Optimizing data processes: Lord Abbett's reporting system upgrade
Finance
About this project
Montrose Software Team developed a robust system that processes over 20,000 instruments monthly and generates more than 5,000 reports. This system integrates various data sources, standardizes information, and supports advanced analytics and reporting, including automated testing and agile development practices to enhance efficiency and accuracy.
scope
Region
New Jersey, United States of America
scope
Region
New Jersey, United States of America
Project team
3 Developers
Tech stack
Java
Python
Azure SQL Server
Batch Script
Perl
Hibernate
TeamCity
About Lord Abbett
Since its founding in 1929, Lord Abbett has maintained a singular focus on managing money. As an investment-led, investor-focused firm, Lord Abbett evaluates every decision from an investment perspective, striving to achieve superior long-term investment performance for its clients. The firm currently manages over $140 billion in assets.
~$199B
assets under management
184
investments professionals
200+
institutional clients
51
partners
The challenge
Data integration
Integrating and normalizing position and reference data from multiple sources was complex and required a consistent approach.Scalability
The system needed to handle a high volume of data (over 20,000 instruments) and generate a large number of reports (more than 5,000) efficiently.Consistency
Ensuring accurate comparison across different benchmarks and managing various policies for split credit ratings posed challenges.Adoption of agile practices
Introducing automated testing, continuous delivery, and agile development practices required significant adjustments and buy-in from the client's team.
The solution
Streamlined data integration
We created a system to seamlessly load, normalize, and classify position and reference data from multiple sources. By assigning unique IDs to securities and using metadata for standardization, we ensured accurate and consistent data integration, accommodating various policies and benchmarks.
Scalable and efficient processing
Designed to handle over 20,000 instruments and generate 5,000+ reports monthly, the system was optimized for scalability. It processed large datasets efficiently, enabling timely and accurate reporting.
Consistent analytics and reporting
The system provided reliable sector and credit quality breakdowns, average durations, and holdings concentrations. It offered flexible reporting options, accommodating different benchmarks and data handling needs.
Agile development practices
We introduced agile practices including automated testing with TeamCity, continuous delivery, and frequent deployments. This improved development efficiency and quality, and was adopted across the firm through collaborative training and support.
Before
- Portfolio statistics were manually summarized and disseminated, leading to inefficiencies and inconsistencies.
- Data handling from various sources was disjointed and lacked standardization.
- Reporting was cumbersome, with limited automation and high manual intervention.
After
- Implemented a monthly process to automate the summarization and dissemination of portfolio statistics, including sector breakdowns, credit quality, and more.
- Created a streamlined system to handle diverse data sources, normalize data, and classify securities efficiently.
- Enabled the generation of over 5,000 reports monthly for various stakeholders, including the Lord Abbett board of directors.
- Introduced agile development practices, including automated testing and continuous delivery, enhancing overall efficiency and reliability.
Project workflow overview
Related case study
Efficient data management: optimizing Lord Abbett's ratings process
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