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Enterprise Data Synchronization and Integration for Post-Merger Financial Operations
  1. case
  2. Enterprise Data Synchronization and Integration for Post-Merger Financial Operations

Enterprise Data Synchronization and Integration for Post-Merger Financial Operations

sombrainc.com
Financial services
Information technology
Business services

Challenges in Post-Merger Data Integration and System Synchronization

The client faces difficulties in integrating and synchronizing client and employee data from an acquired organization due to frequent data changes and disparate wealth management systems. These issues hinder smooth operational continuity and require a secure, scalable, and reliable data migration and synchronization process within tight deadlines.

About the Client

A large, global financial organization acquiring another financial entity, requiring seamless integration of client and employee data post-merger

Goals for Effective Data Migration and System Integration

  • Achieve seamless synchronization of data for over 500,000 clients and up to 5,000 employees post-merger.
  • Implement a reliable, scalable, and secure scheduled data migration pipeline to handle ongoing daily data updates.
  • Reduce integration time and operational disruptions during the merger process.
  • Realize targeted synergies early, with measurable cost savings and operational efficiencies within six months post-implementation.
  • Enhance data security, availability, and observability throughout the integration process.

Core System Functionalities for Data Integration

  • Scheduled, automated data extraction and migration pipelines capable of handling high volume daily updates.
  • Encapsulation of business logic within cloud-native, scalable processing modules.
  • Real-time monitoring, error handling, and alerting features for data pipelines.
  • Secure file transfer mechanisms compliant with industry regulations, utilizing inhouse SFTP solutions.
  • Data storage solutions leveraging cloud-based object storage (e.g., cloud data buckets) for backups, logs, and high availability.
  • Extensive testing frameworks including smoke, regression, integration, and boundary value testing to ensure reliability.

Preferred Technologies and Architectural Approaches

Java-based microservices architecture
Spring Boot, Spring Cloud Task for business logic encapsulation
Quartz framework for task scheduling
Cloud storage solutions (e.g., AWS S3) for data and logs
Secure file transfer protocols (SFTP)

Necessary System Integrations

  • Existing wealth management and investment fund management systems for data extraction
  • Security protocols and regulatory compliance systems
  • Cloud storage and monitoring tools

Key Non-Functional System Requirements

  • Processing time per data batch under 5 minutes
  • High scalability to support large data volumes and future growth
  • High availability with minimal downtime
  • Robust security measures meeting regulatory standards
  • Comprehensive observability through monitoring and logging

Anticipated Business Benefits of the Data Integration System

The project aims to enable the client to realize at least 30% of targeted operational synergies within six months post-implementation, with an annual run rate savings approaching 55% of the initial targets. Additionally, it will strengthen the organization’s growth capacity and confidence during future acquisitions by ensuring seamless, secure, and efficient data integration and system operations.

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