Optimized data and on-demand analytics deliver faster business insights and better user experience for asset management firm
Client
U.S.-based asset management company
Goal
Streamline and improve data and analytics capabilities for enhanced user experiences
Technology Tools
Java, React JS, MS SQL Server, Spring Boot, GitHub, Jenkins
Business Challenge
The client captures voluminous data from multiple internal and external sources. The absence of quick, on-demand capabilities for business users was inefficient in generating customized portfolio analytics on attributes such as average quality, yield to maturity, average coupon, etc.
The client teams were spending enormous amounts of manual effort and elapsed time (approximately 12-15 hours) to respond to requests for proposals from their respective clients.
Solution
Iris implemented a data acquisition and analytics system with pre-processing capabilities for grouping, classifying, and handling historical data.
A data dictionary was established for key concepts, such as asset classes and industry classifications, enabling end users to access data for analytical computation. The analytics engine was refactored, optimized, and integrated into the streamlined investment performance data infrastructure.
The team developed an interactive self-service capability, allowing business users to track data availability, perform advanced searches, generate custom analytics, visualize information, and utilize the insights for decision-making.
Outcomes
The solution brought several benefits to the client, including:
- Simplified data access to generate custom analytics for end users
- Eliminated manual processing and the need for complex queries
- Enhanced the stakeholder experience
- Reduced response time to client RFPs by over 50%
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Data warehouse enhances client communications
Client
A U.S.-based investment bank
Goal
Improve data collation and information quality for enhanced marketing and client reporting functions
Tools and technologies
Composite C1, Oracle DB, PostgreSQL, Vermilion Reporting Suite, Python, MS SQL Server, React.js
BUSINESS CHALLENGE
The client’s existing investment data structure lacked a single source of truth for investment and performance data. The account management and marketing teams were making significant manual efforts to track portfolio performance, identify opportunities and ensure accurate client reporting. The time-consuming and manual processes of generating marketing exhibits and client reports were highly error-prone.
SOLUTION
Iris implemented a comprehensive investment data infrastructure for a single source of truth and improved reporting capabilities for marketing content and client report generation. An automated Quality Assurance process was instituted to validate the information in critical marketing materials, such as fact sheets, snapshots, sales kits, and flyers, against the respective data source systems. Retail and institutional portals were developed to provide a consolidated view of portfolios, with the ability to drill down to underlying assets, AUM (Assets Under Management) trends, incentives, commissions, and active opportunities.
OUTCOMES
The new data infrastructure delivered a holistic, on-demand view of investment details, including performance characteristics, breakdowns, attributions, and holdings, to the client's marketing team and account managers with:
- ~95% reduction in performance data and exhibit information discrepancies
- ~60% improvement in operational efficiency in core marketing and client reporting functions
Contact
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Get in touchDeliver personalization via report automation
Client
A leading asset management firm based in the U.S.
Goal
Help asset managers deliver personalized solutions to establish differentiation.
Tools and technologies
AquaData Studio, Java, Perl, Python, Spring, Hibernate, VRS, PostgreSQL, Composite and MS SQL.
BUSINESS CHALLENGE
- Its front, mid and back office functions needed a lot of manual effort
- Business rules were inconsistent and data duplication was rampant
- User experience on the platform needed significant improvement
- Clients were unable to get a holistic view of their accounts
- Data validation was consuming a lot of manhours
SOLUTION
- Our team streamlined and integrated the client’s front, middle and back office functions. We helped the client integrate their back-office solutions with their custodians, reducing complexity in information exchange, eliminating reconciliation and increasing operational efficiency by more than 75%.
- We automated the creation of more than 7,000 reports.
- Improved experience for retail and institutional clients by automating the generation of complex compliance and strategic reports.
- Developed a strategic reporting module that gave customers a holistic view of their accounts and holdings.
- Set up a business data validation team offshore.
- Enabled self-service option for bespoke reports.
OUTCOMES
- Automated the exhibits process with 75% increase in throughput
- Our report automation solution reduced manual effort by 70% and improved monthly artefact generation throughput by 40%
- Reduced manual effort for customization in client profile management by 60%
- Achieved $50,000 savings monthly in data validation for client profile management
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Get in touchGain speed to market with DevOps solutions
Client
The client is a leading wealth management firm in the U.S.
Goal
DevOps strategy to shorten production timelines and speed time-to-market.
Tools and technologies
JIRA, Jenkins, GitHub, AWS, ECR, Docker, EKS and Helm.
BUSINESS CHALLENGE
The client used multiple legacy applications with a highly complex codebase to run its operations. As a result, it had long production lifecycles and spent several person-hours in integration and deployment.
On the technology front, the client faced challenges in the way server-side applications were defined, stored and managed. Its IT team also had to manage the deployment of multiple Kubernetes manifest files.
SOLUTION
Iris recommended that the client move to a microservices ecosystem. Here’s how we deployed the solution:
- We defined an enterprise-level DevOps strategy using Helm
- Identified the scope of apps that needed to be on-boarded across the enterprise
- Implemented a DevOps pipeline for microservices on the Kubernetes cluster
- Deployed the infrastructure, dependencies and applications with Kubernetes using Helm
- Delivered continuous improvement through Helm release updates and rollbacks
OUTCOMES
The DevOps pipeline significantly improved time-to-market for new releases.
- 20x faster release cycle
- 40% improvement in quality with end-to-end traceability
- 15x improvement in the mean time to deployment (MTTD)
We also put in place robust security control and validation processes; and provided for auditable release requests.
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