COMPLETE REFERENCE GUIDE
Self-serve dashboards
Everything you need to create, configure, share, and maintain dashboards and charts in DevRev - from first click to advanced filtering. Any user can build their own charts on any object, without writing queries or waiting on an analyst. If you already know how to create a view, you already know roughly 80% of what you need.
OVERVIEW
Reporting without a request queue
Self-serve dashboards let any user create charts, widgets, and dashboards on any object in DevRev - without writing queries, depending on an analyst, or submitting a request to engineering. The experience is designed to feel like creating a view, so the learning curve is short.
The guide pairs with two short walkthroughs: Basics builds your first dashboard end to end, and Advanced covers joins, custom fields, and custom objects. The stated success metric is simple - a user should be able to create a dashboard with five or more charts in under 15 minutes (page 1).
Who this is for
The reference maps each persona to the reporting they most commonly need (page 2).
Persona | Primary use case |
|---|---|
Support managers | Ticket volume, resolution times, SLA tracking, severity trends |
Project / engineering leads | Sprint progress, issues per owner, PRs waiting for review |
Sales leads | Pipeline visibility, account activity, custom object reporting |
CS / account managers | Tickets per account, per-tier analysis, SLA metrics |
Any business user | Custom reporting on any stock or custom object |
MENTAL MODEL
A dashboard is a group of charts
Each chart maps to a single object, and one dashboard can mix charts from different objects - ticket charts, issue charts, and code-change charts side by side.
To build a chart you follow a simple hierarchy: create the chart, select the object it pulls data from, choose its layout (chart type), then configure its axes (page 3).
STEP BY STEP
Building a chart, end to end
The walkthrough breaks chart creation into a repeatable sequence (pages 6-11).
Steps 1-3
Create and choose the object
Create the dashboard, then a chart. Name and object are mandatory. Pick the object the chart pulls from - a stock object like tickets, issues, accounts, or code changes, or any custom object.
Step 4
Choose a chart type
Select table, bar, column, line, pie, or donut. The chart type determines which axis configuration you see next, and it can be changed later.
Steps 5-6
Configure axes and filter
Set the dimension and measure for the chart type, then scope the data. Always add at least a date-range filter - skipping filters on large orgs can pull millions of records and slow loading.
Steps 7-8
Save, arrange, and share
Name and save the chart, repeat for each one, then drag to rearrange and resize. Use Share to grant specific users or groups Viewer or Editor access.
Chart types at a glance
Six chart types are available; every type except the table supports auto-generated drill-throughs (pages 14, 19).
Chart type | Best for | Drill-through? |
|---|---|---|
Table | Flat, record-level lists showing ID, title, owner, status in columns | No - already showing records |
Bar (horizontal) | Comparing categories: count of items by owner, severity, or priority | Yes |
Column (vertical) | Same as bar but vertical; good for time comparisons like month over month | Yes |
Line | Trends over time, such as created vs resolved over 90 days | Yes |
Pie / Donut | Distribution of a whole; donut can surface a headline KPI in its center | Yes |
CORE CONCEPTS
The three things people get wrong
The reference calls out the most common points of confusion (pages 15-19).
Axes
Dimensions vs measures
A dimension is what you group by (owner, priority, status); a measure is what you count or calculate (count of IDs, sum of a field). For a horizontal bar: dimension on Y, measure on X.
Filters
Chart-level vs global
Chart filters are baked in at creation and always active. Global filters sit at the dashboard level, are dynamic, and are object-aware - a severity filter only affects charts built on the tickets object.
Drill-throughs
Automatic and accurate
Click a bar, slice, or point to see the underlying records. Drill-throughs are auto-generated on aggregating charts and derived from the chart's own data, so results stay consistent.
OPERATING DASHBOARDS
Sharing, refresh, and limits
Access
Personal by default
A new dashboard is private until you share it. Viewers can see charts, apply global filters, and drill through; editors can also add, edit, and rearrange charts and change sharing.
Refresh
10 minutes, or 30 seconds
Dashboards query the data layer on an interval rather than streaming. The standard refresh is 10 minutes, dropping automatically to 30 seconds when the Computer cache is active.
Limits
Up to 50 charts
A dashboard holds up to 50 charts, with tabs coming soon. There is no hard filter limit - more filters mean more scoped data and faster charts. Data sources are live dim tables today.
Scheduling
Scheduled via Workflows
Native in-dashboard scheduling is not yet available. To email a snapshot on a cadence, build a workflow around the get-snapshot node - this works for both stock and custom dashboards.
Stock vs self-serve, and coming from Zendesk
Stock dashboards are pre-built for standard metrics; self-serve dashboards are the custom ones you build. Teams migrating from Zendesk Explore find the flow familiar (pages 22, 27).
Topic | Detail from the reference |
|---|---|
Stock dashboards | Pre-built by DevRev, stock fields only, not editable; you cannot yet clone one and add custom fields (on the roadmap) |
Self-serve (custom) | Built from scratch, fully editable, all custom fields available |
Zendesk Explore vs DevRev | Same flow (object/dataset -> chart type -> axes -> save), but DevRev reports on any object, not a fixed set of datasets |
Schema-driven | New custom fields appear in the chart builder automatically - no rebuild needed |
Start simple, then grow
Begin with the four core chart types, filter during creation, and build for comparison. On the roadmap: custom formulas, fact tables for historical data, dashboard tabs, cloning stock dashboards, and Computer-assisted chart creation (pages 28, 34).
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