Group the suppressed documents by running Textual Near Duplication after identifying high ranking documents.

After you’ve spotted the high-ranking documents, the workflow doesn’t stop there. In fact, the next move can feel like setting the stage for everything that follows. It’s not about more pages or more words; it’s about making sense of what’s already in front of you. So, what’s the right second step? Group the suppressed documents by running textual near duplication. Here’s why that choice matters and how it plays out in real-world projects.

Let’s start with the big idea: what is “textual near duplication,” and why does it matter after you’ve identified the major players in your pile? In plain terms, two or more documents are near duplicates when they share a lot of the same text and meaning. They might be emails with the same thread, versioned drafts, or documents that replicate a core clause across multiple files. When you group these, you’re not just tidying up; you’re revealing the structure of the data. You’re showing which items are telling you the same story from slightly different angles. That clarity makes everything else faster and more accurate.

Why this grouping is worth doing

• It reduces noise. Instead of reviewing every single item, you review a representative set from each near-duplicate cluster. You avoid chasing the same point over and over.

• It sharpens prioritization. If several suppressed items cluster around the same topic or issue, you can decide which cluster deserves closer attention first.

• It guides later coding and categorization. Once you know which documents talk about the same thing, you can apply consistent tags or categories across the whole group.

• It helps with quality and consistency. When similar documents are treated as a unit, you’re less likely to miss a nuance or to double-review the same content.

Think of it like sifting through a library’s catalog and grouping similar titles in the same shelf. When you know where the core ideas live, you can navigate faster and stay focused on what really matters.

How to execute this step in practice

Let me walk you through a practical approach, keeping the focus on the second step. The goal is to group suppressed documents using textual near duplication so you can work more efficiently later on.

1. Run a textual near-duplication pass across the suppressed set

• You’re not counting pages; you’re tracing content similarity. The goal is to identify clusters where the core text aligns closely across documents.

• Use the analytics tools you’re comfortable with. In many Relativity workflows, there are built-in capabilities that scan for near-duplicate text and return clusters you can review.

• Don’t worry about perfection on the first pass. The objective is to surface meaningful groupings that you can validate and refine.

2. Review the clusters and assign provisional labels

• Once the system surfaces clusters, skim each group to confirm that the shared content is real and not a false positive.

• Give each cluster a short, meaningful label. Something like “Contract_redline_emails_Q3” or “Policy_update_drafts” helps you stay oriented without rereading every document in detail.

• Note any edge cases. A cluster might span multiple topics, or a single document might straddle two categories. Mark those for later decisions.

3. Decide how to handle each cluster

• Determine whether a cluster should be set aside, reviewed more deeply, or flagged for immediate attention. In many workflows, you’ll want to earmark a few clusters for first-pass review, while others can wait.

• Consider privilege, sensitivity, and relevance. Suppressed items often involve privilege or confidentiality concerns, so clustering helps you flag what needs careful handling.

4. Document the grouping rationale

• Record why certain documents landed in a cluster and why others didn’t. Clear notes make it easier for teammates to understand the logic later on.

• A simple summary per cluster goes a long way. It also helps new team members come up to speed without wading through hundreds of items.

5. Prepare for the next steps

• With groups defined, you’re better positioned to move into targeted quality checks, coding, or deeper content analysis. The path ahead becomes more predictable because you’re not starting from scratch with every document.

A useful analogy: organizing a toolbox

Imagine you’re cleaning out a toolbox after a big project. You’ve found some high-value tools (the high-ranking docs). The next sensible action is to group the similar wrenches, pliers, and screwdrivers by type and use. When you know where the screwdrivers live, you’re not rummaging through the entire box every time you need a screwdriver. The same logic applies to documents: you want the near-duplicate groups to become labeled, navigable containers that keep related content together.

Where this step fits in the larger flow

Grouping suppressed documents by textual near duplication is a connective tissue step. It doesn’t stand alone; it sets up everything that comes after. After you’ve established these clusters, you typically proceed to more focused analyses, quality checks, and coding decisions. You’ll find that later tasks—like validating document relevance, tagging for topics, or applying privilege flags—become smoother because you’ve already carved the data into meaningful, related chunks.

A few practical pointers that often help in real projects

• Start simple, then refine. It’s easy to be tempted to chase perfect clusters right away. Start with broad groups and tighten them as you review more content.

• Be mindful of edge cases. Some documents resist clean clustering. Make a note of these outliers and decide how they should be treated in your workflow.

• Keep the human in the loop. Automated clustering is powerful, but your judgment matters. A quick human skim can correct misclassifications and save time in the long run.

• Use consistent labeling. A shared naming convention for clusters prevents confusion down the road and makes collaboration more seamless.

• Track changes. As you adjust clusters, keep a simple log of what changed and why. This transparency helps teammates follow the reasoning and maintain alignment.

A few caveats to avoid

• Don’t over-rely on a single pass. The first round of near-duplication can miss subtle variations. Plan a follow-up check as new documents are added or as interpretations evolve.

• Don’t assume all near-duplicates are equal. Some clusters might contain minor variations; others share a core topic that deserves special attention. Treat them accordingly.

• Don’t skip the privacy and governance layer. Suppressed items can carry sensitive information. Always factor in privilege, confidentiality, and legal constraints as you group and review.

Real-world sense-check: why this makes sense across teams

Whether you’re on a legal review team, a data governance group, or a project squad handling massive document sets, the second step you take matters. It’s the moment where you turn a chaotic stack into stories—not just pages, but narratives that convey the essential threads of the matter at hand. When groups are clearly defined, you can assign reviewers with the right lens, apply consistent coding choices, and keep the team aligned on what’s most important to understand.

A final thought to carry forward

The step after you identify high-ranking documents isn’t a loud, flashy move. It’s a quiet, deliberate reorganization that yields big dividends down the line. Grouping suppressed documents by running textual near duplication creates a foundation for clarity, efficiency, and better decision-making. As you build out the workflow, you’ll likely find that this structure accelerates subsequent analyses and reduces redundancy—saving time and reducing the chance of overlooking critical angles.

If you’re exploring Relativity workflows, you’ll notice how these ideas recur in different flavors across projects. The core principle stays the same: when similar documents stand side by side, you gain a sharper sense of the whole picture. And when you have that, you’re not just moving faster—you’re moving with intention.