 Hello and welcome to Dataversity Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host Shannon Kemp and today we're talking to Manish Suh, the CEO, founder and chairman of RELTU. 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.datavercity.net and use code DVTALKS 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 at 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 help keep up to date in the latest in data management education, go to dataversity.net forward slash subscribe. Now today we are joined by Manish Suh, the CEO, founder and chairman of RELTU and normally this is where a podcast host would read a short bio of the guest but your bio is what we are here to talk about. Manish, hello and welcome. Hi Shannon, it's great to be on the show. Thank you for having me here. Thanks so much for joining us. So tell me, okay, so you're the CEO, the founder and the chairman of RELTU. What is RELTU and what is it, what's this company that you built and what is it that you do? So Shannon, RELTU is focused on unifying core data in real time across the enterprise and you know with this core data when you think about data domains like customer information, product information, or supplier type of information, most of the businesses run on this kind of information. This is core and central to their operations, where they want to drive growth, where they want to optimize costs and most of the companies today have a highly complicated ecosystem of different applications that fragment and silo the data and hence it becomes one of the biggest friction points in the enterprise for companies to be able to execute well and that's where RELTU comes in where we are able to provide the product capabilities that would allow customers to manage data as a product and leverage it across every touch point, across every business process, across every application or insight they're trying to get to. I love it. So very cool stuff. So you have many titles as the CEO, founder, and chairman, but let's talk about the as CEO, CEO of the company and we'll get to the you know the foundation of it here in a bit but what is it that you do? What is your day-to-day in the company? So as a CEO, my day-to-day in the company is essentially driving the strategy, making sure that we're driving and defining the right culture that we want to grow and scale with and focusing on operations so that across all parts of the business, we are able to move in lockstep and get to the targets and the results that we all desire to achieve through the course of the year and as well as as a part of our long-term strategy. Very nice. So tell me, Manish, when you were very young before you even started school, did you dream I'm going to start a company that manages data or what did you want to be when you grew up? I think I never I never as as I was growing up, I never thought of being an entrepreneur or leading a company and you know through different parts of my career you know or let's say when I was in school I was interested in tinkering with stuff you know creating things, building things and what I found interesting was that through those early formative years as well as you know in college my first job out of college data became more and more interesting and you know sort of tangible part of what you can touch and feel and work with as a part of your day-to-day business because if you have to think about driving go-to-market strategy you have to think about what is the size of the market you know you have to look at data that tells you some of that information so there is a lot of information gathering you know just using that as an example and then thinking about what are the signals that you can read out of it so it wasn't something that I dreamt of early on when I was growing up but it certainly became a part of everything that we did and tried to learn along the way where it became very clear that data was going to play a major part or a role in in my career. What did you study in school? What was the passion you're following there? Yeah. So I studied as an engineer and as a mechanical engineering student and in fact when I graduated I went and worked in an area that was all about managing power generation type of units and machinery and automation type of capabilities for large power plants and you know that experience again a lot of the automation work was first of all gave me access to you know our the ability to work with software because automation as you can imagine was tied to software engineering types of work that was associated with it but at the same time you know the critical components that had to be monitored or automated a lot of it brought us back to data and that that sort of started to form this thread of data in that entire journey. Sure, sure. So where did you go from there? So from there you know I got more interested in understanding some of the systems behind behind the business and got exposure to working on a multi-country type of an integration for business. We you know were a part of a larger conglomerate and I was working in their India operations trying to integrate it back to the the mothership in Japan where the larger entity was and you know those types of cross-country type of projects gave me more exposure to why some of the systems needed to be integrated because you know if you're a large global business you are always trying to figure out what is the total global footprint, how much business do we do across geographies, which markets are performing well, which markets are not, are based on the market opportunity, what is the type of investment we should be making in those new markets and that again sort of thematically brought me back to understanding of the systems that were in place and why they needed to be integrated and by the way data was sort of again a common part of that fabric in this and then you know as I was working on that I got an opportunity to move to the US as a part of my work and there you know being in the Silicon Valley I got exposed to some of the newer things that other companies were doing. There was a lot of innovation going on, there was a lot of interest in creating new types of products and capabilities across a wide variety of sectors and just seeing that innovation you know I sort of gravitated towards an early stage startup where all the work that I had done in the past when I heard their pitch I was you know thinking out loud and saying to them this is exactly the type of solution that would have been useful for all the places I have been in my past and you know guess what the theme was data again bringing that data together and you know because I had worked by that point in time I had worked in large industrial organizations, I had worked in telecom industry if you think about telecom industry all the way from the front office to the back office the systems need to be integrated so that you can have automated order flow and provisioning and then billing all of those types of things happening seamlessly but all of this required that there was seamless connectivity between the systems as well as the data flowing across easily so that you could have that end to end flow and you know after living through those experiences looking for are there better ways of streamlining these processes because the number of applications or the number of different systems that number you know there was there was some thinking going on in the market in the early 2000s where some companies large enterprise software companies whether it is SAP, Oracle, IBM they all sort of came you know and said that if you had end to end our capabilities from us then you would not need to integrate all these systems and it would it would all be solved because it all sits inside one single system but those visions then pan out and most of the businesses went after a best of breed type of application landscape and you know that sort of multiplied the complexity of the enterprise landscape where you have more systems more applications more third party data sources than ever before so you know since the early 2000s I've seen this complexity grow and you know by the time we were around the 2011 type of a time frame I decided that this complexity was not shrinking the current systems were not necessarily able to handle where the future was headed so it needed a complete rethink of what types of solutions would really solve the problem for the customers and bring the data together in a useful manner for these organizations to accelerate their ability to do business so that's been you know a little bit of the journey that I've been on through my career and how at every corner at every step of the way it has somehow led me back to data as the core central team in in all of these conversations. That's amazing and so before we get to kind of the next phase which is which is thrill to you know you talk a lot about things that you started working with the different software and different applications and so you know outside of school were you self-taught where did you go how did you you know were you just following a passion where were we going to learn and and grow yourself. I think the two ways to think about it is one is yes you know areas that you're passionate about or find more interesting those often you know tend to gravitate or you tend to gravitate more towards and for me data was certainly one of those areas. The second thing you know I go back to the tinkering where you know being able to shape things and this is where rather than just abstract software type of constructs I found data as a more tangible thing to work with because you could really see the output of you know whatever you were working on in the shape and form of the resulting data that that would come out of it. So that gave me something more tangible to work with and then the third thing that I've always thought of in terms of the guiding spirit for my career is where are the secular trends headed you know are you are you picking something up and getting passionate about something that is also informed by a set of secular trends or is it is it something you know you're doing just because you're passionate about it but there's necessarily no market or opportunity associated with it. So the secular trends in this case every time reinforce that belief that data was going to be a strategic asset that everybody would have to think about and think about in a different way than where we were before and that's the type of transition that I have seen throughout my career and I'll just give you you know one one simple way of thinking about it early in my career you know late 1990s early 2000s the data professionals in a company or the any kind of conversation that was taking place about data was at the lowest levels of the organization you know to the extent that in fact even the offices given to the data professionals were literally in the basement of the building and you're laughing about it but I think yeah no I know it's not the first time I've heard that yeah yeah and and now data is being talked about in the boardroom at the C-suite level as the strategic endeavor that people need to focus on and undertake and I think this is you know at this point in time this is one of the best points in time for anybody to be in a data career right or be a data professional because this is the time and age of data that we are in. So it's really exciting for me to have seen that transition and to be in a place where you know we are helping shape the thinking of what the future can be with data. That's very cool so so then let's talk then you know you found this customer need or this company need and so you decide to start your own company I mean that's not an easy decision that's a big that's a big deal so so tell me a bit about that in the starting of of Reltio. Yeah so I started Reltio in 2011 and you know again I go back to the secular trends the number or the you know think about the silos or fragmentation of data that was growing and it was on an irreversible path because we have only seen the number of systems every company uses go up not down. The second thing was the whole focus on how businesses would need to be more digital in nature and when you think about you know digital experience that companies need to build or deliver or digital processes that they need to support there is no one single out-of-the-box application that is going to magically solve the digital experience problem. In fact you have to build it on top of data that is you know going to help drive that. So the third thing that came to the front with that was where would you build such systems because in a lot of cases you would have digital experiences that would need to be exposed to partners to internal employees to contractors that you're working with or customers that you're selling to and digital experiences cannot sit just within your on-prem four walls because you won't be then able to expose it to the outside world. So cloud was the natural vehicle that it would have to ride and be built on and 2011 was also the time when all of these things were becoming you know focal themes for every organization out there and I thought that at that point in time if this is the direction that everybody's thinking about or focused on then we will need a different set of capabilities to handle, manage, curate the information that is needed for these outcomes and that led me to the starting of relative you know once you once you get an idea stuck in your head and you can't stop thinking about it even when you're sleeping you're still thinking about solving the problem you have to go do something about it. I love it that is that is fantastic that's such a great story. 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 I'm going to jump around a little bit here so with that forward thing this is we're talking about that so much 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? Shannon the number of jobs working with data are going to increase but I think at the same time what you're going to do with the data right or what type of job it is going to be it's going to improve and go higher up in terms of the sophistication you know it's almost like data is a new language we all have to know and understand it and as we learn more about the language you go to the next level of the work that you can do with it so I think a lot of the lower level work will get automated so if it is just shared data entry type of job then automation capabilities can be put to use there right if somebody is reading things off of a printed form and then typing them in those things can be automated now and technology is getting better at that process automation or if you're just you know manually massaging some of the data those types of things will get improved but what that will also do is give the same people the ability to learn something more and go to the next level of that hierarchy or you know the next step function where they will be able to apply their subject matter expertise in a more meaningful manner to the types of jobs that need to be done so you know I see jobs increasing but also the quality or the tight nature of the work that needs to be done you know moving or graduating to some more subject matter expertise that has to be gained and applied to solving these problems. It makes a lot of sense so what is your definition then of data? How do you define it? We haven't worked with it for so long now and sorting a company around it. How do you define it? That's a great question we all talk about data what is data? I think it's you know at the end of the day facts and numbers that help you make a decision and you know in a lot of cases when you start dissecting that and start thinking about what are those details facts or you know numbers then even a simple question that will get asked in a business environment is let's say it's a global organization and somebody in the C-suite level asks the question how much business do we do with American Express across the globe, across all countries, across all lines of business, across all subsidiaries that they have? People scramble they don't have a ready answer for it right? Some facts are missing because you don't have the precise understanding of how many different types of customers do we have in the American Express global family of companies and what does that family look like? Which accounts do we have there? Which ones we don't and by the way if we have different lines of products or lines of business then which lines of business have sold to those different subsidiaries and which ones have not? It just becomes a massive project to assemble all that information together and come up with that answer and both numbers are lacking as well as you know some of the core facts are lacking both have to be put together and that's the type of stuff that I think gets in the way of getting to increasing the speed of your business. Everybody today you know if you go ask any CIO or CEO they all want to increase the clock speed of their business they all want to move faster but unless they get this right or the data right you cannot and that becomes the biggest friction point. So how do you work with data at relative? I mean not in terms of the software but I mean I'm assuming that you know on a day to day I know at data diversity the irony of what we do right educating people and data and then we have to manage it ourselves for our own company right you know how do you work with it and and what are some best practices there? So you know again our core hypothesis is that you have to unify core data because number of silos or number of you know systems that you have that's not going to shrink in fact even our business we use 100 plus different applications to run our business every day but we have to unify information out of it. We have to have a unique identifier for every customer that we work with we have to know everything about that customer how we are engaging across multiple touch points what is our services team doing what is our support team doing what is going on in training you know what is our sales team doing because if it's an existing customer or a prospect understanding all those dimensions round out our perspective. So we assemble those details together in fact we use Reltio as the capability inside Reltio to run our business right so we drink our own champagne and we look at that data as the starting point to inform our decisions and you know that's something that we take very seriously because you know what we go tell our customers is that unless you have high quality trusted information you will not be able to make the right decisions and that is the same principle that we go by where we are able to think about you know what do we need to do to grow faster or be more efficient or to meet compliance type of requirements in all three of those cases it comes back to the high quality trusted information for our customers for our products for our suppliers for our employees all of those details have to be improved on and continuously curated inside our own business. Nice and I love that I love that phrase we drink our own champagne I've heard we eat our own dog food but I much prefer drink our own champagne that's very nice so you know with this what advice would you give to people looking to get into a career in data in any aspect? Yeah that's a great question Shannon because I think first of all as I mentioned the number of jobs in data are going to increase right this is this is a new language that everybody has to understand and learn whether you're in finance or sales or you know IT it doesn't really matter everybody everybody needs to have a good understanding of how to work with data and data systems and that's something that I would recommend you know regardless of whether you go become a data professional or not you should add it as a part of the tools in your toolkit that you will take to you know your career opportunity because even if you're in medicine you know think about the implications of software and you know the type of work that can be done with AIML in the field of medicine but guess what the starting point for that would be data data so this is this is something that everybody has to understand and especially you know what are the different ways in which you can have access to it what are the the tool sets that are available in the market you don't have to be experts at every part of that toolkit you just have to be aware of those tools because if you're working with a team then you would be able to guide them direct them or ask them questions in a manner that will get you get you to the right outcome sooner so you know once again this this has to be a key part of the the set of tools that that everybody has to be aware of and trained well. I appreciate that that's great advice so Manish tell me if if somebody wanted to learn more about RELTO and inquire about your product how do they find you? So go to www.relto.com that's the best source of information you will also see that we have a community at RELTO where you can join that community you can learn more about how other customers are solving you know these tough data problems especially for unification of data across multiple systems and data silos and how they're benefiting from it because Shannon one of the other things that I found lacking you know think about this we always talk about if we have better data we will have better outcomes sounds sounds like a no-brainer to all of us but when you go ask all of the data professionals how much value can you quantify how did it increase or how much did it increase your revenue or how many dollars did you save they don't have a good answer and and I think that's the part where we have to do a better job as data professionals to guide customers or guide your business our business stakeholders on what the quantification of that value is and I think that's that's going to be one of the the big things to solve for and also you know help everybody with that's going to be front and center especially in the given economic conditions. I tend to agree more and that's so great that you have a community and and have made that a focal point in priority it's so important especially I think in the data community I've never seen such a greater need and and and even desire to for for community that's something that we work on and believe here at data diversity as well so and you can you can also find me on LinkedIn happy to you know talk to anybody who has questions about what we do at help here and help them. That's amazing thank you well Maneesh thank you so much for taking the time to chat with us today 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. Thank you for listening to Dataversity Talks brought to you by Dataversity. 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