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Optimizing data processes: Lord Abbett's reporting system upgrade

Optimizing data processes: Lord Abbett's reporting system upgrade

Finance

Optimizing data processes: Lord Abbett's reporting system upgrade

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.

Region

New Jersey, United States of America

Project team

3 Developers

3 Developers

Tech stack

Java

Java

Python

Python

Azure SQL Server

Azure SQL Server

Batch Script

Batch Script

Perl

Perl

Hibernate

Hibernate

TeamCity

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.

stocks

~$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

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

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

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

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

workflow
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