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

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:

Design and implementation of a Cloudera-based data lake on AWS
Ingestion and integration of cargo operations data
Integration of financial data for reporting and analysis
Enablement of analytics and reporting capabilities
Ongoing platform support and maintenance

Benefits

Improved visibility into cargo operations and performance
Faster and more reliable financial reporting
Unified data platform supporting both operational and financial analytics
Scalable cloud foundation for future analytics initiatives
Reduced dependency on fragmented legacy data systems

Impact

Better monitoring and control of cargo business performance
Improved decision-making across operations and finance
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:

Design and execution of a pilot to evaluate next-generation data lake platforms
Comparative assessment of Cloudera Altus and Azure Databricks capabilities
Implementation of HR analytics use cases on shortlisted platforms
Deployment and testing across AWS and Microsoft Azure environments
Validation of analytics performance, usability, and extensibility

Benefits

Clear visibility into strengths and trade-offs of competing data lake platforms
Reduced risk in long-term technology platform selection
Faster validation of analytics capabilities through real use cases
HR teams enabled with actionable workforce insights
Strong foundation for scaling enterprise analytics initiatives

Impact

Confident selection of the most suitable data lake platform
Accelerated readiness for enterprise analytics rollout
Improved decision-making through HR data and workforce insights

Introduction

Travel Data Warehouse Modernization enables travel technology companies to move beyond legacy reporting systems and unlock scalable analytics capabilities. Many travel platforms rely on traditional data warehouse architectures that struggle to process large volumes of commercial and operational data generated across booking systems, sales channels, and partner networks. As a result, analytics initiatives slow down and organizations lack visibility into real-time sales performance.

This case study highlights how a travel technology firm modernized its existing data warehouse by implementing a cloud-based data lake on AWS. By redesigning its data architecture and integrating key sales data sources, the organization improved visibility into commercial performance, accelerated analytics adoption, and established a scalable data platform capable of supporting future advanced analytics and AI initiatives.

Customer

The customer is a travel technology firm providing digital solutions and platforms that support travel commerce and booking ecosystems. The organization manages large volumes of sales and business data generated across multiple channels and services.

Over time, the existing data warehouse environment became difficult to scale and limited the organization’s ability to analyze sales performance efficiently. As the company expanded its analytics ambitions, it required a modern data platform that could support flexible data integration and advanced analytics capabilities.

Business Objective

The primary objective was to modernize the existing data warehouse architecture and transition toward a scalable cloud-based analytics platform.

The organization aimed to implement a cloud data lake that could support growing data volumes and enable new analytics use cases around sales performance. In addition, leadership sought to improve visibility into sales trends and overall business performance across the organization.

Another key goal was to establish a flexible data foundation that could support future analytics initiatives and evolving business requirements.

Scope of Services

The engagement focused on implementing a modern cloud data platform, including:

Design and implementation of a cloud-based data lake on AWS
Modernization of the existing data warehouse into the new data lake architecture
Integration of sales and related business data sources
Enablement of analytics capabilities to support sales performance insights
Optimization of data pipelines for scalability and improved performance

Benefits

Modern and scalable cloud data platform supporting evolving analytics needs
Improved visibility into sales performance and business trends
Faster access to analytics and reporting capabilities
Reduced limitations associated with legacy data warehouse systems
Strong data foundation supporting advanced analytics and future AI use cases

Impact

Enhanced analysis of sales performance across the organization
Improved data-driven decision-making
Increased agility in responding to market and business trends

Introduction

Healthcare Data Warehouse Modernization to AWS enables healthcare providers to move beyond legacy data infrastructure and unlock scalable analytics capabilities. Many healthcare organizations rely on on-premise data warehouses that limit flexibility, increase operational costs, and slow down analytics initiatives. As healthcare systems generate increasing volumes of operational and clinical data, modern data platforms become essential for enabling faster insights and supporting data-driven decision-making.

This case study highlights how an Australian low-cost healthcare provider modernized its data warehouse environment by migrating to a scalable cloud platform on AWS. By transforming its legacy data infrastructure into a modern cloud-based analytics platform, the organization improved accessibility to enterprise data, accelerated analytics adoption, and strengthened its readiness for advanced analytics and AI initiatives.

Customer

The customer is an Australian healthcare provider focused on delivering cost-effective healthcare services while maintaining operational efficiency across its organization.

As the healthcare group expanded its services and operations, its legacy on-premise data warehouse environment began limiting scalability and analytics capabilities. Fragmented systems and infrastructure complexity slowed reporting and reduced visibility into operational performance. Therefore, the organization required a modern data platform capable of supporting analytics growth and future innovation.

Business Objective

The primary objective was to evaluate and implement a cloud-based modernization strategy for the organization’s existing data warehouse.

The healthcare provider aimed to migrate its legacy on-premise data warehouse to a scalable cloud platform that could support growing data volumes and advanced analytics initiatives. In addition, leadership wanted to improve operational insights and enable faster data-driven decision-making across the organization.

Another key goal was to establish a stable, secure data platform that could support long-term analytics needs while ensuring ongoing operational reliability.

Scope of Services

The engagement focused on end-to-end data warehouse and analytics transformation, including:

Feasibility analysis for cloud-based data warehouse modernization
Migration and modernization of the legacy DWH to AWS
Design and development of analytics capabilities on the cloud platform
Establishment of scalable and secure cloud data architecture
Ongoing platform support and operational maintenance

Benefits

Scalable and cost-efficient cloud data warehouse platform
Improved accessibility to enterprise data across the organization
Faster generation of insights for operational and clinical decisions
Reduced complexity associated with legacy data systems
Reliable platform operations supported by continuous maintenance

Impact

Accelerated adoption of analytics capabilities
Improved data-driven decision-making across the organization
Enhanced operational efficiency
Greater readiness for AI and advanced analytics initiatives

Introduction

Commercial Data Warehouse Migration to AWS enables airlines to modernize legacy data environments and support scalable, data-driven decision-making. Many aviation organizations operate complex data warehouse ecosystems built on legacy BI stacks and tightly coupled systems. Over time, these environments become difficult to maintain, expensive to operate, and slow to support new analytics initiatives.

This case study highlights how a large European airline modernized its commercial data ecosystem by migrating its legacy Data Warehouse (DWH) to a cloud-native architecture on AWS. By transforming the legacy platform into a scalable Data Lake and simplifying commercial data workflows, the airline improved data quality, enhanced governance, and enabled faster decision-making across pricing, revenue, marketing, and sales domains. As a result, the organization established a future-ready data platform capable of supporting advanced analytics and AI-driven innovation.

Customer

The customer is a major European airline managing large volumes of commercial and operational data across multiple business units. The airline’s legacy commercial Data Warehouse had grown overly complex due to years of system dependencies, custom pipelines, and fragmented reporting environments.

These challenges made it difficult to maintain data quality, slowed analytics initiatives, and increased operational costs. Therefore, the airline required a modern, cloud-based data architecture that could simplify the commercial data landscape while enabling scalable analytics capabilities.

Business Objective

The primary objective was to modernize the airline’s commercial data ecosystem by migrating its legacy Data Warehouse to a scalable cloud-native Data Lake on AWS.

Key objectives included reducing dependency on legacy BI tools and high-cost infrastructure, simplifying the commercial data landscape, and eliminating interdependency-driven bottlenecks. In addition, the airline aimed to improve data quality and governance while enabling cross-domain visibility across commercial functions.

Another important goal was to support faster decision-making through self-service analytics and unified reporting. Ultimately, the airline sought to establish a future-ready data platform capable of supporting additional business domains and advanced analytics initiatives.

Scope of Services

Platform Modernization & Migration

Re-engineered the legacy commercial domain DWH into an AWS-native architecture
Built a scalable Data Lake using S3, Redshift, Spark, Hive, and NiFi
Migrated complex data pipelines while resolving functional and process interdependencies

End-to-End Reference Architecture

Designed a cloud-first architecture optimized for analytics, storage, and compute
Implemented modular processing layers for ingestion, transformation, and data consumption

Functional Review & Business Rule Redesign

Conducted functional assessment across pricing, revenue, sales, and marketing processes
Rationalized and redesigned business rules to eliminate redundancies
Standardized KPI definitions across commercial units

Governance & Quality Framework

Implemented data quality, metadata management, and lineage tracking
Established governance workflows and role-based data access

Reporting & Insights Enablement

Enabled self-service analytics and reporting for commercial teams
Consolidated insights across pricing, demand, marketing, and revenue domains

Benefits

Significant cost savings through consolidation of technology infrastructure
Simplified reporting environment enabling faster insights
Reduced dependency on IT teams through self-service analytics
Improved data quality and governance through enterprise frameworks
Streamlined business rules eliminating complex interdependencies
Unified commercial data repository supporting cross-functional analytics
Flexible platform capable of onboarding new business domains

Impact

40–60% reduction in operational overhead after eliminating legacy systems
Faster insight generation through self-service access for revenue and pricing teams
Improved commercial decision accuracy through standardized KPIs
Analytics project lead time reduced from weeks to days
Future-ready data platform enabling AI and machine learning use cases

Introduction

Cloud Data Platform Modernization enables healthcare organizations to unlock the full value of enterprise data by replacing fragmented legacy systems with scalable and unified platforms. Many healthcare groups operate multiple operating companies (OpCos), each maintaining separate data repositories. As a result, data becomes siloed, analytics initiatives slow down, and enterprise-wide insights become difficult to generate.

This case study highlights how an APAC-based healthcare group modernized its data landscape through Cloud Data Platform Modernization. By migrating legacy on-premise platforms to the cloud and consolidating fragmented OpCo-level repositories, the organization established a scalable data foundation. Consequently, the healthcare group improved operational efficiency, enabled advanced analytics, and strengthened its readiness for future AI-driven innovation.

Customer

The customer is an APAC-based healthcare group operating multiple operating companies (OpCos) across the region. Each OpCo maintained independent data repositories and infrastructure, which created fragmentation across the enterprise data environment.

As the organization expanded, these siloed systems limited analytics capabilities and slowed down enterprise data initiatives. Therefore, the healthcare group required a modern cloud-based platform capable of supporting unified analytics and scalable data operations.

Business Objective

The organization aimed to modernize legacy on-premise data platforms while consolidating fragmented OpCo-level repositories into a unified environment. In addition, leadership sought to enable cloud scalability and improve operational efficiency across the enterprise.

Another key objective was to support data monetization and advanced analytics use cases while securely integrating third-party data sources. At the same time, the platform needed to maintain 24×7 operational reliability and support business-critical healthcare operations.

Scope of Services

The engagement focused on end-to-end data platform transformation, including:

Migration of on-premise data platforms to the cloud
Consolidation of multiple OpCo data repositories into a unified platform
Design and implementation of scalable cloud-based data architecture
Development of data monetization and analytics use cases
Integration of third-party data sources
Ongoing 24×7 platform support and maintenance

Benefits

Unified and scalable data platform across operating companies
Improved data accessibility and analytics capabilities
Faster development of data monetization use cases
Reduced complexity caused by fragmented data repositories
Reliable round-the-clock platform operations
Strong foundation for advanced analytics and future AI initiatives

Impact

Accelerated data-driven decision-making across operating companies
Improved operational efficiency across the healthcare group
Increased value realization from enterprise data assets
Enhanced readiness for advanced analytics and AI adoption

Customer

This smart transportation digital transformation case study features a leading Australian regional authority responsible for managing and modernizing critical transportation assets, including airports, train stations, and citizen-facing mobility services. The authority serves millions of passengers annually and focuses on delivering seamless, accessible, and future-ready travel experiences across physical and digital touchpoints.

Business Objective

The regional authority initiated a large-scale digital transformation program with the following objectives:

Redesign end-to-end traveler experiences across airports, train stations, and mobile applications
Adopt human-centered design principles to improve accessibility and inclusivity
Reduce Total Cost of Ownership (TCO) through strategic outsourcing of applications and infrastructure
Enable a resilient, scalable multi-cloud environment to support modernization
Incubate innovation using emerging technologies such as AR, VR, and digital walk-throughs
Deliver a next-generation travel platform that improves passenger satisfaction, reduces friction, and enables digital self-service

Scope of Services

BXI Technologies partnered with the authority to deliver an integrated smart transportation digital experience transformation program.

Human-Centered Experience Design

Redesigned digital and physical traveler journeys across new airport terminals and train stations
Developed AR- and VR-enabled prototypes to visualize and test future travel experiences
Improved accessibility and inclusivity for diverse traveler groups, including differently abled and elderly passengers

Strategic Outsourcing (Applications and Infrastructure)

Established an outsourced operating model for application and infrastructure management
Reduced TCO through consolidation, automation, and shared-services delivery
Improved service governance and operational efficiency

Multi-Cloud Enablement and Migration

Enabled hybrid and multi-cloud capabilities to support resilience and scalability
Migrated legacy applications and workloads to modern cloud environments
Modernized existing systems to enhance performance, availability, and reliability

Innovation Incubation

Explored emerging technologies including AR, VR, digital twins, and immersive walk-throughs
Built rapid prototypes to validate and refine next-generation travel experience models

Benefits

The smart transportation digital experience transformation delivered measurable benefits across passenger experience, cost efficiency, and innovation capability:

Immersive AR and VR-powered design enabling intuitive and frictionless travel experiences
Improved accessibility and inclusivity across citizen-facing transportation services
Reduced Total Cost of Ownership through consolidated applications and infrastructure outsourcing
Enhanced system reliability and scalability through multi-cloud adoption
Modernized digital touchpoints across mobile apps, airports, and train stations
A stronger innovation pipeline driven by rapid prototyping and emerging technologies

Business Impact

The transformation resulted in quantifiable operational and experience-driven outcomes:

25–35% reduction in application and infrastructure TCO through strategic outsourcing
40–50% faster delivery of digital enhancements enabled by multi-cloud platforms
30–40% improvement in traveler experience scores through human-centered design
20–25% reduction in operational delays linked to digital process improvements