View source for Definition:Structured data

📊 '''Structured data''' in insurance refers to information organized in a predefined, consistent format — typically rows and columns within databases or spreadsheets — that can be readily queried, analyzed, and processed by [[Definition:Policy administration system | policy administration systems]], [[Definition:Claims management system | claims platforms]], and [[Definition:Actuarial analysis | actuarial tools]]. Examples include [[Definition:Policyholder | policyholder]] names and addresses, [[Definition:Premium | premium]] amounts, [[Definition:Claims | claim]] numbers, [[Definition:Coverage | coverage]] limits, [[Definition:Effective date | effective dates]], and [[Definition:Loss code | loss codes]]. This type of data contrasts with [[Definition:Unstructured data | unstructured data]] — such as handwritten adjuster notes, scanned documents, photographs, or emails — which lacks a fixed schema and requires additional processing before it can be used analytically.

🔧 Insurance operations generate enormous volumes of structured data at every stage of the [[Definition:Insurance value chain | value chain]]. When a [[Definition:Broker | broker]] submits a [[Definition:Submission | submission]], when an [[Definition:Underwriting | underwriter]] binds a policy, when a [[Definition:Claims adjuster | claims adjuster]] records a [[Definition:First notice of loss (FNOL) | first notice of loss]], and when a [[Definition:Reinsurer | reinsurer]] receives a [[Definition:Bordereau | bordereau]] — each transaction produces fields of structured information that flow into an insurer's core systems. The reliability of downstream activities like [[Definition:Experience rating | experience rating]], [[Definition:Reserving | reserving]], [[Definition:Regulatory reporting | regulatory reporting]], and [[Definition:Predictive analytics | predictive modeling]] depends directly on the quality, consistency, and completeness of this structured data. Poor data governance — duplicate records, inconsistent coding, missing fields — cascades through the organization, degrading everything from [[Definition:Pricing | pricing]] accuracy to [[Definition:Financial statement | financial statements]].

💡 The insurance industry's push toward [[Definition:Digital transformation | digital transformation]] and [[Definition:Artificial intelligence (AI) | artificial intelligence]] has elevated structured data from an IT concern to a strategic asset. [[Definition:Insurtech | Insurtech]] companies often gain their competitive edge precisely because they are built on clean, well-structured data architectures from day one, avoiding the legacy data quality problems that plague established carriers. Regulators, too, are demanding more granular and standardized data submissions — as seen in initiatives like [[Definition:Lloyd's | Lloyd's]] market modernization and the expansion of [[Definition:ACORD | ACORD]] data standards. Insurers that invest in robust [[Definition:Data governance | data governance]] frameworks and structured data infrastructure position themselves to extract actionable insights, automate workflows, and respond faster to market opportunities than competitors still wrestling with fragmented spreadsheets and siloed systems.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Unstructured data]]
* [[Definition:Data governance]]
* [[Definition:Bordereau]]
* [[Definition:ACORD]]
* [[Definition:Predictive analytics]]
* [[Definition:Data lake]]
{{Div col end}}