This document discusses how big data from cloud-based translation tools can provide insights through benchmarking and analytics. It provides examples of metrics that can be tracked, such as translation productivity, use of translation memory and machine translation, and project manager performance. Aggregating anonymous data from many users could establish universal performance standards, help identify areas for process improvement, and provide cost savings estimates for tools like translation memory. However, challenges include cleaning and interpreting diverse data from many users and systems.