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To migrate startup data between management platforms, first decide what must move, verify what the destination can import, and map the source data to its target fields. Then protect a backup, test a representative transfer, run a controlled cutover, and confirm both the imported records and the workflows people rely on. The exact steps depend on the platforms: a project-management CSV import, for example, may not preserve the same history, relationships, users, or permissions as a direct importer.
1. Set the scope and define success
Treat a platform move as a controlled change to both data and day-to-day operations—not just an export followed by an import. Name the source and destination systems, the data domains involved, a migration owner, business owners, target date, and acceptable downtime. Decide whether the move will be a one-time cutover, a staged transfer, or ongoing synchronization.
Write down what “success” means before moving anything. Criteria might include importing the in-scope records, retaining approved owners and key relationships, confirming access controls, and getting sign-off from the people responsible for the affected workflows. Microsoft’s data-management checklist recommends planning data sources, mappings, environments, testing, and cutover.
2. Inventory the data and reduce what you move
List the record types and approximate volumes in the source. Include more than the main records: attachments, custom fields, status values, user identities, permissions, automations, integrations, and dependencies can all affect what the destination needs to support.
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For each category, identify its owner and decide whether it is active, obsolete, duplicated, legally required, or out of scope. Moving less unnecessary data can simplify mapping and validation, but retention obligations and access requirements should be decided with the relevant business or compliance owner. Microsoft’s storage-migration assessment guidance calls for cataloging data sources and assessing dependencies, usage, security, performance, resiliency, and cost; AWS’s SMB cloud migration checklist likewise recommends inventorying applications, data, and dependencies.
3. Confirm the actual import route and its limits
Check current documentation for both the source and destination before exporting. Confirm which record types and fields can move, what permissions the import requires, whether the process creates or updates records, and how unsupported fields or identities are handled. Do not assume that a feature available for one product, plan, or workflow applies to another.
Native importers and CSV or JSON
A direct importer may reduce manual formatting, while file-based transfer can be easier to inspect and transform. The trade-off is specific to the platform pair: compare supported fields and metadata, attachments and relationships, history, identity and permission handling, volume limits, repeatability, delta handling, downtime, rollback, and audit needs before choosing.
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For work-management examples, Asana documents CSV imports from monday.com, Trello, Airtable, Smartsheet, Wrike, Google Sheets, and ClickUp in its import guidance. Asana project exports can be JSON or CSV, and its project importing and exporting documentation describes those options. Its CSV preparation guidance says CSV import adds tasks rather than updating existing project tasks, so it should not be treated as a synchronization method. The Asana 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.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsJira Cloud documents CSV and direct import options for tools including Asana, ClickUp, monday.com, and Trello in its import overview. User handling can depend on the workflow and permissions. Atlassian notes that some users who can create team-managed spaces cannot move users; in that case, user fields may be left unassigned and comment tags may become plain text. Review the applicable Jira CSV instructions for the import route you plan to use.
Large Jira Cloud CSV imports
For large Jira Cloud CSV imports, Atlassian recommends splitting the data into files of 1,500 work items each. Its guidance gives an approximate one-hour estimate but says timing depends on data size, complexity, and setup. This is Atlassian’s recommendation for Jira Cloud CSV imports, not a general limit or time estimate for other platforms; confirm the current instructions and test with the actual dataset. See Atlassian’s CSV import guidance.
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API, scripts, or specialist help
Consider an API or scripted transfer when the native routes do not cover required fields, relationships, or repeatable delta transfers. This approach can provide more control, but it also requires technical capacity to handle authentication, errors, rate limits, auditability, and retries. Specialist assistance may be worth evaluating when dependencies are complex or downtime is tightly constrained. Compare these routes against the same acceptance criteria; the documented platform examples do not establish one universally best method.
4. Map fields and decide how to transform them
Create a mapping sheet before importing. For every source field, record its target field, any transformation, how blank or null values should be treated, and who approved the mapping. Include custom fields, users, statuses, relationships, and values that should be excluded or archived—not only the obvious title and description fields.
- Normalize dates, status names, user identities, and multi-select values where the destination expects a different format.
- Decide explicitly how to handle missing owners, unsupported values, and records with no target equivalent.
- Review the import preview to confirm that names and field types are being interpreted as intended before committing the transfer.
Asana’s CSV preparation guidance warns that multi-select values need comma-separated options to be detected as separate values. Check delimiters and quoting so values containing commas are not accidentally split or collapsed.
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5. Protect the source, export, and cutover
Use approved credentials with only the access needed for the migration. Decide who may access exports and transfer files, how they will be protected, and how long they will be retained. Create and verify an independent backup or export before cutover.
Identify integrations, automations, or users that may continue writing to the old platform while data is moving. If updates can arrive during the transfer, plan a change freeze or a delta transfer so the target does not miss late changes. Put the cutover window, communications, decision points, owners, and rollback criteria in a written runbook. AWS’s migration checklist covers backup, security and identity planning, controlled transfer, testing, validation, and rollback.
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 a sandbox before the full move. Use records that exercise important cases, such as custom fields, attachments, relationships, unusual statuses, and different access needs. Record issues, correct the mapping or process, and rehearse again if the changes could affect the result.
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For the live cutover, follow the runbook and record whether each decision point was met. After import, validate both the data and the work people need to do with it:
- Reconcile source and target counts for each in-scope record type, accounting for deliberate exclusions and any records the importer does not create.
- Inspect representative records and confirm critical fields, owners, statuses, attachments, and required relationships.
- Test that intended users can find records and have the right access.
- Run the key workflows and check that connected integrations and automations behave as intended.
- Record discrepancies, their resolution, and business-owner sign-off.
Google Cloud’s migration execution checklist includes a runbook, risk and mitigation list, testing and validation plan, and rollback plan. Microsoft’s Azure migration-planning guidance describes functional, integration, security, and performance testing after migration. Use these as planning references and tailor the actual acceptance checks to the systems being moved.
7. Retire the old platform deliberately
Do not switch off or discard the source simply because an import completed. First obtain owner sign-off and decide what must be retained, who can access it, and for how long. If the source will remain available during a transition, define which system is authoritative so staff and integrations do not create conflicting updates. Retire it only when the startup’s validation, access, and retention decisions support doing so.
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