Case Study
Reengineering BI: From Legacy Logic to Scalable Data
Case Study
Reengineering BI: From Legacy Logic to Scalable Data
Case Study
Reengineering BI: From Legacy Logic to Scalable Data
Client
A leading provider of technology for financial content
Industry
Financial Software Wealth Management Investment Management Financial Services
Services
Data Strategy Transformation Analytics Strategy Data Warehouse
Project Duration
6 months
Client
A leading provider of technology for financial content
Industry
Financial Software Wealth Management Investment Management Financial Services
Services
Data Strategy Transformation Analytics Strategy Data Warehouse
Project Duration
6 months
Client
A leading provider of technology for financial content
Industry
Financial Software Wealth Management Investment Management Financial Services
Services
Data Strategy Transformation Analytics Strategy Data Warehouse
Project Duration
6 months
Problem
A leading provider of technology for financial content review and distribution partnered with Roko Labs to modernize their internal reporting system. Originally built as a simple BI tool, it had grown into a fragile, monolithic platform that was difficult to maintain and scale. Critical analytics and reporting logic were buried in application code and SQL queries, making them hard to trace, test, or reuse. Frequent schema changes in source systems caused data refresh failures, broke hard-coded formulas, and required manual fixes. The system couldn’t handle more than basic transformations, and performance issues were common during data refreshes or report generation. These limitations left the data inaccessible for advanced use cases like machine learning, automation, and real-time insights.
Our Vision
As the company helps hundreds of thousands of marketing and compliance financial professionals access, manage, and utilize financial content effectively each year, they wanted to take advantage of this data by surfacing powerful analytics on financial content trends and to make the data accessible for broader use cases such as machine learning, automation, and real-time insights. This initiative modernizes the company’s data architecture by integrating MySQL & PostgreSQL with an AWS-based data lake, Snowflake warehouse, and dbt for transformation. The project involved rewiring existing reports for accuracy and reliability, integrating multiple data streams to improve session tracking, and implementing a new pipeline, visualization, and reporting solution for better analytics.
Problem
A leading provider of technology for financial content review and distribution partnered with Roko Labs to modernize their internal reporting system. Originally built as a simple BI tool, it had grown into a fragile, monolithic platform that was difficult to maintain and scale. Critical analytics and reporting logic were buried in application code and SQL queries, making them hard to trace, test, or reuse. Frequent schema changes in source systems caused data refresh failures, broke hard-coded formulas, and required manual fixes. The system couldn’t handle more than basic transformations, and performance issues were common during data refreshes or report generation. These limitations left the data inaccessible for advanced use cases like machine learning, automation, and real-time insights.
Our Vision
As the company helps hundreds of thousands of marketing and compliance financial professionals access, manage, and utilize financial content effectively each year, they wanted to take advantage of this data by surfacing powerful analytics on financial content trends and to make the data accessible for broader use cases such as machine learning, automation, and real-time insights. This initiative modernizes the company’s data architecture by integrating MySQL & PostgreSQL with an AWS-based data lake, Snowflake warehouse, and dbt for transformation. The project involved rewiring existing reports for accuracy and reliability, integrating multiple data streams to improve session tracking, and implementing a new pipeline, visualization, and reporting solution for better analytics.
Problem
A leading provider of technology for financial content review and distribution partnered with Roko Labs to modernize their internal reporting system. Originally built as a simple BI tool, it had grown into a fragile, monolithic platform that was difficult to maintain and scale. Critical analytics and reporting logic were buried in application code and SQL queries, making them hard to trace, test, or reuse. Frequent schema changes in source systems caused data refresh failures, broke hard-coded formulas, and required manual fixes. The system couldn’t handle more than basic transformations, and performance issues were common during data refreshes or report generation. These limitations left the data inaccessible for advanced use cases like machine learning, automation, and real-time insights.
Our Vision
As the company helps hundreds of thousands of marketing and compliance financial professionals access, manage, and utilize financial content effectively each year, they wanted to take advantage of this data by surfacing powerful analytics on financial content trends and to make the data accessible for broader use cases such as machine learning, automation, and real-time insights. This initiative modernizes the company’s data architecture by integrating MySQL & PostgreSQL with an AWS-based data lake, Snowflake warehouse, and dbt for transformation. The project involved rewiring existing reports for accuracy and reliability, integrating multiple data streams to improve session tracking, and implementing a new pipeline, visualization, and reporting solution for better analytics.
Solution
Using an open-source visual database display tool allows for rapid development and iteration on charts and graphs.
Solution
Using an open-source visual database display tool allows for rapid development and iteration on charts and graphs.
Solution
Using an open-source visual database display tool allows for rapid development and iteration on charts and graphs.
We selected Superset, an open-source visualization tool, to replicate existing Analytics reports.
Superset offers built-in features like filtering and drill-downs, adding interactivity previously unavailable.
It’s also easy to configure, enabling faster and more efficient delivery of updates and new visualizations without formal development releases.
Technology
The global data architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML), allowing the company to centrally govern, secure, and share data.
Technology
The global data architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML), allowing the company to centrally govern, secure, and share data.
Technology
The global data architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML), allowing the company to centrally govern, secure, and share data.
Company admins are now able to manage access permissions to the data lake
Scaled management simplifies security and governance
Team can gain insights from data securely shared with internal and external users
The system monitors data access and helps achieve compliance along with comprehensive auditing
01.
Global Data Architecture
02.
Architecture Details
03.
Orchestration Details
04.
Data Warehouse: Raw Layer
05.
Data Warehouse: Cleaned & Modeled Layers
06.
Data Warehouse: Transformation Layers
This architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML)
01.
Global Data Architecture
This architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML)
02.
Architecture Details
03.
Orchestration Details
04.
Data Warehouse: Raw Layer
05.
Data Warehouse: Cleaned & Modeled Layers
06.
Data Warehouse: Transformation Layers
01.
Global Data Architecture
02.
Architecture Details
03.
Orchestration Details
04.
Data Warehouse: Raw Layer
05.
Data Warehouse: Cleaned & Modeled Layers
06.
Data Warehouse: Transformation Layers
This architecture unifies information architecture and data governance across data consumers (the core application, internal BI, and AI/ML)
The Results
The results delivered provide significant value across the businesses’ data operations. By increasing strategic agility, they can react more quickly to changes in the market and shifts in data, ensuring decisions are timely and well-informed. Greater operational efficiency reduces maintenance requirements, speeds up report development, and lowers overall development costs. At the same time, stronger risk mitigation practices lessen reliance on fragile or outdated systems, minimizing disruption. Improved data quality ensures consistency, accuracy, and transparency, which directly supports better planning and execution. Finally, user empowerment gives teams self-service access to explore and act on data, fostering independence and faster problem-solving on the warehouse floor.
26
Customer reports updated and data sources refreshed from new pipeline.
75%
Faster report creation time relative to the former, monolithic system.
$10M+
Revenue opportunity in 2026 for the new Analytics product.
The Results
The results delivered provide significant value across the businesses’ data operations. By increasing strategic agility, they can react more quickly to changes in the market and shifts in data, ensuring decisions are timely and well-informed. Greater operational efficiency reduces maintenance requirements, speeds up report development, and lowers overall development costs. At the same time, stronger risk mitigation practices lessen reliance on fragile or outdated systems, minimizing disruption. Improved data quality ensures consistency, accuracy, and transparency, which directly supports better planning and execution. Finally, user empowerment gives teams self-service access to explore and act on data, fostering independence and faster problem-solving on the warehouse floor.
26
Customer reports updated and data sources refreshed from new pipeline.
75%
Faster report creation time relative to the former, monolithic system.
$10M+
Revenue opportunity in 2026 for the new Analytics product.
The Results
The results delivered provide significant value across the businesses’ data operations. By increasing strategic agility, they can react more quickly to changes in the market and shifts in data, ensuring decisions are timely and well-informed. Greater operational efficiency reduces maintenance requirements, speeds up report development, and lowers overall development costs. At the same time, stronger risk mitigation practices lessen reliance on fragile or outdated systems, minimizing disruption. Improved data quality ensures consistency, accuracy, and transparency, which directly supports better planning and execution. Finally, user empowerment gives teams self-service access to explore and act on data, fostering independence and faster problem-solving on the warehouse floor.
26
Customer reports updated and data sources refreshed from new pipeline.
75%
Faster report creation time relative to the former, monolithic system.
$10M+
Revenue opportunity in 2026 for the new Analytics product.
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Have a similar task or project? Let's talk about it!
Copyright © 2025 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2025 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2025 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001
Copyright © 2025 Roko Labs Inc.
All rights reserved.
1250 Broadway, 36th Floor, New York, NY, 10001