How to Plan an Integration Project

Why integration projects fail

Integration projects aren't technically difficult — they're logistically difficult. The technology to connect systems is well-established. What fails is the planning: incomplete data mapping, misunderstood business rules, no error handling, and no monitoring.

A well-planned integration project is faster, cheaper, and more reliable than one that starts coding immediately.

Step 1: Discovery and mapping

Before any technical work, map the complete picture:

• Systems involved: List every system that needs to participate. Include the ones you haven't thought about yet (reporting tools, backup systems, manual processes).
• Data flows: For each connection, what data moves, in which direction, how often, and triggered by what event?
• API availability: Which systems have APIs? Which are well-documented? Which require workarounds?
• Data ownership: For each data element, which system is the "source of truth"?
• Volume and frequency: How many records per day/hour? What are the peak periods?

Step 2: Architecture decisions

Based on your discovery, make these decisions:

• Direct vs middleware: Simple point-to-point connections are fine for 2-3 systems. More than that, consider middleware or an integration platform.
• Real-time vs batch: Not everything needs real-time. Match the approach to the business need.
• Error handling strategy: What happens when a transfer fails? Retry? Queue? Alert? This must be designed, not discovered in production.
• Authentication: API keys, OAuth, certificates? Each integration has authentication requirements.

Step 3: Data mapping

This is where the real work happens. For each data element flowing between systems:

1. Field mapping: "Customer Name" in System A = "client_full_name" in System B
2. Data transformation: Dates in different formats, currency conversions, name splitting
3. Validation rules: What constitutes valid data? What happens with invalid data?
4. Conflict resolution: When the same record is updated in both systems, which one wins?
5. Edge cases: Null values, special characters, multi-language text, extremely long strings

Document every mapping in a spreadsheet. Source system, source field, transformation, target system, target field. This becomes your integration specification.

Step 4: Build and test

1. Build in stages: Start with the simplest connection. Prove it works. Expand.
2. Test with real data: Synthetic test data misses edge cases. Use anonymised production data.
3. Test error scenarios: What happens when the target system is down? When data is invalid? When volume spikes?
4. Parallel run: Run old and new processes simultaneously. Compare results. Only cut over when they match.

Step 5: Go-live and monitoring

• Dashboard: Build a monitoring dashboard showing transfer status, error rates, and data volume
• Alerts: Automated alerts for failures, unusual volume, or data quality issues
• Runbook: Document how to investigate and resolve common issues
• Review schedule: Monthly review of integration health for the first 3 months, then quarterly

Common planning mistakes