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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

Customer

Siebel to Salesforce migration became a strategic priority for a leading Hi-Tech product vendor and system integrator based in the United States. The organization relied heavily on Siebel for core CRM operations but faced limitations in agility, scalability, and user experience. To modernize customer-facing processes and support global growth, the customer sought a trusted partner to lead a full-scale Siebel to Salesforce migration.

Business Objective

The customer aimed to:

  • Transition from Siebel to Salesforce to improve agility and scalability

  • Standardize CRM processes across global regions

  • Improve user experience and adoption for sales, service, and operations teams

  • Enable faster development, deployment, and integration cycles

  • Reduce long-term IT spend and CRM support overhead

  • Increase operational efficiency by consolidating tools and workflows

Scope of Services

BXI Tech was selected as the strategic transformation partner to execute the Siebel to Salesforce migration through a multi-phase modernization program:

  • Migrated legacy Siebel CRM processes to Salesforce.com using a high-speed development model

  • Consolidated fragmented workflows into simplified, optimized Salesforce processes

  • Deployed Salesforce across multiple countries with region-specific configurations

  • Integrated Salesforce with enterprise systems, including:

    • PeopleSoft HRMS

    • Enterprise Data Warehouse

    • SharePoint

    • AppExchange tools such as Conga Merge, DealMaker, Timba Surveys, InsideView, Eloqua, and ClickTools

  • Established a managed services model to support continuous enhancement and operational stability

  • Streamlined CRM services and support structures for long-term maintainability

Benefits

The modernization initiative delivered measurable improvements across CRM operations:

  • Consolidated and standardized CRM processes across regions

  • Faster rollout of CRM capabilities in new markets

  • Stronger integration with enterprise applications and business workflows

  • Improved usability, driving higher user confidence and adoption

  • Reduced complexity through platform consolidation

Impact

  • 35–40% reduction in overall IT spend

  • 60% increase in Salesforce user adoption

  • 8% year-on-year reduction in CRM support team size

  • Service request backlog reduced from 30,000 to 500 over five years