Why using a separate dashboard for each cluster set visualization makes sense in Relativity project management

If you’ve ever tried to cram too many charts onto a single screen, you know the feeling: clutter that hides the signal and makes your eyes ache before you’ve even finished reading the labels. In Relativity, when you’re working with cluster set visualizations, there’s a simple rule that keeps things clean and useful: you typically want a separate dashboard for each cluster set. Yes—that means one dashboard per cluster set visualization.

Here’s the thing, and this isn’t just pedantry. Dashboards are your focused stage. They’re designed to present a specific set of data in a way that highlights clear insights. When you pack several cluster sets into one space, you risk muddiness. Different clusters often come with their own filters, their own styles, and their own angles of analysis. If you jam them together, the interface can become more confusing than clarifying. You end up chasing the wrong thread or missing a crucial pattern because the layout fights with the data rather than supporting it.

Why separate dashboards anyway?

• Clarity over clutter. Each cluster set has its own scope, its own questions, and its own key metrics. A dashboard that’s tuned for one cluster can showcase the exact charts, filters, and annotations that matter for that context. Mixing clusters is like trying to listen to a symphony while someone’s banging a drum in the same room.

• Distinct filters and parameters. Some clusters need different date ranges, keywords, or custodians. If you try to enforce a single set of filters across multiple clusters, you’ll either miss important data or force you into awkward compromises. Separate dashboards let you tailor filters to each cluster without trampling the others’ needs.

• Custom layouts for specific insights. Visualizations aren’t all the same beast. Some clusters shine with stacked bar charts, others with heat maps, and still others with trend lines. A dashboard built around one cluster can arrange panels in a way that flows naturally for that data story, without forcing a one-size-fits-all layout.

• Faster navigation and decision speed. When stakeholders know they’re looking at the right dashboard for the question at hand, they move with confidence. No more guessing which cluster a chart belongs to or how the filters were set. This speeds up discussion and reduces back-and-forth.

• Consistent styling, one story at a time. A separate dashboard gives you the chance to establish a consistent visual language—colors, fonts, label conventions—that matches the cluster’s context. It’s easier to maintain, easier to communicate, and easier to audit later on.

A practical way to picture it: think of dashboards as rooms in a house. Each room has a purpose, a layout tailored to that purpose, and a storyline that visitors follow. The kitchen isn’t the living room, and you wouldn’t decorate them the same way. Similarly, each cluster set deserves its own dashboard so its story is told cleanly.

How to design dashboards that sing

If you’re handed multiple cluster sets, here are some steps that keep the process smooth and the results solid:

• Name with intention. A clear, descriptive title helps everyone understand what they’re looking at at a glance. For example: “Cluster Set A: Custodian Activity by Date” or “Cluster Set B: Topic-Mpecific Privilege Flags.” A good name is a small but powerful time-saver during reviews.

• Use consistent visual language, but tailor the lens. Colors and chart types should feel familiar across dashboards, but you can tune emphasis for each cluster. If you consistently color-code by data sources or document types, keep that convention—just apply it in each dashboard so users recognize patterns quickly.

• Leverage saved searches and panels. In Relativity, saved searches provide a stable data backbone for a dashboard. Attach the relevant saved searches to panels, and keep the filters localized to each cluster’s needs. This avoids the temptation to force one global filter onto everything.

• Separate rather than crowd. If something belongs to a different cluster, give it its own panel set within the same dashboard only if it truly shares a common narrative. More often, a separate dashboard is cleaner. Think minimal, not maximal, and resist the urge to show “everything” on one canvas.

• Plan the workflow, not just the visuals. Consider how a reviewer moves from one cluster to the next. A logical sequence—starting with a high-level overview, then drilling into specifics—helps maintain cognitive flow. The dashboard design should guide that journey, not derail it.

• Keep it legible. Favor readable labels, modest color contrast, and charts that reveal the point quickly. If a panel requires too much squinting to interpret, you’ve probably overloaded it. Simplicity is a feature, not a fault.

• Document the context. A small note in the dashboard or a legend can prevent misinterpretation. If a cluster has unique parameters or assumptions, call them out briefly so viewers aren’t left guessing.

A little realism: common pitfalls to avoid

Even the best intentions can stumble if you’re not paying attention. Here are a few traps to dodge:

• The “everything in one room” temptation. It’s easy to think you’re saving time by combining clusters, but you’ll often pay with confusion. If a panel could belong to two clusters, ask whether it truly clarifies one story or muddles both.

• Inconsistent time frames. If Cluster Set A uses rolling 90 days and Cluster Set B uses 180 days, keep that explicit and consistent within each dashboard. Don’t let cross-dashboard comparisons become a puzzle.

• Mixed data definitions. One cluster might report “documents reviewed,” another “documents produced.” Make sure metric definitions are clear and stable within each dashboard to avoid cross-interpretation.

• Ambiguous labeling. Short labels and acronyms can be clever—but if they’re opaque to stakeholders, you’re creating friction. When in doubt, spell it out.

A quick scenario to bring this home

Imagine you’re overseeing three clusters for a project: Cluster A tracks document provenance and custodians, Cluster B maps topic relevance and key terms, and Cluster C flags privilege and sensitivity indicators. Each cluster asks a different question:

• Cluster A: Who touched what, and when? This benefits from a timeline view, a custodian breakdown, and filters by date and source type.

• Cluster B: Which topics drive action? Here, heat maps and term frequency charts with topic filters can reveal hotspots, while timelines show when activity spiked.

• Cluster C: Where are the sensitive areas? A combination of privilege indicators and document risk scores works well, with a focus on quick yes/no judgments for risk-aware decisions.

If you tried to cram all three into one dashboard, you might end up with a sprawling page where the constellations of data compete for attention. Separate dashboards let each cluster shine in its own right, and when you flip between them, you’re moving through a tidy, purposeful cadence.

A flexible mindset for real-world work

One dashboard per cluster set is a practical rule, but the bigger idea is about clarity, focus, and trust. When teams see data presented in a clean, purposeful way, they can act more decisively. It’s not just about looking smart in a meeting; it’s about making sense of complex information without burning time or patience.

If you’re designing dashboards for several clusters, you’ll likely find a rhythm that serves you well:

• Start with a skeleton. Sketch a layout for each cluster, keeping panels that tell a complete story in a logical order.

• Build iteratively. Add a panel, test whether it communicates clearly, and adjust. It’s easier to adjust on a smaller canvas than after you’ve stacked a dozen panels.

• Review with the user in mind. Put yourself in the shoes of a stakeholder who needs to know, say, “What happened, when, and who was involved?” If the dashboard doesn’t answer that quickly, rework it.

• Iterate across dashboards, not within them. You’ll often find the most effective patterns—like how to present a time trend or a distribution—repeat across clusters. Use that consistency to your advantage while preserving the individuality of each dashboard.

Why this approach matters for project leadership

Clear dashboards aren’t just pretty visuals. They’re decision accelerators. When managers and analysts can point to a dashboard and say, “Here’s where the signal lives,” conversations become sharper and more productive. You reduce the cognitive load on teammates, shorten review cycles, and improve the quality of decisions.

Think of it as truthful storytelling with data. Each cluster set tells its own chapter, and a dedicated dashboard is the best way to present that chapter without spoilers or confusion. In fast-moving projects, that clarity isn’t a luxury—it’s part of how teams stay aligned, stay informed, and stay on track.

Bringing it home

To view multiple cluster set visualizations, you’ll typically want a different dashboard for each cluster set. It’s not just a preference; it’s a practical choice that supports clearer insights, cleaner presentation, and faster decision-making. By creating focused canvases—one dashboard per cluster—you give each data story room to breathe. You reduce clutter, you celebrate precision, and you make it easier for everyone to see what matters most.

So next time you’re mapping out cluster visualizations, imagine the scene: one dashboard for each cluster, each with its own rhythm and voice, all working together to tell a cohesive story about the data you’re stewarding. It’s a small structural choice with a big impact, and it just might be the difference between a good discussion and a good decision. If you’re curious about how this plays out in real-world projects, you’ll notice that teams who embrace this approach tend to move from questions to answers with greater ease—and that’s a win you can feel in every meeting.