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Deepthi Dommaraju

Speaker

Deepthi Dommaraju

3

talks

Head of Customer Success DQLabs

With 15+ years of industry experience , I have been integral to product development, product management, project management, & change management, utilizing Agile methodologies to achieve high-quality results & organizational success. I am well versed in utilizing customer excellence strategies to ensure operational optimization, relationship management, & business acceleration. As a transformational leader, I am skilled in portfolio management efforts, cross-functional leadership, mentoring, coaching, & developing talent to utilize best practices within various industries.

Bio from: Big Data LDN 2025

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Arch Capital Group, a $34 billion S&P 500 specialty insurance leader managing $21.5 billion in gross premiums across 60+ global offices, faced a critical challenge: ensuring data quality and consistency across their complex risk assessment operations. With 25+ predictive models supporting AI-driven underwriting for specialty lines—the industry's most complex and unusual risks—incomplete or inaccurate data inputs threatened the accuracy of critical business decisions spanning property & casualty, reinsurance, and mortgage insurance operations. 

In this session, Sam from Arch Capital shares how the organization partnered with DQLabs to transform their data trust framework, implementing automated quality checks across their global data ecosystem. Learn how this transformation enabled Arch to maintain their disciplined underwriting approach while scaling operations, improve regulatory compliance across multiple jurisdictions, and enhance their ability to respond rapidly to emerging risks while supporting the data accuracy essential for their leadership position in specialty insurance markets.

Join us for an insightful session on the evolving landscape of Data Quality and Observability practices, transitioning from manual to augmented approaches driven by semantics and GenAI. Discover the framework enabling organisations to build the architecture for conversational data quality, leaving behind the limitations of traditional, resource-heavy methods and legacy technology. Learn why context is paramount in data quality and observability, and leave with actionable insights to propel your organisation into the future of data management.