Incremental Build helps keep existing documents intact during indexing in Relativity project management.

Outline

• Opening: data projects move fast, indexing is part of the workflow, and preserving existing documents matters.

• Core question: when you build an index but don’t want to touch current documents, the Incremental Build option is the sensible pick.

• Why not reindex everything: performance costs, potential data state changes, and unnecessary churn.

• How Incremental Build works in practice: it targets only new or updated documents since the last build, leaving existing items alone.

• Real-world implications for project teams: time savings, resource planning, and data stability.

• Quick comparison with other options: why Select All Documents, Exclude Irrelevant Documents, and Archiving aren’t as aligned with the goal.

• Practical tips: plan, validate, and monitor; practical steps to implement successfully.

• Friendly close: a practical mindset for efficient indexing in everyday project work.

Incremental Build: The lean, precise way to index without disturbing what already exists

Let me ask you this: in a busy project, how many times have you wished you could press a button and say, “Just index the new stuff, leave the rest alone”? When you’re building an index in Relativity (or any system with a live document set), that wish isn’t just sentiment—it’s a practical aim. You want speed, you want accuracy, and you want to avoid turning your existing document states into a moving target. That’s where the Incremental Build option comes in.

Why you’d rather not reindex everything

Picture your document library as a well-worn library shelf. Most of the books haven’t changed since yesterday, but every so often a new volume slips in, or a page gets updated. If you choose to reindex the entire shelf, you’re re-reading and re-logging every single book. It’s like a full audit of the entire library every time you add one new edition. It’s noisy, it’s slow, and it creates a lot of unnecessary activity for documents that didn’t change.

There are tangible downsides to reindexing everything. It consumes more CPU, more disk I/O, and more time in your project timeline. It also raises the risk of incidental changes to documents that you didn’t intend to reprocess—something project managers care about because stability matters, especially when teams rely on consistent search results and auditable histories.

Incremental Build: what it actually does

Now, imagine you have a way to tell the system to pay attention only to the new stuff or items that have been touched since the last index. That’s the essence of Incremental Build. In practice, the system tracks what’s new or modified since the previous build and updates only those documents in the index. The rest—the previously indexed documents—stay as they were. The result is a leaner, faster indexing operation with a lower chance of unintended side effects on existing data.

This approach is especially valuable in environments where data stability is non-negotiable. If you’re dealing with legal holds, regulatory records, or high-trust archival content, preserving the integrity of prior states while adding fresh material is a big win. Incremental Build gives you that balance: you keep prior indexing results intact, while you bring in whatever has changed since the last pass.

A practical, project-friendly perspective

For teams, Incremental Build translates into several concrete benefits:

• Time efficiency: you’re not wasting cycles reprocessing unchanged documents. That means faster turnaround for search readiness and quicker validation checks.

• Resource planning: by limiting work to delta changes, you can plan compute and storage needs more predictably. No sprint gets derailed by a massive reindexing burst.

• Stability and trust: stakeholders can rely on the fact that previous document states remain consistent while new material becomes searchable. That consistency is a quiet-but-important risk reducer.

• Incremental validation: with smaller, focused updates, you can perform targeted checks—spotting anomalies in newly indexed items without the noise of a full reindex.

How this stacks up against the other options

If you’re weighing Incremental Build against the alternative choices, here’s how the math lines up:

• Select All Documents option: this reindexes every single document. It’s comprehensive, but it also means every document gets re-evaluated, which undermines the goal of leaving existing documents unchanged. It’s like refreshing the entire shelf every time you add one new book—overkill and slow.

• Exclude Irrelevant Documents option: filtering out some documents sounds sensible, but it doesn’t guarantee that the documents you do index won’t be touched again. The core issue isn’t about filtering; it’s about whether you touch the existing set at all. Excluding irrelevant items helps with noise, but it doesn’t solve the “don’t touch the known good” requirement.

• Archive Previous Data option: archiving old data is a useful data-management move, but it doesn’t directly affect the current indexing process. You’re moving stuff out of the active workspace, not refining how the index is built. It’s a different discipline—data lifecycle management rather than incremental indexing.

In short: Incremental Build is the most precise tool for the job. It’s not just a timing choice; it’s a data integrity choice, a resource management choice, and a reliability choice all rolled into one.

A simple, grounded workflow you can apply

If you want a practical way to incorporate Incremental Build into your day-to-day work, here’s a straightforward approach:

• Establish the last build point: record when the last index was completed and which documents were included. This creates your delta baseline.

• Identify changes: run a delta scan to capture new and updated documents since the last build. Treat these as the “layer” you’ll index this cycle.

• Execute Incremental Build: run the indexing process with delta changes only. Monitor for any unusual errors on the new or modified items.

• Validate incrementally: spot-check a sample of newly indexed documents to ensure searchability and accuracy. Compare counts to expected deltas to catch anomalies early.

• Review and iterate: after the incremental pass, review results with your team. If you notice patterns of late changes or missed updates, adjust the delta criteria or schedule.

• Document the process: maintain a short, clear record of what was indexed, when, and why. An auditable trail adds confidence for everyone who relies on the data.

Real-world analogy to keep it human

Think of incremental indexing like updating a living project notebook. You’ve already penned the core chapters and archived them in a trusted binder. Now, whenever new notes arrive or a section gets revised, you add only those changes to the binder—no need to rewrite the entire notebook. Your past entries stay intact, the latest thoughts are accessible, and the overall narrative remains coherent. It’s an approach that respects the work that’s already done while making room for new insights.

Common pitfalls to be aware of

No approach is flawless, and Incremental Build isn’t a magical shortcut. A few caveats help keep things on track:

• Change sovereignty: define clearly what counts as “new” or “updated.” If there’s ambiguity, you risk missing updates or prematurely indexing items that aren’t truly changed.

• Timing matters: run incremental builds at sensible intervals. If you wait too long, the delta can become large and unwieldy; if you run too often, you may be chasing tiny changes that don’t justify the overhead.

• Validation is still essential: even with incremental updates, perform a focused validation to ensure the index reflects the latest state correctly.

• Prepare for edge cases: automated delta detection can miss non-content changes (like metadata updates). Have a plan for catching those when they matter.

A few notes on tone and technique for PM-focused readers

The practical value of Incremental Build isn’t just about speed; it’s about trust, predictability, and a smoother workflow. In project settings, you’ll hear a lot about delivery timelines, resource constraints, and risk management. This is where a lean indexing approach lines up with a professional mindset: you want clarity about what changes, when they’re applied, and how they affect downstream tasks like search performance or document retrieval.

To keep the rhythm right, mix concrete examples with a touch of color. For instance, you might describe a weekly indexing cycle where new legal messages arrive, or a regulatory amendment adds a batch of new docs. It helps to show the concept in action while keeping the focus on the core principle: update only what’s new, preserve what’s already there.

Closing thoughts: a disciplined, practical stance

Incremental Build isn’t flashy, and that’s not its appeal’s point. Its quiet power lies in precision: update what’s fresh, protect what’s settled, and keep the rest steady. In projects where data stability and timely access to information matter, this approach pays dividends in reliability and speed.

If you’re coordinating a data workflow, consider making Incremental Build a standard part of your indexing toolkit. It’s a simple preference with meaningful impact—one that respects the work that came before, while inviting the new material to join in without disruption. And when your team asks, “What changed since the last index?” you’ll have a clear, auditable answer: just the delta, nothing more.