Data Integration in M&A: The Silent Deal Killer

The Data Integration Reality

Ask any PE operating partner what went wrong in their most difficult portfolio company integration, and data will be in the top three answers. Customer records that do not match. Financial data that cannot be reconciled. Product catalogs with incompatible structures.

Data integration is the most underestimated workstream in M&A technology integration. It is unglamorous, technically complex, and touches every part of the business.

Why Data Integration Is Hard

The Master Data Problem

Both organizations have customer records, product catalogs, and employee records. These records overlap, conflict, and use different identifiers:

• Company A knows a customer as "Acme Corp" with ID 12345
• Company B knows the same customer as "ACME Corporation" with ID A-789
• Their addresses differ. Their contact information conflicts.

Resolving these conflicts across thousands or millions of records requires automated matching algorithms and manual review. There is no shortcut.

Semantic Differences

Even when data appears similar, it may mean different things:

• "Revenue" in one system includes recurring and one-time charges. In the other, it excludes one-time charges.
• "Active customer" means anyone who purchased in the last 12 months in one system, and anyone with a current contract in the other.

These semantic differences surface when business users try to combine reports and get contradictory results.

Data Quality Amplification

Both organizations likely have existing data quality issues. Integration amplifies them — duplicate records within each system become cross-system duplicates, and incomplete records prevent matching across systems.

Data Integration Approaches

Full Consolidation

Migrate all data into a single system of record. Cleanest end state but most complex to achieve. Typically takes 6-18 months for core data domains.

Federated Integration

Maintain data in existing systems but create an integration layer that provides a unified view. Useful when full consolidation is not feasible in the near term.

Hybrid

Consolidate critical data domains (customers, products, financials) while maintaining separate systems for less critical data. This balances unified reporting needs with practical constraints.

Integration Framework

Phase 1: Discovery (Weeks 1-4)

• Catalog all data sources, schemas, volumes, and owners
• Profile data quality in both systems
• Document how each organization defines key business concepts
• Classify data domains by business criticality

Phase 2: Standards (Weeks 5-8)

• Define the target data model for the combined entity
• Establish quality standards and validation rules
• Design integration architecture
• Define data governance model

Phase 3: Execution (Weeks 9+)

• Clean and standardize data before migration
• Run matching algorithms for duplicate detection
• Execute migration in waves with validation after each
• Reconcile migrated data against sources

The Cost of Getting It Wrong

Failed data integration creates ongoing operational problems: customers receive duplicate communications, financial reporting cannot be reconciled, sales teams work with incomplete views, and operational decisions are based on inaccurate data. These problems erode acquisition value over time. Investing in proper data integration upfront is significantly cheaper than remediating data quality issues after they have propagated through business processes.