Jira connector

Use your Jira data for reporting, automation and AI.

Data Panda brings your Jira data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your engineering leads, product managers and service desk use every day.

About Jira

The issue tracker the whole delivery organisation runs on.

Jira was built in 2002 in Sydney by Mike Cannon-Brookes and Scott Farquhar as Atlassian's first product, originally a bug tracker for software teams. Atlassian listed on NASDAQ under ticker TEAM in December 2015 and today reports more than 300,000 customers across its product family. The Jira line itself splits into Jira for general project and software work, Jira Service Management for IT and business service desks, and the product management and agile tooling that sits next to it. The engineering DNA is still visible: sprints, story points, epics, releases, component ownership, native links to Bitbucket and GitHub, and a workflow engine that enterprise teams bend around audit and compliance needs.

For engineering and product leaders, Jira is the system of record for what's being built, what's blocked and what shipped. The built-in boards and reports cover the daily stand-up well. The harder questions live across Jira and the systems around it: how sprint velocity tracks roadmap commitments, where cycle time is drifting, how PR review time compares across teams, and which customer-reported bugs still live as open issues on a renewing account. Pulling Jira into a warehouse is how those questions stop being a one-off JQL export someone runs before every QBR.

What your Jira data is for

What you get once Jira is connected.

Engineering and delivery reporting

Sprint velocity, cycle time, epic progress and release health in one place, across teams and products.

Sprint velocity per team, tracked against roadmap commitments
Cycle time and lead time per issue type, per component
Epic burn-up with real issue resolution dates, not manual status fields

Process automation

Turn CRM, support and call-intelligence events into the right Jira work, without someone copying tickets into issues each morning.

Create bug issues from support tickets with the customer tier and repro steps attached
Escalate customer-reported defects on top-tier accounts to the right component team
Close issues when the linked pull request is merged and deployed

AI workflows

Put issue, comment and change history behind AI that understands how your teams deliver in practice.

Defect-escape risk scoring on open issues based on comments, reopens and component history
AI summaries of sprint and release status for product and exec updates
Intake triage that routes new bugs and requests to the right component and team

Custom apps on your data

Internal tools on Jira data that teams keep rebuilding as dashboards or JQL filters.

Engineering health workbench with velocity, cycle time and PR review time per team
Customer-bug console mapping open issues to CRM accounts and contract value
Release notes app driven by issues resolved between two deploy tags

Use cases

Use cases we deliver with Jira data.

A list of concrete reports, automations and AI features we have built on Jira data. Pick the one that matches your situation.

Sprint velocity trendCompleted story points per sprint and team, against commitment.

Cycle timeTime from in-progress to done, per issue type and component.

Lead timeTime from issue creation to release, per product line.

PR-to-deployMerged pull request to production deploy, per team and service.

Defect escape rateBugs found in production versus bugs caught in QA, per release.

Epic burn-upEpic scope against completed issues, with scope-change history.

Customer-bug loadOpen defects linked to a CRM account, weighted by contract value.

Service desk SLAJira Service Management response and resolution against target.

Component ownershipOpen issue load per component and owning team.

Release notes draftIssues resolved between two deploy tags, grouped for changelog.

Reopen rateIssues closed then reopened within N days, per component.

Intake to triage timeFrom new issue to first triage, per project and priority.

Real business questions

Answers you will finally get.

Is our sprint velocity holding up against the roadmap?

Sprint velocity per team next to the roadmap commitments made at the start of the quarter, with the stories and bugs that consumed the capacity. Product leads see the gap in time to reshape the next sprint, instead of in a retro after the release date has already slipped.

Where is cycle time drifting?

Median and 90th-percentile cycle time per issue type and component, tracked over rolling windows. Where cycle time has doubled on a specific component over the last eight weeks, it surfaces as a number, together with the PR review time and reopen rate that usually move with it.

Which customer-reported bugs are sitting on top-tier accounts?

Open bug issues joined to the CRM account that raised them, weighted by contract value and renewal date. Account managers see the operational picture on the account record before the renewal call, instead of asking engineering to export a filter the morning of.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

Engineering effort per epic and product line next to the budget line it sits on. The finance view of capex-versus-opex on software work stops being a per-quarter reclassification and becomes a living report.

For sales leaders

Customer-reported bugs and feature requests tied back to the CRM account. Account executives see whether the three promised fixes shipped in time, before the renewal call rather than during it.

For operations

Cycle time, velocity, release cadence and service-desk SLA in one view. Engineering managers, product and ops share the same numbers instead of three different exports built the morning of the steerco.

Ideas

What you can automate with Jira.

Pair with Salesforce

Surface customer-reported bugs on Salesforce accounts

Jira bug issues raised from a Salesforce account post back to the account record as open defects with severity, component and expected release. Account executives walk into renewal calls seeing which three bugs are still open and which shipped since the last QBR, without opening Jira. For Jira Service Management customers, SLA breach and open P1 counts land on the same account fiche.

Pair with Zendesk

Turn Zendesk escalations into Jira issues

Zendesk tickets tagged as bug or feature request create Jira issues in the right project and component with the conversation, customer tier and repro steps attached. When engineering resolves the issue, the status flows back into Zendesk so support closes the loop with the customer, instead of shipping a weekly spreadsheet of open escalations to engineering leads.

Pair with Gong

Route feature requests from Gong calls into Jira epics

Gong call moments tagged as feature requests create or update Jira epics in the right product area, with the customer, deal size and the relevant call snippet attached. Product managers see the ten accounts asking for the same capability stacked on one epic, instead of fishing through call notes before every roadmap planning round.

Pair with HubSpot

Link HubSpot accounts to their open Jira bugs

HubSpot accounts and deals tie back to Jira issues raised on their behalf, so CSMs see open bug and feature-request counts per account, weighted by ARR. Account owners are notified the hour a P1 on their account opens in Jira, instead of finding out from the customer the next day.

Data model

Tables we make available.

These are the 6 tables we currently pull from Jira into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.

Accessible Resources
Issues
Issuetype
Projects
Status
Users

Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your Jira data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From Jira to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

Jira connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Do custom fields on issues come across?

Yes. Custom fields land in the warehouse next to issues, so fields like story points, severity, component, affected version or customer account become reportable alongside the core Jira data. When your admin renames or retypes a field, we track the shift so downstream reports do not silently drift.

Does this also cover Jira Service Management?

Yes. Jira Service Management service requests, incidents and SLAs sit on the same core schema as Jira issues, so they land in the warehouse through the same connector. You get the service-desk view on Accounts and SLA next to the engineering view on Sprints and Releases, from a single dataset.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your Jira setup and the systems around it. Together we pick the first thing worth building.

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