How to Review Modern Data for eDiscovery

What is modern data review and why it matters

Modern data includes Slack and Microsoft Teams messages, mobile texts and iMessages, cloud storage (OneDrive, Google Drive, Box), shared documents, comments and reactions, and app logs. These sources are rich in context and metadata but rarely live in neat email-like containers. Many arrive as JSON or proprietary exports that need normalization before they’re review-ready. Why it matters: if you try to force modern data into a legacy email/PDF workflow, you risk breaking threads, losing metadata, and misreading timelines—problems that directly affect admissibility, proportionality, and sanctions exposure.

• Key risk: Producing screenshots or flattened PDFs of chat data can strip timestamps, participants, edits, and deletions—inviting challenges to authenticity.
• When it arises: Employment, trade secret, harassment, and restrictive covenant cases commonly hinge on Slack or Teams chats and mobile messages.
• Immediate action: Issue a legal hold that specifically covers cloud apps and mobile devices; request native exports and audit logs before any auto-deletion triggers run.

For a deeper overview of defensible scoping and methods, see our eDiscovery services page at forensicdiscovery.expert/services/ediscovery/ and our forensic collection capabilities at forensicdiscovery.expert/services/digital-forensics/.

At a glance: fast risks and quick wins

• Auto-deletion windows: Slack free tiers and Teams retention policies may purge data in 30–90 days. Why it matters: move fast to preserve or you may be stuck with partial histories.
• Time zone drift: Mixed time zones across exports muddle timelines. Why it matters: counsel needs reliable sequence to prove intent or rebut allegations.
• Hidden context: Threads, reactions, emoji, edits, and attachments carry meaning. Why it matters: a “thumbs up” can be assent; edits can reflect intent.
• Quick win: Ask for native chat exports plus admin/audit logs and channel membership rosters early—these are low-burden but high-value.

Counsel playbook: a defensible workflow

Below is a practical, court-ready workflow from preservation to production that aligns with proportionality and speeds review.

• Step 1: Expand the legal hold to include Slack/Teams, mobile devices, shared drives, and SaaS apps; disable auto-deletion for custodians and channels in scope.
• Step 2: Decide targeted vs. full collection based on claims and proportionality; for chats, preserve at least entire threads/channels for relevant periods to maintain context.
• Step 3: Request specific natives/logs your experts will need: native chat exports (JSON), attachments, channel/user rosters, retention settings, and audit logs; capture mobile texts in forensic format.
• Step 4: Validate and document chain of custody; confirm hash values, time zones, and completeness with sampling; memorialize admin settings and collection parameters.
• Step 5: Normalize for review: convert JSON to readable, threaded conversations with preserved metadata; de-duplicate thoughtfully to avoid breaking context.
• Step 6: Reporting that maps facts to claims and timelines; provide illustrated timelines tying chats, emails, and files to key events and declarations.

Why it matters: each step translates directly to meet-and-confer leverage and defensible discovery responses. It shows diligence, manages cost, and reduces motion practice risk.

Deep dive: chat and collaboration data in plain English

Chats are not emails. A Slack or Teams thread is a living conversation with replies, edits, emojis, reactions, and linked files. Native exports often arrive as JSON—structured text that machines love but lawyers do not. Our job is to turn that raw data into clean, chronological, searchable conversations without losing the details that make it reliable.

Why it matters: In TROs and preliminary injunctions, a single threaded exchange can make or break a showing of misuse or breach. In sanctions disputes, preserved metadata (who edited what, when, and from which device) proves authenticity and defeats “it was altered” arguments.

• Step 1: Verify scope and integrity by cross-checking user/channel rosters and audit logs against the export to confirm nothing is missing.
• Step 2: Rule out alternate explanations such as time zone offsets, scheduled sends, or bot messages before drawing conclusions about timing or intent.
• Step 3: Ask for production in a review-ready, threaded format with preserved metadata fields (message ID, parent ID, user ID, timestamps, edits, reactions) plus linked attachments.

Example: In a trade secret case, a Slack thread shows “Let’s use the old client list” at 8:04 a.m. The raw JSON reveals it was an edit from “draft the list” two minutes earlier, plus a thumbs-up reaction from the departing employee. Normalized review exposes the edit history and reaction, strengthening the injunction record.

Common mistakes to avoid

• Over-collecting or under-collecting: Capturing every channel for years inflates cost; grabbing screenshots under-collects context. Balance proportionality by date ranges, channels, and key custodians.
• No cross-check: Accepting a chat export without validating against audit logs invites gaps. Corroboration supports admissibility and reduces motion practice.
• Poor documentation: Missing chain-of-custody notes or retention settings can undermine authenticity and spur spoliation arguments.
• Flattening modern data: Converting chats to PDFs strips threads and reactions, increasing ambiguity and review time.

Practical applications for case strategy

Use modern data to shape discovery efficiently and persuasively. The goal is a record the court can grasp at a glance, with low-friction productions that withstand scrutiny.

• Request natives, audit logs, and rosters: Specify Slack/Teams native exports, mobile forensic reports, and the admin retention policies in your RFPs and ESI Protocol.
• Frame plain-language exhibits: Present threaded conversations with clear timestamps and participant labels; annotate edits/reactions sparingly to aid the court.
• Budget and schedule signals: Propose phased discovery—pilot channels or date ranges first—to test proportionality before expanding scope.
• Meet-and-confer posture: Offer reasonable production formats that preserve metadata and reduce vendor processing—then insist on parity from the other side.
• Production formatting: Deliver review-ready, threaded chats with load files that capture message IDs and parent IDs; include linked attachments.

For sample scoping language and defensible methods, browse our insights at forensicdiscovery.expert/blog/ and connect with our experts at forensicdiscovery.expert/services/ediscovery/.

Why typical review breaks down—and what to use instead

Traditional review platforms were built for email. When fed modern data, they often fragment threads, drop reactions, or mis-handle time zones. Reviewers are left stitching context across multiple documents, which drives cost and error rates. Why it matters: you pay more for less clarity, and your presentation suffers.

CloudNine Review offers modern-data-aware ingestion and threading that preserves context while remaining simple for legal teams. It accepts native JSON from Slack/Teams and converts it into coherent conversations with full metadata, attachments, and searchability intact. Why it matters: faster first-pass review, more accurate timelines, and productions that opposing counsel and courts can trust.

Our team deploys CloudNine Review as part of a defensible, end-to-end workflow—from forensic collection to normalized review and properly formatted productions—so your evidence remains reliable and your budget predictable.

End-to-end workflow with CloudNine Review

• Step 1: Preserve endpoints and cloud sources; disable auto-deletion and capture admin settings.
• Step 2: Collect targeted channels, DMs, and mobile texts for defined periods; include attachments and edits.
• Step 3: Normalize chat JSON to threaded conversations in CloudNine Review; align all timestamps to a single time zone.
• Step 4: Search, filter, and tag by participants, channel, and timeframe; triage with saved views to accelerate relevancy calls.
• Step 5: Produce with load files that retain message IDs, parent/child relationships, and linked attachments; deliver a clear production log.

FAQs

• Can I just screenshot Slack messages?: Avoid it. Ask for native exports and produce in a threaded, metadata-preserving format to prevent authenticity challenges.
• How do we validate completeness?: Cross-check exports against admin/audit logs, channel rosters, and retention settings; sample date ranges and key events to confirm no gaps.
• When should I involve a forensic expert?: Early—at hold and scoping—so auto-deletion is halted, the right fields are preserved, and the review platform is configured for modern data.
• What about proportionality?: Phase by custodian, channel, and date; start with high-signal sources and expand only if the results justify additional cost.

Next steps

If your matter involves Slack, Teams, mobile devices, or cloud storage, treat it as modern data from day one. A defensible approach paired with CloudNine Review turns complex sources into reliable, review-ready evidence without overspending.

• Checklist: preserve broadly, collect proportionally, validate with logs, normalize for review, and produce with metadata intact.
• Value: faster timelines, lower cost, stronger admissibility, and clearer exhibits for motions and trial.

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