System modernization helped a multinational educational testing and assessment organization overcome COVID-19 disruptions to deliver seamless and accurate digital testing.
Client
A leading educational testing and assessment services company
Goal
Switch from in-person to online testing
Tools and Technologies
AWS Serverless, Dynamo DB, Node.js, Typescript, Java, Jenkins, and Angular
Business Challenge
Our client, which provides educational testing and assessment services, faced an existential threat with the pandemic-era lockdowns and social distancing requirements. The testing centers it operated at physical locations were unable to open, leaving thousands of students worldwide in a state of uncertainty.
Our client had to switch from in-person testing centers to a digital-first or online testing solution almost overnight. To achieve that, it had to migrate rapidly from legacy systems to the cloud. It also needed to ensure the sanctity and accuracy of its tests while delivering a seamless digital experience to its customers. Other challenges included the ability to dynamically scale up or scale down capacity in response to demand, maintain acceptable service levels, and enable thousands of expert test raters to access and evaluate tests.
Solution
Iris Software stepped in to facilitate a strategic digital pivot in the business model to secure the company’s future. Modernization efforts that were underway at the company even before the pandemic were accelerated as a digital upgrade became imperative. We shifted the data stored on legacy infrastructure to the cloud.
Our team developed a new testing interface that would work overnight across devices, geographies, and different internet connections. Switching the testing operations to the cloud with scalable capacity could help manage the surge in the number of users for the tests. Iris also deployed automation and AI tools to deliver superior experiences for test raters. Those who faced challenges while attempting to grade tests were provided with an always-on AI-based solution to automate the troubleshooting and ticketing process.
Outcomes
The client now has scalable, digital-first testing capabilities to meet all its testing requirements.
- Cloud-based testing enabled on-demand access to students, evaluators, and employees.
- The remote testing options are accurate, secure and safe from external threats.
- A strong focus on automation and user experience has allowed for optimized online offerings.
- Surges in demand for tests can be met rapidly and at scale with minimal intervention.
- Thanks to the always-on cloud offerings, service levels are easily maintained.
- The successful digital pivot has led to strong interest in a hybrid operating model to safeguard the business from threats in the future.
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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.
Good business decisions are often the product of the right people having access to the right data at the right time. Data lineage makes this possible.
An Iris Software Perspective Paper brings you insights on why establishing data lineage has become imperative for organizations across industries, what benefits it can bring to your enterprise, and how you can begin your data transformation journey.
How can data lineage help your organization? Guided by Data & Analytics service providers, data lineage can drive better outcomes such as improved data quality or delivering better insights to business teams. It could also ensure compliance with regulations such as the European Union’s General Data Protection Regulation (GDPR) and BCBS 239, Basel Committee on Banking Supervision's principles for effective risk data aggregation and risk reporting.
The perspective paper highlights the value of a Data Catalog and a Data Lineage solution in an organization. The paper details on how machine learning (ML) and natural language processing (NLP) are used to accelerate the data cataloging process and thereby shorten time-to-value.
The paper also highlights the Iris Data Governance solution framework along-with representative case studies.
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Get in touchHow to transform your risk reporting mechanisms
A leading brokerage firm improved its UI and lowered costs with a future-ready risk reporting platform.
Client
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 as 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.
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The power of in-sprint automation
A large securities firm sped up time-to-market with end-to-end test automation on the cloud.
Client
A leading securities trading firm
Goal
Build a cloud-based automation framework to test client’s trading platform
Tools and Technologies
C#, Ranorex, TestRail, Simulators and Selenium
Business Challenge
The client had a legacy trading platform that had grown and evolved over time. The platform consisted of a stack of 33 applications, built on a variety of technologies and architectures.
Testing new features and additions was proving to be a big challenge. A simple change in one feature would warrant a verification of the complete application. To ensure that any change does not affect other functionality, the client needed to do extensive regression testing and verification.
This was a cumbersome process with over 20,000 or 30,000 test cases being checked and executed manually. The trading firm had to deploy over 20 people to carry out this exercise. The client had tried to automate the testing process with a variety of tools but was not able to get the efficiencies it wanted.
In addition, the client had multiple squads working on different apps, functionality and features. Each squad used its own automation suite. It was becoming a challenge to co-ordinate the work of the different squads and ensure that changes made by a squad did not impact the overall functionality of the platform. Iris’s brief was to design and deploy a common cloud-based test automation framework for the client’s trading platform to ensure that it could launch new features faster.
Solution
Using its cloud-based ready-to-deploy test automation framework, Iris sped up the deployment of new features for the client’s trading platform. The cloud solution, based on Amazon Web Services (AWS), featured continuous testing of multiple products on a common framework layer. It allowed for complete capacity planning of spinned cloud instances and need-based shutdowns.
Iris executed the project using acceptance test driven development (ATDD), a methodology that involves collaboration between customers, business teams and development teams. The teams jointly created the user stories and put down the acceptance criteria for any feature or functionality. Then tests were designed within the common framework to check if the feature met the acceptance criteria.
What was unique about the approach? Typically, automation is introduced towards the end of a development cycle. You would find that, in most projects, developers bring in automation in Sprint 4 for features developed in Sprint 1, 2 and 3. As a result, return on investment isn’t maximized. Our team introduced ‘in-sprint’ automation, enabling 90% test automation with every sprint. This resulted in more efficient and faster testing, and cost savings for the client.
Outcomes
The client’s deployment speed improved significantly with 90% faster execution in each sprint cycle and 80% faster script development.
The cloud-based solution is 100% configurable for on-demand execution on AWS, which reduced the client’s cloud infrastructure costs by 70%.
The new ability for complete capacity planning through the use of infrastructure-as-code (IaC) for spinning up cloud instances helped the client achieve end-to-end (E2E) automation of regression/ functional test cases.
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Data consolidation speeds up drug search
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.
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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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