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Core concepts

A few ideas explain everything in the app. Once they click, the rest is configuration.

Dashboard​

A dashboard is a saved page of charts. You can have as many as you like, organized in the left rail (dashboards can even nest under a parent). Each dashboard has its own datasets, its own widgets, and its own default color palette, and it auto-saves as you work.

Dataset​

A dataset is the set of issues a dashboard charts against. Every widget on the dashboard reads from the active dataset. A dashboard can hold several curated datasets and switch between them. There are four kinds:

KindWhat it is
ProjectAll issues in a Jira project. Also unlocks per-project history and change-log rollups.
Agile boardA Scrum or Kanban board. Required for velocity and sprint burndown.
Saved filterAny Jira saved filter — its JQL becomes the dataset.
Custom JQLJQL you write yourself, with autocomplete for fields, operators, and values.

See Datasets.

Widget (chart)​

A widget is a single chart on the dashboard. Every widget is defined by three choices:

  • Chart type — bar, pie, line, control chart, KPI, gauge, pivot, and more. See Chart types.
  • Dimension — what to group by (the categories along the axis).
  • Measure — what to count or calculate for each group (a stat applied to a field).

Dimensions vs. measures​

This is the most important distinction.

  • A dimension answers "grouped by what?" — status, assignee, priority, component, epic, sprint, created month, and so on.
  • A measure answers "measuring what?" — a stat (count, distinct, sum, average, median, percentile, min, max) applied to a field (issue count, story points, or cycle-time days).

For example, "sum of story points, grouped by assignee" is the measure sum(points) over the dimension assignee. The full catalog is in Dimensions & measures.

Derived dimensions​

Some of the most useful dimensions aren't Jira fields at all — they're computed from each issue's change history:

  • Cycle-time bucket — how long an issue took, bucketed (e.g. 0–2d, 3–5d, …).
  • Time in status — how long issues spend in each status.
  • Status-age bucket (WIP aging) — how long issues have sat in their current status.
  • SLA state — within / breached, against a target you set.
  • Reopen count — how many times an issue went backwards.

The app precomputes these from the changelog and indexes them, so you can group by, filter on, and trend them just like a native field — with no formula language. This is the capability most charting gadgets can't offer. See Derived metrics.

Scope filters vs. cross-filter​

There are two ways the issues on a dashboard get narrowed, and it helps to keep them separate:

  • Scope filters are the filter bar at the top — cascading template variables (status, type, priority, assignee, cycle-time bucket) plus a created-date range. They narrow the whole dashboard deliberately, and they're shown as solid chips.
  • Cross-filter is what happens when you click a segment in a chart. That selection refilters every other widget on the page and shows as an outlined chip. It's exploratory — click to set, click again to clear.

Both can be active at once. See Cross-filtering & drill-down.

Colors & palettes​

Each dashboard has a default palette, and any widget can override it. Beyond palettes you can pin a specific color to a specific segment, or write conditional-formatting rules that color a segment by its value or label (e.g. "Done" → green, cycle time > 10d → red). Palettes include a colorblind-safe option. See Colors & conditional formatting.

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