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🗺️ '''Data mapping''' is the process of defining how data fields from one system, format, or standard correspond to those in another — a foundational exercise in insurance operations where information must flow accurately between [[Definition:Policy administration system | policy administration systems]], [[Definition:Claims management system | claims platforms]], [[Definition:Reinsurance | reinsurance]] reporting tools, regulatory filings, and third-party integrations. In an industry built on structured data — policy numbers, coverage codes, loss categories, geographic identifiers — the ability to translate and align data across disparate schemas is essential for everything from [[Definition:Bordereaux | bordereaux]] reconciliation to regulatory submissions under frameworks like [[Definition:Solvency II | Solvency II]] or the NAIC's data standards in the United States.

⚙️ In practice, data mapping involves creating explicit rules or transformation logic that connect a source field to a target field. A [[Definition:Managing general agent (MGA) | managing general agent]], for instance, may record policy data in its own proprietary format, but the [[Definition:Insurance carrier | carrier]] receiving that data requires it mapped to ACORD standards or a bespoke schema. The mapping exercise defines which source fields populate which target fields, how values should be converted (e.g., translating internal product codes to ISO class-of-business codes), and what validation rules apply. In [[Definition:Insurtech | insurtech]] environments, automated data mapping tools increasingly use machine learning to suggest field correspondences, reducing the manual effort that historically made system migrations and partner onboarding slow and error-prone. Large-scale programs — such as those involving [[Definition:Lloyd's of London | Lloyd's]] market modernization or cross-border [[Definition:Reinsurance treaty | treaty reinsurance]] placements — can require thousands of individual field mappings across multiple jurisdictions and reporting standards.

📊 Poor data mapping is one of the most common root causes of reporting errors, delayed [[Definition:Claims | claims]] processing, and failed technology implementations in insurance. When fields are misaligned or transformation logic is flawed, downstream consequences ripple through [[Definition:Loss ratio | loss ratio]] calculations, [[Definition:Reserve | reserve]] estimates, and regulatory filings. Conversely, well-executed data mapping accelerates partner onboarding, supports real-time data exchange through [[Definition:Application programming interface (API) | APIs]], and enables carriers and [[Definition:Reinsurer | reinsurers]] to aggregate portfolio data across multiple sources with confidence. As the industry moves toward standardized digital data exchange — exemplified by Lloyd's Blueprint Two and ACORD messaging standards — the discipline of data mapping has shifted from a back-office IT task to a strategic capability that directly affects speed to market and operational resilience.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:ACORD]]
* [[Definition:Bordereaux]]
* [[Definition:Application programming interface (API)]]
* [[Definition:Data warehouse]]
* [[Definition:System integration]]
* [[Definition:Extract, transform, load (ETL)]]
{{Div col end}}

Definition:Data mapping - Revision history

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