LGIM
Integration Of The Medallion Architecture For LGIM
LGIM, a global investment firm, faced significant challenges in managing and integrating large volumes of data from multiple sources, including APIs, databases, and file systems. Data fragmentation, inconsistent quality, and scalability issues were key obstacles that hindered their ability to extract actionable insights efficiently. Symufolk partnered with LGIM to streamline its data processes and ensure better decision-making capabilities.
Total Hours
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Overall Efficiency
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Reduced Operations Cost
0
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Satisfaction Rate
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Challenges
- Data Fragmentation: Data was siloed across various systems, preventing a unified view and hindering decision-making processes.
- Quality and Consistency Issues: Data varied in formats, often containing errors, reducing reliability for analytics and decision-making.
- Scalability Issues: The existing infrastructure struggled to keep pace with growing data volumes, delaying data processing and reporting.
- Restricted Access: Business users had limited access to clean, organized data.
- Slow Insights: This made it challenging to quickly gain insights and take meaningful action.
Solution
It optimized LGIM’s data management by using Medallion Architecture and NiFi. It unified data integration through APIs, databases, and files into a pipeline and had automated workflows to clean and standardize the data. The architecture had robust error handling, thus minimizing downtime with continuous operations.
It gave the Medallion Architecture structure, such that the Bronze layer would contain raw data to trace; the Silver would have cleaned and enriched data for analytics; and finally, the Gold would present curated data for business insight. Furthermore, the development process was set up to scale the flow of data.
It gave the Medallion Architecture structure, such that the Bronze layer would contain raw data to trace; the Silver would have cleaned and enriched data for analytics; and finally, the Gold would present curated data for business insight. Furthermore, the development process was set up to scale the flow of data.
our input
Python
GraphQL
Node.js
NestJS
PHP
Microsoft Net
RESULTS
Our approach focuses on enhancing governance, accessibility, and performance to deliver measurable improvements.
Our approach focuses on enhancing governance, accessibility, and performance to deliver measurable improvements.
100%
100%
Enhanced Data Governance
100% traceability of data from ingestion to output, improving compliance and data lineage tracking.
90%
90%
Improved Accessibility
90% increase in data availability for business users, reducing reliance on IT teams for data access.
50%
50%
Optimized Performance
Reduction in manual processes, leading to faster, more accurate data availability.
how we did it
85% Faster Processing of Data
Streamlined workflows with lesser human intervention for faster insights.
Data Accessibility
All users have better availability of data across the organization.
Efficient Automation
Minimized manual processes, increasing speed and consistency.
Standardized Data
Cleaned and transformed data to make it uniform and reliable.
Enhanced Analytics
Reporting and analytics made easier with better data quality.
95% Data Accuracy
Reliable insights through clean, accurate data.
Technologies
WE USED
Creation Process
Data Ingestion
NiFi was used to collect data from diverse sources, including APIs, databases, and flat files, ensuring seamless integration.
Data Standardization
In the Silver Layer, data was automatically cleaned, transformed, and standardized for consistency across all sources.
Data Curation
The Gold Layer refined and optimized datasets, making them ready for business use with easy access and actionable insights.
Error Handling
A robust error-handling framework ensured issues were quickly flagged, logged, and resolved, maintaining smooth operations.
Data Governance
Data lineage and traceability were maintained across all layers, enhancing governance and compliance.