7 Tips to Get the Most Out of Kollate Today

Migrating to Kollate: Step-by-Step Setup and Best Practices

Overview

A structured migration reduces downtime and data loss when moving to Kollate. Below is a practical, prescriptive plan covering preparation, execution, verification, and post-migration best practices.

1. Pre-migration planning

1. Inventory data and workflows: List projects, files, users, permissions, integrations, automations, and custom fields.
2. Set success criteria: Define what “done” looks like (e.g., all projects migrated, <1% data loss, users able to perform core tasks).
3. Map source → Kollate: For each data type, specify destination object, field mapping, and transformation rules.
4. Assess integrations: Identify third-party apps and decide which will be reconnected, replaced, or deprecated.
5. Choose migration window & rollback plan: Pick low-usage time and prepare steps to revert if needed.
6. Communicate: Notify stakeholders and users with timeline, expected impacts, and training plan.

2. Prepare Kollate environment

1. Set up org structure: Create teams, projects, spaces, and folders mirroring your mapped structure.
2. Configure permissions & roles: Predefine roles and test permission levels with sample users.
3. Create custom fields & templates: Add any custom fields, statuses, workflows, and templates needed.
4. Provision users: Invite users and assign roles; consider staged invites for phased migrations.
5. Install/connect integrations: Pre-authorize APIs and connectors you’ll need post-migration.

3. Data migration execution

1. Export from source: Export data in structured formats (CSV/JSON) including metadata, timestamps, and attachments.
2. Transform data: Apply field mappings, normalize values, and convert date/time formats. Automate with scripts or ETL tools where possible.
3. Import in sandbox first: Load into a Kollate test environment to validate mappings and integrity.
4. Validate sandbox: Spot-check records, permissions, linked items, and attachments. Run automated checks for counts, checksums, and referential integrity.
5. Iterate fixes: Adjust mappings, scripts, or environment settings based on sandbox results.
6. Final migration: During the migration window, put source in read-only (if possible), export incremental changes since sandbox run, and perform final import to production.

4. Post-migration verification

1. Smoke tests: Verify key workflows, search, notifications, and integrations.
2. Data verification: Reconcile record counts, sample checks on data accuracy, attachment integrity, and timestamps.
3. Permissions audit: Confirm users can access appropriate projects and nothing is overexposed.
4. Performance check: Ensure load times and API responses meet expectations.

5. User onboarding & training

1. Targeted training: Provide role-specific quick-start guides and recorded walkthroughs.
2. Support channels: Offer office hours, a migration FAQ, and a ticketing path for issues.
3. Feedback loop: Collect and prioritize user-reported issues for rapid fixes.

6. Post-migration cleanup & optimization

1. Decommission old systems: Only after full validation and a retention period; archive exports for compliance.
2. Tune workflows: Optimize automations and templates based on observed usage.
3. Monitor & iterate: Track key metrics (task throughput, error rates) and hold a post-mortem to capture lessons learned.

Best practices checklist

• Backup everything before starting.
• Automate transformations to reduce manual errors.
• Use