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eDiscovery Vet Switches to Automated Redaction for Demanding Client, Ongoing Differentiation

The Challenge

A director-level professional with more than a decade working in eDiscovery shared a very common business challenge – a demanding client with a large case had very specific requests and parameters for how review work could be performed on their cases.

 

Reflecting the deepening digital literacy of legal professionals, this client requested the review tools employ automation to reduce production time as much as possible. The customer estimated receiving roughly 50,000 pages a quarter in the form of several dozen documents approximately 2,000 pages each. A manual review process would have taken months.

 

The client also requested a detailed post-mortem presentation examining metrics from the review. This presentation needed to help the legal team uncover some deeper contextual understanding of such a massive volume of the documents.

Key

Facts

  • 2,000+ Page Documents

  • Introduced Automated Redaction

  • Client-Required Reporting/Optimizations

The Solution

The customer collaborated directly with Milyli’s support. After a briefing call, Milyli proposed that the rule-building functionality in Blackout be used to highlight nuances in the PII of the documents.

 

For example, a rule might be written one way to redact dates found in a particular format while a second rule would be written for another. This method was applied to specific names, Social Security numbers, and account numbers. The team then determined which documents should be reviewed more thoroughly because they contained information about persons of interest. This rule-writing method resulted in markup metrics to inform better information governance, operational process, and more.

 

After this specific case, this customer began offering this approach to rule-building and analysis to other clients. Additionally, because the software consistently shaves enormous amounts of time off tedious work, Blackout is now the go-to redaction and markup tool for the team – particularly on larger cases and litigation on tight deadlines.

Information Blackout Can Flag

  • Personal Identification Numbers

  • Account IDs

  • Credit Card Numbers

  • Dates

  • Contact Information

    • Emails​

    • Phone Numbers

    • Addresses

  • Charts

  • Pivot Table Data

  • Embedded Objects

  • Notes/Comments

And much more.

Blackout uses regular expression (regex) text strings to find terms in documents that match the regex pattern so that they may be marked up, including the ability to redact or markup partial words and phrases. The user’s review team writes these with or without Milyli's assistance and can load them directly into Blackout or import them in bulk.