Iris Software will participate in the exclusive, attendance-capped, annual Leadership Summit hosted by the NC Tech Association, along with its board of directors and advisors, on August 7 & 8, 2024. Our representative, Senior Client Partner, Michel Abranches, will be among the executives gathering for the Summit, at the Pinehurst Resort in Pinehurst, NC, to network and discuss a variety of topics relevant to tech leaders and the projects and associates they manage.
The theme of this year’s summit is Adaptive Leadership. The event includes keynote addresses, executive workshops, and two panel discussions on ‘Why digital transformation is more about people than technology’ and ‘Building resilient tech teams: the power of emotional intelligence.’
As a technology provider to Fortune 500 and other leading global enterprises for more than 30 years, Iris is a trusted choice for leaders who want to realize the full potential of digital transformation. We deliver complex, mission-critical software engineering, application development, and advanced tech solutions that enhance business competitiveness and achieve key outcomes. Our agile, collaborative, right-sized teams and high-trust, high-performance, award-winning culture ensure clients enjoy top value and experience.
Contact Michel Abranches, based in our Charlotte, NC office, or visit www.irissoftware.com for details and success stories about our innovative approach and how we are leveraging the latest in AI / Gen AI / ML, Automation, Cloud, DevOps, Data Science, Enterprise Analytics, Integrations, and Quality Engineering.
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Get in touchDelivering intelligence with speed and scale
Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle in cloud.
Do you trust your data?
Data driven organizations are ensuring that their Data assets are cataloged and a lineage is established to fully derive value out of their data assets.
Through a number of digital initiatives over the past decade, organizations have collated a lot of information. In addition to structured data, they are collating unstructured and semi-structured formats, e.g., digitized contracts and audio/video of customer interactions. The opportunities to apply established and emerging AI/ML techniques and models to this wide variety of information and derive intelligence and enhanced insights have significantly increased.
Cloud and the evolving technologies around Data Engineering, Data & ML Ops, Data Science, and AI/ML (e.g., Generative AI) offer a significant opportunity to overcome the limitations and deliver intelligence with speed and at scale. While the number and sophistication of AI/ML models available have increased and become easier to deploy, train/tune, and use, they need information at scale to be transformed to features. Delivering intelligence in scale would require more than just data lakes and lake-houses. It also requires the overall ability to support multiple modeling/data science teams working on multiple problems/opportunities concurrently. Data Science Engineering and Data & ML Ops are key to enable scaling of the intelligence part of the data monetization lifecycle. Teams need to understand data science/modeling lifecycles to effectively scale intelligence.
In conclusion, organizations demand intelligence in scale and at speed. Emerging technologies like Generative AI demand more powerful infrastructure (e.g., GPU farms). Cloud technologies and services enable these. With support for Python across the intelligence lifecycle, it has become easier to bring together data engineering and data science teams that are easier to provision and use in cloud.
To know more about the benefits, challenges, and best practices for scaling various stages of deriving intelligence from data on cloud environments, read the perspective paper here.
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Get in touchData 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
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Get in touchCloud Data Lakehouse for Single Source of Truth
Client
A medical devices and fertility solutions company
Goal
Establish a cloud data warehouse for a single source of truth and timely month-end activities
Tools and technologies
Azure Data Factory, Azure Data Lake, Power BI, Synapse Analytics
BUSINESS CHALLENGE
The client had multiple instances of ERPs, sales systems, and warehouses built on obsolete technology and frameworks. The existing system siloed the data, resulting in inconsistent versions of the truth. The client's finance and sales teams were struggling to reconcile data offline and feed the same back into the ERPs, causing significant delays in month-end activities. As the record systems were also not synced and legacy reports were built on the interim warehouses, line managers and executive teams were not able to extract actionable and comprehensive insights. A solution to onboard and integrate new datasets on an ongoing basis was required to support the data merger and acquisition process.
SOLUTION
A transformation strategy to transform data and BI applications to MS Azure was finalized. The transformation was executed in phases and included discovery, report rationalization and foundational build of a global system of reporting.
The solution included the data ingestion process with Azure Data Factory, data storage and processing using Azure Data Lake and Synapse Analytics, reports and dashboards with Power BI. A utility to accelerate the onboarding of new data entities was conceptualized and delivered to onboard and integrate new datasets to support mergers and acquisitions.
OUTCOMES
Iris data practitioners helped the client overcome key challenges and advanced data warehousing capabilities by:
- Establishing a "single version of the truth" that enabled data-driven decisions and timely completion of month-end and other critical activities
- Delivering analytics to cash business processes, including subject areas of sales, inventory, shipping, finance, etc., on Power BI
- Facilitating the generation of actionable, insightful reports and dashboards, allowing "self-service" consumption for the business leadership
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Get in touchPlatform re-engineering for operational efficiency
Banking & Financial
Re-engineering data extraction platform for increased efficiency
A legacy data extraction platform was limiting business efficiency and transaction processing capabilities. Iris system modernization and platform re-engineering platform services advanced the operational efficiency manifold.
Client
One of the top 20 brokerage banks in North America
Goal
Modernize an existing, licensed data platform to meet the increasing volume of transactions and product offerings
Tools and technologies
Python, Core Java, Oracle, ETL Framework, Apache Zookeeper, Anaconda, Maven, Bamboo, Sonar, Bitbucket
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Quicker, real-time configuration and execution of 500+ jobs for loading trade feed
- Zeroed downtime, even during the trade reference data changes
- 15% faster onboarding of the new feed or data source
- Nearly 20% faster throughput for various critical feeds with parallel processing feature
- Reduced anomalies and duplication with improved consistency
- 35-40% savings in annual third-party platform/ module license fees.
- Standardized and streamlined onboarding processes and turnaround time, scaling the operations efficiencies
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Get in touchSFTR solution strengthened market leadership
Client
A leading provider of market data and trading services
Goal
Support complex regulatory reporting with automated solution
Tools and technologies
Java, Spring Boot, Apache Camel, CXF, Drools BRE, Oracle, JBoss Fuse, Elasticsearch, Git, Bitbucket, Sonar, Maven
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
- Generating additional revenue from extending the new reporting services to 17 firms.
- Beating the industry benchmark (~91%), achieving a higher transaction acceptance rate (~97%), and match reconciliation at the trade repository.
- Supporting a high throughput of 6 million transactions per hour which is scalable up to 10 million.
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Get in touchReporting transformation with data science and AI
Client
One of the world's leading bank
Goal
Improve efficiency in disclosure and reporting
Tools and technologies
Python – SciPy, Pytesseract, NumPy, Statistics
BUSINESS CHALLENGE
The client relies upon a centralized operations team to produce monthly NAV (Net Asset Value) and other financial reports for its international hedge funds— from data contained in 2,300 separate monthly investment fund performance reports. With batch receipts of rarely consistent file formats – PDF, Excel, emails, and images— the process to read each report, capture key info, and, create and distribute new metrics using the bank’s traditional tools and systems was highly manual, time-consuming, error-prone, and costly.
SOLUTION
Iris developed a Data Science solution that rapidly and accurately extracts tabular data from thousands of variable file documents. Using a statistical, AI-based algorithm featuring unsupervised learning, it auto-detects, construes, and resolves issues for every data point, configuration, and value. Complex inputs are calculated, consolidated, and mapped as per predefined templates and downstream business needs, efficiently generating numerous, distinct, and required period-end financial disclosures.
OUTCOMES
- 90 - 95% reduction in operational efforts
- 99% accuracy in processing variable inputs
- Zero rework effort and cost
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Get in touchPowering shop floor efficiency with data analytics
Client
A leading diesel engine manufacturer
Goal
Reduce bottlenecks on the production line that arise from last-minute changes to orders and ensure compliance with build instructions
Tools and technologies
Windows, SQL Server, C#, .NET, ESB, HTML5, Angular, GitHub, JIRA, Visual Studio, and WebStrom
BUSINESS CHALLENGE
A diesel engine manufacturer based in Detroit faced frequent production delays. The cause of the inefficiency was its build book system. The manufacturer used a printed build book to communicate the specifications of the engine being built to the production floor. But, often after the book was sent to the shop floor, the manufacturer had to make changes to specifications. In such cases, those working on the production line would not be able to use the printed build book. Waiting for a reprinted book would halt production. As a result, the changes were usually communicated outside the manual and assumed to be followed. If the new specifications weren’t followed, they would be discovered only in quality assurance, leading to a loss of time and dollars.
SOLUTION
The client wanted a solution to resolve bottlenecks created by the printed build book and ensure compliance with build instructions. Ideally, the build book is dynamically pushed onto a handheld device assigned to the shop floor. The system would allow managers to update the specifications in the build book on the fly and alert the production team to the changes.
The device would also communicate the status of production to managers. For example, they would know which work center is working on an engine so that relevant pages of the build book could be updated and displayed to those work centers.
Iris custom-built an application that allowed real-time updates of the build book. It was designed to push the build book to work center operators on V10 devices (RFID transponders) with screen sizes ranging from 3 inches to 10 inches. The solution included a consolidated dashboard that provided the management near real-time visibility of work centers and the status of the engine production.
OUTCOMES
During Phase 1 of the project, we deployed 250 V10 devices. After a pilot run of four weeks, the client stopped printing build books; the handheld devices with our application were a superior alternative. The solution helped eliminate printing costs and allowed the manufacturer to accommodate last-minute changes in specifications without disrupting production.
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Get in touchHow to transform your risk reporting mechanisms
Client
The client was a brokerage firm with a strong presence in the capital markets.
Goal
Improve risk reporting and calculations.
Tools and technologies
Dot Net, C#, Greenplum, JUX Proprietary Framework and HTML5.
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
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Monolithic brokerage platform transformed into microservices helps leading brokerage firm achieve improve operations and UX.
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Get in touchAn agile sprint for financial data
Client
Among the world’s largest manufacturers of sportswear, the client sells its products in more than 120 countries and employs more than 13,000 people.
Goal
To significantly reduce turnaround time and ease associated with report creation.
Tools and technologies
Microsoft SQL Server’s Analysis Services (SSAS), Microsoft SQL Server Integration Services (SSIS), Microsoft SQL Server Reporting Services (SSRS), Boomi AtomSphere, and Power BI.
BUSINESS CHALLENGE
SOLUTION
OUTCOMES
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Bank’s multi-channel client communications and portfolio management capabilities improved with investment data warehouse
Cloud Data Lakehouse for Single Source of Truth
Transformation of data and BI applications to MS Azure cloud deliveres “Order to Cash” sales analytics on cloud for a life sciences company.
Platform re-engineering for operational efficiency
A leading brokerage bank improved operational efficiencies with data extraction platform modernization.
Contact
Our experts can help you find the right solutions to meet your needs.
Get in touch