>>Hey everyone. Welcome to this cube conversation featuring next law. I'm your host, Lisa Martin. And today we are joined by Sukkot Sarab CEO and founder of next, next LA Sukkot. Great to have you on the program, >>Lisa, thank you so much for having me here really excited about this. >>Tell us a little bit about next level. What is it that you guys do? >>Yeah. Um, you know, we are in the world of data and one of the biggest challenges that we face, um, as an industry is there is so much data, so much variety. How do we really get it into the hands of people who use data? And, um, the users of data are all across. You know, they shouldn't have to be engineers. They are across the board in different functions. So next, last purpose and mission has always been ready to use data in the hands of the users. So, um, what misled us today is makes it possible for users across the board, whether those are data scientists, whether those are data analysts, whether those are people in various business functions to get the data that they need in the tools that they work with. So, um, we make that possible in a very, no code way, um, for users to get access to the data. Um, and very uniquely actually do that by automating a lot of the data engineering process. We'll talk more about that, but it's an exciting space to be in. >>It is an exciting space to be in. And of course that the volumes of data could just continue to explode and there's, that will not be slowing down anytime. Soon, as we know in businesses, one of the things we saw in the last two years was businesses pivoting so many times and really needing, going from survival mode to thriving mode, but the ability to harness the power and the insights and data is critical for businesses to be successful these days as consumers. We just expect that if it's our business life or personal life, whoever we're interacting with is going to know what we want, and we're going to be able to display that to us quickly. We think about data match. It's a relatively new concept, right? Talk to me about data mesh and what differentiates next left from your competition. >>Yeah. Um, so data mesh is essentially, I would say in the lineage of the concept of democratizing data, the idea has always been that data should get to the users. Now for a long time, these users were dependent on it and engineering to get the data to them. So what the data mesh is doing is it's bringing a framework by which users of data. We call them data, the domains, the different functions that use data. They can have the data to use themselves. They can manage things on their own. And I think that is allowing for a framework in which teams can truly scale. I mean, that bottleneck of depending on engineering to do everything for you is just not going work. And I think in the last two years, even more so we saw that as companies tried to move fast, it started to break down. And I think there is a lot of momentum around this concept of data mesh. For this reason, people are finding that this concept is what can help them scale >>And how does next SLA deliver that single tool so that you can really democratize data and give people with varying levels of technical fluency, the access that they need. I can imagine finance folks with ERP data marketing folks with CRM data. How do you do that with a single tool? >>Yeah. So, um, I think the key thing about getting data in the hands of users, as we think about data democratization has been that, how does it actually happen? How do you give people access to the data? You know, simply giving them passwords to systems is not enough right now the data mesh concept comes with the understanding that there should be an entity, which we call it a data, product data, you know, a data product becomes that sort of common entity that becomes something that people can get access to. They can use, they can collaborate on. Now, what is a data product becomes an important question, of course, and how do we get a data product? So our next step comes in in a very key way is we automatically generate these data products. So again, going back to the thinking that look there, there is not going to be enough engineers to write code for everything. What we are able to do is to say that we can actually, you know, connect to data systems, look to the data, understand it, and package it up as a product, as a data product. And that data product is a core element of the damage. I'm happy to share what a data product is, if it helps people understand and of, >>Yeah. Let's double click into that a little bit. I was noticing on your website about next sets and I wanted to know what that is and how does it reimagine data, product creation. >>Yeah. Um, so let me just break down a little bit about what is the data product in the first place, right. I mean, as consumers, we use products all the time, you know, I'm, I'm, my laptop is here on our desk and that is a product. It is a product made from raw materials, like wood and metal and screws, right. And somebody designed the product, somebody built it and I'm using it. So if we think of the same parallel in the world of data, then API APIs and files and database tables, those are the raw materials. Um, if somebody takes that and packages that up into something that other people can use easily, that is the concept of the data product. Now, what, how is it different from data? Well, you take the core data and you put things around it. Like, what is the distribution of data? >>What is the structure of it? You know, what are the validations that make it work, how to better manage that, who has access to it when you take that raw material and put all of those other structures, it that's when it sort of becomes a data product. And the next step concept in next door is essentially a manifestation of that. It is the concept that these data products do not need to be new copies of data, which is a huge pain by the way. But instead they can be these logical entities. So if I can take us back to the world of compute, where we understand the concept of containers, no, these containers are basically a logical NPP that gives us access to the computation resources. Think of next set as a very similar thing, a logical entity that gives us access to the data resources. And, um, this is something that, you know, we have been able to innovate and automate in such a way that today, when people think of the data mesh and they want to build that, they see us as a component in that whole framework. So data mesh is a much broader framework, but we are sort of the building block for that, through this concept >>Building block. Got it. Talk to me about where your customer conversations are happening. Are they within chief data officer chief information officer? Is it within the C-suite as data is every company these days has to be a data company. >>Oh yeah. Very much in the C-suite. Right. So again, this changes a little bit industry by industry because every industry is organized differently. Um, for example, you know, we have some amazing customer international services there. The conversation often is this the chief data officer or the chief analytics officer. And the key thing that the C suite is thinking is how does this work in the future? How do you know the scale of data challenges are, is the growth of that is so fast. How do we handle things to three years, five years from now? And that's where the strategic conversation is. And that's where things like data mesh become extremely important for companies where we talked to them about, you know, how our technology sort of enables that, right? Um, across other industries, the functions may vary. And one of the things which is very interesting with data, um, compared to other technologies is that it touches almost every aspect of business. It's not limited to engineering. It is your person in HR who is doing HR analytics, source candidates, and profiles are reviewing and all that stuff to finance, to operations, every aspect of business does touch data. So this has to be done in a language and a mechanism that's much more approachable. >>It's gotta be horizontal for all of those different types of users, right. To be able to understand so that ultimately not only did they get access to the data, but they can pull out those insights faster than their competition, whether it's to develop new revenue, streams, new products, new services, you know, the, the person on the other end or the companies on the other end are expecting that real-time interaction. >>Yeah. Yeah. But that's >>No longer a nice to have >>No longer likes to have. And to clarify, right. I mean, the use of data is in multiple ways, right? So analytics is a big use of data, which is how is my business doing and running. Um, we have customers like, um, you know, um, Marchex and Poshmark and bed bath beyond and so on. We'll use us heavily to bring data for the analytics use cases as our companies, for example, like a door dash or Instacart, but that data feeds operational purposes, operational purposes, meaning, understanding the availability of inventory or products across different stores. Not that data has functioned to say, well, if I know what products are available, then I can list them. Then I can go pick them up. And that's not a analytics use case alone. It has a, um, you know, it has an operational use case, right? Um, similarly we see that in audit tracking, we have customers, for example, like Narvar that use us to connect to different shipping tracking system. So the applications of data are in analytics. There are certainly also in operations, which is core business. And they're also, of course, in data science. There's no question that the extension of analytics from looking back on how business is doing to data science, which is, you know, what should we be doing and how should we be more intelligent? So it's across the board, >>Across the board, horizontal, all industries really need to do this, but one of the things that pop into my mind as you were walking through that example was the supply chain challenges that we're all experiencing right now. How can next help organizations mitigate some of the challenges that are going on? >>I think what happens is, you know, technologies like ours, which are the data layer are at a fundamental foundational level. One of the things about next slide is that we are able to bring a data into a much more real time usage. So where in companies, but traditionally moving data on a much more sort of periodic basis. We are our plumbing under the hood. We are completely in real time, which means that we are allowing companies to now get access to that data in a faster way where possible. So again, this is not something that can be fixed overnight, but the role that data can play in is better visibility. Um, and better visibility means those business decisions are being made earlier at the right time. It's more insight. And hopefully that eventually leads to sort of, um, much more efficient, actual on the ground, some movement of products and so on. >>Yeah. That visibility is absolutely critical regardless of the global climate. Right. Talk to me last question here, since we're almost out of time, give me a little bit about your AWS partnership and then talk to me about what's next for next time. >>Um, you know, as, as a technology provider, we ended up, um, running a lot of our own infrastructure on AWS as do many of our customers. And, uh, we have been an AWS partner for multiple years, but very decently, we actually made our product available on AWS marketplace, which means that the access to our technology has become so much easier for companies. Now, uh, next law has started its journey focusing on mid to large enterprises and some of the most complex use cases out there from some of the biggest banks to some of the biggest companies in marketing to some of the core companies in retail, logistics and so on. Now what is happening is that the powerful nature of our product and the ease of use that we have given that need is coming further and further earlier in the life cycle of companies, right? So today new companies are starting up, which said, which are saying that we need to make that sort of investment in data infrastructure earlier. You know, and that's why we have seen even some very small, early startups starting to use next level to come to us for our technology. So we are very much partnered up with AWS because AWS covers the whole gamut from companies that were started yesterday to extremely large enterprises, um, and bring our technology accessible to them. >>Excellent. Well, thank you so much for joining me. It sounds like a tremendous amount of momentum and opportunity at Nexa. We appreciate your insights and best of luck to you. We look forward to hearing more. >>Thank you, Lisa. It's a pleasure talking to it's an exciting space. So time flies, when we talked about that, >>Doesn't it, it really does for sockets sound room. I'm Lisa Martin. You're watching the queue, leave it here for more coverage and a leader in live tech hybrid events.