The Enterprise Data Landscape
Large organizations accumulate data systems the way houses accumulate clutter. Each system made sense when it was built. Together, they create a maze.
The typical enterprise has:
- Multiple ERP systems from acquisitions
- Department-specific databases
- SaaS applications with their own data stores
- Legacy mainframe systems still running critical processes
- Data warehouses and lakes in various states of health
Making these systems work together is one of the most challenging problems in enterprise technology.
Common Integration Patterns
Point-to-Point Integration
The simplest approach: connect systems directly when they need to share data.
Pros: Quick to implement, easy to understand
Cons: Scales poorly—n systems require n(n-1)/2 connections
This pattern works for small numbers of integrations but becomes unmanageable quickly.
Hub-and-Spoke (ETL)
A central system extracts data from sources, transforms it, and loads it to destinations.
Pros: Centralizes logic, easier to manage than point-to-point
Cons: Batch-oriented, latency issues, hub becomes bottleneck
Traditional data warehousing follows this pattern.
Event-Driven Architecture
Systems publish events when things happen. Interested systems subscribe and react.
Pros: Loose coupling, real-time capable, scales well
Cons: More complex to implement and debug, eventual consistency challenges
Modern integration architectures increasingly adopt this pattern.
Common Pitfalls
The "Golden Record" Illusion
"We'll create a single source of truth for customer data."
In theory, beautiful. In practice, different systems have legitimately different views of "customer":
- Sales sees prospects and accounts
- Support sees tickets and contacts
- Finance sees billing entities
- Marketing sees engagement profiles
A single golden record often means nobody's requirements are fully met.
Better Approach: Accept that different contexts need different views. Focus on synchronization and consistency rather than unification.
Underestimating Data Quality
Integration projects reveal data quality issues that were previously hidden. Common discoveries:
- Fields with inconsistent formats
- Duplicate records with conflicting information
- Missing data that was "always handled manually"
- Business rules embedded in application code, not documented
Better Approach: Build data quality into the integration layer. Validate, cleanse, and flag issues rather than propagating garbage.
Ignoring Operational Concerns
The architecture diagram looks elegant. Then production happens:
- What alerts when a sync fails?
- How do you recover from partial failures?
- What's the retry strategy?
- How do you handle schema changes?
Better Approach: Design for operations from the start. Monitoring, alerting, and recovery are features, not afterthoughts.
A Sustainable Approach
Start with Use Cases
Don't boil the ocean. Identify specific business problems that integration solves:
- "We need customer data in the CRM within 5 minutes of account creation"
- "Finance needs daily reconciliation of orders across all channels"
- "Support needs to see customer history from all touchpoints"
Each use case has specific latency, consistency, and completeness requirements.
Build Foundation Infrastructure
Invest in capabilities that serve multiple use cases:
- **Event Streaming Platform**: Kafka, Pulsar, or equivalent for real-time data movement
- **API Gateway**: Centralized access to internal services
- **Data Catalog**: Know what data exists and where
- **Monitoring and Observability**: See how data flows and where it breaks
Implement Incrementally
For each use case:
1. Define the data contract between systems
2. Build the integration with appropriate error handling
3. Deploy with comprehensive monitoring
4. Iterate based on operational experience
Each successful integration builds confidence and infrastructure for the next.
Building Internal Capability
The organizations that succeed at data integration develop internal expertise:
- Engineers who understand both the technology and the business context
- Operational teams who can diagnose and resolve issues
- Architects who can evaluate new integration requirements
This capability is more valuable than any specific integration because the landscape will keep evolving.
