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🗄️ '''Data management''' encompasses the policies, practices, processes, and technologies that an insurance organization uses to acquire, store, organize, maintain, and govern its data throughout its lifecycle. For [[Definition:Insurer | insurers]], [[Definition:Reinsurer | reinsurers]], and intermediaries, effective data management is foundational — it underpins everything from [[Definition:Actuarial analysis | actuarial pricing]] and [[Definition:Reserving | reserving]] to [[Definition:Claims management | claims handling]], [[Definition:Regulatory reporting | regulatory reporting]], and [[Definition:Fraud | fraud detection]]. Given the data-intensive nature of insurance, where decisions depend on the reliability and granularity of historical and real-time information, data management is not a back-office function but a core strategic capability.

🔄 A mature data management framework in insurance typically covers data governance (who is accountable for data quality and policy), data architecture (how data flows between [[Definition:Policy administration system | policy administration]], claims, billing, and analytics systems), master data management (ensuring consistency of key entities like policyholder records and [[Definition:Risk | risk]] identifiers), and data quality assurance (validation, cleansing, and enrichment routines). Regulatory expectations reinforce these disciplines. Under [[Definition:IFRS 17 | IFRS 17]], insurers must trace financial data from source systems through to reported figures with full auditability. [[Definition:Solvency II | Solvency II]]'s data quality requirements for internal models demand documented governance over the data feeding [[Definition:Capital modeling | capital calculations]]. In the United States, the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]]'s Own Risk and Solvency Assessment ([[Definition:Own risk and solvency assessment (ORSA) | ORSA]]) framework similarly expects carriers to demonstrate robust data governance. Asian regulators, including the Monetary Authority of Singapore and Japan's Financial Services Agency, have issued guidance on data integrity expectations within their supervisory regimes.

🚀 Well-executed data management has become a differentiator in an industry where [[Definition:Insurtech | insurtechs]] and technology-forward incumbents are raising the bar. Carriers with clean, integrated data can deploy [[Definition:Artificial intelligence (AI) | artificial intelligence]] and [[Definition:Machine learning | machine learning]] models with confidence, accelerate product development, and respond to [[Definition:Data call | data calls]] efficiently. Conversely, poor data management leads to pricing errors, delayed claims settlements, compliance failures, and an inability to leverage advanced analytics. As the insurance industry increasingly relies on external data sources — [[Definition:Telematics | telematics]], [[Definition:Internet of things (IoT) | IoT]] sensors, social media, and third-party enrichment providers — the complexity of managing data estates grows, making disciplined data management an ever more critical investment for organizations of all sizes.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Data asset]]
* [[Definition:Data mining]]
* [[Definition:Data privacy regulation]]
* [[Definition:Data security regulation]]
* [[Definition:Data ownership]]
* [[Definition:Regulatory reporting]]
{{Div col end}}

Definition:Data management - Revision history

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