Delivering intelligence with speed and scale

Delivering 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.




    Delivering intelligence with speed and scale

    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.

    Download Perspective Paper




      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Data warehouse enhances client communications

      Data warehouse enhances client communications

      Asset Management

      Investment data warehouse enhances client communications

      Account management and marketing teams of an investment bank acquired improved multi-channel client communications and portfolio management capabilities with a data warehouse and a single source of truth.

      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

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Cloud Data Lakehouse for Single Source of Truth

      Cloud Data Lakehouse for Single Source of Truth

      Life Sciences

      Cloud Data Lakehouse for Single Source of Truth

      Data systems built on legacy technologies were delaying month-end activities and actionable insights for finance and sales teams. Transformation of data and BI applications to MS Azure delivered "Order to Cash" sales analytics on cloud.

      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

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Platform re-engineering for operational efficiency

      Platform 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

      The client had a licensed data platform for enterprise-wide risk and compliance operations. Spiked volumes with various financial product offerings and trades were restricting the processes and limiting the analytical capabilities on the existing platform. The system upgrade was required to support related, complex credit risk calculations. These calculations serve as a ground for several thousand bankers/ traders to make loan and investment decisions for customers. System modernization would also cater to the internal transaction and regulatory reporting requirements.

      SOLUTION

      Iris system re-engineering experts designed and implemented a scalable and highly configurable data extraction platform having global data architecture. This ETL framework-based platform enables faster, more efficient onboarding, consolidation, and processing of the numerous variable product and trading data input sources. The re-engineered platform was enabled with value-adds and tools to automate, tabulate, compare, reserve, validate and test data. We integrated the data extraction platform seamlessly with downstream risk applications and system adaptability to accommodate operational/business needs.

      OUTCOMES

      Our data platform re-engineering solution enabled the client to achieve enormous benefits, including user experience, data quality, and risk management capabilities. Key outcomes of the solution constitute:
      • 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

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      SFTR solution strengthened market leadership

      SFTR solution strengthened market leadership

      Risk & Compliance

      Securities Financing Transactions Regulation (SFTR) compliance made easy

      A global trading company solidifies its EU market leadership with regulatory solution and supporting to a throughput of 6 million transactions per hour

      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

      The client offers an automated, integrated solution to its clients in the European Union (EU) for complying with the Securities Financing Transactions Regulation (SFTR). Effective in recent years, SFTR requires timely and detailed reporting based on multitudes of data, systems, collateral, and lifecycle events. The voluminous data is captured from hundreds of millions of daily transactions made to multiple trade repositories registered by the European Securities and Markets Authorities (ESMA). Non-compliance at any stage is risky, potentially very costly, for all trade counterparties, i.e., broker-dealers, banks, asset managers, institutional investors.

      SOLUTION

      Experienced in diverse technologies, big data, and capital markets, team Iris developed a streamlined, end-to-end data reporting platform with complex trade matching and monitoring systems. Improving speed, accuracy, and flexibility, the new architecture supports high trade concurrency and acceptance rates with parallel processing of millions of transactions. The delivered solution also enabled optimal load balancing and matched the reconciliation at the trade repository. Built with microservices to accommodate future scalability, standardization, data quality, and security requirements, the system implemented functional enhancements. A Unique Transaction Identifier (UTI) subsystem was also developed for sharing and matching counterparty transactions, enabling plug-and-play setup for new repositories, and supporting any changes in outbound or inbound data report formats required by ESMA or clients. Improved dashboards and search pages helped the end-users in better configuration and tracking of their transactions.

      OUTCOMES

      The nimble delivery and successful roll-out of the new SFTR platform delivered the desired strategic competitive advantage to the client for maintaining its EU market leader position. The consolidated solution also helped in:
      • 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.

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Reporting transformation with data science and AI

      Reporting transformation with data science and AI

      Banking

      Data science and AI transform disclosure and reporting

      A multinational bank leveraged data automation to achieve major gains in reporting efficiency, with 99% accuracy in processing variable inputs, for its global investment fund.

      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

      The high solution accuracy helped the client’s global NAV reporting team significantly improve precision, efficiency, quality, turnaround time, and flexibility. The delivered solution contributed to:
      • 90 - 95% reduction in operational efforts
      • 99% accuracy in processing variable inputs
      • Zero rework effort and cost
      Our highly customizable and scalable solution can be seamlessly integrated with existing reporting applications and MS Outlook while accommodating additional volumes, report types, and business units.

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      Powering shop floor efficiency with data analytics

      Powering shop floor efficiency with data analytics

      Manufacturing

      Powering efficiency on the shop floor

      An Iris innovation helped a diesel engine manufacturer eliminate production bottlenecks.

      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.

      Related Stories

      Gen AI interface enhances API productivity and UX

      Integrating Generative AI technology into a leading logistics provider’s developer portal reduces API onboarding to 1-2 days.

      Learn more

      Data warehouse enhances client communications

      Bank’s multi-channel client communications and portfolio management capabilities improved with investment data warehouse

      Learn more

      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.

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      How to transform your risk reporting mechanisms

      How to transform your risk reporting mechanisms

      Capital Markets

      How to transform your risk reporting

      A leading brokerage firm improved its UI and lowered costs with a future-ready risk reporting platform.

      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

      The client's market risk reporting and limit monitoring platform was based on products that were reaching the end of their service lines in the foreseeable future — Microsoft's Silverlight for viewing rich content and IBM's Netezza for data warehousing. They wanted to move to a new-technology platform. Among the big challenges was a lack of user-friendliness, a high cost of ownership because of the maintenance needed, and a lack of scalability because the data could not be clustered. The existing systems did not enable efficient audit trails and tracking of users. Iris had to identify alternatives that would sit well with 55 other applications in the system.

      SOLUTION

      We considered building a visualization platform using the latest JavaScript frameworks such as Angular or React but settled on making a fresh user interface and UI framework on HTML5. We developed new UI widgets to provide better user experience, making it possible for users to customize their workspace. We integrated the module to manage a user’s role and access level. In all, we provided a modern, flexible interface for application deployment that was developed in-house.

      OUTCOMES

      We successfully moved all the 55 applications to the new platform. As a result, the total cost of ownership was expected to be 15% lower after the migration. It was also built for the future — a distributed, scalable, mobile-ready platform. It had an integrated module for managing user roles and access levels and could be customized with various themes to provide better user experience. User tracking and audit trails were enabled. 15% Lower total cost of ownership 55 Apps moved to new platform

      Related Stories

      Next generation chatbot eases data access

      Gen AI tools help users find information related to specific needs and complex queries.

      Learn more

      Data warehouse enhances client communications

      Bank’s multi-channel client communications and portfolio management capabilities improved with investment data warehouse

      Learn more

      Brokerage platform transformation improves UX

      Monolithic brokerage platform transformed into microservices helps leading brokerage firm achieve improve operations and UX.

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      An agile sprint for financial data

      An agile sprint for financial data

      An agile sprint for financial data

      How Iris helped a mega sportswear brand’s global operations and financial reports go flexible, agile, and analytical.

      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

      The client’s finance department was using standard SAP reports which limited the flexibility to slice and dice data or add fields to reports. Modifying or creating new reports was either difficult or expensive. Top management, including the CFO and financial controllers, were finding it difficult to create a high-level, integrated view of the company’s finances. The existing process required data transposition between various systems, including the SAP and Oracle systems. Much of this data was extracted and consolidated manually, which was time-consuming, and took around a week.

      SOLUTION

      Iris executed a distributed Agile framework for the client’s global delivery model. Our solution pulled data out of the client’s SAP ERP system using Dell Boomi adapters, and leveraged SSIS (SQL server integration services) to transform it into enriched data. This data was mapped and made actionable through interactive PowerBI Tableau dashboards. With the help of a custom-made finance data model, a data warehouse was created. The easily shareable data cubes not only replaced all legacy reports, but also reduced the number of SAP user licenses.

      OUTCOMES

      With the availability of Power BI dashboards and the capability to slice-and-dice financial data, client managers now have a better view of operations and accounting flows. The data consolidation allows users to create need-based reports without additional licenses. The automation of the entire process from data extraction and transformation to publishing of analytical cubes has enabled the client teams to significantly reduce time required to produce reports – from days to a few minutes. They have been able to achieve a 95% reduction in time and effort.

      Related Stories

      Data warehouse enhances client communications

      Bank’s multi-channel client communications and portfolio management capabilities improved with investment data warehouse

      Learn more

      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.

      Learn more

      Platform re-engineering for operational efficiency

      A leading brokerage bank improved operational efficiencies with data extraction platform modernization.

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch

      A playbook for banks on managing M&A integration

      A playbook for banks on managing M&A integration

      Banking

      A playbook for banks on M&A integration

      Efficient management of the complexities of disparate systems and data after an acquisition saves time and money.

      Client

      Banks that have merged or acquired new businesses.

      Goal

      Manage migration and integration complexity post M&A.

      Tools and technologies

      The Iris business acquisition playbook for banks.

      BUSINESS CHALLENGE

      In a low-interest rate regime, achieving scale is the only way for banks to stay profitable. The top 25 banks are growing at a rate faster than rest of the pack. The search for profitability from scale is predicated upon their ability to ensure that operational costs do not grow linearly with business. A significant part of this growth will come inorganically. Apart from M&As, brownfield expansion comes with banks selling off their books of business for reasons ranging from realigned strategic priorities to the more mundane need of raising cash. Any IT costs in absorbing the new book of work will negate the advantages of size.

      Solution

      Iris has been working with banking clients to create a business acquisition playbook outlining steps to insource with a migration and integration strategy. We have enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. The Iris team delivered solutions specifically tailored for applications in the loan origination and servicing space. We have helped our banking clients:
      • Consolidate multiple acquisition playbooks to create a single standardized framework for their lending business
      • Define insourcing steps for business and technology teams and create a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
      • Assess capability and readiness and help them choose from insourcing options:
      • Achieve full migration of data and systems
      • Achieve partial migration of systems and data migration and integration
      • Manage data integration and connectivity for lending business.

      Outcomes

      We have helped clients achieve 50% savings in cycle time and cost for post-merger integration of business processes, application and data.

      Related Stories

      Gen AI summarization solution aids lending app users

      Custom summarization solution using Gen AI eases lenders’ information access, complex app usage, and new user onboarding.

      Learn more

      Conversational assistant boosts AML product assurance

      Gen AI-powered responses enhance the operational efficiency of the AML global product assurance team and reduce cost.

      Learn more

      Automated financial analysis reduces manual effort

      Analysts in commercial lending and credit risk units are able to source intelligent information across multiple documents.  

      Learn more

      Contact

      Our experts can help you find the right solutions to meet your needs.

      Get in touch
      Copyright © 2024 Iris Software, Inc. All rights reserved