Why clustering and categorization need access to documents in the data source.

If you’re wrangling thousands of documents in Relativity, you quickly learn that not all analytics behave the same way. Some tools want to skim just the surface—the metadata or hidden indices—while others insist you hand over the actual content to get meaningful results. The distinction matters when you’re lining up tasks for a big review, coordinating teams, or just trying to keep the project moving without getting bogged down in data wrangling.

Let me explain the core idea with two analytics that sit at the heart of many discovery workflows: clustering and categorization. These two aren’t just academic terms; they’re practical methods that rely on reading the documents themselves. And that reliance changes how you store, access, and prepare your data.

Clustering: grouping by what’s inside the documents

Think of clustering as a way to find natural groupings among a mountain of documents. If you have memos, emails, or reports, clustering looks for patterns inside the text to put similar items together. It’s not just about repeating keywords; it’s about how ideas, topics, and phrases relate to each other across the corpus.

• How it works in practice: the system analyzes the content—word frequency, term relevance, and the relationships between different passages. It builds a map where documents that talk about a similar thing end up near each other.

• Why the content must be accessible: to decide which documents belong in the same cluster, the algorithm needs to read and compare the actual text. Metadata, file names, dates, or author tags won’t paint the full picture of the meaning or topic without the words on the page.

• A helpful analogy: clustering is like organizing a library not by the color of the spine or the year it was published, but by the ideas inside. When a book and a memo both discuss the same issue, they should naturally sit near each other.

Categorization: labeling based on the text’s meaning

Categorization takes that idea and adds a layer of labels—predefined categories you’re aiming to assign to documents. Maybe you’re tagging items as “privilege,” “PII,” “legal hold,” or “contractual.” The goal is to sort documents into buckets that your team can act on quickly and consistently.

• How it works in practice: the system reads the content to determine which category fits best. It’s not enough to know the file type or the creation date; you need to understand what the document is saying.

• Why the content must be accessible: a good label comes from interpreting the document’s substance. Keywords help, but understanding context, synonyms, and nuances often requires seeing the full text.

• A real-world touch: imagine you’re organizing a mixed bag of communications from a large project. Categorization helps you tag every item so reviewers know at a glance which folder it belongs to, which in turn speeds up decision-making and reduces misfiling.

Keyword analysis: when metadata and indices can do the job

Now, where does keyword analysis fit in? It’s the more permissive cousin here. Keyword analysis can often operate on metadata, field values, or pre-built indices that don’t require pulling the entire document content into the analysis pipeline.

• What that means for data handling: you can scan titles, authors, file types, production dates, and other structured fields to surface items that match what you’re looking for.

• Why it’s different: because it doesn’t demand the full text, keyword analysis can be faster and lighter on data access. It’s great for quick triage or baseline filtering when you don’t need deep semantic insight.

• A practical note: if you later decide to drill down, you might still unlock richer conclusions by loading the actual content for more thorough clustering or categorization. It’s not a binary choice; it’s about choosing the right tool for the right moment.

Putting the pieces together in Relativity workflows

So, which analytics require the documents to be in the data source? In practice, clustering and categorization rely on the content being accessible in the data source. They’re content-driven, and that access lets the analytics do their job with depth and nuance.

• When to lean on content-driven analytics:

• You need to discover hidden topic groupings that aren’t obvious from metadata alone.

• You want consistent labeling across a broad set of documents to support reviews, production decisions, or privilege reviews.

• When you can get by with metadata and indices:

• You’re performing fast triage or high-level filtering without pulling in the full text.

• You’re dealing with very large datasets where reading every word isn’t practical, at least not upfront.

The practical upshot is simple: if your goal is to understand what the documents say at a granular level, you need the documents themselves available for analysis. Clustering and categorization are about content connections, and those connections live in the text.

A few everyday tips to keep this smooth

• Prepare your data source with accessible content: ensure documents are readily retrievable and not locked behind obscure permissions. When your team can fetch the text cleanly, clustering and categorization become more reliable and faster.

• Pay attention to indexing: for keyword analysis, robust indices can do a lot. But for content-driven analytics, you’ll want clean, well-organized text that the algorithms can compare across items.

• Balance speed and insight: it’s tempting to push for quick results with metadata alone. If you hit a wall or you notice gaps in the insights, consider loading the documents into the analysis stream to unlock richer patterns.

• Mind the privacy touchpoints: not every document is meant to be read in full by analytics. Keep privacy controls in place, especially when the content includes sensitive or privileged information. You don’t want to trade insight for risk.

Putting it into a real-world rhythm

In any sizable project, you’ll find yourself toggling between quick keyword checks and deeper content-driven analytics. Think of it like planning a complex trip: you scout routes with a map (metadata and indices), but you trust your GPS to interpret the terrain (the actual content) when you need it most. Clustering acts as the navigator’s eyes—seeing patterns across the landscape—while categorization tags the terrain so reviewers know what they’re looking at without a second guess.

If you’re coordinating a team, a practical approach works well:

• Start with a metadata-driven pass to identify obvious lots or clusters of activity.

• Move to clustering to reveal topic neighborhoods that aren’t evident from titles alone.

• Apply categorization to assign consistent labels that reflect your project’s review priorities.

• Use keyword analysis sparingly to spot focal points or to validate findings you’ve surfaced through content-rich analytics.

A closing thought

Analytics in Relativity aren’t just about numbers or neat slices of data. They’re about understanding a complicated tapestry—the words, ideas, and contexts that give meaning to a collection of documents. By recognizing which tools require the actual content and which can work with lighter data, you design workflows that are both efficient and deeply insightful. And when you see clustering and categorization doing their job well, you’ll notice how much smoother the entire process can feel—like a well-tuned workflow where each part knows its lane and stays focused on the goal.

If you’re curious to explore this further, you can think about a simple exercise: pick a small set of documents, run a basic keyword pass to map metadata, then run a clustering pass on the same set after loading the content. Compare what you learn from each approach. The differences will make the value of content-driven analytics crystal clear, especially in large-scale projects where precision and context matter most.