We live in an era of data proliferation, where content is born quickly, but ages quicker. The standards enforced by regulatory bodies and customer expectations are continuously on the rise, increasing the rate at which simulation content becomes qualitatively stale. A year, a month, or less goes by and once-fresh satellite imagery turns into a history lesson; ex-state-of-the-art 3D models are reassessed as blocky embarrassments; formerly “photorealisticâ€� textures appear to our modern eyes as impressionist blurs; databases defined in yesterday's format de jour aren't even compatible with the latest hardware or IG. The constant churn means that, for many organizations, they are losing value to data obsolescence faster than they can add value through data acquisition. This paper delves into the issue of maintaining deep content libraries while meeting rising quality standards. We break down the problem space into a set of overlapping topics: • Scalability – Generating static data faster, leveraging dynamic data • Compatibility – Standards, converters, and outpacing the format treadmill • Timeliness – Capturing real-world changes We survey classical and novel solutions to these problems, make predictions based on current trends, and propose best practices.