Multi-Source Data Analysis Challenges

At least three factors are converging to make multi-source data analysis pervasive in the near future. Digital data acquisition is becoming easier and cheaper. Computational simulations are gaining fidelity and detail while becoming more practical to compute. And everything is becoming networked so data from many sources can be reached by a single user or application. From cross validation of computational and experimental models to steering computational simulations with real world observations, bringing data from multiple sources together is much more powerful than using each source separately. And computer systems can provide support for users in situations where they would be overwhelmed by volume or complexity without the support. But multi-source data analysis is harder than single source data analysis, and designing, building and deploying tools for others to use for it is very hard.