The Industry Landscape
Big Data Demands Smart Operations

In re-insurance, handling complex data efficiently is essential to meeting business objectives, managing client needs and remaining competitive. Re-insurers rely on cohesive, structured data to develop analytics models, assess risk and meet regulatory obligations.

As data sources and regulatory requirements grow, a standardized data governance framework supporting advanced analytics and Machine Learning (ML) capabilities becomes critical. A robust data strategy empowers firms to streamline operations, cut costs, make informed decisions and unlock new opportunities for growth and innovation.

The Client Challenge
Building a Unified Data Foundation to Power Advanced Analytics

Our client sought to augment its data management and governance to drive advanced analytics initiatives. To achieve this, the client needed to address key areas such as:

Streamlining fragmented data practices

With data acquisition, organization and delivery spread across various systems, the client sought to create a unified platform to better support its business objectives.

Expanding analytics potential

The lack of a standardized data platform also restricted the re-insurer’s ability to implement self-service analytics and ML models, essential for their advanced analytics initiatives.

Creating a unified data strategy

To become a truly data-centric organization, the client sought a comprehensive, modern data strategy that could scale seamlessly across people, processes and technology, enabling effective integration and strategic decision-making.

The Solution
Shaping a Scalable, Secure, Future-ready Data Strategy

Stepping in as a transformation partner, WNS Analytics delivered a scalable and enterprise-grade data strategy to support timely decision-making. Our comprehensive data strategy covered secure and flexible data architecture, data integration, data quality, an advanced data governance framework and an Enterprise Data Office (EDO) setup to support current and future analytics needs.

Key Solution Components

Establishing robust, automated workflows to optimize collaboration and decision-making and reduce operational costs

Developing tailored algorithms using Natural Language Processing (NLP) and ML to enhance data extraction and classification

Strategically deploying domain experts to monitor and curate critical competitive intelligence and refine analytical models, ensuring the value of insights delivered

Harnessing digital platforms alongside AI and ML to transform the content aggregation process, expand global insights coverage and personalize experiences to retain / expand customer base

This comprehensive methodology ensured that every facet of the transformation – from data ingestion to actionable insights – was addressed holistically, driving innovation and efficiency across the organization.

1. Modern data infrastructure design

• Enterprise data architecture
  We defined a robust data architecture for both batch and real-time data integration, creating a unified and responsive data ecosystem.

• Cloud-based data warehouse
  We proposed a Snow󹀹ake-based warehouse hosted on Azure, aligned with the client’s cloud modernization strategy to optimize scalability and performance.

• Technology selection
  We conducted a rigorous tool comparison using our proven in-house framework, evaluating platforms such as Snowflake, Synapse Analytics and Databricks Delta Lake. Final selections were directly aligned with enterprise objectives for improved business outcomes.

2. Comprehensive data extraction and integration

• Standardized integration model
  Using MuleSoft, we established a standardized framework, unifying data across policy administration systems, Customer Relationship Management (CRM), sales and human resources pipelines, and third-party sources.

• Dynamic ETL configuration
  Introduced a dynamic Extract-Transform-Load (ETL) process to simplify and scale data integration processes.

• Technology selection
  We conducted a rigorous tool comparison using our proven in-house framework, evaluating platforms such as Snowflake, Synapse Analytics and Databricks Delta Lake. Final selections were directly aligned with enterprise objectives for improved business outcomes.

3. Scalable analytics

• Advanced analytics platform
  We developed an enhanced semantic model-driven analytics platform supporting AI and ML use cases.

• Self-serve Business Intelligence (BI)
  Accelerated decision-making by enabling business teams to generate insights independently using self-serve BI tools.

• Centralized Key Performance Indicator (KPI) library
  Maintained consistent performance measurement with a centralized KPI repository.

4. Data governance and security strategy

• Federated data governance implementation
  We implemented a federated data governance model to standardize enterprise data management. This was preceded by a thorough evaluation of leading platforms like Azure Purview, Collibra and Databricks Unity Catalog to identify the best 󹀼t for the client’s governance and security needs.

• Data Security & Compliance Framework
  We enforced granular row and column-level security controls, ensured General Data Protection Regulation (GDPR)-compliant handling of Personally Identi󹀼able Information (PII), and established comprehensive data policies and standards for operational and regulatory alignment.

• Data strategy assessment framework
  We utilized WNS' comprehensive assessment framework with pre-built questionnaires across different data modules to de󹀼ne and align the enterprise data strategy.

5. Data marketplace and monetization

• Centralized Catalog
  We developed a unified catalog to maintain the organization's data assets and reporting objects while establishing a secure data sharing model for information exchange.

• Data Monetization Model
  We introduced a comprehensive data monetization framework with Data-as-a-Service (DaaS) capabilities, enabling end users to easily access information and driving monetization across the enterprise.

6. Centralized data operations

• Enterprise Data Office (EDO)
  We established an EDO that streamlined data access operations, enhanced data ownership and fostered a collaborative data-driven culture while optimizing data management across acquisition, analysis and distribution.

The Outcome
Smarter, Scalable, Cost-effective Decision-making

The new data platform and governance architecture resulted in significant improvements across operations, decision-making and cost management, ultimately transforming the client’s data-driven capabilities:

Infrastructure savings

percent reduction in infrastructure costs due to data modernization

Data quality improvements

percent reduction in data outliers and anomalies, as the automated engine significantly improved data quality, aligning it with set improvement standards

Increased platform adoption

percent rise in platform usage, owing to the data marketplace-driven data governance

percent increase in end users’ confidence in the data, owning to certification-based data availability

Centralized data repository

Consistent and reliable data for reporting and decision-making from the centralized repository acting as a single source of truth

Accelerated decision-making

Teams were empowered to undertake analytical use cases owing to increased platform responsiveness and timely, data-backed decisions across strategic areas