Sharable Content Object Reference Model (SCORM) afforded major benefits to the learning and training industry by creating an environment of interoperability for e-learning content and systems. However, the data that resulted from a learner experiencing SCORM content was often stored in proprietary data stores. As a result, potentially important data was locked away and unable to be used. Recently, emerging trends in big data, predictive analytics and data visualization renewed interest in accessing massive amounts of learning experience data. Paradata and correlations can be evaluated to provide learner recommendations for relevant content, to present visualizations to teachers so they can see how their content is being used, and to view meaningful analytics that among other things, can be used to refine and improve learning content. But how can this be accomplished when the requisite data is locked in proprietary learning management systems? This paper will discuss a novel method of intercepting SCORM communications and translating to standard Experience API (xAPI) ‘statements'. The xAPI is an emerging technology that allows tracking of experiential data and provides secure access to data once stored. After applying this solution, SCORM run-time data is stored in a learning record store (LRS) allowing secure access for analysis and visualization. It is possible to apply this solution in two distinct ways: content or server-side updates. Both of these are viable, and in some cases almost automatable solutions to exposing vast amounts of SCORM data. This paper will explore both methods for removing legacy data silos, will discuss the pros and cons of both content and server side updates, will report on the feasibility of these methods by describing software proofs-of-concept, and will illustrate several use cases and examples of the value of leveraging SCORM e-learning data once it is available en masse.