Data Mesh Adoption in Financial Enterprises: A Survey of Six Organizations and a Production Case Study

Jeevan Krishna Paruchuri

doi:10.36948/ijfmr.2023.v05i04.75351

Data Mesh Adoption in Financial Enterprises: A Survey of Six Organizations and a Production Case Study

Author(s)	Jeevan Krishna Paruchuri
Country	United States
Abstract	The data mesh pattern, articulated by Zhamak Dehghani and elaborated through a body of subsequent literature, proposes that the central data team a familiar bottleneck in any data-intensive organization should be replaced by domain-aligned teams that own their data as products, governed by a federated set of platform-wide policies. The pattern has been adopted enthusiastically in technology and e-commerce organizations whose constraints align with mesh assumptions, and more cautiously in insurance, where regulatory compliance, audit trails, and claims-data governance create particular challenges for autonomous domain teams. This paper combines a survey of six insurance enterprises at varying stages of data mesh adoption (two large property-and-casualty insurers, two life insurance firms, one workers' compensation specialist, and one health insurer) with a production case study of an insurance data platform that scaled from 83 legacy ETL feeds to 1,900+ domain-owned data products, grew its team from eight to twenty-four engineers organized into six domain pods (policy, claims, underwriting, actuarial, customer service, and platform), and migrated from a centralized ETL model to a domain-aligned mesh architecture. The migration produced measurable delivery improvements: time to deliver a new claims analysis dataset fell from 3-5 weeks under the centralized model to 1.5-2.5 weeks under domain mesh ownership. The case study platform reaches 86% adoption of Snowpark programmatic access with 12-30 ms p99 overhead, runs on Snowflake with AWS S3 external stages, and has logged one production incident in two years under a governance model that blends domain ownership with central platform controls for compliance and regulatory reporting. We argue that insurance-specific constraints require a modified mesh pattern that retains strong domain autonomy while enforcing central guardrails for regulatory compliance, audit logging, and cross-domain claims and policy data. Across the six surveyed organizations, mesh maturity ranges from Level 1 (exploratory adoption with traditional ETL foundations) to Level 3 (optimized in production with full domain pod autonomy); the pattern of partial adoption with centralized compliance enforcement is more common than pure mesh implementation in insurance. We document the boundary problems claims cross-domain access for settlement analytics, shared policy reference data across domains, regulatory reporting that spans domain boundaries and the architectural and organizational mechanisms that make them tractable in heavily regulated environments. The contribution is an insurance-focused survey intended to bridge the gap between the data mesh literature's technology-industry prescriptions and the specific compliance, audit, and governance constraints that define modern insurance data platforms.
Field	Computer Applications
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-08-06
DOI	https://doi.org/10.36948/ijfmr.2023.v05i04.75351

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Data Mesh Adoption in Financial Enterprises: A Survey of Six Organizations and a Production Case Study

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