3051: Data-Driven Innovation: Cloudera on AI and Leadership in Modern Tech

[00:00:04] How does one balance a thriving corporate career while also the entrepreneurial drive to launch multiple startups?

[00:00:12] Well, today I'm going to be digging deep into this question with my guest from Cloudera.

[00:00:18] And he's been on a remarkable journey from moving from India to the US to pursue a Masters in Computer Science.

[00:00:25] My guest today, he's built an impressive career in data and AI leadership.

[00:00:30] That's only a very small part of this story. Alongside his corporate success, he's founded two innovative startups,

[00:00:38] Farmioc, an agriculture data analytics platform designed to empower Indian farmers and so much more.

[00:00:46] But I don't want to reveal any spoilers.

[00:00:47] So I want to learn more about how he makes it look so easy to skillfully balance these two worlds

[00:00:52] from navigating the entrepreneurial landscape to shaping industry trends in data and AI at Cloudera,

[00:00:59] and how he's helping businesses embrace hybrid and multi-cloud architectures, generative AI and modern day frameworks like Lakehouse, Fabric.

[00:01:09] So what does it take to thrive in both corporate and startup worlds at the same time?

[00:01:14] And how can businesses leverage these emerging technologies to drive their own transformation?

[00:01:19] Let's find out.

[00:01:21] So a massive warm welcome to the show.

[00:01:25] Can you tell everyone listening a little about who you are and what you do?

[00:01:29] Hello, Neil. Thank you for having me on the show.

[00:01:32] My name is Surinu Talapati and I'm currently the data and AI practice lead for Cloudera Professional Services in the Americas.

[00:01:40] I lead teams in delivering data lakehouse, machine learning, AI ops, and generative AI solutions.

[00:01:47] I also oversee the development of our accelerators for AI and data lake house, ensuring our customers achieve successful outcomes with our solution delivery.

[00:01:58] In addition to my corporate role, I'm also the founder of two tech startups, Tridal Apps, which is a cloud-native SQL editor platform and a farm-yacht and agriculture data analytics platform on marketplace.

[00:02:14] Well, I'm looking forward to finding out much more about your work and everything that you do there.

[00:02:19] But although this is a daily tech podcast, one of the things I love doing is also shining a light on the origin story of my guests.

[00:02:27] And when I was doing a little research on you, I was thinking about your personal journey, which took you from growing up in India to building a successful career in the United States.

[00:02:36] And I suspect that background has shaped your approach to technology and leadership today, too.

[00:02:42] But for anyone listening, can you just tell me a little bit more about that story and that personal journey and how it shaped your career?

[00:02:50] So growing up in India, I was always fascinated by technology, which, you know, drove me to pursue my master's degree in the computer science in the United States.

[00:03:01] So I moved here almost a decade or like a half years back.

[00:03:06] So post my graduation, kind of opened my doors for my role set of FedEx and the charter communications and also eventually the cloud era.

[00:03:16] But honestly, a pivotal part of my journey has been the decision to venture into the entrepreneurship while working in corporate roles.

[00:03:27] So I founded two startups, as mentioned earlier, Farm Yak and Trade Labs, along with my full time work here.

[00:03:34] Coming from an agriculture background family in India.

[00:03:38] So I really wanted to build something for the Indian agriculture sector.

[00:03:43] So I started Farm Yak with the problem of how we solve the problem of agriculture sector, especially in India with the crop yields market strategies.

[00:03:54] How do they get better profits and make it more sustainable?

[00:03:58] So we developed an agriculture data analytics platform that imports traders and farmers with the data driven tools and also the dashboards and price discovery to optimize everything from crop yields to marketing their products.

[00:04:14] So with the trader labs, I created a cloud native SQL editor for modern databases.

[00:04:20] So this tool basically connects to almost 100 databases.

[00:04:26] It supports connecting 200 plus databases where data teams can use it for querying and building dashboards on their data.

[00:04:37] And then they can also collaborate with their team members to write queries for their development work.

[00:04:44] So balancing these two ventures alongside my corporate role has really shaped my approach for technology and the leadership.

[00:04:52] It has taught me the value for adaptability, empathy and fostering the innovation culture.

[00:05:00] Whether I'm leading the startup or contributing at the cloud era, I learned that, you know, great leadership is about creating an environment where people feel empowered to innovate and take ownership of their work.

[00:05:13] What a great story.

[00:05:14] What a great story.

[00:05:14] I love the journey you've been on now and your passion for farming, agriculture and how it's come together with technology to solve real world problems.

[00:05:23] And as a result, you've had an impressive career progression, including, as you mentioned, Trader Labs, Farmy Arc.

[00:05:31] I've curiously, how did those entrepreneurial ventures influence your perspective on data and AI?

[00:05:38] Because I think we looked at our news feeds, we've seen all the AI and data aspects, but it's your unique approach and story that seems to be shaping everything here.

[00:05:48] Yeah.

[00:05:49] Thank you, Neil.

[00:05:49] So, so founding Trader Labs and Farmy Arc gave me a hands on understanding of immense power of the data and AI, all in transforming these industries, right?

[00:05:59] So at Farmy Arc, I saw how data could be used to solve the critical problems in agriculture sector for optimizing crop yields to enabling smarter trading decisions.

[00:06:10] Agriculture in India is especially data rich, but it's a very underutilized industry for tech, especially.

[00:06:19] There is a very, the technology is not widely used across India for the agriculture sector.

[00:06:26] So for that, what we had to do was like, we had collected data from multiple sources, whether it's trading or acreage or yields or, you know, satellite data, weather data and all kinds of this data that we accumulated.

[00:06:41] So we developed machine learning models to analyze these real-time weather patterns, soil conditions in those areas, and also the market fluctuations with the trading each year.

[00:06:54] So we pro we build those machine learning models to kind of provide insights on like what's the next year accuracy is going to be, what's the next price for the paddy or whatever the pulses or anything is going to be.

[00:07:08] So this kind of helps the agriculture sector reduce waste and improve the sustainability.

[00:07:16] And also with the latest innovations in the generative AI, this agriculture sector, I think we can take it further.

[00:07:25] So similarly with Trader Labs, we are trying to simplify how the data teams interact with the modern databases.

[00:07:33] So we created that collaborative tool to empower them to, you know, kind of tackle advanced analytics and use cases more efficiently and then collaborate with their team.

[00:07:46] So these two ventures have reinforced my belief that, you know, when applied thoughtfully, data and AI can unlock immense value, not only for larger enterprises, but also for the traditional industries like agriculture as well.

[00:08:01] So this perspective has deeply influenced my approach to technology where I focus on leveraging data and AI to drive meaningful real-world impact with any of the customers that I work with.

[00:08:14] So today in your role at Cloud Air, how are you helping enterprises leverage data and AI to drive transformation and innovation in your customers' businesses?

[00:08:26] Can you tell me a little bit more about that?

[00:08:27] So in my role, I guide and mentor customers through their data and AI adoption journey, whether that involves scaling AI applications into production or developing data-driven solutions using Cloud Air as a suite of products.

[00:08:43] So I work with many customers over time.

[00:08:46] So we recognize that each organization's data and AI journey is unique.

[00:08:52] So we collaborate with our customers on a day-to-day basis to develop tailored solutions that address their specific needs.

[00:09:02] From initial strategy to building a proof of concept or scaling them solutions into production as well.

[00:09:09] So for example, we recently helped one of the largest European telecom to fast-track their AI project from proof of concept to production in just four weeks on the Cloud Air stack.

[00:09:24] All right.

[00:09:24] So I work for professional services within Cloud Air.

[00:09:30] So we offer a comprehensive portfolio of services.

[00:09:33] Our smart services covers like AI development, streaming analytics, data lake house, and many other use cases where customers can partner with us to kind of reduce the time to production and then release their potential of data and AI initiatives.

[00:09:51] Love that.

[00:09:52] And for people listening around the world, Cloud Air does have somewhat of a reputation for its AI initiatives.

[00:10:00] And it's all everyone's talking about at the moment.

[00:10:02] So to bring to life what we're talking about here, do you have any recent use cases or success stories that you've seen of how AI has significantly impacted business outcomes?

[00:10:13] Because there's a lot of businesses at the moment, they want to do stuff with AI.

[00:10:17] But it's what business value does it generate?

[00:10:20] What problem are we solving?

[00:10:21] And this is something that I know you're very passionate about.

[00:10:25] Yeah, sure thing, Neil.

[00:10:26] So Cloud Air has been at the forefront of this AI innovation that's been going on and helping business unlock their new possibilities through the power of data and AI.

[00:10:38] So one of the examples that I could provide is the OCBC bag recently, which leveraged the Cloud Air's platform to power their next best conversation, a centralized machine learning platform that analyzes real-time customer interactions.

[00:10:54] So this enabled the OCBC bank to deliver 250 million personalized insights annually, improving financial management and significantly increasing the campaign conversion rates.

[00:11:08] So additionally, OCBC used the AI-driven chartboards to handle 10% of their customer interactions, streamlining services and also boosting their operational efficiency.

[00:11:20] Similarly, Bank Negara Indonesia partnered with Cloud Air to accelerate its AI-driven use cases, applying Genry to AI especially to enhance risk management and decision making.

[00:11:33] Also, Japan Exchange Group, which leveraged the Cloud Air to create their G-Lake, a data service platform for real-time analytics and AI.

[00:11:47] By centralizing data management and applying advanced analytics, it significantly improved their operational efficiency.

[00:11:54] This helped them scale business operations and accelerate their digital transformation as well.

[00:12:00] So these are some of the success stories in recent times from our customers, Neil.

[00:12:07] Love that.

[00:12:08] And I'm curious, based on your experience founding Trider Labs and Pharmaoc, were there any lessons that you learned from those days about applying data and AI that have been valuable in your current role at Cloud Air?

[00:12:21] Because very often we don't realize at the time just how much we learned that and how relevant it will be in the future.

[00:12:27] But is there any lessons that you're using today in your current role?

[00:12:32] Yeah, definitely, Neil.

[00:12:34] So one of the most critical lessons that I have learned from these startups that I have built is addressing the business problem rather than starting with what technology to use, right?

[00:12:48] So at Pharmaoc, we began to focus on how we could help traders and farmers, especially the agriculture sector in India, to make decisions using data.

[00:12:59] So this helped us finding what data sources we should collect.

[00:13:04] How do we build?

[00:13:06] What should we build to give the users that better decision-making capacity?

[00:13:12] That's where we start of using the technologies like machine learning and other analytics to build that intelligence to provide to the users.

[00:13:23] Also with the Trider Labs, same thing.

[00:13:26] How we started with how data professionals can query the modern databases, right?

[00:13:32] As each day there is some new database coming in, whether it's in cloud or on-premise.

[00:13:37] So making the process more efficient, user-friendly for users to query the databases is what we thought with Trider Labs as well.

[00:13:48] So addressing the business problem first, considering that as the core is the very important aspect.

[00:13:54] And also when building something, scalability is also another thing that we got to consider, whether this can scale to the next level when there is a huge increase in the user base or huge increase in the use cases as well.

[00:14:11] And another thing that I firmly believe is the data trust and governance has proven to be crucial as well, especially for the AI-driven solutions.

[00:14:20] So at Pharma, for example, having the right data and then having the trusted data for building the machine learning models is a key point for providing better insights, right?

[00:14:35] So those are the three key areas I still take whether I'm working with a clouder or any other customer.

[00:14:42] Addressing the problem and scalability and data trust and governance are key aspects for me.

[00:14:48] And of course, the data and AI landscape is evolving incredibly quickly right now.

[00:14:54] So are there any industry trends that you think will maybe define the future of AI and cloud computing in the next few years?

[00:15:01] I know it's almost impossible to predict the future and it's moving at such breakneck speed.

[00:15:06] But are there any trends that you're noticing here?

[00:15:08] Yeah, yeah, it's a great question, Neil.

[00:15:11] So there are several key trends that are set to define the future of data, AI and cloud computing as well.

[00:15:18] So I got three of these things that I have observed in the market.

[00:15:24] So one of the first thing is hybrid and multi-cloud architecture are gaining importance.

[00:15:29] So allowing organizations to leverage both public cloud and on-premise seamlessly for flexibility, scalability, and also the local compliance with the local regulations.

[00:15:42] So this hybrid approach is increasingly becoming a foundation for the data and latest AI workloads.

[00:15:50] And the second thing is another significant trend is the raise of generative AI and large language models, which are transforming industries by enabling businesses to create more advanced applications like chatbots and AI assistants.

[00:16:07] AI model inference and deployment will play a key role in this, scaling AI applications from PVC to production and enable the customers to deploy and host open source models privately on their own VPC at a lower cost.

[00:16:25] And then third thing is the adoption of modern data architectures like Data Lakehouse, Data Fabric, and Data Mesh that supports diverse analytic workloads are also increasing.

[00:16:36] So this enables businesses to break down the data silos across their organization, providing more seamless access to data and support real-time data on the AI applications.

[00:16:48] So these are the three major trends that are going on in the industry based on my perspective.

[00:16:53] And the question I've got to ask, of course, is how are you at Cloud Air positioning yourself to remain at the forefront of some of these trends?

[00:17:01] And what would you say are some of the companies' most exciting innovations in AI and data management that you're able to share today?

[00:17:08] Yeah, definitely, Neil.

[00:17:10] So Cloud Air has been in the forefront of AI with its latest innovations.

[00:17:15] So within Cloud Air products, we have AI assistants like SQL AI Assistant and Cloud Air Co-Pilot, which helps users leverage AI for their data analytics and SQL queries.

[00:17:27] The inbuilt AI assistant tools, let's say user automate insights, building queries, or improving, you know, developing their data analytics use cases.

[00:17:38] So we have AI offerings with the Cloud Air products as well.

[00:17:43] And along with that, Cloud Air also introduced AI inference service, enabling enterprises to scale AI applications rapidly with high performance and security across hybrid environments.

[00:17:56] I'm so excited about this new inference where we can host open source models faster and securely on the customer VPC.

[00:18:07] Also, the acquisition of VARTA strengthens Cloud Air's operational AI platform, enabling customers to better manage AI models at scale.

[00:18:17] And most recently, Cloud Air announced a new suite of accelerators for machine learning projects called AMPS.

[00:18:24] So these are pre-built to open source machine learning projects that helps organizations to deploy solutions rapidly with these pre-built projects.

[00:18:36] So one of the AMP that we have built from a professional services arm is the DocGenius AI.

[00:18:43] This is a chatbot reference architecture for the chatbot based on your documents.

[00:18:52] Another exciting initiative is Cloud Air's Open Data Lakehouse powered by Apache Iceberg, which offers a unified scalable environment for managing data across public cloud and on-premise as well.

[00:19:05] So these are the key things that Cloud Air is focused to position itself in the forefront of these trends.

[00:19:10] So looking further into the future, especially now as we're months away from life in 2025, what do you think are the biggest opportunities and indeed challenges for organizations out there at the moment that are trying to harness the power of data and AI?

[00:19:27] Yeah, good question.

[00:19:29] So the biggest opportunity lies in the ability to monetize the data and use AI to drive business transformation.

[00:19:37] So organizations that succeed in integrating AI into their day-to-day operations can experience significant improvements in decision-making, customer experience, and operational efficiency as well.

[00:19:51] So AI is poised to take accelerate innovation in industries like healthcare, finance, and retail.

[00:20:00] However, there are also challenges.

[00:20:03] One key issue with the AI especially is the data governance and complaints.

[00:20:13] So business needs to ensure that AI models are transparent, fair, and align with regulatory standards to avoid any consequences or discrimination.

[00:20:26] Additionally, scaling AI from pilot projects to production remains a significant challenge for many customers.

[00:20:33] So that's something that organizations are trying to help as well to help customers deploy AI solutions faster from POCs to production.

[00:20:45] And also, there's almost a constant pressure for us all to be in a state of continuous learning.

[00:20:52] And as someone that's leading the way here, I've got to take a peek behind the curtain and ask, how do you continuously learn and self-educate and keep up to speed with these trends?

[00:21:03] Is there any tips you can share around that?

[00:21:05] Yeah, you're absolutely right, Neil.

[00:21:08] So continuous learning is vital in our rapidly evolving field.

[00:21:13] And I make it personal priority to stay ahead of the curve.

[00:21:17] So I self-educate through various methods, either it's reading books like Atomic Habits or Psychology of Money,

[00:21:24] or taking online courses on emerging technologies,

[00:21:27] also working on hands-on projects on my startups,

[00:21:31] and engaging with the community through conferences and online forums as well.

[00:21:37] And another thing I do on a day-to-day basis is also mentoring others and speaking at the events,

[00:21:43] reinforce my knowledge.

[00:21:45] And also the podcasts and audiobooks also keep me informed during my break times or when I have a free time as well.

[00:21:54] So by integrating these practices and my daily routine,

[00:21:59] I maintain the mindset of that curiosity and learning what's going on in the industry as well.

[00:22:05] Lovely.

[00:22:06] Well, I can't thank you enough for coming on and joining me on the podcast today and sharing your inspiring story.

[00:22:12] But for anyone listening, just want to find you or your team online and find out more information about anything we talked about today.

[00:22:19] Is there anywhere you'd like to point everyone?

[00:22:21] Yeah, sure.

[00:22:22] Definitely, Neil.

[00:22:23] So thank you all for listening.

[00:22:25] So anyone who wants to check out me, please check my profile, linkedin.com slash in slash Thamuaiwo,

[00:22:35] where I frequently share my insights on cloud data and AI.

[00:22:39] And also please explore my startup.

[00:22:41] So try the labs.com and the farmiac.com as well.

[00:22:45] To learn more about the work we are doing at Cloudera,

[00:22:48] you can visit cloudera.com for insights and the industry reports on data and AI.

[00:22:53] Thank you again for all listening.

[00:22:55] Thank you, Neil, for the opportunity to share my thoughts and expertise through this podcast.

[00:23:01] Well, a huge thank you to you, my friend, for taking the time out of your busy day to sit down and share your story of the journey from India to the US,

[00:23:10] the career progression, entrepreneurial ventures with everything from Trider Labs to Farmioc.

[00:23:17] And I love how your origin story began in farming and agriculture, but you're using technology to solve some of those big problems.

[00:23:25] And then obviously now present day, big data and AI and modern enterprises with your role at Cloudera and also AI initiatives.

[00:23:35] So many success stories and the future trends in big data, AI, cloud computing.

[00:23:40] We could have talked about this for another hour, but thank you so much for sitting down and sharing your story today.

[00:23:46] Yeah, thank you so much, Neil.

[00:23:49] So what stood out to you from listening to today's episode?

[00:23:53] From my guest's insights into balancing entrepreneurship with a demanding corporate role to his vision for data and AI's impact on the future.

[00:24:02] So much to reflect on.

[00:24:04] And as we wrap up, think about the lessons shared today, whether you're an entrepreneur, a tech leader, business leader or someone navigating the intersection of business and technology.

[00:24:14] How can you apply these ideas to accelerate innovation in your own field?

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[00:26:35] Bye for now.