- INDUSTRY: FinTech
- Vertical: BigData
- Location: USA
- Completed Date: 21-10-2023
Executive Summary
A leading global investment management firm faced the challenge of unifying data from multiple subsidiaries and third-party sources to gain a comprehensive view of their operations. The firm partnered with OktaBytes to develop a robust, scalable Unified Data Platform (UDP) on AWS. This case study explores the challenges, the solution implemented, the technologies leveraged, and the significant benefits achieved, illustrating the power of cloud-based data solutions in the financial industry.
Vision
The investment management firm, with trillions of dollars in assets under management, operated across numerous subsidiaries and relied on diverse third-party data providers. This fragmented data landscape led to inefficiencies, hindered analytical capabilities, and increased operational risks. The firm recognized the need for a centralized data repository to improve data quality, governance, and accessibility.
Challenge
The firm faced several key challenges:
-
Data Fragmentation: Data was scattered across multiple subsidiaries and third-party systems, making it difficult to obtain a unified view.
-
Business Logic Embedded in Legacy Systems: Critical business logic was embedded in database procedures with rigid schemas, hindering agility.
-
Lack of Centralized Governance: Data quality and governance were inconsistent across different data sources.
-
Limited Analytical Capabilities: Inconsistent data and lack of a unified platform limited the ability to perform comprehensive analysis.
-
Scalability Concerns: Existing systems struggled to handle increasing data volumes and diverse data types.
-
API Exposure: Difficulty in exposing downstream APIs for data products.
Solution
OktaBytes team helped the real estate organization develop a Generative AI powered chatbot with the following features:
-
End-to-End Data Pipeline: Developing and implementing an end-to-end data pipeline to ingest, process, and deliver data.
-
Multi-Layered Architecture: Building a multi-layered data platform (Raw, Curation, Organize) to ensure data quality and organization.
-
Data Ingestion: Ingesting data from various sources, including structured, unstructured, third-party, transactional, and application data, in both real-time and batch modes.
-
Data Processing: Utilizing AWS services for data transformation, cleaning, and enrichment.
-
Data Storage: Storing data in various AWS services based on access patterns and requirements (S3, Redshift, RDS, DynamoDB, Glacier).
-
API Development: Exposing downstream APIs for creating data products and enabling data access for various applications.
-
Data Governance: Implementing data governance and security measures to ensure data quality, consistency, and compliance.
-
DevOps Practices: Employing DevOps principles and CI/CD pipelines for efficient development and deployment.
-
Monitoring and Logging: Setting up comprehensive monitoring and logging using AWS CloudWatch.
-
Automation: Using Infrastructure as Code (IaC) with Terraform for automated provisioning and management.
Tech Stack
Python
Terraform
AWS Services
S3
SageMaker
API Gateway
Lambda
RDS
EventBridge
SNS
EMR
Glue
Redshift
Kinesis
StepFunction
Impact
-
Unified Data View: The platform provided a centralized repository for data consumption, offering a holistic view of the firm's operations.
-
Improved Data Quality and Governance: Centralized data governance and quality checks ensured consistent and reliable data.
-
Enhanced Analytical Capabilities: The unified platform enabled comprehensive analysis and data-driven decision-making.
-
Increased Efficiency: Automated data pipelines and workflows reduced manual effort and processing time.
-
Improved Data Accessibility: APIs and reporting tools made data easily accessible to various teams and applications.
-
Scalability and Performance: The AWS-based platform scaled effortlessly to handle increasing data volumes and diverse data types.
-
Cost Optimization: Optimized data storage and processing reduced operational costs.
-
Accelerated Development: DevOps practices and CI/CD pipelines accelerated development and deployment cycles.
-
Cloud Maturity: The project significantly advanced the firm's cloud maturity and adoption.
Conclusion
The Unified Data Platform project successfully transformed the investment management firm's data landscape. By leveraging AWS cloud services and implementing a robust data architecture, the firm achieved a unified, scalable, and secure data platform. This transformation improved data quality, enhanced analytical capabilities, and increased operational efficiency, enabling the firm to make more informed decisions and drive future growth. The project serves as a testament to the power of cloud-based data solutions in addressing complex challenges in the financial industry.
