Why limiting a data source to nine million documents helps project teams stay focused and efficient

When you’re steering a large data review—or any project that rides on clear decisions and tight timelines—the numbers you pick can steer the whole ship. In Relativity Project Management, one number in particular tends to show up again and again: 9 million documents. That’s the cap you’ll hear about most often. So, what’s the thinking behind keeping a data source to about nine million items? Let me walk you through why this limit makes sense, how it actually plays out on projects, and how you can apply the idea without losing sight of what matters.

Why 9 million? A practical way to think about it

Imagine you’re hosting a big review. If the pile is too tall, reviewers get bogged down, timelines slip, and the risk of missing the important threads grows. If the pile is too small, you might miss critical context that changes the story. Nine million is a sweet spot for many teams because it hits a balance point: it’s large enough to include diverse custodians and relevant timeframes, but small enough to manage within a reasonable window and with a predictable workflow.

• Manageability. A finite set keeps tasks like deduplication, near-duplicate detection, and keyword search actionable. When the total is stable, you can tune filters, run measurements, and judge progress without constant re-scoping.

• Speed and consistency. With a larger data set, processing time can balloon, and variance in results can creep in. A capped figure helps maintain consistent processing times and reviewer throughput.

• Focused insights. A defined quantity nudges teams toward prioritizing the most relevant materials first, rather than chasing every possible document. It’s about quality over quantity, with the cap acting as a guardrail.

A real-world feel: what the cap looks like in practice

Let’s say your project lands on 9 million as the cap. The team starts by mapping custodians, dates, and file types to understand what’s in scope. Then you might:

• Run an initial sampling. Look at a representative subset to gauge proportions—privilege logs, confidential data, or highly duplicative sets.

• Apply filtering. Narrow by date range, key custodians, or specific matter areas to tighten the first pass without throwing away everything that could matter later.

• Use targeted analytics. Early case assessment (ECA) tools, clustering, and keyword analytics help surface the most meaningful documents quickly.

• Triangulate with governance. If you notice a few critical custodians or timeframes are swelling the total, you re-evaluate scope, always with a clear rationale and stakeholder alignment.

The cap isn’t a ceiling so much as a control mechanism

Think of the cap as a governance tool rather than a strict constraint. It’s about building predictability and ensuring your team isn’t overwhelmed. When teams drop into a routine with this kind of cap, they can:

• Allocate reviewers more efficiently. With a known scale, you can plan staffing, split work by timezone, and optimize QA checks.

• Improve decision speed. Clear boundaries help leadership see where to allocate attention—summaries, risk flags, or key evidence—faster.

• Reduce rework. A finite data set makes it easier to confirm whether your conclusions hold when the scope changes or new data arrives.

How to decide your cap for a given project

The number nine million isn’t a universal prescription. It’s a guideline that often aligns with typical project constraints, but your context matters. Here’s a practical way to think about setting a cap that works for you:

• Assess platform capacity. What’s the hardware and software stack you’re running on (Relativity, of course), and how does it handle indexing, analytics, and workload distribution? If throughput is smooth at nine million, great. If not, you might adjust down or plan staged passes.

• Gauge reviewer capacity. How many reviewers can you sustain with consistent speed and quality over the project’s timeline? If your team is lean, a smaller cap may prevent burnout and keep risk in check.

• Consider data diversity. If the matter features a wide variety of document types, languages, or custodians with very different review needs, the cap might be set conservatively to allow for smarter sampling and targeted reviews.

• Factor in criticality. If certain timeframes or custodians carry outsized importance, you might preserve those materials for a deeper pass later, using the cap as a base layer for initial triage.

• Plan for iteration. In many setups, you won’t lock in nine million forever. You’ll start with a cap, learn from the first pass, and re-scope if needed. The key is to have a documented, rational reason for any shift.

Practical steps to implement the cap without losing sight of the bigger picture

• Start with a data survey. Before you commit to a number, run a quick inventory: how many unique documents, how many duplicates, and what’s the distribution across custodians and date ranges.

• Create a staged workflow. Use the cap as the first stage. After the initial pass, you’ll review findings, take in new inputs, and decide whether to expand or refine the data set.

• Use sampling strategically. Rather than analyzing every last record at once, pull statistically representative samples to validate assumptions about relevance, privilege, and responsive material.

• Leverage governance levers. Date ranges, custodians, file types, and data sources are your levers. Use them deliberately to shape the cap while preserving the ability to adjust when warranted.

• Build a transparent rationale. Document why you chose a cap, what you expect to gain, and how you’ll determine if a change is needed. Stakeholders appreciate clarity about scope decisions.

Common misconceptions and how to avoid them

• Misconception: A smaller cap always means faster results. Reality: It helps, but if you prune too aggressively, you risk omitting material that changes the story. Balance is still essential.

• Misconception: A cap locks you in. Reality: It’s a starting point. You should reassess as you learn, not as a reactionary move when things go sideways.

• Misconception: Every matter needs the same cap. Reality: Different cases demand different scales. The nine-million benchmark is a helpful reference, not a universal law.

A few relatable analogies

• Think of it like building a playlist. You want enough tracks to tell the story, but not so many that you can’t hear the songs clearly. If a playlist grows to 9 million tracks, you’d want to curate, sample, and structure it so the best tunes surface first.

• Or consider a garden. You plant a wide bed, but you don’t seed every possible plant at once. You start with a core set, observe growth, prune what doesn’t belong, and then expand thoughtfully if needed.

• Even in a kitchen, you wouldn’t sous-chef a lemon into a huge pot of soup without tasting. You’ll sample, adjust seasonings, and decide if the batch needs more citrus—or if you’ve already captured the essential flavor.

A quick takeaway

The 9 million document cap is a practical rule of thumb designed to keep data review manageable, efficient, and focused. It helps teams allocate time and energy where it matters, makes decision-making snappier, and reduces the chance of overlooking critical content. If you’re shaping a project plan, use this guideline as a starting point, but tailor it with a clear, data-backed plan for your own matter.

Tips you can apply tonight

• Map capacity and scope in one page. Note how many documents you can process per day per reviewer, and how long each stage should take.

• Run a one-page data profile. Capture custodians, date ranges, languages, and common file types to spot where the cap would bite or where it would glide.

• Build a pilot pass into your timeline. The first pass with a nine-million framework should reveal whether you’re on track or if you need adjustments.

• Keep a change log. If you shift the cap, record the reason, the expected impact, and the new plan. It keeps stakeholders aligned.

Relativity isn’t just a tool set; it’s a workflow partner. The cap isn’t a cage—it’s a compass that helps you steer through complexity with clarity. When teams know they can rely on a stable scale, they’re freer to focus on the meaningful questions: Which documents actually tell the story? Where do key facts come from? How can we defend our conclusions with crisp evidence?

If you’re walking through a project that involves a sizable data pool, that nine-million idea can be your ally. It gives you a concrete starting point, a sensible pace, and a framework to justify how you’ll sift through the noise to reach the signal. And in the end, isn’t that what good project leadership is really about—turning a mountain of data into a story that’s easy to hear?