As modern main-memory optimized data systems increasingly rely on fast scans, lightweight indexes that allow for data skipping play a crucial role in data filtering to reduce system I/O. Scans benefit from data skipping when the data order is sorted, semi-sorted, or comprised of clustered values. However data skipping loses effectiveness over arbitrary data distributions. Applying data skipping techniques over non-sorted data can significantly decrease query performance since the extra cost of metadata reads result in no corresponding scan performance gains. We introduce adaptive data skipping as a framework for structures and techniques that respond to a vast array of data distributions and query workloads. We reveal an adaptive zonemaps design and implementation on a main-memory column store prototype to demonstrate that adaptive data skipping has potential for 1.4X speedup.