Data Synchronisation

Bi-directional synchronisation where both systems can create and update records — changes in either system flow to the other without creating duplicates, losing data or triggering sync loops. Built for the most common B2B scenario: CRM and ERP that both hold customer and order data.

• Change detection in both systems: new, updated and deleted records
• Sync loop prevention: changes originating from sync not re-synced
• Deduplication logic on initial sync and ongoing
• Master system definition for specific fields when needed
• Sync direction locking: some fields only update from one direction
• Bi-directional delete handling: archive or soft-delete on both sides
• Full test run against real data before go-live
• Data volume estimation and performance planning

Real-time data sync triggered by events — a customer record updated in the CRM is pushed to the ERP within seconds via webhook, not waiting for the next nightly batch. Gives every team current data without the latency that makes batch sync unusable for time-sensitive workflows.

• Webhook listener for inbound events from source system
• Event processing pipeline: receive, validate, transform, push
• Idempotent processing: receiving the same event twice is safe
• Processing queue to handle traffic spikes without data loss
• Processing latency target: under 5 seconds end-to-end
• Error isolation: one failed record does not block the queue
• Event replay: reprocess failed events after fix
• Real-time sync monitoring dashboard: throughput and error rate

Scheduled batch sync for systems that don't support real-time webhooks or where nightly reconciliation is sufficient — extracting changed records since the last sync, transforming them for the target system and loading them reliably with error handling and reporting.

• Incremental sync: only records changed since last run
• Last-modified timestamp or sequence number tracking
• Full refresh option for systems without change tracking
• Transform pipeline: clean, map and validate each record
• Batch size control to avoid API rate limit exhaustion
• Run scheduling: hourly, daily, weekly per data type
• Post-run report: records processed, created, updated, failed
• Retry on transient failures with backoff strategy

Conflict handling for cases where both systems update the same record before the sync runs — detecting the conflict, applying a configured resolution strategy automatically for clear-cut cases and queuing ambiguous conflicts for manual review.

• Conflict detection: compare both systems' version and timestamp
• Last-write-wins strategy as default resolution
• Master-system-wins: one system is always authoritative per field
• Field-level conflict resolution: different rules per field
• Manual conflict resolution queue for complex cases
• Conflict alert to integration admin
• Resolution history log
• Conflict frequency reporting to identify data entry process issues

Data transformation layer that converts data from the source system's format to the target system's format — field mapping, value translation, date and number format conversion, field combination and splitting, and derived field calculation.

• Field mapping: source field A → target field B
• Value translation: source code "DE" → target value "Germany"
• Date and time format conversion and timezone handling
• Number format: decimal separator and currency conversion
• Conditional mapping: map differently based on record type
• Derived fields: calculate from multiple source fields
• Field split: one source field to multiple target fields
• Field combine: multiple source fields to one target field

Robust error handling that prevents sync failures from causing data inconsistency — failed records are isolated, queued for retry, and reported to the integration admin with enough detail to diagnose and fix the root cause quickly.

• Per-record error isolation: one bad record does not halt the batch
• Error classification: validation, auth, rate limit, server error
• Retry strategy by error type: immediate, backoff or manual
• Dead letter queue for permanently failing records
• Error alert to admin: email or Slack on new errors
• Error detail log: record, error message, stack trace
• Error resolution tracking: mark as fixed and reprocess
• Error rate monitoring: alert when error rate exceeds threshold