Powering the Future: How Real-Time Analytics and AI Are Transforming Data-Driven Enterprises in India

Mr Venkatesan Vijayaraghavan, Executive Vice President and Global Head of Technology Service Lines at Virtusa, spoke to The Hans India about how real-time analytics and artificial intelligence are reshaping the future of Indian enterprises. In a candid conversation, he explored how businesses are moving beyond static reports to embrace smarter, faster, and more adaptive decision-making in an increasingly data-driven world.
Venkatesan Vijayaraghavan - Executive Vice President and Global Head of Technology Service Lines Virtusa
With India’s data analytics market projected to grow significantly, what role do you see real-time analytics playing in this growth?
Businesses today must respond to situations in real time and with the appropriate context. This is especially critical in banking, healthcare, public health, and disaster management. The ability to incorporate real-time data into decision-making empowers organizations to deliver a more personalized and enhanced customer experience—now a vital factor in staying competitive. Customers expect agility in both the speed and quality of the services they receive. At the same time, organizations gain the advantage of offering products and services relevant to the immediate situation, creating more meaningful engagement.
For example, we currently use a customer’s real-time location and recent transaction history to deliver area-specific offers and promotions.
What are the key challenges businesses face when adopting real-time analytics, and how can they overcome these obstacles?
Adopting real-time analytics comes with several challenges, particularly in collecting, storing, and integrating data into decision-making processes. In the world of IoT, businesses have access to an overwhelming amount of real-time data. The real challenge, however, is determining which data is truly valuable. Not all of it is relevant for decision-making.
The core challenge is knowing how to sift through this vast amount of data and identify the small fraction—typically just 2–3%—that is critical for making timely, informed decisions. We apply a "divide and rule" approach, carefully isolating key data, modeling it effectively, and merging it with stored analytical insights to deliver actionable outcomes that drive impact.
With the emergence of Agentic AI, much of this decision-making process can now be automated, increasing efficiency and speeding up response times.
Ultimately, overcoming the challenges of real-time analytics requires a strategic approach to data identification, effective modeling, and leveraging AI-driven automation. This combination enables businesses to transform raw data into immediate, actionable insights that improve customer experience and operational efficiency.
Additionally, real-time analytics should follow a consumption-driven model. In this approach, business hypotheses and use cases drive the preparation of data for real-time analytics, as opposed to the producer-driven model, which often results in an overwhelming amount of data that may not be relevant for decision-making.
How is Virtusa helping businesses shift from static reports to AI-driven, real-time insights?
Virtusa has established robust frameworks for architecting and executing real-time or streaming data solutions. Our architectures, thoroughly vetted by all major cloud providers, are designed to be ACID and Lambda compliant. With the rise of cloud-based data, we offer custom APIs that seamlessly integrate curated datasets with real-time data, enabling models to deliver real-time outcomes efficiently.
How do you envision the future of data-driven enterprises in India over the next decade?
The future of data-driven enterprises in India looks bright and transformative. Data-driven companies are proven to grow and sustain themselves 70% better than others. Using data to make decisions is no longer just a business need—it has become a regulatory requirement. For example, responsible AI, once a "black box," is now becoming more transparent, with organizations required to explain how decisions are made.
With regulations like GDPR and CCPA, businesses must clarify why they choose datasets and ensure they have permission to use them. India’s Data Protection Law, the India Data Protection Act (IDPA), is gaining strength, pushing companies toward more accountability and transparency. Indian businesses will have to be more open about how they make decisions.
We see global enterprises starting to be data-driven enterprises, maturing to Monetizing the data and to finally the nirvana of productizing the data. This trend will be seen in future in Indian enterprises too given the data producers and consumers in India will be multi-fold than many of the world economies.
What is Virtusa’s approach to leveraging real-time data processing, and how does it contribute to optimising business operations and enhancing customer experiences?
Virtusa’s approach to real-time data processing follows a divide-and-rule strategy. Streaming data from platforms like Kafka or Splunk is continuously polled for specific data elements and directly moved to the consumption layer. This data is temporarily stored in tables that are copies of the permanent data stores in the mart layer. These temporary tables are refreshed in real time from the data feeds, with minimal transformation applied to the data.
A semantic layer is built on top of both temporary and permanent tables, with permanent tables being updated through batch processing. The semantic layer is then polled by applications within the value stream, consuming the data either in real-time or near real-time (NRT). This setup is a clear implementation of Lambda architecture.
What differentiates Virtusa is its focus on performance, which is critical in real-time scenarios. Virtusa works closely with cloud platform providers to ensure optimal performance in specific situations. It’s crucial to carefully select the right use cases for implementation to avoid cluttering the system. Some of the areas where Virtusa has successfully recommended real-time analytics solutions include fraud detection in banking transactions, maintenance of heavy-duty machinery, and healthcare administration in ICUs.
How is the landscape of big data analytics evolving in India, and what opportunities does this present for enterprises looking to adopt these technologies?
First of all, let's address the common misconception about Big Data. As data practitioners, we avoid using the term "Big Data" because the way we handle data does not change based on its size. The models that are use and the way that the data is consumed is also not different based on the data volumes.
Now, the data usage in India, it varies across different industries. Sectors like banking, pharmaceuticals, telecommunications, and manufacturing are actively adopting global best practices. India has a unique advantage in that it did not have to go through the technology lifecycle that many developed countries did. Instead, India has quickly adopted proven technologies from Western markets, allowing many Indian organizations to reach high levels of maturity.
In summary, the opportunities in India are no different from those in developed markets. Awareness, adoption, and technology transformation are just as real and impactful in India as they are in any other country.
What role does AI play in transforming data analytics into actionable insights, and how does it contribute to proactive decision-making in businesses?
AI has always been around, but it's only now getting the attention it truly deserves. For years, industries have used models to predict outcomes and suggest actions based on historical data—especially in fields like actuarial science and equipment maintenance. Earlier, due to the high cost of storing and processing data, AI was limited to specific areas. But with modern, affordable storage and faster processing power, AI has become much more accessible and widely used.
Industries that once relied on basic models like linear or time-series regression have now moved on to more advanced techniques like machine learning and deep learning. Today, AI does not just predict the next best action, it also learns from the outcomes to improve over time. It is called reinforcement learning, where models continuously adapt and optimize based on results. A well-trained AI model that can learn and self-correct can make decisions that are close to, or sometimes better than, human expertise.
Take healthcare as an example. A doctor interpreting X-rays, MRIs, or CT scans has an average accuracy of about 83%. AI, on the other hand, can reach up to 89% accuracy. AI is also ahead of humans in tasks like image processing and summarizing long texts. With Generative AI becoming more advanced, Natural Language Generation (NLG) has become even more powerful.
To become fully data-driven, businesses must embrace AI. Organizations that use data well consistently perform better than those that do not. So, it is important to speed up AI adoption—with the right guardrails in place—to stay competitive and future-ready.