The most effective dashboards in Insights are not the ones that show the most data. They are the ones that answer a clear set of questions, guide the reader through a logical flow, and make it easy to know what to do next. This guide covers how to design dashboards that work, for the people who use them and for the AI assistant that can help explore them.

Before you add a single tile, be clear on who will use this dashboard and how. A dashboard built for a revenue manager who reviews it every morning should look very different from one built for a general manager who checks in weekly.

Ask yourself:

If you are building for a mixed audience, focus on the most common use case. You can always build a more detailed dashboard for power users separately.

The most common mistake when building a dashboard is starting with the data, pulling in everything that seems relevant and arranging it until the page looks full. The result is a dashboard that shows a lot but answers very little.

Start instead with two or three clear business questions. Write them down before you add anything:

Example: Pick-Up Analysis

Instead of "show everything about bookings," the questions might be:

Those four questions tell you exactly which tiles belong on the dashboard, and which do not.

If a tile does not help answer one of the core questions, it probably does not belong on this dashboard.

The single biggest design trap in Insights is trying to recreate an Excel spreadsheet as a dashboard, every stay date, every segment, every metric, for every day of the year, all on one canvas.

Spreadsheets are designed for exploration. You scroll, filter, and drill into whatever row or column you need. A dashboard is designed to surface answers. It should tell you what is happening, surface the most important signals, and give you a path to go deeper when something needs investigating.

If you want to see every day's OTB, budget, and forecast broken down by segment, that belongs in a workbook. A workbook lets you query and explore freely. A dashboard should present the output of that thinking: the trend, the comparison, the key exception. Not the raw grid.

If you find yourself planning to download the dashboard and continue working in Excel, ask whether a scheduled workbook export would serve you better. Dashboards and workbooks are different tools, using the right one makes both more useful.

Good dashboards tell a story. They start somewhere, move through a logical sequence, and finish somewhere. A user who opens a dashboard should know immediately where to look first and what to do next.

Most analysis naturally moves from high-level signals into deeper detail. Your dashboard should reflect that progression:

Example: Pace and Performance dashboard

A user can orient at section 1 and go as deep as they need to. Position your most important insight at the top, this is where attention lands first. Use tile size and section headers to signal what deserves the most focus.

Name sections to communicate purpose, not just content. A section title in the format Topic: Analytical Angle tells the viewer what the section is for, not just what it contains. "Segment Performance: Where the Revenue Gap Occurs" is immediately more useful than "Segment Breakdown." Apply this to every section that has an analytical angle worth stating.

End each section description with a forward pointer. The closing sentence of a section description should point the viewer to the next logical step: "If you see a gap in the overview above, use this section to identify which segment is responsible." This keeps the viewer oriented without them needing to scroll back up to understand where they are in the analysis.

The best dashboards do not make you read every number to find the problem. They point you to it. Design for this deliberately:

A dashboard does not need to show every metric it contains. Keep the number of charts focused, a smaller number of well-chosen tiles is almost always more useful than a large grid of everything that could be relevant.

A tile can include additional measures in its underlying analysis that provide useful context without displaying all of them on screen. For example:

This keeps the dashboard visually focused while preserving analytical depth. The same principle applies to filters, include the ones most users need most of the time. If you are adding a filter "just in case," ask whether it is earning its place.

Curate for clarity, not completeness. Strong dashboards prioritise signal over noise, the goal is not to reduce information, but to organise it so answers are easy to reach.

Dashboards work best when they support questions your team asks on a regular cadence: pick-up monitoring, forecast versus budget tracking, market benchmarking, segment performance review.

If an analysis is exploratory, one-off, or highly specific to a single moment, it belongs in a workbook. If you cannot answer "what is this dashboard for?" in one sentence, it is probably trying to do too much. Split it, or simplify it.

Before sharing a dashboard with your team, walk through it as a first-time user:

If a dashboard needs explaining every time it is shared, it is doing too much of the explaining in conversation and not enough in its own design.

The Dashboard Agent uses the dashboard as its starting context. When a dashboard is well curated, clear questions, logical flow, focused tiles, the Agent can understand what the analysis is about, connect insights across tiles more effectively, and give you faster, more accurate answers to follow-up questions.

If a dashboard is overloaded or unfocused, the Agent will reflect that, both users and the Agent will struggle to navigate it.

In practice, good dashboard design improves the experience for everyone: the person reviewing it, the team sharing it, and the AI assistant helping to explore it.

The way you arrange tiles, sections, and navigation shapes how quickly people find what they need. A few structural choices that consistently make dashboards easier to use:

Group content into named sections. Every tile sits inside a container, a section that groups related charts together. Give each section a clear name: "Pick-Up by Segment," "Occupancy Overview," "Stay Period Breakdown." Named sections make the dashboard read as a deliberate sequence rather than a wall of tiles, and make it much easier to maintain or hand off to a colleague. See Containers for more.

Use pages for distinct topics. When a dashboard covers more than one analytical angle, separate them into pages with tab navigation. The Detailed Performance Report uses four pages, Overview, Business Mix, Production Sources, and About, so a revenue manager checking business mix goes straight there without loading everything else first. Pages also improve performance: each page only loads its own analyses when opened. See Dashboard Pages for more.

Put filters where they belong. Global filters, stay date, property, comparison period, live in the filter bar at the top and apply everywhere. Section-specific filters, such as a timeframe drill that only affects the stay period charts, sit inline directly below the section they control. Placing a filter next to the content it affects makes its scope immediately clear. See Dashboard Filters for more.

Always include a visible title and description. The first elements of every dashboard should be the dashboard name and a one to two sentence description of what it shows and what decision it supports. Many viewers reach a dashboard via a shared link, a scheduled delivery, or a bookmark, they do not see the breadcrumb navigation. In PDF and email outputs, the breadcrumb does not appear at all. A visible title and description is the only reliable orientation in every context.

Write section descriptions that do two things. A brief section description (one to three sentences) is enough for sections with a clear purpose and self-evident charts. For complex diagnostic sections with multiple metrics or non-obvious reading logic, use a more structured format: an intro sentence explaining what the section highlights, labeled reading cues for each key column or measure, and a closing sentence that tells viewers what to do if they find something. The goal is that a viewer can understand what to look for before they start reading the data.

Give every dashboard an About page. Include a dedicated About page as the last tab on every dashboard. It holds the full context: what the dashboard shows, who it is for, how to use each filter and control, and the key definitions and caveats. An About page can be excluded from scheduled deliveries so it does not pad PDF or email output, and it is always findable in the same position regardless of how long the rest of the dashboard is.