Merging data after an acquisition is complex and high risk. Use this practical checklist covering strategy, governance, security, architecture, testing, change management, and post-merge optimization to integrate systems with confidence.

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1) Strategic and Organizational Alignment

• Define the integration strategy—full merger, partial consolidation, or coexistence (system of record vs. system of engagement).
• Establish ownership & governance—who approves mappings, business rules, and transformations?
• Prioritize by impact—start with mission-critical domains (customers, financials, inventory, employees).
• Identify overlaps—duplicate customers, vendors, products, or territories.

2) Data Inventory and Assessment

• Catalog systems & sources—ERP, CRM, HRIS, data warehouses, file stores (SharePoint/OneDrive), etc.
• Profile data quality—completeness, consistency, accuracy, timeliness.
• Compare data models—field types, constraints, relationships, reference data.
• Align entity definitions—what “Customer,” “Product,” or “Revenue” means in each org.

3) Mapping and Transformation

• Field-to-field mapping—schema alignment, data types, naming conventions.
• Standardization—dates, units, currencies, country/state codes, time zones.
• Master data harmonization—declare the authoritative system per entity.
• Deduplication & identity resolution—matching rules, survivorship, golden records.
• Business rule reconciliation—KPIs and calculations (e.g., margin, lead stages) must match.

4) Security, Compliance, and Privacy

• Classify data—PII/PHI/financial, contractual restrictions, retention policies.
• Regulatory compliance—GDPR, CCPA, HIPAA, SOC 2, industry regs across regions.
• Access control alignment—roles, groups, conditional access, least privilege.
• Auditability—who moved what, when, and how; immutable logs.

5) Technical Integration and Architecture

• Choose integration patterns—ETL/ELT, APIs/eventing, iPaaS/middleware.
• Use staging & sandboxes—safe spaces for transformation and rehearsal loads.
• Plan coexistence—if both systems run in parallel, design sync & reconciliation.
• Performance & resilience—batch sizing, throttling, retries, idempotency, rollbacks.
• Tooling—e.g., Azure Data Factory, SSIS, Talend, Informatica, Power Automate.

6) Validation and Testing

• Reconcile counts & totals—row counts, aggregates, checksums/hashes.
• Business validation—reports and dashboards produce the same answers.
• UAT with power users—test real-world workflows and edge cases before cutover.

7) Change Management and Communication

• Stakeholder updates—cadence, risks, decisions, and status transparency.
• Training & documentation—new data definitions, report paths, SOPs.
• Transition plans—login changes, redirects, app retirement timelines.
• Hypercare—post-go-live support & escalation channels.

8) Post-Merge Governance and Optimization

• Data stewardship—named owners for quality and metadata.
• Monitor KPIs—data accuracy, error rates, adoption/utilization.
• Iterate—capture feedback, refine mappings and rules.
• Decommission legacy—only once stability and completeness are verified.

Common Pitfalls

• Underestimating time for data cleanup and dedupe.
• Missing documentation of legacy logic and reference data.
• Ignoring dependencies (BI datasets, Excel macros, workflows).
• Rushing testing or skipping UAT.
• Not planning for historical/archived content.

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Bottom line: Successful post-acquisition data integration balances people, process, and technology. With strong governance, clear mappings, disciplined validation, and thoughtful change management, you can turn fragmented data into a unified, trusted asset.