In a dynamic landscape where data reigns supreme, Snowflake, a data platform company, is eyeing boundless opportunities for India’s burgeoning digital economy. Leading this charge is Vijayant Rai, MD (India) of Snowflake, who sheds light on the company’s strategic priorities and vision for the future. As Snowflake marks its fourth year in India, Rai emphasizes Snowflake’s focus on scaling its enterprise and commercial businesses, expanding its partner ecosystem, and pioneering India-scale solutions. Rai discusses Snowflake’s cloud-agnostic approach, making its platform available on Azure and AWS in India, and emphasizes the pivotal role of data strategy in unlocking the full potential of AI. “You need to have a data strategy before you have an AI strategy,” Rai asserts, highlighting Snowflake’s expertise in empowering organizations with a robust data foundation.
Some edited excerpts from the interview:
What are some of the key priorities for Snowflake India this year? Request you to also comment on short-term and long-term initiatives?
Snowflake is about a four-year-old organization in India. I would say its in its infancy from a coverage perspective, as we’re building to the next level. Some of the key areas we’re going to build on are our enterprise business to the next level. When we started the operations here, it was during COVID, so the first wave of customers we had were mainly from the digital native segment. Lots of them and really good ones. That’s a segment which continues to grow very well for us. For example, some of the largest food aggregators. My charter now is to expand the overall coverage into different verticals.
As we grow the business here, we would be looking at verticalization with verticals like BFSI, digital natives, manufacturing, public sector, etc. We will also look at a bigger foray in the enterprise segment. And so that’s going to be the largest focus in terms of building our enterprise business. We would also look at expanding our commercial business and scaling it, that served us very well. Commercial business looks at the SMB and the mid-market for us. We would be looking at expanding that and scaling that. And then the third piece is expanding our partner ecosystem. So it’s more of an expansion focus that we will have right now in the immediate term. In the long term, we would be looking at creating some India scale solutions and interventions, because we believe as a data platform, we have that opportunity to build out solutions for very, very large problems. So it could be for the public sector, could be for different enterprises. Right now its called ‘powered by Snowflake.’ So we have people building on our platform and then offering different solutions for different use cases. We would be looking at scaling that to the next level.
So you have ISVs as part of it?
Yes. So ISVs would be a very big part of it. We already have a very large set of ISVs, which are ‘powered by Snowflake’, which is that they run their solutions on the Snowflake cloud. And obviously that gives them a lot of reach in terms of not only India as a geography, but globally as well. So a lot of Indian ISVs currently are already working with us. But the opportunity to scale it to a larger set is definitely there. So we’d be looking to expand in ISVs.
Any specific areas within the ISV ecosystem?
We are currently seeing traction in areas of customer experience. A lot of the customer experience ISVs are working with us. They basically look at the data platform for use cases like analytics of their customer inputs. So a lot of sales analytics and customer analytics. So right now, the areas of traction are areas like customer experience is one. But we have some very niche and very interesting ISVs as well. This includes players from the electric ecosystem or ESG ecosystem, etc. These are extremely interesting use cases where they’re actually catering to utility companies for very specific solutions for green energy. So pretty much anything which is data-driven, but obviously, larger scale is where we will probably play a bigger role.
Are there plans to make Snowflake more of horizontal platform where your ecosystem of partners build public data sets, which when combined with your expertise in data can translate into a huge opportunity?
So I see this as a very big opportunity. So if you look at it, it’s threefold. At the first level, when you look at enterprises, it’s sort of breaking the data silos, getting on-prem stuff together, all of that. The second level is when you start sharing it with your ecosystem. For example, if you’re an airline, can you share data with, say, a hospitality company or a car rental company and unlock the value of that data for cross-sell, up-sell usage. And then the third is, like you said, you create those data sets and you actually put it on our marketplace and monetize it. That is happening at scale even now, especially in the U.S. And we believe that’s a massive opportunity in India. So we have a reference story of BlackRock. BlackRock has this application called Aladdin, which is on the Snowflake marketplace. It’s a data set which AMCs globally can use to enrich their data and work on. So that’s the sort of opportunity which Indian organizations will have. So, like I said, the maturity as you get to that for enterprises, of course, breaking silos. Then ecosystem, and then creating a marketplace. It doesn’t have to happen sequentially. You can do it parallelly.
So you already have this marketplace in place in India?
Yes, the marketplace is globally available. Additionally, we are collaborating with bureaus and other agencies that possess datasets relevant for various use cases. For instance, in financial services, if you’re providing a loan, you might need to access data from sources like CIBIL. Currently, this involves API integration. You can bring those data sets into the marketplace where people can then quickly bring in… because the advantage is, if you are a customer on Snowflake, the dataset is there natively, making it much faster and secure, so the data does not get compromised. We believe the biggest opportunities will happen around areas like data sharing and data collaboration. Today, as you look at unlocking the value of the data, you have a lot of opportunities. Very large conglomerates, for example, are talking to us to figure out how they can share data between their different companies, some of which may be separately regulated. We have a solution called “data clean room”. Data clean rooms are a very key solution, especially from an Indian context, where we have different regulated entities. It’s very important because regulation doesn’t allow Personal Data (PI Data) to go out. What the solution does is anonymize and encrypt the data, creating certain hashtags so that no PI data moves. It does a matching, and then you can actually monetize or share that data.
Can you give us an example?
Imagine there’s a food delivery app already on Snowflake, and you are in a different industry. This can be automobiles or airlines, and you want to utilize the same solution. The discussion is not just about de-duplication of customer sets or data warehouses or data lakes now; it’s moving towards sharing and unlocking the whole value, which has tremendous potential. In our marketplace, unlike marketplaces from other very large vendors, it’s essentially applications, but in our marketplace, it’s data sets. So you come to our marketplace because you want to have your data set there to be monetized and for others to utilize.
Purely from an opportunity point of view, where do you see yourself as a solutions company?
We call ourselves a Data platform. We are a SaaS platform and that’s a key differentiation against a lot of players where you need to build the platform in a PaaS format. In our case everything is already built. Long time back, when the company started, we separated storage from compute, which unlocked a lot of innovation and elasticity. So what we do is whenever we add any capability, it is immediately available to our customers. You don’t have to set up a separate system for it. Our philosophy is that your data stays in one place, and you bring applications to the data. This eliminates concerns about data security and governance. This is a big differentiation. Another interesting differentiator Snowflake has is that we are cloud-agnostic. We are available in all the clouds globally. In India, we’re available in two clouds, which are Azure and AWS. But globally, we are available on all three clouds. Being multi-cloud differentiates us from others in the same space because they wouldn’t have that advantage of being on different clouds.
AI is also talked about a lot. We also are heavily invested in generative AI because we believe that that is a substantial unlocking of value for customers. But the key thing we profess to every one of our customers is that you need to have a solid data foundation. You need to have a data strategy before you have an AI strategy, because if you don’t have a data strategy, you don’t have an AI strategy. So the foundation has to be strong. You need to have, all of our data in one place, governance, all of those tenants of data, to utilize the benefits of Gen AI. You would have heard the announcement of our new CEO sometime back, a few weeks back actually, Sridhar Ramaswamy. He comes in from an acquisition which Snowflake did called Neeva, which was focused on AI. He was also running our AI charter for the past one year before he got announced as CEO. And he created a framework called Snowflake Cortex. And Cortex was essentially looking at creating our generative AI play. And we currently have three solutions in public preview. They should be announced shortly for general availability. There’s one for document, it’s called Document AI, which is essentially a large language model which can look at documents, summarization, all of those things that you do on text. The second is a solution for SQL. It’s a co-pilot for SQL. And the third is a solution for Universal Search or Enterprise Search. These are all generative AI solutions, which will run on the platform natively. We’re building our generative AI strategy.
Any reason why these specific areas have been chosen? Document, SQL and Universal Search?
If you look at Neeva when it started off, when we acquired them, they were looking at Enterprise Search. And that’s how Universal Search came by. And of course, Document AI is a very specific area. There is huge opportunity in terms of looking at documents and then getting intelligence out of it. And SQL is because we are in data and it’s all about querying data. If you are a person who needs to query data, you’ll actually now have a co-pilot. Instead of writing queries in simple English language, you can query and get your outputs. Its very complimentary to our data platform strategy.
Where do you think the growth will come from for you? Would it be in the data sets?
India still is a very large on-premise country. Very little data has moved to the public cloud. For a SaaS company, the biggest opportunity is on-premise, helping enterprise customers – to educate them, move data to the cloud, to get them, to show them the value of the public cloud and a SaaS solution like ours in the cloud and what that unlocks for them. That’s the biggest opportunity because a huge amount of data is still out there. Breaking silos is the second one because enterprise data is scattered all over the place. That’s a massive opportunity. We believe that the next level of growth will come from ecosystem and data sharing. Which is where we want our customers to get to, because then they start monetizing the data.
Everybody talks about data as a new oil, but if you’re not able to sell it or monetize it or utilize it, it stays in the ground.
What significance does data sharing hold, particularly for big conglomerates who have cross sell opportunities as well?
Individuals within large enterprises tend to be protective of their data, viewing it as their own. This cultural aspect highlights the need for executive sponsorship at various levels to encourage a strategic approach to data sharing, preventing the reemergence of silos on the cloud. Addressing the softer side, fostering a data-driven culture is crucial, alongside executive sponsorship to enforce this approach. Otherwise, familiar issues may resurface.
Would you consider running hackathons for startups to develop datasets, aiming to break down silos within enterprises?
In enterprises, we implement this approach to break silos. They observe that picking up data from one department often resolves specific issues, minimizing duplication. If data resides in Snowflake, retrieval is straightforward, eliminating the need for data movement. Hackathons focus on unlocking use cases collaboratively with ISVs and partners. Recently, I discussed with a partner exploring customer experience analytics and integrating it into their GTM strategy. Hackathons serve as a catalyst for both scenarios.
From a Gen AI perspective, are there specific areas in India that you would like to highlight where you believe there might be a large, potentially unfulfilled gap that could be addressed?
I think in Gen AI, again, having the right data, the right data sets, and all of that can be substantial unlocking in most large areas. For example, in enterprises, we see legal as a significant unlocking point, right? I mean, legal departments have piles of data. And you can apply the same argument to the public sector and the legal system we have in the country. It needs to get the foundations right, obviously, which is a humongous task, as you can imagine. But just think about it, if you do that, the capability to query old cases in the legal system could be unbelievable. There are very simple use cases, for example, which we’ve seen in generative AI of an insurance company, where you have a transcript of an accident. And you basically use something like Document AI. It has all the data behind, and you ask a question like, where did this accident happen? It’s Mumbai. Did somebody get hurt? No. And then you ask a bunch of questions, like should I approve this claim? It says yes. And then you give me three good reasons. The reasons you give are probably better than what a human would think about. And that’s the advantage of something in generative AI because it can deal with that level and volume of information, which possibly a human would find tough to do. So in those sorts of use cases, it could be legal, contracting, insurance, underwriting, all of that. So those would probably be in my mind, and we’re working with one of the largest insurance companies in the country, in the private sector, actually. And we’ve recently signed up with them. So we would be, you know, and those are the sorts of use cases they’re discussing, saying that once we are good with our data foundations, these are the sorts of things we want to do. But the thing is, you should do it when you’re prepared. If the data foundation isn’t there, then, you know, it’ll just be half-baked, and you might end up redoing the services part also.
Do you engage in the consultative process of cleaning up the data or establishing the appropriate framework?
So typically, what we do is provide various tools for organizations to examine their foundations, but we collaborate with partners. We work with all the major advisory companies, niche companies as well as regional players, who handle the advisory aspects for customers and even deployment, including any necessary customization and other requirements.
Can you share any indicative examples highlighting India’s growth story specifically?
If you look at the organization, it’s about 12 years old now. We are four years old in the country. So the comparison would not be a straight one. But for us, the headroom is massive. I mean, we’re just starting off. So when I look at, like I mentioned in the beginning, the space for us to expand in the enterprise, we have a bunch of marquee anchor customers we’ve already acquired. But what we can scale up is completely open to us. Second is the opportunity on just the on-premise piece to scale to the next level on the data side. And then the new innovative use cases around collaboration, sharing, the data clean room, for example, which we bring to the equation. That will give us the growth. But globally, we just announced our results. It’s publicly available. So, you know, in my mind, since we are at a small base, we should be growing much, much higher. That’s the sort of expectation I have. And that should be the case. India has been identified as a fast track territory for Snowflake globally.