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Virtusa Announces Digital Transformation Studio

Virtusa Corporation announced its new Digital Transformation Studio (DTS), a proprietary platform and approach designed to increase the delivery speed and dramatically reduce the costs of business critical digital transformation projects.

DTS was designed to mitigate many of the typical issues plaguing traditional digital transformation efforts, while enabling a 30% overall productivity improvement.

DTS allows Virtusa teams to set specific performance goals with each client focused on areas including reducing technical debt, improving time to market, or reducing costs.

DTS includes three key components:

- Engineering Tools that drive SDLC automation to improve quality, enable speed, and increase productivity. These include Smart Application Lifecycle Management tools to improve user stories and provide a story point estimation model; proprietary Gamified Dashboards to promote transparency, quality, and productivity metrics; and end-to-end CI/CD pipeline that automates code quality review, testing, and release management.

- Reusable Industry Assets collected and improved through Virtusa’s Open Innovation Platform that strives for a near-zero approach to coding. These include AI Model Zoo and Data Lake with over thirty pre-trained AI/ML models trained on synthetic datasets with 10MM customers, and 500MM transactions; Cloud Native Middleware with prebuilt microservices, boilerplate code generators, and multiple legacy system connectors; and API Lifecycle Toolkits to manage onboarding, QoS, security, distributed tracing, and logging of deployed services.

- Certified Teams pre-trained on agile processes, technology, domain, and Virtusa's engineering tools and assets. A Developer Portal opens challenges and hackathons to a community of developers. Using an agile methodology, teams are configured in squads and tribes to promote scalability and growth. Teams also use a Solutions Assembly Sandbox to assemble Digital Solutions from the asset library.

“Given today’s challenges, enterprises need to maximize every dollar invested in digital transformation initiatives,” said Kris Canekeratne, chairman and CEO, Virtusa. “DTS was built from the ground up to increase the speed and success rate of these business critical projects dramatically. With gamified dashboards to promote transparency and improve performance, reuse of industry assets to save time and money, and pre-trained and certified teams with experience in key industries, we can predict digital transformation successes very early in engagements with clients.”

The impact of DTS is expanded by its integration with Virtusa’s Global Technology Office and xLabs, which combine design thinking and digital engineering to reduce the time and costs associated with identifying, evaluating, and exploiting new technologies to create a competitive advantage. Once new solutions are built, tested, and successfully deployed for clients in Global Technology Office and xLabs, those solutions are transferred to DTS for componentization and reuse.

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Virtusa Announces Digital Transformation Studio

Virtusa Corporation announced its new Digital Transformation Studio (DTS), a proprietary platform and approach designed to increase the delivery speed and dramatically reduce the costs of business critical digital transformation projects.

DTS was designed to mitigate many of the typical issues plaguing traditional digital transformation efforts, while enabling a 30% overall productivity improvement.

DTS allows Virtusa teams to set specific performance goals with each client focused on areas including reducing technical debt, improving time to market, or reducing costs.

DTS includes three key components:

- Engineering Tools that drive SDLC automation to improve quality, enable speed, and increase productivity. These include Smart Application Lifecycle Management tools to improve user stories and provide a story point estimation model; proprietary Gamified Dashboards to promote transparency, quality, and productivity metrics; and end-to-end CI/CD pipeline that automates code quality review, testing, and release management.

- Reusable Industry Assets collected and improved through Virtusa’s Open Innovation Platform that strives for a near-zero approach to coding. These include AI Model Zoo and Data Lake with over thirty pre-trained AI/ML models trained on synthetic datasets with 10MM customers, and 500MM transactions; Cloud Native Middleware with prebuilt microservices, boilerplate code generators, and multiple legacy system connectors; and API Lifecycle Toolkits to manage onboarding, QoS, security, distributed tracing, and logging of deployed services.

- Certified Teams pre-trained on agile processes, technology, domain, and Virtusa's engineering tools and assets. A Developer Portal opens challenges and hackathons to a community of developers. Using an agile methodology, teams are configured in squads and tribes to promote scalability and growth. Teams also use a Solutions Assembly Sandbox to assemble Digital Solutions from the asset library.

“Given today’s challenges, enterprises need to maximize every dollar invested in digital transformation initiatives,” said Kris Canekeratne, chairman and CEO, Virtusa. “DTS was built from the ground up to increase the speed and success rate of these business critical projects dramatically. With gamified dashboards to promote transparency and improve performance, reuse of industry assets to save time and money, and pre-trained and certified teams with experience in key industries, we can predict digital transformation successes very early in engagements with clients.”

The impact of DTS is expanded by its integration with Virtusa’s Global Technology Office and xLabs, which combine design thinking and digital engineering to reduce the time and costs associated with identifying, evaluating, and exploiting new technologies to create a competitive advantage. Once new solutions are built, tested, and successfully deployed for clients in Global Technology Office and xLabs, those solutions are transferred to DTS for componentization and reuse.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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