1. A method of processing data, comprising: collecting data about a set of transactions, each transaction including a plurality of components associated with a plurality of tasks, said data including time series data for each task based on execution times of components associated with said each task during said set of transactions; determining a normal execution time for each task based on execution times of components associated with said each task; determining a threshold deviation for each of said tasks dynamically based on execution times of said components associated with said each task during a number of transactions of said set, said determining said threshold deviation for each task includes applying weighted averaging and trend identification to said execution times of said components associated with said each task, said threshold deviation for each task is a threshold deviation from said normal execution time for each task; determining whether said transactions have execution time values beyond a threshold; for each transaction having an execution time value beyond said threshold, automatically identifying one or more components using said threshold deviations based on a deviation in time series data for a task that is associated with said one or more components of said each transaction; and reporting said one or more components for said each transaction, said reporting includes providing an indication that said one or more components are a potential cause for said each transaction having an execution time beyond said threshold.