Key challenge is preparing on-premises data estates for the cloud: Snowflake MD

The rise of generative artificial intelligence (GenAI), at the one hand has prompted the companies to rethink about their data management techniques and on the other side, created an opportunity for the cloud services providers. Sensing the potential, data cloud company Snowflake aiming to help businesses unlock the full potential of their data. The company focuses on data management, AI, and data sharing. 

In a conversation with TechCircle, Vijayant Rai, Managing Director for India at Snowflake, discussed about the company’s ongoing efforts and future plans. Rai also touched on the significance of the Indian market for Snowflake. Edited Excerpts:

What growth potential do you see for Snowflake in the Indian market, especially with the rise of Gen AI?

Generative AI has significantly transformed the landscape since the introduction of ChatGPT in late 2022. At Snowflake, our primary focus is on data and AI. Although AI has been around for a long time, Gen AI has made it far more accessible.

In the past 18 months, we've seen Gen AI move towards mainstream adoption. While many proof-of-concepts (POCs) are underway, it's only a matter of time before we see large-scale use cases.

From an India perspective, Snowflake is intensifying its efforts with enterprises. Our team is working on partnerships across various sectors. A key area of focus is ensuring organisations are prepared to harness the power of GenAI.

GenAI is now widely available, but to benefit from it, organisations need to ensure that their data is well-organised. At Snowflake, we believe there that can be no AI strategy without a solid data strategy. We help enterprises develop these strategies and ensuring their data is in shape so they can effectively apply AI.

India is on a remarkable growth trajectory, rapidly becoming a data-driven economy. This rapid data accumulation brings challenges such as security, which we address by helping enterprises develop robust data and AI strategies.

Snowflake sees a massive opportunity in India. As enterprises move beyond testing and POCs, they will start realising the immense business value of generative AI. Our focus is on helping them achieve business outcomes by ensuring their data estates are prepared to leverage AI and generative AI, taking their operations to the next level.

What specific challenges do Indian enterprises face when adopting Gen AI?

We need to understand that India is still largely an on-premises country, with most data remaining on-premises. Although there has been a movement to the cloud in the past two to three years, accelerated by Covid-19, enterprises have started migrating to the cloud more significantly. Digital natives, who were born in the cloud, adopted it much faster.

The key challenge today is preparing these on-premises data estates for the cloud and utilising the cloud's power in terms of elasticity, computing, and storage. The biggest challenge remains educating enterprises and SMBs on how to maximise cloud benefits for their data estates, applications, and outcomes. Ensuring customers that cloud security is robust — likely more secure than on-premises is crucial.

Innovation in tech is happening on a big scale, and no one should miss out on this opportunity. However, there are cultural and process-related aspects to consider. The transition involves three elements: technology, process, and culture. Overcoming these challenges is essential for greater cloud movement, enabling people to leverage cloud data and the benefits of AI and generative AI.

What is the current state of cloud adoption among enterprises in India?

We're witnessing significant positive movement in cloud adoption across various enterprise verticals over the past two to three years. Financial services have been leading this charge, with digital leaders being the first to adopt. Many of these organisations were born in the cloud, which is not surprising. IT and ITS companies have also embraced the cloud extensively.

Manufacturing is progressing well in cloud adoption, and the public sector is exploring cloud solutions for specific use cases. Overall, cloud adoption is gaining momentum across different sectors, although there is still much to be done. A major challenge remains the substantial number of applications and data still hosted on-premises. Companies are developing cloud strategies to address this and leverage the benefits.

A significant driver of this shift is the advent of generative AI, which promises transformative outcomes. Enterprises are eager to capitalise on these advancements, accelerating their move to the cloud. Despite the challenges, the opportunities are immense, and most enterprise verticals are making significant strides toward cloud adoption.

When discussing Gen AI, there's often a concern that India faces a data scarcity issue compared to the West, which can impact training these AI systems. Do you think this is the case, or is there a different perspective?

India, as a large country, has its own unique opportunities and challenges. The availability of data varies by industries, but there is no shortage overall. The focus should be on the kind of data we need moving forward. For instance, large language models and applications designed to address India's specific needs rely on harnessing and building on existing data, particularly in Indian languages.

There is significant progress being made in data across various sectors. However, more can always be done. The challenge lies not in the availability of data, but in effectively harnessing it to produce meaningful outcomes.

Organisations like Snowflake are working to skill people, ensuring there are enough resources from both a generative and data perspective to help organisations tackle data challenges. Additionally, India's digital public goods, such as Aadhaar and the payment stack, operate on massive datasets, demonstrating the country's capability in this area. 

In conclusion, the key issue is not the lack of data but how we harness its power and apply it to achieve meaningful results.

How Snowflake enables enterprises to build LLMs on their own data while ensuring data governance and security?

We believe in being a software-as-a-service (SaaS)-based data platform. This means that customers’ data resides on our platform. Our philosophy is that when applying LLMs or creating applications, the generative AI or LLM comes to the data; the data doesn't leave the platform.

We profess that this is the most secure way to apply generative AI or perform other activities on your data. Snowflake manages all security aspects. In India, we ensure compliance with local regulations by being present in the country's data centers on all major hyperscalers.

As mountains of data are added every second, we aim to be the most secure destination for data. Large financial companies already working with us keep their data on the Snowflake platform, performing analytics and creating applications on it while keeping the data secure and in place.

How does Snowflake enable real-time data processing and analytics for enterprises, and why is this important?

Organisations with a long history, sometimes spanning 30 to 50 years, often possess a substantial amount of legacy data. They also gather new data through various channels, both structured and unstructured. Snowflake offers a secure platform to manage all this data seamlessly.

A significant outcome of using this data is analytics. Organisations can leverage Snowflake to gain insights, such as understanding customer patterns or creating comprehensive customer profiles. For example, a financial services firm might use it for regulatory reporting and data sharing with regulators.

There are numerous use cases where Snowflake proves beneficial. For instance, a large food aggregator delivering millions of food parcels daily can analyse delivery efficiency through rider analytics. Similarly, a financial services company issuing loans can study consumption patterns across different regions.

Snowflake helps these companies extract meaningful analytics for various purposes, including sales, customer service, and strategic planning. Notable clients, such as Piramal Finance and Swiggy, are leveraging Snowflake to achieve remarkable results in their respective fields.

How important is the Indian market for Snowflake's overall business strategy? Any expansion and investment plans?

India has been identified as a key area for accelerated growth from a global perspective at Snowflake. The country's importance is a high priority for our global leadership.

India boasts one of the largest pools of skilled technical professionals and the third-largest startup ecosystem in the world. Snowflake is significantly invested in India, with around 500 employees in our Pune Center of Excellence. These employees handle crucial functions such as support and operations and collaborate closely with our go-to-market teams in India. For instance, the "Snow on Snow" program demonstrates to customers how Snowflake utilises its own technology for various analytics purposes.

We are planning to expand our operations significantly over the next 12 to 18 months. There is a strong focus on increasing our enterprise presence, building and scaling verticals, and forming strategic partnerships with Indian GSIs and ISP partners. Additionally, we are committed to growing the startup ecosystem and fostering strategic partnerships with large enterprises in the country.

Are there any technologies beyond AI that you’re planning to invest in?

From a technological perspective, there are a few key areas of focus. First, there's the core data itself, which continues to see ongoing development and investment. Second, we're heavily investing in Gen AI, including partnerships with industry leaders and organisations to expand our capabilities.

The third critical area is data sharing. Snowflake helps organisations create native applications and break down data silos. We assist companies in leveraging their data by moving it to the cloud and monetising it effectively. Our platform also enhances data sharing both internally and with external partners in a secure and seamless manner.

In terms of new innovations, Snowflake has a range of developments. For example, Snowflake Cortex introduces several generative AI solutions, including Document AI, an SQL co-pilot for developers, and Universal Search. These tools are either available now or will soon be in public preview. These advancements aim to make GenAI a practical tool for enterprise customers.