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Data Integration and Management Platform for Financial Data Processing
  1. case
  2. Data Integration and Management Platform for Financial Data Processing

Data Integration and Management Platform for Financial Data Processing

spyro-soft.com
Financial services
Information technology

Identified Challenges in Financial Data Management and System Integration

The client faces difficulties managing large volumes of data across multiple internal and external systems, leading to inefficiencies in data preparation, analysis, and reporting. As operations expand, there is a pressing need to transform and standardize data to align with regulatory standards and transmit information securely to supervisory authorities. Existing processes lack automated, tailored functionalities for data cleaning, aggregation, and integrity assurance, impacting overall operational efficiency and data quality.

About the Client

A mid-sized financial services firm specializing in equity analysis, research, and capital markets services, seeking to optimize data handling and compliance processes.

Goals for Streamlining Data Operations and Enhancing System Integration

  • Develop a comprehensive internal and external data management system to facilitate data preparation, analysis, and report generation.
  • Implement automated data transformation, cleaning, and aggregation functionalities to ensure data accuracy and consistency.
  • Enable seamless integration with various internal and external systems to support large-scale data retrieval and transmission.
  • Enhance data security through encryption, controlled access, and compliance with relevant regulations.
  • Leverage cloud infrastructure and modern CI/CD practices to improve system scalability, reliability, and performance.
  • Support data adherence to diverse standards for transmission to regulatory bodies, reducing manual interventions and errors.

Core Functional Features for Data Handling and System Integration

  • Data preparation module supporting formatting, transformation, and validation of large datasets.
  • Custom functionalities for data cleaning and aggregation to ensure accuracy and consistency across reports.
  • APIs to facilitate data exchange between internal systems and external partners or regulatory bodies.
  • Secure environment with access controls, encryption, and audit trails for sensitive data management.
  • Automated deployment pipeline using CI/CD practices to ensure continuous system updates and maintenance.
  • Cloud-based deployment environment to enhance scalability, availability, and performance.

Preferred Technologies and Architectural Approach

Cloud computing platform (e.g., Azure or equivalent cloud services)
Containerization with Docker and orchestration with Kubernetes
Angular or modern web framework for front-end development
SQL database for structured data storage
API development for system interoperability
CI/CD pipelines for automated deployment

External and Internal System Integrations Needed

  • Data sources for large-scale data retrieval and input
  • External regulatory reporting systems
  • Internal systems for research, analysis, and reporting
  • Security infrastructure including encryption and access control systems

Critical Non-Functional System Requirements

  • Scalability to handle increasing data volumes as operations expand
  • High performance with minimal latency in data processing and retrieval
  • Robust security measures including encryption, access controls, and audit logs
  • Regulatory compliance with data handling and transmission standards
  • Maintainability and ease of updates through automated deployment practices

Expected Business Benefits and Performance Improvements

The implementation of this data management and integration platform aims to significantly enhance operational efficiency by automating data workflows, reducing manual errors, and enabling faster report generation. It is expected to improve data accuracy and security, support compliance with regulatory standards, and scale effectively with business growth, ultimately resulting in a more streamlined data ecosystem and reduced operational costs.

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