JSM to Zendesk is a schema translation: map issues to tickets, preserve comment visibility, and design around API rate limits. No native path exists. Budget 1–3 weeks.
There is no native migration path between Jira Service Management and Zendesk; the migration is fundamentally a data-model translation problem. JSM issues are Jira issues wrapped in ITSM workflows, projects, and layered comment visibility, while Zendesk tickets are flat, conversation-centric objects organized by forms, tags, and a fixed status taxonomy. Custom API-based extraction and transformation work is required to preserve comment visibility (public vs. internal), map request types to ticket forms, collapse priority levels, and rebuild SLA policies, workflows, queues, and approval processes manually in Zendesk.
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Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
TL;DR — Migrating from Jira Service Management (JSM) to Zendesk is a data-model
TL;DR — Migrating from Jira Service Management (JSM) to Zendesk is a data-model translation problem, not a file transfer. JSM issues are Jira issues wrapped in ITSM features; Zendesk tickets are conversation-centric objects. No native migration path exists between the two platforms. The biggest risk is exposing internal comments as public replies if your migration script doesn't set public: false on each comment. SLA policies, workflows, approvals, and automation rules cannot be migrated programmatically — they must be rebuilt in Zendesk. Realistic timeline: 1–3 weeks for most environments. Teams with fewer than 5,000 tickets and standard fields can attempt a DIY API script. Everyone else should use a managed migration service.
The single biggest failure mode in a JSM to Zendesk migration is comment visibility
Validate public/private comment counts before cutover, and spot-check tickets that contain escalation, security, finance, or incident-response notes.
Jira Cloud's /rest/api/3/search/jql endpoint has known pagination issues
Multiple users have reported broken nextPageToken behavior. If you encounter this, fall back to the legacy /rest/api/3/search endpoint with startAt pagination, or filter by ID ranges.
The standard Zendesk ticket creation API overwrites historical dates with the current timestamp
To preserve original JSM creation and resolution dates, you must use the Ticket Import API, which accepts created_at and updated_at parameters.
Zendesk's Ticket Import API rejects comments with empty bodies
JSM allows comments that contain only attachments with no text. Add placeholder text (e.g., "(Attachment only)") or the import will fail silently for that comment.
Disable welcome emails before importing users
In Zendesk Admin Center → People → Configuration → End users, deselect "Also send a verification email when a new user is created by an agent or administrator." Otherwise, every imported user receives a password reset email.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
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Pull the real numbers out of Jira Service Management
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Zendesk can hold your support model
Walk your current workflow through Zendesk: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
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Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Jira Service Management → Zendesk timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
Jira Service Management → Zendesk specifics
- External customer support is the primary use case
- JSM is architected for internal ITSM — incident management, change requests, engineering escalations. Zendesk is purpose-built for external customer conversations across email, chat, social, and phone. Teams that adopted JSM for customer support often hit friction with JSM's rigid issue-based UI and limited native omnichannel capabilities. Atlassian's own comparison content describes a pattern where customer-facing support stays in Zendesk while internal escalations live in JSM. (atlassian.com)
- Agent experience and speed
- Zendesk's agent workspace is optimized for high-volume ticket triage. JSM's interface is optimized for workflow-heavy ITSM processes with screens, transitions, and validators that slow down agents handling simple customer queries.
- Ecosystem and integrations
- Zendesk's marketplace has 1,500+ pre-built integrations for e-commerce, CRM, and customer data platforms. JSM's strength is Atlassian-ecosystem integrations (Confluence, Jira Software, Bitbucket).
- < 500 tickets, no attachments
- CSV export of users/orgs + manual ticket recreation or a marketplace tool.
- 500–20K tickets, standard fields
- A third-party tool or a dedicated engineer building API scripts (budget 2–3 weeks of eng time).
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Jira Service Management export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Jira Service Management export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Jira Service Management where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
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Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing
Jira Service Management → Zendesk specifics
- Search endpoints
- Use POST /rest/api/3/search or /rest/api/3/search/jql with JQL queries. The primary extraction query is something like project = "YOUR-PROJECT" ORDER BY id ASC.
- Pagination
- Default page size is 50 results. Jira Cloud enforces a hard ceiling of 10,000 results per search window. To paginate beyond 10,000 issues, filter by issue ID (id > {last_seen_id}) and re-query. (developer.atlassian.com)
- Comments and attachments
- Require separate API calls per issue. Use /rest/api/3/issue/{issueIdOrKey}/comment for comments and the attachment field on the issue response for attachment metadata. For accurate public/internal visibility, use the JSM-specific endpoint: GET /rest/servicedeskapi/request/{issueIdOrKey}/comment. (developer.atlassian.com)
- Rate limiting
- Atlassian documents default burst limits of approximately 100 GETs/sec and 100 POSTs/sec per endpoint, plus per-issue write limits. Some endpoints are stricter — certain Service Desk endpoints are limited to 5 requests/sec. Exceeding limits returns HTTP 429 with Retry-After and RateLimit-Reason headers. (developer.atlassian.com)
- Attachment downloads
- JSM attachment URLs require active authentication. Your extraction script must pass basic auth or OAuth tokens in the request header.
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
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Generate the first-pass Jira Service Management → Zendesk field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Jira Service Management → Zendesk field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
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Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Zendesk has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
Jira Service Management → Zendesk specifics
- 20K+ tickets, custom fields, attachments, compliance
- A managed migration service. The engineering hours burned on throwaway ETL code almost always exceed the cost of a specialist.
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Zendesk sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Zendesk sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
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Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
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Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Zendesk's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
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Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Zendesk, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Jira Service Management stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Zendesk's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
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Freeze Jira Service Management and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
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Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Jira Service Management read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Jira Service Management and Zendesk record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
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Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Zendesk, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Jira Service Management read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
How Map JSM Objects to Zendesk
| Jira Service Management field | Zendesk field | Notes |
|---|---|---|
| Issue (Service Request) | Ticket | JSM issue key (e.g., HELP-1234) is lost — Zendesk assigns new IDs. Store the key in external_id and a visible custom field. |
| Reporter / Customer | Requester (End-User) | Must exist as an active user in Zendesk before ticket import. Map JSM accountId or email to Zendesk email. |
| Assignee | Assignee (Agent) | Must be an agent in Zendesk with the correct group membership before ticket assignment. |
| Project | Group or Brand | No direct Zendesk equivalent. Map to Groups (for routing) or Brands (for multi-brand setups). |
| Organization | Organization | 1:1 mapping. Import organizations before users. JSM allows multiple orgs per user; Zendesk supports this on specific plan tiers. |
| Request Type | Ticket Form + Custom Field | Zendesk has no "request type" object. Use Ticket Forms or a custom dropdown field. |
| Issue Type (Incident, Problem, etc.) | Ticket Type | Zendesk supports: question, incident, problem, task. Custom issue types must map to a custom field. |
| Priority (5 levels) | Priority (4 levels) | JSM has Highest/High/Medium/Low/Lowest; Zendesk has Urgent/High/Normal/Low. Decide how to collapse. |
| Labels / Components | Tags | Direct mapping, but Zendesk tags are flat strings — no hierarchy. |
| Public Comment | Public Comment (public: true) | Preserve author_id and created_at timestamps using the Ticket Import API. |
| Internal Comment | Internal Note (public: false) | Critical. Failure to set this exposes internal notes to customers. |
| Attachments | Attachments | Upload via /api/v2/uploads first, then reference the token in the comment payload. |
| Request Participants | Collaborators or Followers | Zendesk Ticket Import exposes collaborator_ids and follower_ids. Decide one global mapping rule. (developer.atlassian.com) |
| SLA Policy | SLA Policy | Cannot be migrated. Must be rebuilt manually in Zendesk. |
| Queue | View | Cannot be migrated. Rebuild as Zendesk Views using conditions. |
| Workflow / Transitions | Triggers + Automations | Cannot be migrated. Rebuild manually. |
| Approval | — | No Zendesk equivalent. Zendesk has no native approval workflow. |
| Assets (CMDB) | — | No Zendesk equivalent. Zendesk does not have a CMDB. |
| Knowledge Base (Confluence) | Zendesk Guide | Separate migration. Not covered by ticket migration. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Tickets (Issues) | medium | JSM issue keys are lost during migration since Zendesk assigns new IDs, requiring the original key to be stored in external_id and a custom field for cross-reference. |
| Internal Comments | high | Failure to explicitly map JSM's visibility role property to Zendesk's public:false boolean will expose confidential internal notes, escalation details, and security information to customers. |
| Public Comments | low | Public comments map directly between platforms, though author IDs and timestamps must be preserved using the Zendesk Ticket Import API. |
| Users and Organizations | medium | Users and organizations must be created in Zendesk before ticket import, and JSM's support for multiple organizations per user may not be available on all Zendesk plan tiers. |
| Custom Fields | high | Field type mismatches (cascading selects, version pickers, multi-user pickers) require transformation, and historical tickets referencing deleted picklist values will be rejected unless legacy values are recreated in Zendesk. |
| Attachments | medium | Attachments require separate API calls per issue for extraction and a two-step upload process in Zendesk, adding complexity and time to the migration. |
| SLA Policies | high | SLA policies cannot be migrated programmatically and must be completely rebuilt in Zendesk, risking configuration drift or gaps in service level enforcement during cutover. |
| Workflows and Automations | high | JSM workflows with transitions, validators, and screens have no portable equivalent and must be manually redesigned as Zendesk triggers and automations. |
| Request Types and Ticket Forms | medium | JSM request types with hidden agent-only fields do not map directly to Zendesk ticket forms, requiring a redesign and confirmation that the Zendesk plan tier supports multiple forms. |
| Approvals and Assets (CMDB) | high | Zendesk has no native approval workflow or CMDB, so these JSM capabilities are entirely lost and must be addressed through third-party apps or process changes. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Comment Visibility Mapping
JSM uses a visibility property with role-based rules for internal comments, which must be explicitly translated to Zendesk's public boolean on each comment to prevent exposing internal notes to customers.
Data Model Translation
JSM issues are Jira issues with projects, issue types, workflows, and screens, none of which have direct equivalents in Zendesk's flat ticket-centric model, requiring structural redesign using ticket forms, custom fields, and tags.
Workflow and Status Mapping
JSM's multi-step Jira workflows with customer-facing status mappings must be collapsed into Zendesk's fixed status categories (new, open, pending, hold, solved), making 1:1 workflow porting impossible.
SLA and Automation Rebuild
SLA policies, queues, automation rules, and approval workflows cannot be migrated programmatically and must be manually rebuilt in Zendesk using its native triggers, automations, views, and SLA features.
API Extraction Constraints
Jira Cloud enforces a hard ceiling of 10,000 results per search window and requires separate API calls per issue for comments and attachments, with some Service Desk endpoints limited to 5 requests per second.
Custom Field Type Mismatches
JSM supports cascading selects, multi-user pickers, version pickers, and date-time fields with time zones that have no direct Zendesk equivalents and must be transformed to simpler field types like dropdowns or text fields.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Jira Service Management to Zendesk without losing data?
You can migrate tickets, comments (public and internal), users, organizations, attachments, and custom field values without loss using the Zendesk Ticket Import API. However, JSM SLA history, workflow configurations, automation rules, approval chains, CMDB/Assets data, and CSAT survey data cannot be migrated programmatically — they must be rebuilt manually in Zendesk. Original JSM issue keys are also lost; Zendesk assigns new ticket IDs.
How long does a Jira Service Management to Zendesk migration take?
A typical migration takes 1–3 weeks end-to-end. Small instances under 5,000 tickets can complete in 3–5 days. Enterprise environments with 100K+ tickets, complex custom fields, and heavy attachment volumes typically require 3–6 weeks including discovery, test migrations, full migration, and validation.
How do I prevent JSM internal comments from becoming public in Zendesk?
Extract comments using the JSM request comment endpoints where public/internal visibility is explicit, then set the boolean value public: false on every internal comment in the Zendesk import payload. Treat this as a QA gate — validate public/private comment counts before cutover, not after.
Does Zendesk preserve original ticket timestamps from JSM?
Yes, but only if you use the Zendesk Ticket Import API (/api/v2/imports/tickets). It allows setting created_at, updated_at, and solved_at on imported tickets, plus created_at on individual comments. The standard ticket creation API overwrites all dates with the current timestamp.
What data cannot be migrated from Jira Service Management to Zendesk?
JSM SLA policies and historical SLA data, workflow configurations, automation rules, approval chains, CMDB/Assets data, Opsgenie-backed service data, CSAT survey data, and original issue keys cannot be migrated programmatically. These must be rebuilt manually in Zendesk or archived separately.