Although many data analysis tasks are highly or even embarrassingly parallel in nature, climate scientists don't typically make much use of parallelism when they're analyzing the output from climate models. I certainly never did, until I was asked to bias-correct a large dataset with a short deadline, and the only way to get it done in time was to do it in parallel. In this talk, I will tell the story of what the process of shifting from serial to parallel data analysis using NCAR cyberinfrastructure looks like from a user perspective: the problem that motivated me to make the switch, hurdles I encountered along the way, what worked and what didn't, how I had to change my thinking and the way I structured the problem, how much effort it took, and what I was able to get out of it in the end.