In the EDW modernisation program, data quality and data catalogue pose significant challenges for a leading life insurance provider in India. The organisation manages vast and complex datasets across policy administration, claims, distribution, regulatory reporting, and digital channels. Key issues include:

Data Quality Challenges​

• Inconsistent identifiers for customers, policies, and intermediaries across legacy systems leading to duplication and reconciliation gaps.​
• Incomplete or inaccurate attributes (e.g., policy details, KYC fields, claims status) impacting downstream analytics, compliance, and risk reporting.​
• Lack of standardized business rules for validation, creating multiple versions of truth across business functions.​
• Data latency and errors in integration from core insurance systems to the warehouse, affecting timeliness of insights.​

Data Catalogue Challenges​

• Absence of a unified metadata repository, making it difficult for business users and IT teams to discover and trust datasets.​
• Limited visibility into lineage and ownership of data elements, which hampers impact analysis and regulatory compliance (e.g., IRDAI reporting).​
• Siloed documentation practices, resulting in poor knowledge sharing and heavy reliance on SMEs for data interpretation.​
• Difficulty in harmonizing technical metadata with business glossaries, reducing adoption of self-service analytics.

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To address these challenges, the insurance provider implemented a products from Talend technologies:​

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Talend Data Quality: To ensure clean, accurate, and cleaned customer, policy, and claims data – driving trusted insights and compliance.​

Talend Data Catalogue: To provide a single source of truth with data lineage, business glossary, and ownership – enabling faster discovery and self-service analytics.​

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• Improved Data Trust – Accurate, complete, and deduplicated customer and policy data across systems.​
• Faster Insights – Business users can quickly discover and understand data, reducing dependency on IT.​
• Operational Efficiency – Reduced time spent on reconciliation and error correction, enabling focus on analytics and decision-making.​