 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 Mona Brachiebe, the co-founder and CEO at Tomai. 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.dataversity.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 are joined by Mona Rokkebe, the co-founder and CEO at Tomai, and normally this is where a podcast host will read a short bio the guest, but in this podcast your bio is what we're here to talk about. Mona, hello and welcome. Thank you so much Shannon. Hello. What a fantastic Friday evening to be with you and talk about my journey. Oh, I'm so excited for you to be here and to learn about you. So you're the co-founder and CEO at Tomai. So tell me about Tomai. What's the company about and what is it that the company does? So Tomai is a data observability company that me and my co-founder Max Lukic have started almost two and a half years back. What data observability is that we look at the data using our machine learning statistical analysis and we extract insights about the data that tells us about the health of the data and we constantly monitor that. The company is, as I mentioned, two and a half years old, but we have built it out very rapidly. We have customers like Markov, ClearBit, Datastacks. We have built a partnership with GCP, Databricks, Snowflake and so on. The product has already been loved. We have great reviews on G2 and also what leaders in the GigaOM report for data observability. So seed stage, so far I've raised eight and a half million seed round of funding for Tomai and we are on a roll. That's exciting. So congratulations on starting this business. I can't wait to hear it until we get to that point. But tell me, as the founder and CEO, I'm sure you're wearing many hats at this point. So what is your typical work week look like? This is a very interesting situation in an early stage company specifically like Tomai. There's a lot to do always. And specifically with the role of a founder CEO, it cuts across many different dimensions and many different business units of Tomai from operations to sales to marketing because we don't have such a built out team in all these different functions. So first thing, my primary focus is to make sure that we build a strategy for the company. That's number one thing across like specifically around the product market fit. Once we set that strategy, there is a tremendous amount of distractions that can go in a startups world. So how do we make sure that we set the focus areas for the team month over month, quarter over quarter, so that we are all aligned and march in the same direction and reduce the noise and focus on the signals that we need to focus on. So those couple things are high level and how we deliver this is through a good culture and good team. So one of my key role is to hire the right team who are super top in what they do. Because at our stage, it's very difficult to execute if you don't have the right team. So the right team and the right culture and keeping that momentum. Having said that on a tactical level, if I just have to look today, I had a meeting about legal contract review sales negotiation. It's all over the place. Discovery call and somebody told me very beginning when we started the company, you have one role, which is to both of us, me and Max found a role. And that role is a very different role. You got to do whatever it takes to move the ship forward. So it is a very overloaded term. It's a very complex thing. I did not realize what that meant till I really started. It's literally anything that comes your way, right? I've been talking to people, whatever it takes to get this ship moving constantly. Oh, but how exciting and how fun to put all of your passion into this and build that up. It is. It is absolutely. And I feel fortunate that I even have this opportunity. Congratulations again. So let's back up a little bit before we get into the why and when you started. So so tell me, Mona, is this what you wanted to be when you grew up? Was this the dream? So I'm going to be a CEO of Telma when I grow up. And I'm going to co-found this company on what was the dream? So this is super funny because I am born and raised in a very small town in India. Or it is not super small, small, but it's definitely not the big city. So to put it really straightforward, my dreams also couldn't reach where I am today. So my dreams were humble back then. If I had dreamt about being a working person growing up, that itself was a big thing. So being a co-founder CEO of a tech company is something that I don't think my dream would have reached back then. So I have done my dream long ago. So it was a very humble dream. So I've come a far ahead from that part. So yeah. Oh, wow. So so tell me about that. So growing up, so then when did you start evolving your education? And what did you start focusing on as you got older and start creating those dreams? Yeah, so so a little bit like I've grown up in a family where like my mom was a homemaker, my dad, a government agricultural officer. So a lot of the town where I grew, Nasek, there were good colleges, but the engineering colleges were very tough to get in. And specifically computer science was a hard one. So I definitely did my graduation in computer science, programming is something that I like specifically was good in the logic behind programming. From there on, I stepped out of my town to a bigger city where I did my masters in computer science. That itself was a big step like kind of reasoning out with my dad that it's the right thing to go to go to leave the home and not have an orange marriage. And then one was getting my first job, which was on campus much bigger than what I could have imagined or what my father could have thought that I would do. So that's where my career started. I got my first job as an engineer, started working as a developer in Java. Get away from there, started moving. I was a risk taker from the beginning, like moving out from my town and stuff. So that was one thing that always came along. And then I took another big risk of moving to U.S., like with not too much cash, not too much money is like, okay, what worst can happen? I don't like, I don't find myself settled. I'll come back again. Like things won't go that bad. So here I was with a couple of suitcases and now came to U.S. And then again, continue to work here, join companies like Oracle. Back then I was still working as engineer in Java side. Slowly what I realized that my inclination was more towards customer empathy, solve, using technology to solve problems. So when I started pivoting towards like understanding how the product is used by customers and move towards quality engineer, quality engineer, and I started actually doing functional quality assessment, like using the product, how customers would use. Started writing about that at Oracle, complex product capabilities, breaking them down into how customers would use it. Slowly that kind of carved out my path to product management, which was one of the pivotal change for my career. And I started moving more and more towards customers, so much that I used to love doing this day in a life of customers. So basically taking a technology, spinning it around, looking at the why part of why will this technology even be relevant? Why would somebody care about this? Like, are we doing it just to innovate or are we doing it to solve some real problems? And that's how from like, I was heading Oracle BPM product, sweet move, helped move to the cloud, then joined a startup. Right after that was my entry into the startup world at a master data management company called Reltio. I was the founding product manager. That's where I entered the startup world, which was so fascinating to be honest. It was so amazing. The pace at which I could make a change in a startup was awesome. Like the whole thing that I mentioned, working closely with customers, that was all tenets in a startup, right? Like you have a problem, you just get stuff done. There is no waiting for anybody. There's no, like just move. So I was, I joined there as a founding product manager, grew into heading their platform. The company was early stage and I was with Reltio till it's hyper growth into its unicorn status and so on. After that, the startup is what I got the bug, right? So when I quit Reltio, I'm a mom of two daughters by the way. So when I quit Reltio and I thought I wanted to take a break and my co-founder called me and he's like, you know, this problem we were working on and we kept seeing at Reltio and other companies, let's solve it with AI and data science. And I have this amazing idea. And as I told you, I'm a risk taker, right? So it took exactly two minutes. I had a two year old back then at home. Most people would be like, I have so much going on. But I was like, you know what, let's do it. Like let's just start a company. I don't know what to think these things. It felt right. The problem is something I knew. I told you it's important to have the right team and stuff. I knew this person. I knew the problem. And as always, I was like, what is there to lose? Like, yes, of course there's things I could over calculate. I could calculate a lot of things, but it feels right. The industry needs this. We have an idea, a novel idea to solve it. So let's go for it. So that's really how my journey was through engineer, turn product manager, turn founder, CEO. Oh my goodness. That's quite the journey. So let me ask you a couple of questions. So I'm so impressed that you were so confident in yourself. You knew what you wanted. You stood up for yourself and said, no, I'm going to go do this. I'm going to go get my masters. That's so impressive. It takes a lot to do that. And I don't know that risk taking it as much as just confidence and self-awareness of what you wanted to do. Yeah, it's both, honestly speaking. I'll tell you something, but you're scared. Like everybody else. Right, yeah. And you're confident outside, but you know that you're fighting against your parents and your family. So you have additional pressure to prove what you did was right. And it was meant to be, right? Like I had that extra pressure of like going back to my dad and saying that, hey, that was the right thing to do. See, a good thing you send me out and everything out. And you're also paving a path for others who want to do the same thing. Yeah, so it may seem on the surface being confident or confident, but that also is a little bit of a scary thing. Oh, it certainly, I'm sure it was not easy. So confident, yes, but maybe a hard thing to do. So that's really impressive. And so what was the catalyst then too that made you decide you wanted to move to the U.S. and make this other big huge, and this was certainly a risk, you know, moving to another country. Yeah, so a lot of it was opportunities, right? It was a lot like, I was engineering and back then, now my husband, I was dating him, he's also in tech. And we both were thinking that it is time to kind of get out of our smaller pond and explore what we can do beyond, right? More opportunities being specifically, we were very curious and interested in coming to Silicon Valley. A lot of innovation happens, where a lot of advancement happens and again goes back to that mindset that, yes, we, at the most, we will feel that this is not right and we will come back. That path is always there. It might put additional challenges, but best case we go out and we know that there is something that we wanted, and like many immigrants, right? The funny story is like, I came on H-1B and I told that, look, my family is here, your family is here. We are not going to renew our H-1B. We'll stay for three years. We'll experience a new country, a new way of working. We'll be in a faster paced, more innovative place. And then we can, we'll go back, but that never happened, right? That three years and no renewal, that never happened. This is home now. I have kids, build a company. I feel like we did a lot for ourselves, like here. Everything said and done, this country is awesome for the amount of support we received as first generation immigrants here from career perspective, from a opportunities perspective. It was fantastic. Oh, that's so great. 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 so then tell me a little bit then, so you had this conversation with Max, your partner, to talk about this problem that you wanted to solve. So you just decided to jump in and sometimes people freeze at that moment, but you dove in and you just did what it took and now you have seed funding and that is amazing. So just to go after that and... Yeah, so that journey was the immediate, yes, was super fast. And then the rubber meets the ground, right? Then you actually start making things. You have to get the company registered, start talking to people, fundraising, bootstrapping, like putting our savings into getting the prototype ready. Because until you put and show that really we strongly believe in this so much that we both quit our jobs. Both of us, although I had quit a little bit earlier, Max also quit and both of us were at peak of our careers where we were at that point. And we had a strong conviction. Now we had to work towards it. So we started doing, we first built out a prototype. Then we realized that when we are building an AI-based product, the funding is needed because you need to bring in some data science engineers. This is hard to just bootstrap with our own saved money. So at that point we started fundraising. We also got into Y Combinator, which also gave us seed money but also accelerated our learning curve as founders. So we were in Y Combinator summer 21 program. The same time we got into Y Combinator, we did a pre-sid round with .406 Ventures and Zeta Venture Partner, both our data infrastructure and AI-first investors. With that money, we were able to build out the product, push it out. Now again, there's a lot of ups and downs, right? Like fundraising is hard. Getting the first customer is super hard, super. I mean, I thought I knew so many people but it is hard for anybody to put their job online for somebody who has a great vision but be happy for those early adopters and that's not at all easy. It was a tough one. You have to go talk to people, prove out that your vision about the product is real but once you get those early believers then you have to prove out a lot more in terms of the product and because they have put their jobs on the line you have to make sure they are successful and you make them look really good in front of their company. So all of those things started like as we moved on. A start-up life, there are challenges. We had a team partially in Russia. The Russia war happened so we had to overnight change a lot. Our team was almost brought up to engineering, to a couple engineers and we had just onboarded like large enterprise customers. So a lot of some of these challenges started but one thing what happens very quickly in an early stage company like Telma is that you learn to adapt to these challenges very quickly and you develop a mindset of how do I solve this and move on quickly and not get stuck up and that's oh my god why did this happen kind of a mindset. So we kept doing that, we embraced that and within even before company turned two years we had three large customers. We had earlier this year we built all these partnerships and now we are on a super good growth momentum. So with those ups and downs in the end with the right team with the right vision we were able to kind of do a lot in these two years, two and a half years. So tell me then with all these different changes and risks that you've taken what has been your biggest lesson so far in your career? From a career perspective the way I would say Shannon is that if you go by a typical corp especially I've worked at large companies the way a growth chart is built it's based on statistics and data like most people they become an engineer after that senior engineer if they like the individual role they can continue to be a top class engineer and or an architect or then you pick the other ladder which is like people manager and stuff but often people get caught up in that and feel like that's the path they have to go with but that's not the truth you have often time you have to look inward what is my personality for me that was the moment right I love technology but I loved solving problems with technology right now there was no path like this to figure out right so when I started getting signals I just kind of moving a little bit lateral here and there QA product manager data company if problem solving is what you like then and if you're in a little bit of a different ladder then you need to figure out how to get out of that ladder right because the org structures are not designed for you they are designed on the masses and statistically designed right so sometimes you have to carve your path outside of that so that's that's my learning if I had tried to always figure out how fast I can go through that chart I would have missed these opportunities oh that's really really good advice and and and a really great lesson so you know especially then too as you as you're hiring people um in data 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 I think it will increase and increase increased dramatically because there's a lot of sentiment in the industry that data and AI will take jobs but I look at it differently AI will make jobs easier AI will make things faster, cheaper, better what you are doing now which means that the human skills of like analyzing things differently putting a little bit of empathy will become a very premium thing right and you use AI for everything I am I use data for everything all decisions all whether it's marketing product decisions which what what should we do what features should we do let's figure out like what is the data show what is the user feedback show what does our research show so there's data there what where do we focus on marketing efforts how do we segment our market that's again data driven right so data is going to be everywhere people who adapt data-driven mindset will I mean will definitely have a premium will have a lot of room to grow and continue to grow problem solving with data will also be very crucial so I do feel like the importance of data will increase the jobs will be there the focus should not be so much specifically on the tools but on the tools and technology because they constantly well just in my career there has been so much of evolution the focus should be on problem solving with these tools and data and if you master that then everything around can keep changing but your skillset will never go out of fashion or whatever that's really important so you know and you mentioned data and how you use it all the time and you know it's a key component to your your company so what is your definition of data so my definition of data is exactly something that helps you what you do start cheaper and better right that is my simple way of thinking about data but there's so much complexity in that faster because now you have data to make decision and not just like hypothesis now you have some information that can help you quickly drive your decisions and everybody's impatient today right to everybody wants I mean think about the time when cloud came right the adoption curve was so like it took its own sweet time for everybody to adopt if you think of generative AI in November we all played with open chat GBT open AI and today so many companies are already have production models and like everything has become fast and then data helps things get even cheaper right like because in terms of now we have the technology to make decision faster cheaper and better that our bar for better has increased dramatically right every even for startup our stage people expect top-notch service top-notch product everything right and I feel data gives us these three things in general at every level that is really a great definition and I really I agree with that whole heartedly so then I'm gonna you know especially as you know what advice would you give then to people looking to get into a career and data in any aspect yeah so so first thing is data is here to stay AI is here to stay so the sooner we all kind of accept that fact and start learning and getting our skills updated and at every level we have to do it even at my level I have to always be two-step ahead of what is happening on technologies run so I would definitely advise everybody to embrace that first thing data is here to stay AI is here to stay so build that skill set and when I say skill set sometimes it's not so important that you start coding and doing everything start learning every single data data science algorithm but what you need to know is what how do these things apply to what you are doing and how can you solve what you're doing with the use of data data science and then the technologies will evolve and you have to keep learning so my top advice would be like embrace this start leveraging some of the modern technologies start updating yourself with what's happening in the AI world especially generative AI we did that at Telma we have like a hackathon and we are encouraging everybody to constantly see what can we do what we are doing better with AI it doesn't have to be in our product and as a part of our product but it could also be like co-pilot and how can we write better test coverage using AI and make our lives easier so really embrace that mindset that's that's great advice and so tell me what how do you keep up with the latest technology and what's happening in tech so I definitely read up most of the time so I read up I used to read books before a lot but now I even listen to books that helps me kind of do multiple things together I follow folks whom I feel have a good point of view whether it's in data science and data itself so who have a clear articulation who have good customers customer driven approaches to explaining solutions so I follow that I feel those are easier way to consume shorter nuggets the other thing that I personally like doing is I like to write blogs for tell my myself at least the outline and that enforces me to do a lot more research right it's a forcing factor and I have a personal objective of at least getting one or two blogs out which means and I pick a topic which I should research so let's say when large language model came and there was a lot of hype around it what is the impact of data quality on model training now that's the topic I knew there is impact I knew as somebody who's in the space that there is going to be impact of bad data on how you train your model fine tune your model so I said why don't I write it and if I'm writing it I have to do a very thorough research so that then I start reading about it and then I start following few authors and then so it kind of puts me in this forcing factor kind of a thing and by the end of it because I'm writing about it I kind of develop a subject matter expertise and point of view on that oh very nice I like the multitasking it's a very multi-purpose yeah oh well Mona this has been so great so I'd be remiss if I didn't ask if people wanted to learn more about Tamai where would they go so our website n.ai which is getting updated in next couple of weeks will have much more but there's a lot on Tam.ai I'm very accessible on LinkedIn I individual because as I told you before I love talking to people so I'm one of those people I have another kind of thing that I'd like to do is like people especially in the data space so having at least four or five meeting with new people and just understanding what their pain points are what they are learning about what they are seeing in their use case in their world is something that I absolutely derive a lot of insights from so easy I'm relatively very easy to get told off on LinkedIn and stuff so but learning about my www.tam.com tam.ai my bad and we have blogs under that and we keep writing about topics in the data reliability space data observability space also about data science and how we use AI to do what we did traditionally in a different differentiated approach so there's a lot on that blogs site of our website very nice and I'll be sure and grab those URLs from you and we'll get them posted on the podcast page as well for everybody awesome so we will mona thank you so much for joining us today I really appreciate it's been a joy getting to know you thank you so much and I'll stay in touch with you but this was really good conversation I hope my journey inspires I'll leave you with one closing thought I I'm a pretty mediocre person in every which way and that's why I I hope the audience learns this that do not try to pattern match you with others because that's very limiting if you're trying to look at people who are trying for dropout or MIT dropouts or MIT grads who are building stuff or that's not the case because anybody the message that I would like people to do is like just carve out your own path there's no pattern here for especially for starting things and not to say that I'm super successful or anything but at least carve out your path of your career so specifically since you're doing this for career for people I think that's a useful thing to walk away with such sage advice and and thank you for for saying that so that's that is our hope there is no straight path in data there's no straight path in any career and it's so true just following your passion following being true to yourself and as you say carving your own way it's it's yeah you don't have to follow the plan that's mapped out for you yeah I think a lot of us just try to pattern match with others that doesn't always work no I wish I had learned that a lot earlier in life I wish the same for myself also like a lot of time I kept like comparing and thinking yeah I mean that's that's one thing I would tell even my daughters like it's your path it's your journey and don't try to match it with others what a great note to end on Mona thank you so much for being here thank you so much and it was such a pleasure talking to you and good luck with everything likewise and to all of our listeners out there if you'd like to keep up and up to date on the latest podcast news 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 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