Visualizations

Visualizations transform raw data into meaningful insights that are easy to interpret, explore, and share. Whether you’re working in the Tile Editor or the Data Explorer, you can choose from a range of visualization types designed to highlight different patterns and relationships within your dataset.

By configuring the best visualization for your data’s structure and the story you want to tell, you can make your findings more compelling, intuitive, and actionable.

Visualization types

Each visualization type provides a unique perspective and comes with its own configuration settings tailored to its ideal use cases.

The initial types available may vary based on your dataset, but you can use Shaping or SQL Analytics to manipulate your data so that it can be visualized in more ways.

Name
Description
Displays raw data in a structured, sortable table format. Best for viewing detailed records, inspecting query results, or validating data.
Shows a single value, such as a total, count, or percentage. Best for KPIs or summary metrics (for example, “Total Sales” or “Average Resolution Time”).
Plots values over time or a continuous variable. Best for tracking trends, identifying spikes or drops, and time-based analysis.
Compares categorical values or aggregated data side-by-side. Best for ranking categories, comparing quantities, or showing part-to-whole relationships.
Displays key metrics in colored blocks or tiles for quick visual scanning. Best for dashboards highlighting several KPIs or categorical counts.
A circular chart representing parts of a whole. Best for showing proportional breakdowns (for example, “Tickets by Status”).
Displays external or custom content directly within your dashboard. Best for integrating third-party dashboards or internal portals.
Displays a single value against a defined range. Best for monitoring progress toward a goal or threshold.

Troubleshooting visualizations

Which visualizations are offered to you depends on the data available, for example Line Graph will only be offered if there is time series data in your dataset.

When a warning triangle is shown it means that that particular visualization cannot be shown at this time. It may be that shaping the data will allow others visualization to be used.

A visualization type with a warning triangle

Example fixes

If your ticket data does not currently display a Line, Bar, or Donut visualization, try reshaping it with the following operations:

  1. Filter | Group | Sort > Group by > Created Date > Bucket by > Day
    → Adds time buckets to support trend visualizations (for example, Line or Bar charts).
  2. Filter | Group | Sort > Group > Aggregation type > Count
    → Creates aggregated data suitable for Bar or Donut charts.

Once the data structure supports the selected visualization, the warning will disappear and the chart will render.

Resetting visualization settings

If your configuration becomes complex or you want to start over, use the Reset button in the Visualization panel. This reverts all visualization-specific changes to their defaults without affecting your underlying data or filters.

Tips for choosing the right visualization

Choosing the right visualization is one of the most important steps in turning data into a story that resonates. The type you select can be the difference between a well clarified message, or confusion and noise. A well chosen visualization not only highlights what matters but also guides your audience toward the insights you want them to see.

Keeping the following principles in mind will help you select the most effective visualization:

  • Understand your goal:
    Are you comparing, tracking over time, summarizing performance, or exploring patterns? Your objective determines the best visual.
  • Match visualization to data type:
    • Time-based →Line Graph
    • Categorical →Bar or Donut
    • Single metric →Scalar or Gauge
  • Keep it simple:
    Too many visual elements or overly complex designs can distract from the insight you’re trying to convey.
  • Validate your data shape:
    Make sure your data is properly grouped, aggregated, and structured before switching visualization types. Clean inputs make stronger visuals.

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