Confluence connector

Use your Confluence data for reporting, automation and AI.

Data Panda brings your Confluence spaces, pages and blog posts together with the data from the rest of your business. From one place, we turn that knowledge base into dashboards, automations, AI workflows and custom apps your engineers, product managers and ops people use every day.

Confluence
 Data Panda Reporting Automation AI Apps
Confluence
About Confluence

The team wiki most engineering organisations already run on.

Confluence shipped from Atlassian in 2004 as the docs-and-collaboration sibling to Jira, and it has stayed in that slot for two decades. Today it runs in two main editions: Confluence Cloud, hosted by Atlassian, and Confluence Data Center for organisations that still self-host. Atlassian reports more than 300,000 customers across the product family, and Confluence sits inside a sizeable share of those, especially in software-engineering organisations where Jira is already the system of record for delivery work. The product is built around spaces (one per team, project or topic), pages organised in a tree, blog posts for announcements, and now also Whiteboards, Databases and richer templates aimed at moving more of the day-to-day documentation work onto a single platform.

What makes Confluence different from a free-form workspace like Notion is the structured shape: spaces, page trees, page restrictions, labels, version history and a deep native link into Jira issues, releases and projects. That structure is also what makes it hard to keep clean. A 200-engineer organisation has thousands of pages, hundreds of spaces, runbooks that nobody opens until the on-call alert hits, and decision-log pages that link to Jira tickets that have since been closed, moved or deleted. Pulling Confluence into a warehouse is how those questions stop being a once-a-year wiki gardening project and become a number a content owner can act on.

What your Confluence data is for

What you get once Confluence is connected.

Wiki and runbook reporting

Page health, space coverage, ownership and edit cadence across the whole knowledge base.

  • Pages untouched for N months, by space and owning team
  • Spaces with no recent activity and no clear owner
  • Runbooks that link to Jira components or services that no longer exist

Knowledge automation

Let Confluence events drive the rest of your stack, instead of someone watching the wiki for changes.

  • Page edits on critical runbooks notify the right Slack channel with the diff
  • New incident postmortems open the linked Jira improvement tickets automatically
  • Sales playbook updates push a summary into the deal-team HubSpot record

AI workflows

Put your real wiki behind AI that knows your runbooks and decisions, not a generic assistant guessing from training data.

  • Internal Q&A grounded in actual Confluence pages, with source links back to the page and version
  • Stale-content scoring that flags pages where the linked Jira tickets have closed or the named owner has left
  • Runbook summarisation for on-call handover, refreshed when the page changes

Custom apps on your data

Small tools on Confluence content for people who do not want to learn the space tree.

  • Page-owner review queues with stale runbooks ranked by service criticality
  • Decision-log explorer that joins Confluence pages to the Jira epics they reference
  • Read-only customer or partner portal backed by specific Confluence spaces
Use cases

Use cases we deliver with Confluence data.

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

Stale runbook auditPages tagged as runbook, ranked by months-since-edit and service criticality.
Orphan space detectionSpaces with no active editors and no inbound links from other spaces.
Page ownership coverageShare of pages with a current employee set as owner, per space.
Broken Jira link driftConfluence pages linking to Jira tickets that have closed, moved or been deleted.
Decision-log coverageArchitecture decisions documented per service against changes shipped from Jira.
Label hygienePages missing the labels their space template requires, per team.
Editor activityEdits and creators per person and team over rolling windows.
Most-read versus orphanedHigh-traffic pages versus pages no other page links to.
Postmortem completionIncident postmortem pages opened versus closed Jira incidents in the period.
Restricted-page sprawlPages with view or edit restrictions, by space and restriction age.
Real business questions

Answers you will finally get.

Which runbooks haven't been touched since the systems they describe changed?

Pages tagged as runbook, joined to the Jira components and services they reference. Pages where the underlying Jira project has been renamed, deprecated or restructured surface as a list, ranked by service criticality, so on-call leads see which docs are lying about reality before the next incident, not during it.

Which spaces are dead weight nobody owns?

Spaces with no edits over the trailing twelve months and no inbound links from active spaces, with their nominal admin and current size. The output is a list a wiki gardener can take to the relevant team for archive-or-keep decisions, instead of guessing which of the 400 spaces is safe to retire.

Where do our Confluence pages still reference Jira tickets that are gone?

Confluence pages joined to Jira issue keys parsed from page bodies and inline links. Tickets that have been closed for over a year, moved to a renamed project or deleted entirely surface against the Confluence pages that still point to them. That's the list to fix before someone follows a link in a runbook at 2am.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

Confluence pages tied to budget approvals, vendor reviews and capex decisions get tracked next to the actual finance ledger. The audit trail of what was decided, when, and by whom stops being a manual page-search the week before an audit.

For sales leaders

Sales playbooks, competitor pages and pricing docs are indexed by recency and ownership, so reps can see which playbook is current and which one was last edited before the last pricing change. Account teams stop quoting deprecated terms because the page wasn't flagged.

For operations

A live picture of runbook coverage, postmortem completion and ownership across spaces. Ops and engineering managers run targeted wiki cleanups based on actual rot, instead of broad cleanup weeks where everyone tackles their own pet pages.

Ideas

What you can automate with Confluence.

Pair with Jira

Tie Confluence decision logs to the Jira epics they describe

Confluence decision-log and architecture pages join to the Jira epics, components and tickets they reference, so engineering leads see which decisions are still backed by active work and which point at epics that closed or got renamed. New decision pages can also open the matching Jira tracking ticket, instead of relying on someone to remember to open one after the meeting.

Pair with Slack

Push runbook and key-page edits to the right Slack channel

Edits on flagged Confluence pages (runbooks, on-call docs, security policies) post a compact diff to the Slack channel that owns the topic, with a link back to the page and the editor name. Page owners stop relying on email watch lists, and the team sees a lightweight audit trail of who changed what next to the conversation where it matters.

Pair with GitHub

Cross-reference GitHub PRs with the Confluence docs they should update

GitHub pull requests that touch a service get matched to the Confluence runbooks, architecture docs and decision logs that reference that service. The PR description picks up a checklist of doc pages worth reviewing, and pages that haven't been touched since the PR merged surface in the next stale-content report. Engineering leads see drift between code changes and the docs that describe them as a number, not a hunch.

Pair with HubSpot

Surface sales-playbook updates on the matching HubSpot deal

Edits on Confluence sales playbooks, competitor pages or pricing docs push a short summary to the HubSpot deal records that touched the relevant segment in the last quarter. Account executives walk into the next call knowing the playbook moved, instead of using last quarter's pricing because nobody told them the page changed.

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 Confluence data lives.

Power BI logo
Power BI Microsoft
Microsoft Fabric logo
Fabric Microsoft
Snowflake logo
Snowflake Data warehouse
Google BigQuery logo
BigQuery Google
Tableau logo
Tableau Visualisation
Microsoft Excel logo
Excel Sheets & pivots
Three steps

From Confluence to answers in three steps.

01

Connect securely

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

02

Land in your warehouse

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

03

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.

  • Confluence 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.

Does the sync pull full page content, or only metadata?

Both shapes are available, and worth picking per use case. Page metadata (title, space, labels, owner, created and updated timestamps, version, restrictions) lands as structured columns and covers most reporting and audit work. Full body content (paragraphs, headings, embedded macros, attachments) is heavier and goes through the Confluence content API per page. We pull bodies where AI search or stale-content scoring needs them, and skip them where metadata is enough.

Page content can be sensitive. How are restrictions and permissions handled?

Confluence page and space restrictions are pulled into the warehouse alongside the content. The default behaviour is to land everything the connector account can read and respect the original restriction model in downstream views, so a person who could not open a page in Confluence does not see it in a dashboard either. For sensitive spaces, the connector account can also be scoped to skip them entirely.

Does this work for both Confluence Cloud and Data Center?

Confluence Cloud is the default supported edition and uses the Atlassian REST API. Confluence Data Center (the self-hosted edition) is reachable too over its own REST API, with the connector account configured against the on-prem URL and an API token. The two editions surface broadly the same content shapes, so the warehouse model and downstream reports stay the same regardless of where Confluence runs.

GDPR-compliant
Data stays in the EU
You own the warehouse

A first deliverable live in four to six weeks.

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

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