Be Public is a key requirement for Saved Searches in Active Learning projects.

Why a Saved Search in Relativity has to be Public — and why that matters to your team

If you’ve spent any time wrangling data in Relativity, you know the value of a good Saved Search. It’s the shorthand you use to pull the exact documents you need, without re-running every filter from scratch. In the context of an Active Learning project, there’s one rule that stands out as a real game changer: the Saved Search you create must be Public. Yes, public. Not private, not restricted to a single user, but accessible to everyone involved in the project.

Here’s the thing: in an Active Learning setup, the way you collect, label, and test data is a team sport. You’re not just hunting for documents; you’re training a model, validating results, and refining your approach as you go. If the Saved Search isn’t open to the whole team, you end up with silos. People can work in parallel, sure, but they end up duplicating effort, second-guessing the data pool, and chasing the same tails in isolation. Public visibility keeps everyone rowing in the same direction, with the same map.

A vivid analogy might help. Imagine you’re coordinating a group cooking project. One person has found a recipe for a spicy tomato sauce, another has a secret spice blend, and a third is tallying allergies. If each person works in a separate corner of the kitchen and never shares what they’ve found, you’ll end up with a chaotic meal that doesn’t blend well. But when everyone can see the sauce, the spices, and the dietary notes in one shared notebook, you can adjust on the fly, taste together, and end up with something better than any one chef could craft alone. That shared transparency—that openness to the team—makes the process efficient and predictable.

What exactly makes “Public” so powerful in this setting? First, it elevates collaboration. When a Saved Search is public, it becomes a living component of the project’s knowledge base. New team members don’t waste time chasing where to find the right data; they dive into a clear, common starting point. Second, it reduces bottlenecks. If only one person can access a critical search, others wait, which slows learning cycles. With a public Saved Search, you remove that wait time and keep the momentum moving. Third, it helps with quality control. When everyone has visibility, you gain a shared audit trail: who made changes, when, and why. That transparency is gold for learning, iteration, and accountability.

Now, you might be curious about the other settings you sometimes see in Saved Searches. Options like returning extracted text, including Families, or suppressing duplicates—these can be useful in certain contexts. They’re not requirements for an Active Learning project, though. Think of them like optional gear for a hike: useful depending on the terrain, but not what gets you off the ground. The essential trait here is accessibility for the whole team. If your goal is rapid iteration and shared understanding, making the search public is the anchor.

Let’s translate that into some practical takeaways you can apply right away.

• When you set up a Saved Search for an Active Learning workflow, default to Public. It’s a simple, high-leverage choice that sets the stage for collaborative review, quick feedback, and faster learning cycles.

• Treat public visibility as a feature, not a risk. Yes, you’re handling potentially sensitive data, but Relativity provides permissions and role-based access that protect you while keeping the core team aligned.

• Use public Saved Searches as your project’s heartbeat. They tell the team what data is in play, what labels or decisions have been validated, and where attention should go next.

• Remember that other options can still matter. If you need to constrain a search for a particular sub-team or a pilot, you can create backups or companion searches. The public one stays the main reference point, while the others serve targeted purposes.

If you’ve ever watched a team workshop where data and decisions move in lockstep, you know the magic of shared visibility. It’s the moment when someone says, “Yes, we’re on the same page,” and everyone relaxes just a notch because ambiguity melted away. In Relativity’s Active Learning flow, that moment often hinges on a single choice: making the Saved Search public.

A few lightweight caveats to keep the flow smooth:

• Public doesn’t mean reckless exposure. You still organize data governance, review who can modify the search, and ensure sensitive information is handled according to policy. The goal is clarity, not chaos.

• Document the rationale. If you adjust a Saved Search, capture a brief note about why the change was made. It helps everyone understand the evolution of the data pool and the learning model.

• Foster a culture of shared ownership. When team members see that their colleagues can access the same search results, they feel more invested in the project’s outcomes. That shared ownership is what keeps the energy up through long cycles.

If you’re the kind of person who loves a crisp checklist, here’s a compact version to keep near your workspace:

• Create the Saved Search with the Active Learning project in mind.

• Set the visibility to Public for the team-wide access.

• Attach a short explanation of the search’s purpose and scope.

• Review periodically to ensure it still aligns with the project’s goals.

• Consider companion searches for specialized tasks, but keep the primary one public.

Now, you might wonder, does this approach ever clash with what you’re trying to protect? It’s reasonable to worry about oversharing. The answer is balance. Public visibility is about enabling collective intelligence, not exposing everything indiscriminately. Use the Relativity permissions framework to control who can modify or delete the search, while keeping the data and results visible to the core team.

Let me explain why this single rule—Be Public—often becomes the hinge that makes an Active Learning effort more successful. Shared access accelerates learning. It reduces the risk that someone will go off on a tangent because they’re unsure what the others have found. It creates a natural rhythm: discover, share, reflect, adjust. When everyone trusts the same starting point, you don’t waste cycles debating which data matters; you focus on what to do with it.

If you’re new to this, you might be surprised how quickly a seemingly small choice compounds into meaningful gains. A public Saved Search isn’t just a file in a folder. It’s a living reference that binds the team’s curiosity to a concrete data set. It’s what keeps the torches burning during a marathon data review session. And let’s be honest: in teams that learn together, the energy to press on often shows up as quick, friendly nudges—“Hey, did you see this result?”—that keep everyone moving forward.

To wrap up, here’s the bottom line: for an Active Learning project in Relativity, the central requirement for a Saved Search is that it be Public. That openness is what unlocks collaboration, clarity, and momentum. The other features you’ll encounter—like extracted text outputs, family groupings, or duplicates suppression—are useful tools to tailor the outcome, but they don’t replace the core need for shared access. So, when you’re setting up your next Saved Search, make it Public first. The rest can follow, but the public stance is the spark that fuels collective progress.

If you’d like, I can help translate this into a quick one-page reference you can pin near your workspace. It could include a short reminder of why public access matters, plus a few best-practice notes tailored to your team’s workflow. After all, a tiny nudge of clarity can make a big difference when the data starts piling up and decisions have to be made fast.