Understanding the UNCLUSTERED group in Relativity: why some documents aren’t text searchable

Outline

• Set the stage: why document grouping in Relativity matters for fast, accurate review

• Define UNCLUSTERED: what it covers and why it’s there

• Address the statement directly: True or False? Why the answer is False

• Explore the practical impact: how searchable vs non-searchable text changes workflow

• Tips to optimize grouping: OCR, text extraction, and keeping your workspace tidy

• Quick takeaways you can apply right away

How grouping affects the flow of a Relativity project

If you spend any amount of time dealing with large document sets, you’ve probably learned that not all files behave the same way. Some are text-rich; others arrive as images or scans with no obvious search terms. In Relativity, the way you group and classify these files isn’t just a nice-to-have. It shapes how you search, how you review, and how quickly you can surface the exact documents your team needs. Think of it as giving your data a proper filing system so a search doesn’t become a scavenger hunt.

UNCLUSTERED: what it means in plain terms

Let me explain it in simple terms. The UNCLUSTERED group is typically tied to documents that don’t contain searchable text. In other words, these are the files that look like they’re just images to a machine—scanned pages, faxes, certain image-based PDFs, or other non-OCR’d materials. If a document has text that can be indexed and searched, it’s usually placed in a different grouping or category that’s designed to support fast text queries. The key distinction is not about the document’s content per se, but about whether the content is readily searchable by the system.

This is where a lot of people trip up. It’s easy to assume that every file should slide into a “text-enabled” bucket, but the reality is a bit more nuanced. A scanned contract, for instance, might technically be readable to a human, yet unless OCR (optical character recognition) has converted those characters into actual text, the computer won’t be able to search inside it. That’s the moment you see the UNCLUSTERED label pull its weight: it flags a type of content that needs extra handling if you want robust search results.

Falsehoods to clear up

The statement in question—“The UNCLUSTERED group is created for documents that contain searchable text”—is False. Here’s the logic behind that:

• UNCLUSTERED is a cue for non-searchable content. If a document already has searchable text, it belongs in a cluster or grouping designed to leverage that text for indexing and retrieval.

• The real value of UNCLUSTERED lies in highlighting documents that require OCR or other text extraction steps before they can participate in keyword searches.

• Keeping non-searchable files separate from searchable ones helps reviewers allocate time and resources more efficiently. You don’t waste time running a search against a pile of images when you know those files can’t yield text without OCR.

What this means for your workflow

When you’re organizing a Relativity workspace, this distinction matters because it directly affects how you search and how you triage documents during review. If a chunk of your set is UNCLUSTERED, here are a few practical implications:

• Searchability: You’ll likely need to run OCR to unlock text-based search capabilities for those files. Until OCR is completed, you won’t be able to rely on keyword searches to reveal relevant passages.

• Review speed: For non-text-based files, reviewers often skim the content visually or use metadata (like file name, author, dates) rather than keyword hits. That means you may rely more on filters, tags, and threaded reviews to move quickly.

• Center of gravity for resources: OCR processing can be resource-intensive. By clearly marking UNCLUSTERED content, you can schedule OCR runs during low-demand windows, freeing up time for other tasks.

• Bi-directional retrieval: Once OCR is done, a chunk of UNCLUSTERED material can convert into a searchable asset. The moment that happens, the document’s value in fast triage improves dramatically.

A practical example to anchor this idea

Picture a corporate litigation matter with thousands of scanned invoices and supplier communications. The invoices are high-resolution scans with no embedded text. In Relativity, those invoices might sit in UNCLUSTERED until OCR is run. Once OCR produces searchable text, you can query terms like “net terms” or “invoice number” and instantly surface relevant invoices. In the meantime, reviewers can still work with the non-text files by leveraging metadata, subject lines, or custom tags. The end result is a smoother, more predictable review flow.

Getting the text where it matters (without losing context)

If you want to turn non-searchable files into searchable gold, a few best practices help:

• Run high-quality OCR: Choose OCR settings appropriate for language, font types, and the document’s layout. Some pages with complex formatting may require manual adjustments to maximize accuracy.

• Verify accuracy: OCR isn’t perfect. It’s smart to spot-check a sample of OCR’d pages to ensure the extracted text matches the source. Small errors can cascade into missed hits later.

• Preserve original context: Don’t strip away the non-text aspects while OCR-ing. Keep the image alongside the text so reviewers can verify content visually when needed.

• Re-cluster thoughtfully: After OCR, re-evaluate whether the documents should move from UNCLUSTERED to a text-enabled grouping. A clean handoff between non-text and text-enabled sets makes discovery smoother.

• Leverage metadata: Even without text, you can use fields like author, date, file type, and production notes to guide the review. Metadata often carries as much weight as the text itself.

Common pitfalls and how to dodge them

Like any workflow, there are easy missteps that slow you down. Here are a few I’ve seen and how to sidestep them:

• Underestimating OCR needs: Some teams assume all essential documents already have text. A quick scan often reveals a sizeable chunk of non-text files. Plan OCR capacity accordingly.

• Overloading UNCLUSTERED: Keeping everything non-searchable in one bucket is tempting, but grouping too broadly can slow targeted searches. Try to maintain a light touch—only keep what truly lacks text in UNCLUSTERED.

• Skipping OCR quality checks: If you skip validation, you might chase search results that aren’t precise. A pragmatic sampling approach helps keep accuracy high without bottlenecks.

• Ignoring metadata potential: In the absence of text, metadata can be a powerful ally. Don’t overlook these fields when building filters and saved searches.

A few tips to keep the rhythm steady

• Build a simple taxonomy: Create clear rules about what goes into UNCLUSTERED versus text-enabled groups. A short, written guideline helps new team members align quickly.

• Schedule OCR thoughtfully: If you’re dealing with huge batches, stagger OCR runs. You’ll maintain momentum without starving other tasks of resources.

• Regularly audit a sample: Periodically check a subset of UNCLUSTERED files after OCR to confirm you’re getting meaningful search results. It’s a quick sanity check that saves headaches later.

• Balance form and function: Remember that some reviewers rely on the visual fidelity of the original document. Keep a link between the image and text so you can switch seamlessly as needed.

Why understanding this distinction matters beyond a single project

This isn’t just about a single question or a single document set. The way you classify documents as UNCLUSTERED or text-enabled echoes through every downstream step—search efficiency, review speed, and the reliability of produced outputs. When teams have a shared mental model of what each grouping represents, conversations about scope, risk, and timelines become clearer. It’s the small clarity that compounds into big gains when you’re handling thousands of files across multiple custodians and sources.

A final reflection

If you’re navigating Relativity, the UNCLUSTERED label isn’t a stubborn tag to memorized trivia; it’s a practical signal. It tells you, “This content needs extra work before it can be fully surfaced in keyword-driven searches.” That awareness helps you plan, allocate resources, and keep the review moving. In the end, the goal is simple: make the right documents easy to find, without getting bogged down in the clutter of files that quietly resist search.

So, the next time you peek at a batch of non-text-based documents, you’ll know exactly what the UNCLUSTERED category implies. You’ll also know the right steps to take to bring those files into the searchable fold—without losing sight of the context, the metadata, or the human judgment that matters most.