Reducing Review Costs through Effective Data Filtering

By Matthew Felten

The uncertainties surrounding forensic data collection and the e‑discovery process make establishing a budget for impending litigation expenses a daunting task. How much data is going to be collected, how long it will take outside counsel to review those documents, and how many documents will actually be produced are just some of the unknown variables.

Approximately 75% of litigation costs are generated from attorney review, so reducing the amount of data that needs to be reviewed is one of the most obvious ways to cut costs. However, efforts by organizations to limit data volumes often come into direct conflict with the recommendations of outside counsel, who tend to prefer over‑inclusive collections. So where is the middle ground?

Utilizing an Early Data Assessment (EDA) tool can effectively reduce your data population, but will also give outside counsel peace of mind that nothing was missed. DeNIST, de‑duplication, search terms, and date restrictions can all be applied prior to sending the data to outside counsel. Search term hit reports can also be provided to outside counsel to ensure defensibility. These reports show not only what was searched, but also what wasn’t. EDA also allows for effective search term negotiation through iterative sampling.

So how do you know which EDA platform to use? Do you purchase something that sits behind your own firewall or partner with an established EDA service provider? There are pros and cons to both scenarios, so it’s important to thoroughly test the product and associated workflows. Use a data set that was previously processed with search terms, ingest that data into an EDA platform, then run the terms and compare the results of the responsive hits. Did you get more hits, fewer hits, or about the same amount? What was the quality of the tool’s reporting mechanism? Were you able to export the filtered data in accordance with your downstream review tool?

The right EDA tool enables you to be proactive and establish a tested workflow for data filtering—both of which are key to the effective reduction of data and review costs.