Europe & UK Archives - Page 2 of 3

Introduction

Conversational AI service desk automation helps transportation organizations improve support efficiency, reduce resolution time, and enhance service reliability. A world-leading high-speed rail service provider operating across the UK and Europe faced challenges in managing high volumes of support requests across systems and users. Manual processes increased response time and operational costs. By implementing this automation, the organization streamlined support operations, enabled multi-channel assistance, and improved overall service consistency.

Customer

A world-leading high-speed rail service provider connecting the UK to major European cities.

Business Objective

Reduce mean time to resolution (MTTR)
Improve service desk efficiency
Lower operational support costs
Enable automated, multi-channel support
Enhance service reliability across operations

Scope of Services

Implementation of conversational AI–based service desk automation
Multi-channel bot integration for support requests
Automation across applications, infrastructure, and support functions
Integration with existing service desk platforms
Enablement of intelligent workflows for issue resolution

Benefits

Faster and more consistent issue resolution
Reduced dependency on manual support processes
Improved service desk efficiency
Scalable support model across multiple systems
Enhanced user experience through conversational interfaces

Impact

Faster issue resolution across support functions
Reduced manual intervention
Improved service consistency
Lower operational overhead in support management

Introduction

An AI-driven HR analytics platform enables organizations to access real-time insights, reduce dependency on manual reporting, and improve decision-making efficiency. An organization in the Professional Services industry faced challenges in generating accurate HR metrics due to reliance on manual analysis and external analysts. This slowed decision-making and limited scalability. By implementing an AI-driven HR analytics platform, the organization enabled self-service reporting, automated insight generation, and improved accessibility to workforce data across functions.

Customer

An organization seeking faster, simpler, and more accurate HR analytics and reporting to support decision-making without increasing dependency on in-house or outsourced business analysts.

Business Objective

Enable quick and accurate extraction of HR insights
Reduce manual reporting and analysis effort
Empower HR teams with self-service analytics
Support data-driven decision-making
Scale analytics without increasing analyst dependency

Scope of Services

HR data ingestion and normalization
Text analytics and multilingual language understanding
AI-driven metric generation and insight automation
Multi-tenant analytics platform enablement
Self-service reporting and dashboard delivery

Benefits

Reduced dependency on manual reporting and analysts
Faster access to accurate HR insights
Improved decision-making through real-time data
Scalable analytics platform supporting multiple users
Enhanced visibility into workforce performance

Impact

Enabled tracking of key HR KPIs including:
- Time-to-Hire
- Time-to-Fill
- Recruiting Channel Efficiency
- Applications per Vacancy
- Interview-to-Offer Ratio
- Offer Acceptance Rate
Improved efficiency in HR analytics and reporting
Stronger data-driven workforce decisions

Introduction

HR lifecycle automation platforms enable organizations to streamline employee onboarding, movement, and offboarding while improving efficiency and compliance. An organization in the Professional Services industry faced challenges with manual processes, access management errors, and delays during employee lifecycle transitions. These inefficiencies impacted employee experience and increased operational overhead. By implementing HR lifecycle automation, the organization created a seamless, audit-ready, and scalable system that improved process efficiency and reduced manual intervention.

Customer

An organization aiming to modernize and automate employee onboarding, movement, and offboarding processes to improve efficiency, compliance, and employee experience.

Business Objective

Reduce time and effort in employee lifecycle processes
Eliminate manual errors in access and asset management
Improve employee experience during transitions
Ensure auditability and compliance
Lower operational costs through automation

Scope of Services

Automation of onboarding, transfer, and offboarding workflows
Integration between HRMS and ITSM platforms (e.g., ServiceNow)
Access control and asset lifecycle management
Approval workflow automation
Audit-ready process execution and tracking

Benefits of HR Lifecycle Automation Platform

Reduced manual effort across HR and IT processes
Improved accuracy in access and asset management
Faster employee onboarding and transition processes
Enhanced compliance and audit readiness
Consistent and standardized lifecycle workflows

Impact

End-to-end self-service automation of HR and IT tasks
Single-click approvals via email or SMS
Improved cost efficiency through reduced manual effort
Strong audit trail and compliance readiness
Significantly improved employee experience

Introduction

A serverless data platform is critical for organizations handling massive and rapidly growing datasets. The UK’s telecommunications regulator faced increasing volumes of mobile and broadband data, making traditional infrastructure inefficient and difficult to scale. Limited flexibility and high operational overhead restricted timely analysis. By implementing a serverless data platform on Azure, the regulator enabled scalable data ingestion, reduced infrastructure complexity, and strengthened its ability to generate real-time regulatory and market intelligence insights.

Customer

The UK’s telecommunications regulator responsible for overseeing mobile and broadband markets and enabling data-driven decisions.

Business Objective

Handle rapidly growing telecom data volumes
Enable scalable ingestion of multi-terabyte datasets
Support regulatory and market intelligence analytics
Reduce infrastructure management overhead
Ensure reliability, scalability, and cost efficiency

Scope of Services

Advisory to define serverless data architecture and strategy
Design and implementation of data lake on Microsoft Azure
Ingestion and processing of large-scale telecom datasets
Enablement of analytics access for regulatory teams
Ongoing platform support and operational maintenance

Benefits of Serverless Data Platform

Seamless ingestion and processing of massive datasets
Faster access to actionable market intelligence insights
Reduced operational overhead through serverless architecture
Scalable and cost-efficient data platform
Improved support for regulatory analytics and decision-making

Impact

Enhanced market intelligence capabilities
Improved regulatory oversight through data insights
Increased agility in responding to telecom market changes

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

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 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

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

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