 Hello and welcome to my career in data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp and today we're talking to my uncle Madden from Lemongrass. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launch pad for career success. Browse the complete catalog at training.dataversity.net and use code dbtalks for 20% off your purchase. Hello and welcome my name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is my career in data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education go to dataversity.net forward slash subscribe. Today we are joined by my uncle Madden the head of data and analytics at Lemongrass and normally this is where a podcast host would read a short bio of the guest and this podcast your bio is what we're here to talk about. My uncle, hello and welcome. Hello Shannon, how are you? Good, how are you doing today? I'm doing great, thank you. Awesome, so tell me, okay, so you're the head of data and analytics at Lemongrass. So tell me, what is what is the business of Lemongrass? What is what does Lemongrass do? So Lemongrass is a software enabled service provider, mainly focused on four key services to start with, we migrate SAP from on-prem to cloud. Second is security operations, SecOps, third is FinOps, finance operations and fourth is data analytics. Now, if I say our core business is basically migrating SAP from on-prem on to cloud, we started by doing it on AWS. We are one of the premier partners and first in the cloud, you know, who actually did migration on to cloud for SAPs. And since then, now we have built partnership with all cloud providers. Oh, that's exciting. So those are major undertakings, yeah? Yeah, yeah, yeah. And as you know, you know, SAP is kind of a business critical application enterprise application for major enterprises. So this is the way to modernize and, you know, get into the modern enterprise. So one of the key element which all of our customers are looking for is how to basically get on to the cloud. How do we migrate this business critical application on cloud? Well, that's very exciting. So as the head of data analytics, what do you do for Lemongrass? What's your typical work week look like? So as a head of data analytics, my focus is specifically how to generate value from data. So understanding data challenges customers are having with respect to how do they use data from business, which is SAP data, along with and combining with external data sources, which may not be business. For example, weather data or trending data. So you can say that what's the current trend in UK, right? And how do we combine that to, for example, understand buying patterns in a particular region? So my focus is, you know, to work with customers, understand what challenges they have in terms of integrating these data sources together, what kind of reference architecture they build on to cloud in a cloud native manner. And then how do they analyze this data together? What are the kind of use cases they have? So these are a few things what I do as part of my role. And my week mostly is basically interacting with different customers. Majorly CIO, CTOs, head of SAP, head of data analytics to understand the kind of problems they have, the kind of use cases they are working on, going through that and working through also technical a little bit on, you know, what does the enterprise architecture look like? What their strategy and roadmap is for data? Very nice. So you work a lot with data yourself? Oh, yes. Yeah. Quite a lot, yeah. Well, we'll get into that a little bit more, but for right now. So let me ask you, is this what you wanted to be when you grew up? Is this the dream? Like when you were six, you know, hope to say I want to be the head of data and analytics? That is a question I would say no to start with, because, you know, I come from a very small town in India, center of India, where I grew up and at that time I was not aware of anything and what I need to do. And being the eldest of the family, you know, a joint family, I was, you know, like thinking of doing anything which, you know, was, you know, presenting itself to me, you know, becoming an actor or doing something or, you know, becoming an engineer. Yeah. So engineering is part of something what I was looking for, but definitely, you know, haven't thought of, you know, becoming and going into data analytics. So well, then tell me, so as you started growing up and developing your passions, you know, what started leading you, where did you go as you got older and started choosing your schooling and things like that? That's, again, a good question. It's a journey, actually, you know, so my town, you know, I didn't have any studies after GCSE or what we say is after 10th. So I had to move out of my town, go to a city to, you know, study more and they are, you know, because as you move to city, you meet new people, you basically interact and get to know, OK, there are options available. And that's when, you know, in 1998, you know, I started doing that, exploring a little bit more, you know, getting that little bit of mentorship from friends, OK, after talking to them. And that's how I got into engineering and I chose computer technology. So I did my engineering there, I was campus selected, I joined a company service provider, very well known, Tecma Hindra. And that was the kind of foundation, you know, when I started interacting and actually working in computers and data. So starting with database developer. Oh, very nice. So you got into data just right away. Yeah. So tell me more about that job and where you progressed from there. Yeah. So when I joined Tecma Hindra, there was no cloud, OK, at that time, 2003 to 2002, I am talking about there was no cloud and the information available on the Internet was also not that much, right? So it was on your own and with the training program you need to learn. So I started working on Oracle databases, you know, operational systems to start with. And as I started working in, my interest grew in terms of that, you know, how these companies use this data. Operational element is one thing, OK, to get things done as a service, for example. But how do they collect this data? How do they store this data and how do they analyze history? So I was working for British Telecom as a client and they have this 21th century network program, you know, roll out of fiber and everything going on at that time. And I got a chance to work on that. How do we basically do forecasting for these net equipment's capacity? OK, so I kind of led that team of data and the BI to start with business intelligence. And that was my journey to start on actually data analytics. But that was purely structured data, what we call in today's world, you know, in relational database. From there, as I grew, I moved on to get more exposure into consulting. So I joined Capgemini, where I was a consultant working for retail and government clients, where I got a chance to, you know, focus more on industry, right? So going through a retail data model, going through that, you know, how the store operates and analyzing data at scale. But again, you know, at that time, the challenge was that how do we analyze data at scale? Because again, people were not moving to cloud at that time. All the systems were on-prem, right? That was a turning point again, wherein I realized, OK, there are new things coming up. And at that time, big data was getting popular. And I'm talking about year 2013, 2012-2013. One of the things what I have done is, you know, as I see and the new technology evolution, I try to do hands-on. So working for a customer, me and one of my colleagues realized that this is a data problem, we need to solve at scale. We can't solve on databases. So why don't we do some pilot and POC? So I started on Hadoop platform, which is, you know, let's do a big data platform and demonstrate to customer. And that's where, you know, I started learning and, you know, doing things on big data, you know, combining other data services together and unstructured data together. And then I joined a company called Hortonworks, which was a pioneer of Hadoop as an architect and later grew in Hortonworks to become an instructor working with customers across Europe, Middle East and Africa, focusing on delivering big data platforms and realizing the value from unstructured data. So I think it's a long journey. After that, continued in big data, joined Vitus Stream as a cloud provider and later on worked for an AI company where, how do we leverage videos and images and analyze videos and images? So it's unstructured data. So it's a 15 journey in short, 15 years of journey in short in how I grew and got an opportunity, but I have kind of experimented, evolved from there, learned from there and then went on. Oh, I love that. And is that when you moved to Lemongrass? After that? Yeah. So Lemongrass is an interesting opportunity, frankly. Why so? Because as I said, one of the key systems customers have is ERP, business critical systems. And not all of, I think more than 50% systems are still on-prem, they're not on cloud. They, so those customers are struggling to innovate and get the flexibility, innovation, scalability of cloud, not realize for them yet, right? Not only that, so that's not my core area of migration, that other core team looks at, but my role came in as, okay, how do we realize? Because one part would be let them migrate in here, but second is how do you innovate from there? How do you combine business data with non-business data in a proven architecture and apply latest technologies and tools and open data ecosystem and tools? For example, now JNAI is popular. So how can I use it? Of course, right now, I can't say yes or no, but it really depends on the use case. And that's how I work with customers and understanding their problem, what they have and accordingly apply kind of other techniques to solve it. So in short, if you say it's a great opportunity because all enterprises have got ERP systems and they want to migrate it and they want to innovate from there, they are looking forward to merge business data and non-business data together. And the opportunity came in at right time and I thought, I think this is a great opportunity to get a diploma across. That's exciting. And Monk, it sounds like you've been chasing a lot of the new texts you mentioned, innovation. As a word in there many times and you became a data architect and as you were expanding and going through your career, how did you learn these skills? And how did you gain these skills? Was it just through experience and where did you get the training and to continue to pursue these advanced technologies? Right. So I would divide it into two, three points, right? So first is to always, always be up to date on the trends which are coming in. It doesn't necessarily mean that you have to do it, right? Because sometimes it could be a hype but you need to experiment it, right? And you can do experimentation with current technologies and tools in the market. You can experiment it very quickly. You can fail fast. So that is something what I do is, you know and whatever foundation I have built in my career, starting with basics, right? Learning SQL, right? Playing with data, playing with structure data that really helps. So combining that foundational data element with awareness in the market, what is coming in and actually doing a little bit of hands-on, right? So at my role, I try if I could do a little bit of hands-on as well, right? Because unless until you do a little bit of hands-on you don't get that confidence even though you have that experience working on different kind of architecture. But if there's a new technology, even if you do a few things, I think you know that it works and then you can confidently talk to your customers. That is something what I look for to do. And third is having people around which are experts. So training is something you can now get it online, right? So you go on YouTube, you go on Coursera. There are so many kind of training portals available. You can go and get yourself trained. So that is I think two, three points I would say people should be doing. More and more companies are considering investing in data literacy education, but still have questions about its value, purpose and how to get the ball rolling. Introducing the newest monthly webinar series from Dataversity, Elevating Enterprise Data Literacy where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at dataversity.net. So that's fantastic. And so what has been your biggest lessons so far in your career? Right, biggest lessons so far I would say would be people, you know, keeping people and keeping mentors around, right? That is one of the key thing, right? As you grow into different roles, it is important that you may learn the technology, you may learn to do the things, but what we are, we just interact with people. Even if we talk to our customers, they are just people. So how do we communicate things? How do we articulate things? I look forward to my bosses when they speak, when they talk, I look forward to my old mentors, my friends, I always go to them. And it's not only the business problems you go to. Sometimes we are human being, right? You may have some problem going on and you need somebody to talk to. So my biggest lesson, which I realized later on, you know, after 10 years of my career is to be open and go and talk to the, you need to have a coach, you need to have a mentor in your life which can provide you, you know, guidance at a right time. And that would be really, really helpful to grow your career. Okay. That is such a good lesson and it's when I can totally relate to, you know, trying to do it so hard for so long by myself. It's just not possible. You need the community. Yeah, yeah. Exactly, exactly. I love it. So, you know, having worked with data, really since the onset of your career, what is your definition of data? So data to me is any information, right? It may be a personal information, business information, where, which you can securely store. Now I'm calling this jargon securely stored because in current world where we are geographically distributed, there are different compliances, different regulations, right? Even for the personal data, right? Securely store and analyze. Analyze in the sense, you know, combined with each other or probably, you know, as it is, you can retrieve and access that data, okay? So that is, that is a data basic definition to me. And now that data could be numbers or facts, could be some information about customer, could be anything. You know, I'll give you, I normally give classy examples, like my gardener, he is still old school. When he comes in, he has a small diary he comes with and he notes down the schedule, right? Okay, I'm going here next. Okay, so I ask for next appointment, he notes down, okay, I'll come on this day. He still maintains that whenever he needs to call a customer, he opens it, he finds out the number and call it, right? And it's secured in his pocket. It's with him, right? So this is a basic example, but, you know, moving that to now cloud terms and all, you can correlate that, you know, you need to have that. And how do you govern it, you know? So all these, you know, principles come into picture. Oh, I love that. That comparison is very, very good. It's still data, right? Even handwritten is still data. Yeah. And so, you know, and having worked again with data for quite a while and leading a company now in data and analytics, you know, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? That's a great question. That's a great question. I would say definitely increasing. Now there are various factors to it, right? And we are in a so fast-paced world now that every day there is a new company coming up on how do, how can they leverage data? How can they, you know, kind of analyze data? How can they apply gen AI? And all, why all these things happening? Why are companies so or new companies are coming up? You know, it's because of explosion of data. Explosion of data is not only, you know, that, you know, now business data is moving to cloud. It is all about 5G is being rolled out. Now people are thinking about 6G advancement in GPUs, you know, like companies like NVIDIA is focused on. Quantum computing is coming in, right? So all this advancement is making it possible what was not possible before. Now, when there is a possibility to do things better, it requires a lot and a lot of data to be combined and analyzed. And that's why, you know, gen AI, which are, which is, you know, created or the model, the foundational model. So LLMs are created based on billions of parameters and days and months of computing, you know, or model training, it is now possible, but it requires a lot of data. And now I see because of the gen AI, we'll see how it basically applies to different industries, but I'll see that more jobs will come in sectors like data engineering, data analytics, machine learning. And the new one would be now, you know, what we are calling it as a prompt engineering or how do we leverage generative AI in the industries and businesses? I was, you know, I was looking at the news day before and Apple has now, you know, committed to invest $1 billion in gen AI. And my view is, you know, if companies like Apple is investing, that means it's gonna impact your daily lives as well. So it is not only business we are talking about now, it is gonna impact your daily lives as well. So I definitely see that, you know, that there will be a lot of more jobs, but alongside, there will be a lot of automation, there will be a lot of, you know, robotics, which will come into picture. Yeah, yeah, it's very true. Yeah, it's a shift, right? And yeah. Next industry revolution we're talking about, yeah. Yeah, and we've seen so much, you know, that as companies try to stand up, all this cool new tech, they realize they also need to invest in headcount, which manages data governance and quality and the prep of the data. Exactly, exactly. That becomes crucial, you know, and very, very important. Yeah, absolutely. So then, you know, especially, you've given some great advice already, you know, but especially for anybody who's just now just starting to look for a career in data and consider that possibility, especially as data has become a mainstream topic, what's your advice for somebody getting into data? So my advice would be that to learn the foundation of data, you know, data principles to start with, you know, like I gave an example of how do you store data, analyze and process data? How do you apply governance and security? The basic principles, get on to learn basics like Excel. You know, that's a very good way to learn analytics, right? Even, you know, by using workflows and formulas, you get to learn more, right? Learn SQL, right? Which is the foundation of, you know, structured data, you know, a lot of new also companies which are coming in, you know, you can use NC SQL to, you know, query business data. So these are, you know, some foundational things which you should be doing. Apart from, get your hands dirty, you need to do it, you know, without doing it, without doing a lot of, lot of reading, it is not gonna happen, right? You know, if you see that, okay, you'll go on a project and then start doing it, don't wait for it, you know, people who are already doing, in the university, they need to build something. And it is so easy to do it these days. You can log on to cloud, cloud provides free tier, you can just do experimentation there. So get your hands dirty, I would say get hands on experience at very early stage of your career. Such great advice. And like you say, there's so many different places to achieve that possibility. Exactly, exactly. Learning is like very easy these days, right? It is just that, how do you learn and what to learn? And that's where your mentors and coach, you know, comes into play. So you can, you know, kind of structure your career path, you know, from early. Right, so important. You know, it's funny too, we've been talking a lot about that here, you know, at the university taking the time to learn, not just do the work and get the work done, but taking the time to learn so that we can also progress to the next step, right? Like if we're not growing ourselves, then we can't grow the company, right? Exactly, exactly, exactly. Yeah, I love it. Mark, and I would be remiss if I didn't ask, if people wanted to learn more about Love and Grass, where would they go? So they can reach out to us, like they can reach out to, first of all, they can go and understand, you know, our services on our website. They can reach out to me, they can reach out to any of, you know, our team, and we provide details of our offices and our contacts on the website as well. We are doing loads and loads of events, okay? Like SAP Insider, we go to AWS Reinvent, we go to Google Next, we go to, you know, events with Microsoft, our partners, right? So we can meet there, we are meeting our customers and our prospects and there as well, and talking about our services, you can contact us and, you know, arrange a workshop with us, where we talk about in detail, and we have got a great references of our tier one customers, okay? We have done thousands of, you know, SAP migration, SAP systems migrations, okay, we are expert in that. So we can, you know, talk through about all of it and required, you know, we can provide references. Oh, perfect. And what's the URL for your website? It's 11grasscloud.com. Perfect. Well, and we will post that link into the podcast as well, so y'all can get that information. Mya, it's been such a pleasure to get to know you and hear about your journey. Likewise, and really, really looking forward to, you know, be working sometime next time as well and talk about, you know, how the trends are going in data and how the things are changing in the world. Oh, I love that. I really look forward to that too. Mya, well, thank you so much again for taking the time to chat with us today. Yeah, thank you. And for all of our listeners out there, if you'd like to keep up to date on the latest podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe. Until next time, stay curious, everyone. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational webinars at dataversity.net forward slash subscribe.