 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 Jatine Solanke from DeCube. With a robust catalog of courses offered on demand and industry-leading live online sessions throughout the year, the DataVersity Training Center is your launchpad for career success. Browse the complete catalog at training.dativersity.net and use code DVTOX 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're joined by Jatine Solanke, the founder of DeCube, and normally this is where a podcast host would read a short bio, the guest, but in this podcast your bio is what we're here to talk about. Jatine, hello and welcome. Hey, Shannon. I'm super excited. Thank you for having me here and excited to share my journey and how I reached to this position. I'm so excited to connect with you and so tell me you're the founder of DeCube. So tell me what type of business is this? What have you started here? Well DeCube is a unified platform which manages data observability, data catalog, and governance. And the reason why we have a unified platform is because we don't want people to juggle between multiple applications and also the biggest struggle of a data leader or data managers is the single source of truth. And that's exactly what we're trying to solve with our unified platform and that's what DeCube does at a very, very high level. I love that a lot. It's definitely a need out there for sure. We hear that from our community all the time. So you're in a sweet spot there for sure. So what do you do then? What does your typical work week look like as a founder? I'm sure you work with lots of data yourself. Yeah. As a founder of DeCube, my job is to bring the data domain expertise within the team itself. Our team comprises of software engineers, data engineers, and even machine learning experts. My job is to ensure that everything is tight and we are focused on one region itself. How does a typical week look like? Though I'm a founder, but we need to sell what we build. So I spend a lot of time on the discovery call talking to data leaders of what kind of challenges they are facing, especially around data observability. And we try to see whether DeCube can fit into that environment. And if it doesn't, I also take the feedback from them and also that will help me to sharpen the product for the future roadmap. So that's precisely how our week looked like for me. And I love doing that every minute, every day, every week. Yeah, you have to be passionate about something to found a start a company, right? Absolutely. I mean, you have to be passionate. You have to love what you do. Otherwise, you don't have the fire within you. And if you are not motivated enough, it's quite a challenge for you to bring that same motivation within your employees and get the similar kind of output. So tell me, was this the dream when you say you were six years old? Did you say I'm going to grow up and I'm going to found a company to solve data governance problems? Honestly, no. My dream was, I was very, very fascinated with the commercial pilots. So I always dreamt like, how can I be the commercial pilot? And today also, I do have a flight simulator at home, which is like a proper flight simulator. I do enjoy that. It's also one of the areas you can love flying aircrafts and all. But of course, I didn't sort of focus on that. I want to be a founder of a startup and all. But the way it actually, the journey is more from an in-section point of view, where my father was an entrepreneur. So he had a large business. I lost him quite early in 2003. But there are a couple of learnings I got from him, especially that when you want to do, especially on the business. So I was always also loving what the business is and what it takes. And of course, if you're being a founder or if you're a businessman, you definitely earn a lot of wealth. But that's not like something which is guaranteed. You need to work towards it. I definitely wanted to be like a commercial pilot, but I didn't know and I will end up into a businessman. But again, here, one of the things which I learned from my father was don't jump the ship. First learn about it. First spend the time. Build the domain expertise. See where you want to head to. And that's precisely what I did. I spent a lot of time with the data space, data domain. And then I decided that this is the problem I need to solve. And that's how I ended up as a founder. Oh, very nice. And first, you know, I want to say I'm sorry for your loss and how nice that you're building his memory into your business. So tell me those. So as you started growing up, then how did you start change the passion and from like I want to be a pilot to what did you start studying and where did you start? How did you start moving your career forward before getting into data? Yeah, so one was the fantasy world, you know, that the commercial pilot you want to be. The other is when I was graduating in my academics, I found out and even, you know, along with my brother, my with my father, if I found out that I'm pretty good in mathematics, right? And gradually, you know, the passion towards numbers sort of grew within me. So I did spend, you know, from an academic perspective, I did my bachelor in business. But again, I did my masters in finance with minors and statistics from one of the top B schools in India. And after that, also, I did postgraduate diploma in data science and AI. So sort of, you know, the academics, which is more like a foundation for you, I've spent a lot of time there. And as I was just mentioning, as I was graduating towards my academic career, that's the passion towards number grew. And it was like when I got a first campus offer, it was in the data field itself. And which is why I decided that, hey, this is something which I really love, spending my time, understanding data, crunching numbers at that time, and which is why I continue in that field. Again, the passion, you know, grew as a sort of, you know, sort of progress in my career. And this is one of the fields which I don't regret, you know, sort of, you know, starting my career with. And I love doing it. Amazing. So you just really got into data just right away. I love that a lot. So, you know, so what's been the biggest lesson so far in your career? Yeah, so a couple of lessons out there. I guess, you know, the biggest lesson is the foundational layer. So I'm talking into sort of, you know, fourth year. So one is one is at the team side. No matter, you know, when you're heading a department, or you're a manager, or you're in a very early stage of your career, I guess, you know, you need to spend a lot of time, you know, building that foundational layer, because if you're, if you are a head of department, and if the bottom layer of your team is not performing well, it's going to impact your metrics, your performance. So ensure that, you know, the bottom layer is always happy. If they are happy, the middle layer will be happy. And if they are happy, you are happy. Right. So to spend more time with them, understand what is, what is the motivation? And most of the times, I also learn money is not the motivation, not the driver every time. And I always have, I always have a tendency to spend, you know, a little bit of time on understanding the personal kind of goals to, right? You know, understanding about the family, because that impacts the performance. If a one person has a pipe with his wife, that morning is not good for him, it's not productive. So if you understand that, that sort of, you know, background of the person, it really helps you to get the best out of the person itself. On the, on the personal side, I think so, you know, the collaboration between other departments is super crucial. And when you are a data leader, or even though for the matter data manager, it's very, very crucial for you to, you know, manage your stakeholders. And one of the lessons which I have learned in past was, so I was building a machine learning model, and I was ready with the model. But one of the biggest things which I didn't do properly was the stakeholder management, especially from the product management side. And so I was ready with the API and the product said, hey, you know, it's two months away from now. So, so again, you know, boiling down is the collaboration, the stakeholder management is something super, super crucial. Considering that the data department is also like one of the, is the center part of the business value chain. You are supplying the data, you are working with multiple stakeholders. You need to ensure that, you know, you spend tremendous amount of time there. Wow, I love those lessons. And, you know, I love that you mentioned that the lessons come from, you know, a challenge and a misstep, so to speak, in order to learn it. And how, what a great message to like how important the people are no matter what you're doing. And you're talking about building, you know, AI and, you know, it's not replacing your people, but the people are so important to what it is you are building. That's really, really nice. So, so tell me, you know, having worked with data for so long now, and, and, and it being your primary focus, what is your definition of data? Well, any, even today we're talking on the podcast, there's a, there's a ton of moment of data out there, the way I speak, the way you speak, my, my gesture, even the words which I emphasize on can define my personality. So, for me, the data could be in any shape and form, mostly I look at whether there's a structural data, whether it's unstructured data, but even you take an image on your iPhone or, or any phone for that matter, that's a data. Even when you're talking to anyone on the phone, that's data. So, data is like any form in a digital format, right, or even if it's necessary to say digital, but it could be in a physical format too, but there's any exchange of information in, in any language, either it's a binary language, either it's a one-necular language, it's English. That to me is a definition of data and, and nowadays there are so many tools out there where you can collect whether your unstructured data, that's your image, audio, and any other files, and, and the other thing is the structural data, what, what kind of interactions you do with the application, that is, that is something more like structured data. But yeah, that's, that to me is data, but the next step towards the data is how do you translate that into a knowledge and that is something which is very, very crucial, you know, as, as a data leader, as a data manager. Very true. Any best practices to translate that data and put it into context? Well, there are a lot of libraries available where you can sort of translate that data into, into, you know, a certain form, right, like just to give you an example, if there is an image, you can definitely use libraries to convert that image into a binary language, right, where you can, we can just convert like all the colors, like you know, it's, you know, the colors have a range of 0 to 256, so you can define that, you can actually pixelate that into, into a matrix into a numbers. The other form which is the very, very, you know, simple is you dump data lake and nowadays a lot of companies are adopting that structure where you sort of ingest, sort of store everything in a likes of Amazon S3 or something like, you know, Google, so it's more like a bloop storage, that's what I'm predominantly going through, so that's that's the, I would say one of the best practices out there. Very nice. So especially having worked with data for so long, 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? Well, this is, this is a tricky question but at the same time, you know, in a very early stage, after discussing with few of the data leaders out there in the industry, the data management is definitely going to evolve as companies get to invest in generative AI and I believe that having generative AI, the roles will redefine within data itself, we may have less amount of data analysts because the generative AI is able to crunch the numbers, is able to slice the numbers, business will be able to directly interact with those models and understand the data pretty easier and faster but at the same time, for your model to perform well, you need to feed the data in a accurate fashion or you need to feed the reliable data. So that's where the dependency on your data engineers, your data ops people becomes more critical. So I see the weightage changing, right? You may end up having more data engineers, more data operation people because as company invests in the generative AI, you need to have more pipelines, more data going in, more unstructured data and you need to take out the meaning out of the data. So again, there you will require some of the ML experts and AI engineers too to maintain that model and continuously monitor that model too. So I see the shift happening in and definitely one of the reasons why we are focusing and every company needs to focus on the data reliability because that lays the foundation again for whether it's your AI model or whether it is your data products internally. If you have a right and reliable data, accurate information, you will get accurate output. Otherwise, it's a typical thing we have is garbage and garbage out. So that's the paradigm shift which I am, which early signs are showing. But again, as the models evolve, as the generative AI evolves across in the coming few years, I guess we also get to sort of change that within our data management as a community. But again, just to answer that, from an outcome perspective, I see there's definitely going to be an increase in the data management people within the organization itself. 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. And you mentioned the garbage and garbage out and how important it is to have quality data going into your models. So putting an emphasis and stress on then the importance of data governance in order to manage that, yeah? Yeah, absolutely. I guess the data governance, as you graduate into your journey of the data management, sometimes I have seen that the data governance is not required on day one itself. But as it is, I mean, based on my best practices, I would include that into day one, but I'm just more talking towards the reality part of it, right? Because as the company is developing the data practices internally, they hit a wall where they need to bring in more control, right? They have understood what it takes to drive the data internally for growth and which is where now they want to bring a control saying that we would require a modular framework like data governance because we have so much data internally, we have external data, IoT data, we have internal sensitive data, we have certain inter, we have transactional customer data and so on and so forth, which is where then access becomes like a challenge, right? How do you provide access to the people and how do you monitor those access? And that's one element. The other element is you have certain sensitive information, some of the financial companies have credit information about you as a person. You need to ensure that those are protected from even the internal employees and that's where the requirement of data governance comes into picture and which is why it's very, very crucial. At the same time, people need to realize the importance of it. It is the long-term gain. It is not something like a magic wand. It is not like an AI model also where you deploy and you may see the results in a month or so. It's also a cultural change, right? It's the way you function, the way you define your processes internally. So that's where the data governance comes into picture. Again, I also emphasize the data governance is more like a framework which you need to bring across in the organization, the cultural changes. Then you should look at the tooling part. We solve the tooling problem, but if you deploy the tool now and there's still not a change in the culture or the frame or the process itself, it's not going to reap any benefits for you. So that something is very, very important for all the companies to sort of emphasize on is the importance of the framework. You know, it's interesting. We still hear from a lot of people that they're struggling to get funding for or get executives to buy off on even starting a data governance initiative. What would you say to those executives, say, hey, this is why it's not... So many executives think that the data governance is a dirty word and it's really just about adhering to laws, but it's so much more like you just said than that and it's so important to the management of the data within the company. So what would be your sales pitch to somebody who's not yet on board to understand the importance of why it could benefit the company? Yeah, this is super important and this is also a very important question for a lot of data leaders out there, even for executives. The way I help the data leaders because we work very closely with the data leaders to sort of champion internally and be sort of a sponsor in this particular data governance project. Where they can onboard us. So it's actually for me, it's agreed out there. But in reality, even when I was a data leader and when I wanted to pitch in the data governance framework, the way I got the buy-in was you don't need to talk to the data, the technical language in front of your executives. They really won't understand what you are trying to define, what is the kind of impact. So what I did was I simplified things for them. Because we as maybe exacts or even like a human, we are more reactive than preventive. And data governance is also part of preventive frameworks. So we always have to showcase that what is the impact if your data gets leaked out. So if you sort of, this is just one example, if you tell your executives that hey, if your data gets leaked out, first of all, you get a loss of millions of dollars. The other thing is the reputation of the company. And we all know the negative news spread across like a bush and a fire. And but at the same time, if it is like, so if the reputation gets impacted, it also impacts your customer sentiments. They might not do business with you. And even if it is a B2B companies, the businesses might not want to engage with you because you're not safeguarding your data in a right fashion. So this is just one of the replications you will see. And if your data gets leaked out. So if you are able to explain that, and I'm sure executives will give you a bind for that. At the same time, you also need to tell them what it takes to bring that project in. What is the absolute dollar value? And what's the absolute dollar value in terms of returns you will be getting in the next three or four years? What's the kind of savings you have? So it's more like a total cost of ownership kind of model. But again, you also need to understand what's the value you're driving internally. That is something which is very crucial. And the value sometimes is not always about absolute dollar because subjects like data governance, and even for that matter, data observability, these are subjective. These are productive tools. It helps you to improve productivity internally. So you will see the gradual change in the coming months slash years, which is where you will always see the data governance is not like a SaaS tool which you can install it today and uninstall it tomorrow. So that's something which doesn't happen that day. So if you sort of focus on all these elements and put a very simplified version to your executives, you will get a high, it's a high probability for you to get the approval slash the kind of stamp on your project, saying that please go ahead and do the procurement. Very nice. So then tell me, what advice would you give to people who are looking to get into a career in data management, especially with the shift, as you mentioned, happening? So what do you think people should focus on? And how do they get into these data engineering role, per se? Yeah. So again, as I just personally speaking, we have to emphasize more on the foundational layer of what it takes to be a data engineer, to be ML experts or AI experts out there. But if you notice, there are a couple of things which are pretty common. Maybe it's writing codes on Python, maybe it's writing codes on some other language for that matter. So having, I would say, basic skills about the coding skills about Python or some other common languages would be really, really important. But at the same time, the reason why the foundational knowledge helps you is, tomorrow, if there's a new language which comes in and it sort of overpowers Python, you need to have the skills to immediately shift into those new languages itself, which is why if you have the right foundation, like maybe some of the basics of computer science, doesn't matter. You don't have to do a degree in computer science, but maybe just understanding some of the modules out there, which there are a lot of websites available today where people can learn that. The other thing is about spending about time of how data, what is the data framework, what are the types of data you have, what it takes to write a SQL query. So there are this basic functional knowledge which is already available. I'm more inclined towards the data engineering part where you need to have a basic of how does an SQL, how do you write an SQL script, some of the basic Python scripts, but also some of the technologies nowadays have become quite common. What is Kafka? What is the event streaming? Because as companies are investing in the data management, people want real time and there are solutions out there which does this real time streaming. So it's better to have a knowledge of it, maybe not the deployment knowledge or not the advanced knowledge, but when you're preparing yourself for an interview or even that you want to graduate into this particular role, build that, have that particular layer itself defined for you, invest there. I would say if I would not jump into getting into interview rounds but I would jump into, I would actually spend more time in sort of preparing myself, upskilling myself, take a pause there and then sort of go in the interview and start sort of, you know, discussing important roles. Oh, what's your name? Thank you so much. I mean, that's really very helpful and really great advice. And I will be remiss if I didn't ask, you know, if somebody want to learn more about DeCube and how it can help their business, how would they find you? Well, they can just type in the word DeCube in Google and I'm sure we will be the first link appearing there. Alternatively, you can also search on just type in DeCube.io and you will be on our website. It's a very, I try to again simplify things for all the data people out there. What we do and how we sort of interact with your data infrastructure. So yeah, you will find more information. Alternatively, you can also find me on LinkedIn and I love to talk with data people and we're all happy to spend some time and even simplifying what exactly the framework of data observability, governance, even data catalog for that matter. Oh, very nice. Well, thank you so much. And we'll get those links posted on the podcast page for everybody so they can click on those and chat with you. Thank you. That will be fantastic. That will be an easy browse for everyone. Indeed. Well, today it's been such a pleasure to me. Thank you so much for taking the time to chat with us today. Thank you so much once again for having me and I'm excited to see the published version of it once again. Thank you so much. You guys have a fantastic day and a week ahead and look forward for I've already subscribed to your talks. I love hearing more from you guys and thanks for again helping the other data people to sort of learn from data leaders out there in the market. And this kind of learning is definitely required and you are also building a community out there which I'm super excited for. Well, thank you so much. We really appreciate that. And I look forward to the community learning more about your product. It just again, it's a nice sweet spot that you've got going on there. And to all of our listeners out there, if you'd like to keep up to date on the latest in podcasts and the latest in data management education, you may go to dataversed.net forward slash subscribe. Until next time, stay curious everyone. Thank you for listening to Dataversed Talks, a podcast brought to you by DataVersity. 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