Transforming Portfolio Data Management

How QuantSpark helped a leading asset manager replace manual, error-prone portfolio data processes with a modern, automated data pipeline—enhancing accuracy, efficiency, and scalability.

The Challenge: Overcoming inefficiencies in portfolio data processing

A leading asset manager faced significant challenges in managing public and private equity portfolio data.

Their reliance on Excel-based processing led to:

• Time-consuming manual work, increasing operational inefficiencies.
• Error-prone reconciliation processes, resulting in data discrepancies.
• Limited analytical capabilities, restricting data-driven investment decisions.
• Inconsistencies in books of record (BOR), complicating performance reporting.

The Solution: A unified data pipeline for accurate performance reporting

QuantSpark developed a scalable and automated data infrastructure, focusing on:

1. Data ingestion and warehousing

• Designed and built a Snowflake-based data warehouse aligned with the asset manager’s data model.
• Enabled seamless data storage and access, eliminating dependency on Excel-based processes.

2. Modern pipeline orchestration

• Established automated ETL pipelines to transfer data to the asset manager’s Redshift database via an S3 bucket.
• Leveraged AWS Glue, DBT, and Terraform to automate data movement, validation, and transformation.
• Ensured scalability and efficiency by using modern cloud-based orchestration tools.

3. Expansion to additional data sources

• Initial Proof-of-Concept (POC) focused on integrating Bloomberg data.
• Expanded pipelines to ingest data from multiple sources, including IQ-EQ and fund administrators’ data.
• Created a scalable framework to onboard future data sources with minimal effort.

The Results: Enhanced data consistency and efficiency

The implementation of this modern data pipeline delivered tangible benefits, including:

• Faster deployment and simpler data management through fully managed services and pre-configured connections.
• A single, unified book of record (BOR), reducing data inconsistencies and reconciliation efforts.
• Improved data traceability and accuracy, ensuring pipelines remain in sync with dynamic data sources.

Through this engagement, QuantSpark empowered the asset manager with a robust, automated, and scalable data infrastructure, enabling more accurate reporting and informed decision-making.

Struggling with inefficient, error-prone portfolio data management? We specialise in transforming complex data processes into streamlined, scalable solutions.

Contact us today to explore how we can help you unlock greater accuracy, efficiency, and insight.