Introduction
A governed data platform is essential for investment firms to ensure accurate, secure, and compliant analytics. A leading Europe-based investment firm faced challenges in managing and analyzing portfolio data from multiple sources, including BlackRock Aladdin. Lack of governance and scalability limited the effectiveness of analytics. By implementing it on Azure, the firm established a secure, unified environment for investment data, enabling reliable portfolio analytics and better decision-making.
Customer
A Europe-based investment firm seeking to build a governed data platform for managing and analyzing investment data.
Business Objective
- Establish a governed data platform with data lake and warehouse
- Securely ingest and manage BlackRock Aladdin data
- Enable portfolio allocation analytics
- Ensure data governance, quality, and compliance
- Support scalable analytics capabilities
Scope of Services
- Advisory to define governance and architecture strategy
- Design and implementation of data lake and warehouse on Microsoft Azure
- Integration of BlackRock Aladdin data
- Enablement of analytics and reporting
- Ongoing platform support and maintenance
Benefits
- Trusted and governed investment data across systems
- Improved visibility into portfolio performance
- Faster and reliable analytics
- Reduced data risk through governance controls
- Scalable platform for future analytics
Impact
- Improved investment insights and allocation decisions
- Enhanced operational efficiency
- Strong compliance and audit readiness
Introduction
A rate quote issue platform is critical for insurers to deliver fast, accurate, and scalable policy issuance. A large insurance carrier offering specialty and standard commercial lines struggled with legacy systems that slowed quoting, reduced accuracy, and limited responsiveness to brokers. By modernizing its rate quote issue platform on AWS, the organization improved operational speed, streamlined underwriting integration, and created a scalable foundation for future insurance product innovation and growth.
Customer
A large insurance carrier providing specialty and standard commercial insurance products.
Business Objective
- Modernize core quoting and issuance capabilities
- Improve speed and accuracy of quote generation
- Enable scalable policy issuance
- Support diverse insurance product lines
- Enhance responsiveness to brokers and customers
Scope of Services – RQI Platform Modernization
- Design and implementation of a cloud-native platform
- Enablement of rating and quoting workflows
- Integration with underwriting and policy systems
- Deployment on Amazon Web Services
- Optimization for performance, scalability, and reliability
Benefits of Modernized Insurance Platform
- Faster quote turnaround and improved response time
- Better pricing accuracy and consistency
- Reduced dependency on legacy systems
- Scalable architecture supporting business growth
- Improved efficiency across sales and underwriting teams
Impact
- Accelerated new business processing cycles
- Improved broker and customer experience
- Increased agility in launching and scaling insurance products
Introduction
Policy administration system modernization is critical for insurers aiming to improve agility without disrupting ongoing operations. A UK-based Property & Casualty insurance provider faced challenges in transforming its legacy PAS while maintaining business continuity. Traditional migration approaches carried high risk and downtime. By adopting a modern policy administration system modernization strategy, the organization enabled continuous data migration, reduced transformation risk, and built a scalable cloud-ready platform on AWS.
Customer
A UK-based Property & Casualty insurance provider seeking to modernize its policy administration system with minimal disruption.
Business Objective
- Accelerate policy administration system modernization
- Reduce risk in large-scale transformation
- Enable continuous data migration without downtime
- Improve interoperability through API-based integration
- Build a scalable cloud-ready PAS foundation
Scope of Services
- Advisory to define modernization and migration strategy
- Design of API-enabled continuous migration framework
- Real-time data interchange between legacy and modern systems
- Migration of PAS data to Amazon Web Services without disruption
- Post-modernization support for platform stability
Benefits
- Faster modernization of core insurance systems
- Reduced transformation risk through phased migration
- Improved flexibility via API-enabled architecture
- Minimal disruption to live policy operations
- Scalable foundation for future enhancements
Impact
- 60% reduction in PAS modernization time
- Improved operational continuity during transformation
- Increased agility for future platform upgrades
Introduction
Cloud reporting platform modernization helps insurance enterprises improve reporting speed, scalability, and data visibility. A multi-specialty insurance multinational corporation faced delays in operational and financial reporting due to legacy systems and fragmented data sources. These limitations impacted decision-making and operational efficiency. By implementing a cloud reporting platform, the organization modernized its reporting environment, reduced turnaround time, and enabled scalable, data-driven insights across insurance functions.
Customer
A multi-specialty insurance multinational corporation seeking to improve the speed and efficiency of operational reporting.
Business Objective
- Reduce reporting turnaround time
- Modernize legacy reporting systems
- Enable scalable cloud-based data management
- Improve visibility across insurance product lines
- Build a stable and reliable reporting platform
Scope of Services
- Advisory to define cloud data lake and reporting strategy
- Design and implementation of data lake on Amazon Web Services
- Integration of operational data across PR, Surety, Credit, and Financial Lines
- Enablement of analytics and reporting using Snowflake
- Ongoing platform support for performance and reliability
Benefits of Cloud Reporting Platform
- Significantly reduced reporting turnaround time
- Faster access to operational and financial insights
- Unified reporting across insurance product lines
- Improved scalability and performance
- Reliable and supported reporting environment
Impact
- Faster operational decision-making
- Improved reporting efficiency
- Enhanced visibility across business lines
- Strong foundation for future analytics initiatives
Introduction
Cloud data platform consolidation enables healthcare organizations to reduce costs while improving data accessibility and analytics capabilities. A healthcare group operating with fragmented on-premise applications faced rising capital and operational expenditure, along with limited scalability. By implementing a cloud data platform consolidation strategy, the organization unified its data landscape on AWS, simplified architecture, and created a scalable foundation for analytics and decision-making.
Customer
A healthcare group seeking to reduce CAPEX and OPEX by consolidating on-premise applications into a cloud-based data platform.
Business Objective
- Reduce infrastructure-related CAPEX and OPEX
- Consolidate on-premise applications into a cloud data platform
- Enable cloud-based data warehousing and analytics
- Improve enterprise data accessibility
- Build a scalable and cost-efficient data foundation
Scope of Services
- Advisory to define cloud data platform and analytics strategy
- Consolidation of on-premise applications into a unified data lake
- Design and implementation of a cloud data warehouse on Amazon Web Services
- Enablement of analytics use cases across the platform
- Optimization of architecture for cost efficiency and scalability
Benefits of Cloud Data Platform Consolidation
- Reduced capital and operational expenditure
- Simplified data architecture replacing fragmented systems
- Faster access to analytics and insights
- Improved scalability for growing healthcare data
- Cost-efficient foundation for AI and analytics initiatives
Impact
- Lower infrastructure and maintenance costs
- Improved operational efficiency
- Increased agility in decision-making
- Better utilization of enterprise data assets
Introduction
Cloud data warehouse modernization is essential for healthcare organizations aiming to reduce costs and improve system performance. A healthcare group faced high infrastructure and licensing costs due to its Oracle-based data warehouse environment. Limited scalability and availability further impacted operational efficiency. By implementing a cloud data warehouse modernization strategy, the organization migrated to AWS, improved resilience, and established a scalable foundation for future healthcare analytics.
Customer
A healthcare group seeking to reduce costs and improve availability by modernizing its data warehouse and migrating from Oracle to a cloud platform.
Business Objective
- Reduce Oracle infrastructure and licensing costs
- Improve availability and system resilience
- Modernize legacy data warehouse architecture
- Migrate databases to a scalable cloud platform
- Enable a future-ready analytics foundation
Scope of Services
- Assessment of Oracle databases and data warehouse environment
- Migration of databases, warehouse, and GoldenGate components to Amazon Web Services
- Redesign of data warehouse for cloud scalability and high availability
- Optimization of data pipelines and workloads
- Validation of performance, reliability, and availability
Benefits of Cloud Data Warehouse Modernization
- Significant cost reduction from Oracle licensing and infrastructure
- Improved system availability and reliability
- Modernized cloud-aligned data architecture
- Enhanced scalability for healthcare data growth
- Reduced dependency on legacy systems
Impact
- Lower total cost of ownership for data platforms
- Higher availability and resilience
- Improved operational efficiency in data management
- Strong foundation for analytics and AI initiatives
Introduction
IoT analytics is transforming how energy-intensive organizations monitor and optimize consumption. An organization operating IoT systems in the energy sector faced challenges in leveraging large volumes of sensor data effectively. Without a unified IoT analytics approach, energy usage insights remained fragmented and underutilized. By implementing an IoT analytics solution, the organization enabled real-time visibility, improved decision-making, and built a scalable foundation for energy optimization across operations.
Customer
An organization operating IoT systems in the energy sector, focused on improving energy efficiency through advanced analytics.
Business Objective
- Capture and process high-volume IoT sensor data
- Build an IoT analytics solution for energy monitoring
- Enable data-driven energy optimization
- Support scalable IoT data ingestion and analytics
- Establish a future-ready energy analytics foundation
Scope of Services – IoT Analytics Implementation
- Integration of sensor data into a centralized IoT analytics platform
- Design and deployment of IoT analytics solution on CGP
- Real-time and batch processing of sensor data
- Analytics enablement to identify energy usage patterns
- Optimization of data pipelines for scalability and reliability
Benefits of IoT Analytics
- Improved visibility into energy consumption patterns
- Identification of energy optimization opportunities
- Better utilization of IoT sensor data
- Scalable platform for growing IoT workloads
- Strong foundation for smart energy initiatives
Impact
- Reduced energy usage using IoT analytics insights
- Improved operational efficiency in energy monitoring
- Enhanced scalability of IoT-based energy solutions
Introduction
In today’s digital-first banking environment, customer interactions span multiple channels—mobile apps, web platforms, and digital services. However, many financial institutions still struggle with fragmented data systems, limiting their ability to deliver personalized and consistent experiences.
This case study highlights how a leading European Bank transformed its data landscape by building a unified customer data platform, enabling real-time insights, improved decision-making, and a scalable foundation for omnichannel banking.
Customer
A leading European bank operating across multiple regions and digital platforms faced increasing challenges in managing fragmented customer data. The lack of a unified data strategy impacted customer intelligence, operational efficiency, and the ability to deliver seamless banking experiences.
Business Objectives
The bank aimed to transform its customer data ecosystem to support modern banking experiences:
- Create a unified view of customer data across all digital channels
- Eliminate fragmented data silos impacting decision-making
- Enable consistent, reliable, and real-time customer insights
- Support omnichannel banking initiatives
- Build a scalable data foundation for analytics and personalization
Scope of Services
1. Unified Customer Data Platform Development
- Designed and implemented a centralized customer data platform
- Enabled a single source of truth for customer data
2. Multi-Channel Data Integration
- Integrated data from web, mobile, and digital banking channels
- Ensured seamless data synchronization across touchpoints
3. Data Consolidation & Standardization
- Consolidated structured and semi-structured datasets
- Standardized data for consistency and governance
4. Analytics Enablement
- Built analytics-ready datasets
- Enabled faster access to customer intelligence and reporting
5. Cloud-Based Scalable Architecture
- Deployed on Amazon Web Services (AWS) integrated with Hortonworks
- Created a secure, scalable, and high-performance data environment
Services Delivered
- Centralized customer data hub implementation
- End-to-end data integration across channels
- Data consolidation and governed architecture design
- Cloud deployment and infrastructure setup
- Enablement of analytics and reporting frameworks
Benefits
- Unified and consistent customer data across channels
- Improved visibility into customer behavior and interactions
- Faster access to actionable insights
- Reduced complexity from fragmented systems
- Strong foundation for personalization and advanced analytics
Impact
- Enhanced customer intelligence across digital channels
- Improved operational efficiency in data management
- Accelerated readiness for omnichannel banking initiatives
- Established a future-ready data foundation
Introduction
Airline Cargo Analytics Data Lake Implementation enables aviation organizations to unify operational and financial data for better business visibility and decision-making. Airlines often manage cargo operations and financial reporting across multiple systems, which creates fragmented data environments and delays insights. As cargo operations grow in complexity, airlines require scalable data platforms capable of integrating operational data with financial performance metrics.
This case study highlights how a Gulf-based airline modernized its data landscape by implementing a cloud-based data lake on AWS. By consolidating cargo operations and financial data into a unified analytics platform, the airline improved visibility into cargo performance, accelerated financial reporting, and established a scalable foundation for future analytics initiatives.
Customer
The customer is a Gulf-based airline managing a large cargo business alongside its passenger operations. Cargo performance plays a significant role in overall airline revenue and operational planning.
However, fragmented systems made it difficult to track cargo operations efficiently and generate timely financial insights. As a result, the airline needed a unified data platform capable of consolidating operational and financial data while supporting analytics-driven decision-making.
Business Objective
The primary objective was to establish a scalable cloud-based data lake that could consolidate cargo and financial data.
The airline also aimed to improve monitoring and tracking of cargo business operations while enabling accurate and timely financial reporting. Additionally, leadership wanted a unified data platform capable of supporting both operational analytics and financial insights.
Another key goal was to create a stable data environment that could support ongoing analytics initiatives and future data-driven decision-making.
Scope of Services
The engagement focused on building and supporting a cloud-based data lake, including:
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Design and implementation of a Cloudera-based data lake on AWS
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Ingestion and integration of cargo operations data
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Integration of financial data for reporting and analysis
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Enablement of analytics and reporting capabilities
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Ongoing platform support and maintenance
Benefits
-
Improved visibility into cargo operations and performance
-
Faster and more reliable financial reporting
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Unified data platform supporting both operational and financial analytics
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Scalable cloud foundation for future analytics initiatives
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Reduced dependency on fragmented legacy data systems
Impact
-
Better monitoring and control of cargo business performance
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Improved decision-making across operations and finance
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Increased agility in responding to market and business demands
Introduction
Data Lake Platform Evaluation for Enterprise Analytics enables organizations to select the right data foundation before scaling analytics initiatives across the enterprise. As companies adopt advanced analytics and AI-driven insights, choosing the appropriate data lake platform becomes critical for ensuring performance, scalability, and usability. However, evaluating competing technologies often requires practical validation beyond theoretical comparisons.
This case study highlights how a travel technology firm conducted a structured data lake platform evaluation to determine the most suitable architecture for enterprise analytics. Through a hands-on pilot comparing Cloudera Altus and Azure Databricks, the organization tested platform performance, scalability, and usability while implementing real HR analytics use cases. As a result, the firm gained clear insights into platform capabilities and established a strong foundation for future analytics expansion.
Customer
The customer is a travel technology firm focused on building advanced analytics capabilities to support operational and strategic decision-making. As part of its data transformation initiative, the organization explored next-generation data lake platforms that could support enterprise-scale analytics workloads.
To validate the right technology choice, the firm decided to run a structured pilot focused on HR analytics use cases. This approach allowed the organization to assess platform capabilities in real-world scenarios while minimizing long-term implementation risks.
Business Objective
The primary objective was to identify the most suitable data lake platform for supporting enterprise analytics initiatives.
The organization aimed to compare Cloudera Altus and Azure Databricks through a hands-on pilot that evaluated scalability, performance, and usability. In addition, the firm wanted to demonstrate business value through HR analytics use cases.
Another important goal was to establish a flexible analytics foundation that could support future data-driven initiatives across additional business domains.
Scope of Services
The engagement focused on structured platform evaluation and analytics enablement, including:
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Design and execution of a pilot to evaluate next-generation data lake platforms
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Comparative assessment of Cloudera Altus and Azure Databricks capabilities
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Implementation of HR analytics use cases on shortlisted platforms
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Deployment and testing across AWS and Microsoft Azure environments
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Validation of analytics performance, usability, and extensibility
Benefits
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Clear visibility into strengths and trade-offs of competing data lake platforms
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Reduced risk in long-term technology platform selection
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Faster validation of analytics capabilities through real use cases
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HR teams enabled with actionable workforce insights
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Strong foundation for scaling enterprise analytics initiatives
Impact
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Confident selection of the most suitable data lake platform
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Accelerated readiness for enterprise analytics rollout
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Improved decision-making through HR data and workforce insights
