Asset management is largely a data driven process as one of the key elements of asset management is using data to support decisions. However, the databases representing inventory and historical records of road, bridge, and roadside assets collected using video logging, automated pavement distress survey, regular inspections, structural health monitoring, and other methods can rapidly explode. Such data is key to maintaining physical assets in a state of good repair and addressing safety issues. Simple tasks such as capture, curation, storage, search, sharing, and analysis are challenging as our ability to collect data expands. Ideally “better” data will be understandable, transparent, interoperable, automated, and visual. Some of the experiences with “big data” in other fields may help to manage, more pro-actively, our data assets to support the management of our physical assets.  “Big Data” refers to data sets that are so large and complex they are not easily manipulated using the commonly available database tools. These challenges are characterized by the three “V’s” – velocity, volume and variety. This project will identify areas where big data may be an issue for asset management in DOTs and develop strategies for dealing with big data.