Challenge
A client was faced with the daunting task of processing and reviewing over 400,000 unfiltered Slack messages, including attachment links but without the actual attachments, all provided in Slack’s JSON format. The client had less than one week to identify key conversations and participants—a task that, using traditional methods, would have taken an estimated three to four weeks due to the volume and complexity of the data.
Solution
Leveraging StreemView, Slack JSON data was imported, preserving attachment references while making all channels, groups, multi-party instant messages (MPIMs), and direct messages (DMs) immediately searchable. This enabled the client to begin filtering and reviewing data within hours rather than days or weeks.
Key steps in the solution included:
- Rapid data ingestion: Slack JSONs were processed directly into StreemView, making conversations and metadata instantly accessible.
- Efficient filtering and tagging: Within four days, the client identified and excluded 75,000 bot/system messages, pinpointed relevant conversations, and tagged 14,000 key messages for final review.
- Targeted attachment retrieval: Instead of bulk-downloading unnecessary data, the team selectively retrieved only the attachments relevant to the tagged messages.
- Seamless export: The client was able to generate 24-hour RSMF (Relativity Short Message Format) files in just a few clicks for promotion to review for production, significantly streamlining the process.
Results
The implementation of StreemView led to unprecedented efficiency gains, dramatically reducing review time and effort.
Key Metrics
- 400,000+ Slack messages across 1,100 conversations processed.
- 725 participants with multiple identities normalized.
- 75,000 bot/system messages automatically excluded.
- 1,100 StreemView conversations compared to what would have been 27,000 RSMF documents under traditional methods.
- 96% reduction in Slack data, narrowing down to 14,000 highly relevant messages and attachments.
- Review completed in under one week, compared to the traditional 3-4 weeks.
Conclusion
By leveraging StreemView, the client was able to efficiently ingest, reduce, and export Slack data at an unprecedented speed. The case demonstrates how purpose-built technology can drastically reduce manual review time, enhance data relevance, and streamline workflows in Slack-based investigations and litigations.
