To migrate startup data safely, inventory what you have, confirm what the destination can import, map fields and identities, rehearse the move, then validate records and workflows before retiring the old platform. The exact steps depend on the products and data involved: project-management tools, CRMs, finance systems, HR platforms, and file services do not necessarily preserve the same records, relationships, history, or permissions.
1. Set the scope and define success
Name the source and destination platforms, the data domains involved, a migration owner, business owners for important records, a target date, and acceptable downtime. Decide whether you are doing a one-time cutover, a staged move, or ongoing synchronization. Treat the move as an operational change—not just an export and import—because integrations, automations, and team processes may also need to change. Microsoft’s data-management checklist recommends planning data sources, mapping, environments, testing, and cutover.
Write down measurable acceptance criteria before work begins. For example, specify which record types and key fields must reconcile, which relationships must work, and which teams must be able to access and use the new system. The checks should reflect the startup’s actual workflows rather than a generic notion of a successful import.
2. Inventory the data and decide what to move
Make an inventory before choosing a migration method. Include the records and their approximate volume, data owners, attachments, custom fields, users, permissions, integrations, automations, and dependencies. Note whether records are active, obsolete, duplicated, legally required, or outside the project’s scope. This both reduces unnecessary payload and helps expose data that may be difficult to map.
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- Records and metadata: Identify objects such as projects, tasks, contacts, deals, invoices, or employee records, plus custom fields and status values.
- Relationships: Record links between objects, such as task-to-project assignments, parent-child records, dependencies, or account-to-contact associations.
- Files and history: Check whether attachments, comments, activity history, and audit details need to remain available, and whether the selected route can carry them over.
- People and access: List users, owners, roles, groups, and access rules. Decide how identities in the source will correspond to identities in the destination.
- Connected systems: Identify integrations and automations that read or write data, including processes that could keep changing the source during the move.
Microsoft’s storage migration assessment calls for cataloging sources and assessing dependencies, usage, security, performance, resiliency, and cost. AWS’s small-business cloud migration checklist likewise recommends inventorying applications, data, and dependencies and identifying data-quality gaps.
3. Confirm a supported migration route
Read the current documentation for both platforms before exporting. Confirm which record types and fields are supported, what permissions are required, whether an importer creates or updates records, and how it handles unsupported fields, users, relationships, and files. A native direct importer, a CSV or JSON export-import, and an API or scripted transfer can differ in metadata preservation, repeatability, technical effort, downtime, and rollback options. Specialist help may be worth considering when dependencies are complex or downtime is tightly constrained, but no route is universally best.
The following examples apply to the documented work-management workflows; they are not promises that other platform pairs support the same features.
Asana import and export options
Asana documents CSV imports from monday.com, Trello, Airtable, Smartsheet, Wrike, Google Sheets, and ClickUp. Column names guide mapping, and custom fields can be used. Its instructions tie Trello CSV export availability to a Trello Business Class subscription and mention an extension as an alternative. Check current availability and assess the extension’s access and security before relying on it. See Asana’s import instructions.
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Jira Cloud import options
Jira Cloud documents CSV imports and direct imports from several tools, including Asana, ClickUp, monday.com, and Trello. The available workflow and permissions matter: Atlassian says some users who can create team-managed spaces cannot move users. In that case, user fields may be left unassigned and comment tags may appear as plain text. Review Jira’s import overview and, for CSV, the software-project CSV instructions.
For large Jira Cloud CSV imports, Atlassian recommends splitting the data into files of 1,500 work items each. Its page estimates approximately one hour but says the time depends on data size, complexity, and setup. This is Jira-specific guidance, not a general migration limit or time estimate; test with the actual dataset and check the current Atlassian CSV import guidance.
4. Build and approve a field map
Prepare a source-to-target mapping sheet before importing. For each field, record the source name and type, target field, any transformation, the treatment of empty or invalid values, and the person approving the mapping. Include users, statuses, relationships, custom fields, and values that require normalization—not just the obvious columns. Mark data to exclude or archive so it is not accidentally treated as a migration failure.
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| Mapping item | What to decide |
|---|---|
| Field and type | Which destination field receives the source value, and whether the data types are compatible. |
| Users and ownership | How source identities map to destination accounts, and what to do when a matching account is unavailable. |
| Status and category values | How source values translate to destination options, including any values that should be combined or split. |
| Relationships | Whether links such as parent-child records, task dependencies, or account associations can be preserved or must be rebuilt. |
| Missing or excluded data | Whether a blank value stays blank, receives an approved default, is archived separately, or is intentionally omitted. |
Normalize dates, status names, user identities, and multi-select values where necessary. Asana’s CSV guidance warns that multi-select values need comma-separated options to be detected as separate values; review delimiters and quoting so a value is not silently collapsed. Check the importer’s preview and confirm field names and types before committing the import: Asana CSV preparation guidance.
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5. Protect the source, export, and transfer
Use approved credentials with only the access needed for the migration. Decide who can access exports and transfer files, and how those files will be protected. Create an independent backup or export before cutover and verify that it is usable. Identify integrations and automations that can continue writing to the source; plan a change freeze or a final delta transfer if records may change while the move is underway.
Document the cutover window, communication plan, decision points, and rollback criteria. AWS’s migration checklist covers backup, security and identity planning, controlled transfer, testing, validation, and rollback. For cloud workload planning specifically, Microsoft’s Azure guidance recommends documenting encryption and security or identity configurations; treat that as cloud-planning guidance, not as a universal requirement imposed by SaaS importers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Rehearse, cut over, and validate
When practical, test with a representative sample or in a sandbox before moving the full dataset. Include records that exercise custom fields, unusual statuses, relationships, attachments, and identity mappings. A sample import can reveal mapping errors and unsupported data before they affect the whole workspace.
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Use a written runbook for the production move. It should identify who performs each action, the sequence and timing, how teams will be notified, when to pause, and what conditions trigger rollback. Google Cloud’s migration execution checklist includes a runbook, risk and mitigation list, testing and validation plan, and rollback plan.
Validate records and real workflows
After import, compare source and destination counts for the in-scope record types and inspect critical fields. Then test representative records and the work people need to do in the new system. Microsoft’s Azure migration planning guidance describes post-migration functional, integration, security, and performance testing.
- Reconcile record counts and check required fields, dates, statuses, and ownership.
- Inspect representative records, including edge cases, and confirm that important relationships and attachments are present where expected.
- Test whether the right users can find, view, edit, and act on records under the intended permissions.
- Run key workflows and integrations, including automations that create or update records.
- Document exceptions, obtain business-owner sign-off against the agreed acceptance criteria, and use the runbook’s rollback decision if critical checks fail.
7. Retire the old platform deliberately
Do not deactivate the source simply because the import finished. Once owners have signed off, decide how long the old data must be retained, who may access it, and how its integrations and credentials will be handled. Keep the old system available for the agreed transition or retention period if needed, then retire it under the startup’s access and record-retention policies.
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