An automated cloud-based consolidation application for R&D data helped a pharmaceutical company improve turnaround time to create Approval for Product Release (APR) documents.
Client
A U.S.-based pharmaceutical multinational corporation
Goal
Reduce turnaround time for APRs
Tools and Technologies
Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming
Business Challenge
In pharmaceutical R&D, data is generated from several sources: the process, patients, retailers, and caregivers, among others. Pharmaceutical R&D organizations that use the traditional way of creating APRs manually consolidate paper specifications into binders across all R&D functions.
Specific regional rules, compliance mandates, and external regulations were slowing down the client’s workflow. Many spreadsheets in multiple formats were leading to errors from manual entry and duplication of data — the inevitable “swivel effect” that results from data being pulled out from disparate, unconnected software packages.
Iris was approached to improve the process of collecting and using data from multiple sources; the improvement would help the client identify and develop new potential drug candidates faster.
Solution
Iris’s team of 12 specialists designed, developed, tested, and deployed a cloud-based application that integrates data from multiple regions and eight different systems into a single, unified interface for the client’s users. Our application unified the creation and management of the client’s workflows across its lines of business and 20 different product families.
The development environment included Amazon’s AWS OPCx, Webmethods, natural language processing (NLP), neural networks, and Python programming.
Outcomes
Within a year of the application’s release, 2,800 users were using the application, with 55% of APRs turning around in 10 calendar weeks or less. Thanks to the in-memory data grid, the response time of transactions across the board has been brought down to nearly 2 seconds.
The cloud-based application developed by Iris ensures that data is automatically and seamlessly shared between systems that were previously stand-alone and required the tedious manual entry of data.
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Cloud-native app opens new markets for trading services
A prominent bonds trading network expands its market reach with new products and geographies.
Client
The world’s leading provider of trading services for fixed income products
Goal
Create an IT architecture to support growth across markets and products
Tools and Technologies
AWS Cloud, Java, Springboot, React JS, React, Redis, Kafka, C#, Ranorex and Test Rails
Business Challenge
The client, a market leader in bonds trading, was expanding to new markets, acquiring new businesses, introducing new products and adding features to existing offerings. To support its growth plans, it needed an agile, modern, cloud-based platform.
Some of the business needs the client wanted to address with the new solution were:
- How do we achieve scale with minimal latency in operations and service?
- How do we integrate new businesses seamlessly and without disruption?
- How do we roll out new features faster to improve customer experience and get a competitive edge?
- How can we use data to help customers make better trading decisions?
- How can we monetize the data?
As a solution partner, we had to not only create a new IT architecture for the client’s trading platform but also constantly re-engineer and improve the architecture to quickly meet emerging business needs.
Solution
We deployed a scalable, highly available auctions solution on the AWS cloud using Java, Springboot, React JS, React, Redis, and Kafka.
Optimized algorithms now achieve best matching with minimal latency while offering full price transparency. Artificial intelligence (AI) and machine learning (ML) provide greater insight and real-time price discovery for specific asset classes.
The new cloud-based architecture enabled the client to create products and monetize market data. Those products helped customers get accurate data in real-time to take better and faster trading decisions.
Test automation across the trade lifecycle using a combination of C#, Ranorex, Test Rails helped the client update user interfaces (UI) without reducing performance. It also eased integration linkages between the acquired solution’s frontend and the client’s existing backend.
Outcomes
The introduction of Agile methodology and the cloud-native application has helped the client significantly speed up time-to-market for new releases. It is now able to make releases several times a year.
The new IT architecture now allows the client to offer trading in Muni bonds (an acquired product) and U.S. treasuries (a new service). The solution also enables the client to support Chinese markets.
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Real-time tracking of a global container fleet
A cloud-based GPS-integrated solution delivered, for our client, real-time visibility of its shipments, bringing efficiencies in time, cost and regulatory compliance.
Client
A diversified global business group with a strong presence in maritime services.
Goal
Real-time tracking of a global container fleet
Tools and Technologies
Microsoft Azure, Node JS, C#, Bootstrap, and HTML5
Business Challenge
The pandemic has made it critical for transportation and logistics firms to manage their assets (or fleets) more efficiently.
Demand is high for real-time asset tracking and tracing solutions. For the marine services business of our client, real-time management of a global fleet of containers required continuous, global tracking.
Tracking the container fleet and meeting compliance requirements was laborious and error-prone, often leading to delays and lost opportunities. The client also did not have a mechanism to track customer usage of pay-per-use containers.
We were tasked with creating multi-tenant based cloud application that would be optimized for cost and yet not compromise the user experiences of customers and field teams.
Solution
Our decade-long experience in radio-frequency identification (RFID) helped us address this challenge. Our information engineers, UX designers, solution architects, and business analysts worked onsite with the customer to test scenarios and create a new architecture along with new user experiences and interfaces.
We built the solution using the latest Microsoft Azure cloud services and BizTalk and RFID middleware for an integrated framework involving multiple customers (tenants) and services. An offshore team engineered the new application with the capabilities sought by the client; the architecture team helped create new systems and environments for development, quality assurance (QA), user acceptance testing (UAT), and production.
The solution was capable of handling large amounts of streaming data with the help of edge services located near-site. Customized workflows captured container movement within each site, while GPS services tracked container movement across the globe. The application, which was deployed well within the scheduled deadline, enables post-production maintenance and enhancements from our offshore center.
Outcomes
We engineered real-time tracking and tracing for the client’s global container fleet, with map support and enhanced personalization for users based on location and asset class. Responsive design ensured that the application could be seamlessly used across various devices. Our experience with RFID and cloud-based engineering helped us anticipate and prepare the scenarios well in advance so that solutions could be delivered to the client fast. This significantly reduced the time for the client to prepare cost and compliance status reports, and location-based dashboards for KPIs. The near-real time tracking of containers reduced the time to calculate exact container usage and certificate status from days and hours to a few minutes.
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Data & Analytics
Using data science to cut BI costs
A proven strategy for report rationalization.
Organizations today rely more than ever on accurate and efficient reporting to help them make data-driven decisions. A key part of any modern business intelligence (BI) system are the reports that provide insights to drive change, pinpoint market opportunities and eliminate inefficiencies. Consequently, it is important for organizations to have a clear understanding of how all reports are managed, including how these reports are being used, how often, and by whom. Rationalization using data science can empower organizations to determine the right set of reports expeditiously.
Report rationalization provides significant benefits to those embarking on business intelligence transformation or consolidating reports arising out of mergers and acquisitions. For example, some 60% of organizations in the next three years plan to migrate or move BI/analytics workloads to the cloud, according to the 2020 IDG Cloud Computing Survey. Migrating a BI system (or multiple disparate BI systems) and often thousands of reports can slow the pace to a crawl. This can make the journey to modern BI more expensive and less efficient.
Unfortunately, most report rationalization processes are primarily manual in nature. In our experience, the typical manual process for report rationalization requires approximately 10 hours per report — very time consuming and cost prohibitive even for a relatively small set of 100 reports.
The need for a new approach
Clearly, businesses need an alternative solution to manual report rationalization, as well as a way to perform rationalization cost-effectively on a regular basis. Fortunately, a new approach has emerged that utilizes the promise of data science to reduce the cost, time and complexity of managing and consolidating reports. This white paper will explore how data science and artificial intelligence (AI) are being used as part of a new strategy to modernize report rationalization.
Overall, the reporting ecosystem in many organizations today can be described as a mashup of document formats within multiple, siloed BI systems, underlining the need for report rationalization. Large numbers and varieties of reports must be managed, and this task becomes more complex if reports are distributed across different lines of business, products, geographic locations, regions and countries.
Mergers and acquisitions pose yet another challenge of migrating a large volume of reports. Costs increase as IT requires a larger budget to keep up with increasing computing costs.
Cost reduction is another driver for any report rationalization effort. For instance, a major investment bank that used the Iris rationalization accelerator reduced its report volume of 4,000 by 25%, which helped it trim maintenance costs.
In our experience, it is estimated that it costs a business between $4,000 and $10,000 to build and manage a single report within a BI system over the life of the report. Now multiply those numbers by the hundreds of reports generated over the years to accommodate the deluge of BI data flowing into multiple disparate systems across the enterprise. Not only is this cost prohibitive, but unfortunately, maintaining a large volume of reports does not necessarily result in better business insights.
An intelligent approach to solving these challenges is to abandon manual processes and adopt a modern report rationalization strategy that leverages data science — specifically, natural language processing (NLP) and machine learning (ML) techniques.
Applying data science for report rationalization
A new approach to report rationalization is gaining traction where the tools and techniques of data science are applied to simplify and improve the benefits of report rationalization. Data science applies a variety of different methods, systems, processes and algorithms to both structured and unstructured data to glean valuable insights. ML is based around the use of algorithms that automatically improve through experience while NLP is primarily focused on using computers to process and analyze large amounts of natural language data.
Using NLP to extract and read report headers or data labels — not the data itself — in any document type and from any platform enables rationalization to be both BI-application and format agnostic. In addition, this new approach also leverages machine learning (ML) to provide a comprehensive review of all the existing reports and documents.
With ML it is possible to automatically identify duplicates (reports providing data on the same report fields) as well as similar reports (reports with an overlap of report fields) so that only the ones that are required by the business and used regularly will be consolidated. The machine learning component also helps data and business analysts in their efforts to standardize and define metadata.
Advantages of report rationalization with data science
Data science-driven report rationalization is platform agnostic and is beneficial to organizations across industries. The key benefits of this solution are:
Cost reduction of 25-40%: Report rationalization driven by data science has consistently delivered more than a 30% reduction in the total volume of reports. In our experience, the volume of reports has been reduced by over 70% and costs by up to 40% in some cases. Industry estimates indicate that the savings from even a 10% reduction of reports will pay for the cost of the rationalization effort itself.
More insights from fewer reports: BI consolidation can provide better insights from a smaller, more efficient report set. This makes it easier to identify duplicates, and identify any redundancies — as well as significantly reduce maintenance costs.
Supports BI modernization: Additionally, the results of a data science-driven report rationalization solution can be leveraged in the execution of a BI transformation initiative aimed at migrating reports from a traditional BI platform to a modern one. After rationalization, the optimal number of reports can be migrated to the new system.
Key takeaways
- For organizations seeking to reduce report volume and costs, or undergoing a BI transformation, report rationalization driven by data science makes it easier, faster and less expensive.
- When applied to BI consolidation and transformation initiatives, report rationalization typically reduces the total number of reports by approximately 30%.
- When driven by data science, rationalization recommendations can be obtained in a matter of weeks.
- Often considered an afterthought, report rationalization should be performed upfront in any BI modernization or transformation effort.
- Whether part of a larger initiative or as a standalone process, now is the time for businesses to take advantage of emerging trends in NLP, machine learning and AI by adopting a faster, proven, report rationalization solution that is driven by data science.
Explore more on our solutions and services in Data & Analytics.
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Get in touchA legacy portfolio gets a makeover
How one of the Big Four advisory firms turned around an under-performing, high-cost application portfolio to meet growing business needs.
Client
The client is one of the Big Four advisory firms
Goal
Modernize the legacy application to meet growing business needs
Tools and Technologies
MS SharePoint, MS .Net and MS SQL Server
Business Challenge
- Lack of integration: Most of the integration with upstream and downstream systems was manual, resulting in common data getting obsolete quickly
- The client was finding it difficult and expensive to hire and retain resources to maintain the legacy apps
- The legacy system was prone to security breaches and couldn’t be deployed on the enterprise-level stack
- The existing system supported only single-user applications, and it wasn’t possible to roll them out to multiple users
Solution
After a comprehensive analysis, we rationalized, classified and distributed the client’s applications portfolio in four areas:
1) Upgrade and continue to maintain
2) Rewrite to modernize
3) Consolidate overlapping applications using a framework approach
4) Retire
The idea was to deliver maximum value at the lowest cost possible and ensure the system complied with security standards.
Here’s what we did for the client:
- Upgraded the technology stack for the application(s) to lower maintenance costs, improve efficiency and meet growing business needs
- Used an in-house technology modernization framework to reduce development and maintenance costs
- Consolidated applications that were doing similar tasks and had similar features and modernized them
- Retired applications, whose features were available through other applications
The success of our solution was based on our ability to quickly gather complete information about the existing applications. To do that, we used a questionnaire that we have developed and refined over the years that helps us gather information in a structured and comprehensive manner about the architecture, user base, maintenance methodology, etc.
Outcomes
- With the legacy modernization and application consolidation process, we reduced the client’s application portfolio from 45 applications to less than 10.
- Reduced the resources required for maintenance from six to two.
- The framework-based approach accelerated time-to-market, a critical differentiator for the client.
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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 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.
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A playbook for banks on managing M&A integration
Efficient management of the complexities of disparate systems and data after merger and acquisition (M&A) integration 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 created a business acquisition playbook for our banking clients outlining steps to insource with a migration and integration strategy. We defined insourcing steps for business and technology teams and created a migration strategy with quantifiable recommendations and a reusable checklist for insourcing activities.
Our solutions enabled clients to deal with post-merger integrations and create a single source of truth for transactional data and positions. We consolidated multiple acquisition playbooks and created a single standardized framework for their lending business. The solution also included data integration management and ensured connectivity for the lending business. The solutions were specifically tailored for applications in the loan origination and servicing space.
Outcomes
Our solution rendered several significant benefits and helped clients:
- Achieve 50% savings in cycle time and cost for post-merger integration of business processes, application, and data
- Capability and readiness assessment and assistance in choosing from insourcing options
- Achieve full migration of data and systems
- Achieve partial migration of systems and data migration and integration
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Deliver personalization via report automation
A leading asset management firm personalized offerings by automating processes to glean customer insights.
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
We partnered with the asset manager to deliver better digital experiences to all its stakeholders. We created a robust data ecosystem and used advanced technologies such as artificial intelligence/machine learning or AI/ML, intelligent automation, cloud computing and test automation.
- 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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Setting a high standard for member experience
We helped a global standards organization build a cloud-native microservices-based platform to store and share data on billions of products with members.
Client
One of the world’s leading retail standards organization
Goal
Build a 24x7 cloud-based platform capable of storing and sharing data on billions of products
Tools and Technologies
Java, Python, NodeJS, .NET, Azure PostgreSQL, Azure SQL, MongoDB, Redis, Azure DevOps, Pipelines, Git, Docker, Kubernetes, Azure App Service
Business Challenge
Our client is a global standards organization. Its products and codes are used by millions of brand owners, retailers and supply chain partners around the world.
The organization needed an always-on, scalable cloud-based platform capable of storing and sharing data on billions of products and related information with members and partners across the world.
The client also wanted the ability to onboard member organizations quickly and seamlessly.
They wanted the business capabilities developed on a platform to be available as modern and secure enterprise-level APIs.
Solution
We chose a microservices architecture for high agility, loose coupling, independent deployability and maintainability.
We followed an API design-first approach and designed according to the standards-based API specification (OpenAPI Specification).
In line with best practices for securely publishing and maintaining APIs, our team deployed the Azure API management solution. We used Azure APIM developer portal to deliver a superior developer onboarding experience. The solution had other features as well:
- Design and implementation of the Azure Virtual network for securely hosting the platform.
- A cloud-native architecture using the Azure AppService and an Azure-managed Kubernetes platform.
- Comprehensive performance testing and optimization at all levels to meet strict SLAs.
- Security testing and vulnerability assessment to ensure secure APIs.
Outcomes
- A robust and secure API platform that handles 200,000 API requests per day.
- 50 million codes uploaded in 40 product categories across more than 130 countries.
- Delivered a developer portal for quick onboarding of application developers.
- New 7-step verification mechanism led to the creation of new revenue streams.
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An API ecosystem expedites customer onboarding
A global leader in logistics created an API ecosystem that significantly transformed its customer experience.
Client
The client is a leading logistics and transportation services provider, ranked among the top 10 in the world
Goal
Rapid onboarding of partners and customers; improve customer experience
Tools and Technologies
Azure, Dell Boomi APIM and oAuth 2
Business Challenge
The growing volume of business required the client to respond faster to market demands. On their current systems, it was a challenge for the support staff to onboard new clients and service their requests.
The client needed a solution that would help it respond quickly to diverse business needs, including:
- Last mile shipment tracking and alerts across carrier networks
- Quotes for multiple shipment options
- Special fulfilment orders
- Order personalization for seasonal surges
Solution
We proposed and developed a comprehensive API layer that allowed customers and partners to access client systems using APIs.
Customers and partners could easily track shipments and get quotes on their own. This reduced the need for support personnel to service routine requests. The team configured these APIs on Boomi’s API Management to enable seamless real-time integration.
Outcomes
- Improved onboarding speed: 20+ partners per month
- Enabled client to handle large volumes of requests, with a service capacity of 35,000 API requests a day
- Improved customer experience through real-time rates and quotes
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