The document discusses re-thinking eDiscovery processes. It provides examples of case studies where analyzing and culling large amounts of electronic data upfront saved significant costs compared to loading all data for review. One case study details how indexing 500GB of data and analyzing the results culled it down to only 12GB for review, saving $407,600 compared to loading all data. Another case used analytics to filter 600GB of data down to reviewing only 1/4 of that, costing $28,000 total before review. The document advocates frontloading the analysis process to make more informed scope and review decisions.