Tackling Costly Slack Data Surprises: 96% Reduction in One Week

A client was faced with the daunting task of processing and reviewing over 400,000 unfiltered Slack messages, provided in Slack’s JSON format. The export included attachment links but not the actual attachment files. 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 given the volume and structural complexity of the data.

The Challenge

The data arrived in raw Slack JSON format, spanning 1,100 conversations and channels including public channels, private groups, multi-party instant messages (MPIMs), and direct messages. Before any substantive review could begin, several layers of complexity had to be addressed.

Identity normalization. Slack users often appear under multiple display names, usernames, or identities across an export. Without normalization, participant analysis is unreliable—the same person may appear as a different custodian depending on how their account was configured at different points in time. The dataset contained 725 participants whose identities required normalization before meaningful filtering could occur.

Bot and system message volume. Modern Slack environments generate significant automated traffic—workflow notifications, integration alerts, bot-generated updates—that is rarely responsive and always adds noise. This dataset contained 75,000 bot and system messages that needed to be identified and excluded before review began.

Attachment access. The export contained attachment references in the form of Slack-generated URLs, but the files themselves had not been bulk-downloaded. Pulling every attachment in the dataset regardless of relevance would have been both time-consuming and unnecessary. Targeted retrieval—downloading only attachments linked to messages already identified as relevant—was the only practical approach given the timeline.

RSMF production. The client’s downstream review platform required 24-hour RSMF (Relativity Short Message Format) files. The workflow needed to conclude with a production-ready export in that format.

The StreemView Workflow

Leveraging StreemView, the Slack JSON data was imported in full, preserving all attachment references and making all channels, groups, MPIMs, and DMs immediately searchable. The client was able to begin filtering and reviewing data within hours rather than days.

Rapid data ingestion. Slack JSONs were processed directly into StreemView, making conversations and all associated metadata instantly accessible and searchable across the full dataset.

Efficient filtering and tagging. Within four days, the client had identified and excluded the 75,000 bot and system messages, pinpointed the relevant conversations, and tagged 14,000 key messages for final review. StreemView’s conversation-native structure meant that filters applied to channels, participants, date ranges, and keywords operated against the full conversation graph rather than against individual message records—enabling the team to move quickly without sacrificing coverage.

Targeted attachment retrieval. Rather than bulk-downloading the entire attachment population—which would have been both slow and largely irrelevant—the team selectively retrieved only the attachments associated with the 14,000 tagged messages. This kept the scope manageable and the timeline on track.

Seamless RSMF export. With relevant messages tagged and attachments retrieved, the client generated 24-hour RSMF files in a few clicks for promotion to review and production, completing the workflow within the required timeline.

The Results

The implementation of StreemView led to efficiency gains that would not have been achievable through traditional methods:

• 400,000+ Slack messages across 1,100 conversations processed and made searchable
• 725 participants with multiple identities normalized for accurate custodian analysis
• 75,000 bot and system messages automatically identified and excluded
• 1,100 StreemView conversations compared to what would have been 27,000 RSMF documents under the traditional 24-hour conversion approach
• 96% reduction in the Slack data population, narrowing from 400,000+ messages to 14,000 highly relevant messages and attachments for review
• Review completed in under one week, compared to the traditional estimated timeline of three to four weeks

Why the RSMF Comparison Matters

The contrast between 1,100 StreemView conversations and 27,000 RSMF documents is worth unpacking. Traditional 24-hour RSMF conversion takes every message in a conversation and groups it by calendar day, producing one document per participant per day of activity. A conversation that spans 30 days between 10 participants produces 300 documents—most of which contain only a fraction of the actual exchange, severed from its context at midnight boundaries.

StreemView preserves conversations as coherent units. Filtering and review happen against whole conversations, not against daily fragments. The 96% reduction to 14,000 items was achieved by working with the data as it was actually structured—and only promoting the relevant portion to RSMF at the end, after relevance had already been established.

Lessons from the Matter

This case demonstrates several principles that apply broadly to Slack-based eDiscovery under time pressure:

Start with ingestion, not conversion. Processing Slack JSON natively before converting to RSMF preserves the full conversation structure and makes filtering far more effective. Converting to RSMF first forfeits the ability to filter at the conversation level.

Purpose-built tools compress timelines that traditional methods cannot. The three-to-four-week traditional estimate for this matter was not a conservative projection—it reflected what the workflow actually requires when Slack data is processed without tools designed for it. StreemView’s ability to ingest, normalize, filter, and export natively is what made the one-week completion possible.