In today’s digital economy, data is often called the new oil. But just like crude oil, its true value lies in how it’s refined and used! As businesses across the world race to harness the power of their data, platforms like Snowflake are becoming essential partners in this journey. In this exclusive interview with APAC Media and CXO Media, Vijayant Rai, Managing Director-India, Snowflake, shares how the platform is helping Indian enterprises unlock the full potential of their data through powerful cloud technologies, secure and easy data sharing, AI integration, and a growing ecosystem of partnerships.
What are the primary solutions and services provided by Snowflake?
Snowflake is a cloud-based data platform that helps organizations—ranging from large enterprises and SMEs to the public sector—manage, organise, and derive value from their data. The core mission of Snowflake is to help businesses break down data silos and centralize all types of data, including structured, semi-structured, and unstructured, onto a single, scalable platform. This not only enables better data organization but also unlocks deeper insights and analytics, helping organizations make more informed business decisions.
With AI and generative AI becoming increasingly critical to modern businesses, Snowflake enables the application of these technologies directly on data within the platform. In addition to advanced data management and analytics, Snowflake also allows organisations to collaborate and even monetize their data assets. It has evolved into a comprehensive AI Data Cloud that supports seamless scalability, high-performance analytics, and cross-functional collaboration—all within a secure and unified ecosystem.
What are the main product offerings in Snowflake’s portfolio today?
Snowflake’s portfolio includes a wide range of integrated capabilities designed to handle the entire data lifecycle. This includes data warehousing for high-performance analytics, a data lake for managing semi-structured and unstructured data, and data engineering features that support complex data pipelines. Snowflake also offers a feature-rich collaboration environment through Snowflake Data Sharing, which enables secure and efficient data collaboration without physically moving the data.
Developers can leverage Snowpark to write custom code using familiar languages like Python, Java, and Scala directly within the platform. Additionally, Snowflake provides built-in tools to support AI and machine learning workloads at scale. The recent launch of Snowflake AI Data Cloud enables organizations to embed AI and generative AI into their data strategy. Moreover, the introduction of Snowflake OpenFlow allows seamless ingestion of data from any source, further simplifying the data integration process and expanding Snowflake’s utility across use cases.
In a competitive market, what differentiates Snowflake from other cloud-based AI data service providers?
Snowflake stands out in a crowded market through several key differentiators, the foremost being time-to-value. Organisations can quickly derive actionable insights from their data due to Snowflake’s simplified ingestion process and unified platform design. The newly introduced OpenFlow capability allows businesses to bring in data from virtually any source and start working with it almost immediately.
Another critical advantage is Snowflake’s advanced data sharing feature, which enables organisations to collaborate with partners and stakeholders securely without physically moving the data. This makes inter-organizational data collaboration seamless and efficient. Furthermore, Snowflake supports AI and ML workloads at scale, offering businesses the ability to apply cutting-edge models directly to their data. The combination of rapid data onboarding, AI-readiness, seamless scalability, and secure data sharing makes Snowflake one of the most innovative and differentiated data platforms in the market today.
Snowflake emphasizes the democratization and easy access to data for enterprises. How relevant is this in the Indian context?
India remains a unique market where a significant portion of data infrastructure still operates on-premises. Many traditional enterprises still house their data in local data centers or on in-house servers, which limits their ability to truly capitalize on the power of cloud-native analytics and AI. Our primary goal in the Indian context is to support these organisations in their journey to the cloud. Once data is moved to the cloud, organisations can integrate various types of data—structured, semi-structured, and unstructured—from multiple sources. This convergence is critical to unlocking the true potential of data.
The democratization of data in India begins by breaking down these internal silos. Today, companies have fragmented systems—for sales, HR, customer relationship management, and more. For example, a bank catering to millions of customers often has customer data scattered across transaction systems, CRM platforms, and other channels. This makes it difficult to build a unified customer profile. Snowflake helps solve this by consolidating all the data into the Snowflake AI Data Cloud, enabling advanced analytics, better customer experience, improved risk management, and more. Generative AI has also played a transformative role, particularly in enabling the combination of structured and unstructured data for richer insights.
What is your go-to-market (GTM) and marketing strategy for Indian enterprises?
From a GTM perspective, our focus in India is on helping large enterprises, mid-market companies, and even SMEs get more value from their data. This starts with educating them on how to organize their data more effectively, and then partnering with them through their digital transformation journey.
Beyond just providing the technology platform, our strategy also involves thought leadership and addressing the cultural and organizational nuances of data ownership. In many companies, data is owned by multiple stakeholders, and there’s often internal resistance or political friction when it comes to sharing or centralizing that data. That’s why a top-down approach—led by leadership—is vital. Fortunately, we see that in India, data and AI have become board-level discussions. CXOs and business leaders are increasingly aware that unlocking data can drive better customer acquisition, improved internal efficiencies, and overall business growth.
The challenge, however, lies in execution. It’s not just about adopting a technology like the Snowflake AI Data Cloud—it’s about cultivating a data-first mindset within the organization. True democratization comes not just from centralizing data, but also from transforming the culture and encouraging cross-functional collaboration. This combination of robust technology and internal transformation is what we see as the real differentiator in our strategy for India.
Could you share some recent success stories of Indian enterprises that have adopted Snowflake’s services? Which sectors are gaining the most traction?
Today, almost every company is re-evaluating how to derive more value from its data, although some are further along this journey than others. Across our go-to-market efforts, we engage with enterprises from a wide range of sectors—BFSI (banking and financial services), manufacturing, retail, digital-native companies, and even small and medium businesses. Snowflake has built a robust presence across all these verticals in India, with a strong focus on helping customers unlock data value through specific use cases rather than a one-size-fits-all approach.
Take, for example, Bajaj Allianz General Insurance, one of the leading general insurance providers in India. They are working with us to truly democratize their data. With numerous sources of data currently in use, the company is centralizing all of it within the Snowflake AI Data Cloud. This consolidation is empowering them to create unified customer views, enhance risk management, and deliver improved customer experiences. It’s a significant data transformation initiative that is already yielding tangible benefits.
Another compelling example is Piramal Finance, where data was previously scattered across different systems, making it difficult to extract meaningful insights. By leveraging Snowflake’s platform, they are now able to bring all this data together in one place. This not only simplifies operations but also opens up powerful opportunities for advanced analytics, risk assessment, and customer insight. The value of centralized data is something that Piramal is now actively realizing.
On the digital-native front, we’re working with Chalo, a public transportation tech company that operates across over 60 cities in India. If you’ve seen the “Chalo” branding on BEST buses in Mumbai, that’s them. Their app provides real-time bus tracking for commuters and operational insights for fleet managers. This kind of application generates massive volumes of data, and they’re using the Snowflake AI Data Cloud to ingest, process, and analyze it all in real-time. This helps commuters know exactly when the next bus will arrive and enables fleet owners to manage vehicle maintenance and efficiency far more intelligently. It’s a great example of data directly improving daily lives and operations.
On the traditional enterprise side, IPL Biologicals, a major player in the manufacturing sector, is also using the Snowflake platform to eliminate data silos. They’re bringing together data from multiple systems to drive more informed decision-making through analytics. This transition is allowing them to apply use cases across production efficiency, supply chain optimization, and more.
These stories reflect the breadth of Snowflake’s impact in India—from large financial institutions and manufacturers to cutting-edge digital platforms. What ties all these cases together is a commitment to leveraging data more effectively, and Snowflake is proud to be a part of that journey.
What do Snowflake’s newly introduced features, like OpenFlow and Cortex AI, offer to enterprises?
Both OpenFlow and Cortex AI represent significant advancements in making data more accessible and usable for Indian enterprises.
OpenFlow, which we recently announced at Snowflake Summit, is designed to streamline the process of bringing data from multiple sources into the Snowflake AI Data Cloud. Traditionally, data ingestion—especially from legacy systems—requires a high level of technical effort and expertise. OpenFlow simplifies and accelerates this process, allowing seamless integration across hundreds of systems. This drastically reduces deployment time, making data access and activation much easier for enterprises of all sizes.
Cortex AI, on the other hand, serves as our robust AI framework. A core philosophy at Snowflake is that data should remain secure and within the customer’s environment. With Cortex, customers can apply artificial intelligence to their data without moving it out of the Snowflake platform. This ensures not only data sovereignty and security but also enables high-performance analytics and AI use cases directly on the data. Enterprises can access top-tier LLMs (large language models) such as those from OpenAI, Anthropic, and LLaMA, directly within the Snowflake environment. That means organizations can implement generative AI solutions without exposing their data to external platforms—a key consideration for regulated industries like BFSI and healthcare. Cortex empowers businesses to build and deploy powerful AI applications securely and efficiently.
As Snowflake continues its expansion in India, what kind of partnerships are critical for this growth, globally and specifically in the Indian context?
Partnerships are absolutely central to Snowflake’s growth strategy. We view our partner ecosystem as a true force multiplier. These are the professionals who work closely with customers through their data transformation journeys—not just from a platform perspective, but also from a people and process standpoint. Partners play a crucial role in ensuring high-quality data ingestion, proper architectural planning, and effective deployment strategies. They help address the classic “garbage in, garbage out” challenge by ensuring that only the right data enters the system, leading to better outcomes.
Our partner ecosystem is broad and includes multiple layers. At the top, we have advisory partners like Deloitte, EY, PwC, and KPMG who bring strategic expertise. Then there are regional system integrators who work closely with us in cities across India. Global partners such as TCS, Wipro, Infosys, and others also play a significant role in both Indian and international implementations.
Our job is to continuously enable these partners through training, certifications, and product updates, ensuring they stay aligned with the latest capabilities of the Snowflake platform. We’re particularly proud that nearly 50% of the APJ region’s partner ecosystem is based in or operates out of India, making the country a vital hub for Snowflake’s broader growth strategy.
What’s your take on the maturity and adaptability of AI in India’s ecosystem right now? What are some of the gaps that exist, and how can they be addressed?
AI is poised to become a massive game-changer for a country like India. We’re one of the fastest-growing developing economies in the world. On top of that, India is quickly becoming one of the most data-driven economies, thanks to strong digital public infrastructure like India Stack and the UPI payment ecosystem. These platforms are excellent examples of large-scale, data-powered innovations, and they’ve laid the foundation for an economy centered around digital capabilities. What’s particularly promising is how digital-native companies in India—many of which are unicorns—are already operating at an impressive scale and applying AI in cutting-edge ways, serving hundreds of millions of users. This signals that India is not just ready but already leading in many ways.
That said, the pace and success of AI adoption will vary across sectors. For instance, regulated industries may adopt AI a bit more cautiously due to compliance and governance considerations. Enterprises across the board, however, are now recognizing the need to get their data estates in order because, very clearly, there is no AI strategy without a data strategy. Data is the foundation, and organizations are now actively working toward optimizing it to support AI initiatives.
Skilling is another key pillar in this journey. India has massive potential due to its scale, but keeping up with the rapid pace of AI advancement is critical. The speed of change in AI is incredibly fast—we’re seeing breakthroughs and new large language models (LLMs) being introduced every few weeks. So, it’s important to ensure our workforce is constantly upskilled to match that pace.
At Snowflake, we are deeply committed to contributing to India’s AI and data readiness. We’ve partnered with NASSCOM to skill over 100,000 developers in India in data and AI technologies. Through this initiative, we’re offering free courses to the community to ensure they stay updated with the latest developments. We also run a global initiative called the Million Minds program, which is focused on preparing the developer community globally for the AI-driven future. In India, beyond just metro cities, we organize developer events in various cities to ensure access to learning and resources is inclusive and wide-reaching.
What are Snowflake’s key priorities going forward when it comes to talent development, partnerships, customer engagement, and upcoming product launches? What are you most excited about moving forward?
We’re extremely excited about the road ahead for Snowflake, both globally and in India. When we look at the market today, there’s a lot of talk about data being the new oil. But honestly, we believe we’ve only just skimmed the surface of what data can do for our customers. Our top priority is to partner closely with enterprises, SMEs, and digital-native organizations in their data and AI transformation journeys. We truly believe that Snowflake is the ideal platform to accompany businesses through this evolution, from organising their data to enabling advanced AI-driven insights.
One of the biggest challenges organisations face with digital transformation is complexity. Snowflake’s value lies in simplification. We make the process of adopting and scaling data and AI much more straightforward and manageable. Our platform is built around key principles: time to value, ease of use, and most importantly, trust. Since we’re in the business of data, trust is non-negotiable. We promise our customers that the Snowflake platform adheres to the highest standards of security, compliance, and data governance. Whether it’s a large bank in the U.S. or a startup in India, the security and capabilities we offer are the same—no compromises.
As a SaaS-based AI Data Cloud, Snowflake ensures that every customer, no matter the size, has access to the same enterprise-grade features and functionality. This democratization of technology is what sets us apart. Going forward, our focus remains on enabling faster outcomes—helping customers move from raw data to actionable insights as quickly and efficiently as possible.
