Blackout Automations Eliminate Days of Work to Meet DSAR Deadline
As one of Milyli’s service provider customers can attest, Data Subject Access Requests (DSARs) continue to grow in complexity, volume, and frequency. Over the last year, they noticed DSARs increasingly require meticulous redaction on tens of thousands of documents over short 30-days-or-less delivery timeframes.
For this provider, one such recent DSAR touched about 29,000 image and Excel files. Given the size of this document batch, even a reasonable manual redaction rate of 60 seconds per doc required an estimated 80+ business days to review. The provider realized they needed to automate work to meet the deadline with their team’s availability and scale.
“It’s hard to get an exact dollar amount for how much money was saved using Blackout; however, the additional 300 hours was certainly time the [case team] did not have to meet the DSAR deadline.”
This long-time Blackout partner used their existing knowledge of the software to concept a new DSAR workflow for their Relativity users. The drafted workflow implemented a mix of the customer’s existing library of terms, phrases, and regular expressions to discover PII and some additions and modifications to identify the specific PII relevant for the task.
Their library-based approach enabled the provider to segment the documents for quick anonymization with high-speed image and spreadsheet projects. They were able to locate, redact, and anonymize PII throughout the documents of the DSAR and eliminate several review passes over these documents. The batched documents then entered a review combining the coding of disclosable docs with the quality control checks on the redactions placed.
This task also featured 200 extremely large Excels with duplicate information. As a manual workflow, these Excels would require about 5 minutes each. However, with Blackout’s spreadsheet propagation projects, they automated and eliminated nearly 17 hours –about two full business days– of unnecessary review time.
Used accumulated work product, standard terms, and expressions to predictably and consistently anonymize data
Capitalized on the pairing of targeted, automated projects with manual review to effectively segment and isolate docs correctly
Optimized by integrating Blackout into the early stage of review to combine multiple actions and stages (i.e., QC and disclosure review)
Eliminated the need for additional tooling by using Blackout to produce native Excels with production text
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