Case Studies

Read some of our case studies or get in touch to discuss more of our data stories

Case Study 1: Comprehensive Cloud Migration for Legacy Systems in the Automotive Industry

Client: A leading global automotive manufacturer

Objective: To transition a large-scale, outdated legacy system to a modern cloud environment, enhancing efficiency and scalability.

Approach and Technologies Used:

  • Legacy System: The original setup included proprietary on-premises database servers supporting in-house developed software applications crucial for manufacturing processes.
  • New Technology: Amazon Web Services (AWS) was selected for cloud storage and computing, with Kubernetes for orchestration and Docker for container management. AWS Database Migration Service was utilized alongside custom scripts to facilitate efficient data transfer.
  • Data Size and User Base: The migration involved over 5 terabytes of data critical to operations, supporting around 10,000 users across production, logistics, finance, and administration departments.

Implementation Phases:

  1. Planning and Assessment: Detailed analysis and mapping of existing data and application dependencies.
  2. Pilot Migration: A smaller, non-critical segment of data was migrated to test the process and tools.
  3. Full-scale Migration: Gradual migration in stages to minimize operational disruption, closely monitored for issues.
  4. Post-Migration Optimization: Tuning and optimization to leverage cloud capabilities fully.

Challenges:

  • Downtime Management: Ensuring minimal downtime was critical to avoid disrupting manufacturing processes.
  • Data Integrity: Maintaining data integrity during the transfer, given the complexity and size of the datasets.
  • User Adaptation: Facilitating smooth transition and training for the diverse user base to adapt to the new cloud environment.

Opportunities:

  • Scalability: The cloud environment offers scalability options that are cost-effective and quick to implement as the company grows.
  • Performance Enhancements: Improved data processing speeds and reliability of services.
  • Cost Reduction: Significant reductions in operational costs due to optimized resource management and reduced need for physical infrastructure maintenance.

Results:

  • The migration was completed successfully within the set timeframe and budget, meeting all performance benchmarks.
  • Notable improvements in system reliability and operational efficiency were achieved.
  • The client reported a 30% reduction in operational costs and expressed high satisfaction with the enhanced scalability and flexibility provided by the cloud solution.

Case Study 2: Enhancing Health Data Integration in APAC Health Systems

Industry: Healthcare

Objective: To upgrade and integrate diverse health data systems across the Asia-Pacific region to improve healthcare delivery and policy-making for migrant populations.

Technological Approach:

  • Legacy System: Localized health data systems with limited interoperability and data sharing capabilities.
  • New Technology: Implementation of a centralized Health Information Exchange (HIE) platform using Oracle Health Sciences Data Management Workbench and Apache Hadoop for big data processing.
  • Data Size and User Base: Migration and integration of approximately 15 terabytes of health data, with the system intended to support over 20,000 health professionals across the region.

Implementation Phases:

  1. Initial Assessment and Planning: Analysis of existing data infrastructures and formulation of a standardized data model.
  2. System Development and Pilot Testing: Development of the HIE platform and initial pilot testing in select locations to ensure functionality.
  3. Full-Scale Rollout: Gradual rollout of the HIE across the region, including training programs for users.

Challenges:

  • Data Standardization: Unifying diverse health data sets that vary widely in format and granularity.
  • System Integration: Seamlessly integrating new technologies with existing health data infrastructures without disrupting ongoing healthcare services.
  • User Training and Adaptation: Ensuring that all users are proficient in using the new system.

Opportunities:

  • Enhanced Data Accessibility: Improved access to health data across the region, facilitating better disease tracking and health service management.
  • Data-Driven Decision Making: Enabling policymakers to make better-informed decisions based on comprehensive, real-time data.
  • Scalability: Establishing a scalable infrastructure that can accommodate future data growth and technological advancements.

Results:

  • Successful integration of the HIE platform, leading to more efficient data sharing and improved healthcare outcomes.
  • Significant improvements in the speed and accuracy of health reporting and analytics.
  • High user satisfaction and increased capacity for handling complex health data needs in the APAC region.

Case Study 3: Implementing Data Governance in a Mid-Size Australian Bank

Industry: Banking and Financial Services

Objective: To establish a robust data governance framework to enhance data quality, compliance, and security across the bank's operations.

Technological Approach:

  • Legacy System: Disparate data management systems with manual processes and minimal oversight.
  • New Technology: Integration of IBM InfoSphere for data governance, including tools for data quality, lifecycle management, and security.
  • Data Size and User Base: The new system manages over 100 terabytes of financial and personal data for 5,000 users, including bank staff and management.

Implementation Phases:

  1. Assessment and Strategy Formulation: Conducted a thorough assessment of existing data practices and developed a comprehensive data governance strategy.
  2. Technology Deployment: Deployed IBM InfoSphere and trained technical teams in its use.
  3. Rollout and Compliance Alignment: Gradual rollout across departments, ensuring compliance with APRA standards and GDPR for data protection.

Challenges:

  • Integration Complexity: Integrating new technologies with existing legacy systems while maintaining uninterrupted banking services.
  • Compliance and Security: Meeting stringent regulatory requirements and ensuring robust data security measures.
  • Cultural Change: Shifting the organization's culture to prioritize data governance and management.

Opportunities:

  • Enhanced Data Quality: Improved accuracy and reliability of the bank's data assets.
  • Regulatory Compliance: Strengthened compliance with banking regulations and data protection laws.
  • Operational Efficiency: Streamlined processes and reduced operational risks related to data handling.

Results:

  • Successful implementation of a data governance framework that enhanced data security and compliance.
  • Positive feedback from users on the ease of access to high-quality data.
  • Recognition from industry bodies for leading practices in data management.

Case Study: Data Cleansing Initiative for Australian Insurer

Industry: Insurance

Objective: To enhance data quality and accuracy across the insurer's extensive customer and transaction databases.

Technological Approach:

  • Legacy System: Older relational database systems prone to errors and duplicates.
  • New Technology: Adopted Talend Data Quality tools and integrated them with a modern data warehouse solution, Microsoft Azure Data Lake.
  • Data Size and User Base: Managed over 500 terabytes of data, servicing more than 8,000 users including customer service agents, underwriters, and claims handlers.

Implementation Phases:

  1. Data Audit and Problem Identification: Conducted a comprehensive review of existing data sets to identify inconsistencies, duplicates, and outdated information.
  2. Data Cleansing Implementation: Deployed Talend Data Quality tools to clean, validate, and standardize data across systems.
  3. Continuous Monitoring and Improvement: Established ongoing data quality monitoring with automated alerts for anomalies.

Challenges:

  • Scale of Data: Managing the vast amount of data efficiently while minimizing disruption to daily operations.
  • Integration with Legacy Systems: Ensuring that new data quality tools seamlessly integrate with existing databases and infrastructure.
  • User Training and Adoption: Equipping staff with the necessary skills to utilize new technologies effectively.

Opportunities:

  • Improved Data Integrity: Enhanced accuracy and reliability of data used for critical business decisions.
  • Increased Operational Efficiency: Reduced time and resources spent on manual data corrections.
  • Better Customer Experience: Improved data quality led to faster claims processing and more personalized customer service.

Results:

  • Significant reduction in data-related errors, improving overall operational effectiveness.
  • Enhanced compliance with data governance standards and regulations.
  • Positive feedback from users about the improved data environment.