 Hi, this is your host, Sopil Bhartia, and welcome to TFR. Let's talk. And today we have with us Russ Kennedy, Chief Product Officer at Nassuni. Russ, it's great to have you on the show. Thank you, Sopil Noah. Very nice to be with you. It's my pleasure to host you today. Since this is the first time you and I are talking, I would love to know more about the company. What do you folks do, and how old is the company? Sure. So Nassuni is a 12-year-old company. We're headquartered in Boston, Massachusetts. We deliver a hybrid file data platform for customers that primarily want to consolidate all of their file data in the cloud, but then leverage that data in any location wherever their operations happen to be. That could be in physical offices. It could be in data centers. It could be in manufacturing facilities. Wherever they need to access that file data, we provide access capability, but it's a hybrid solution because part of their data exists locally at each of those locations and part of their data exists in the cloud. It's not wrong to say that we kind of live in a data-centric or data-driven world. I mean, we can talk about genetic AI. We can talk about Tesla. No matter what we talk about, it's all about data. And they say, hey, we just build boxes around data to do different things with data. Can you talk about the importance of data in modern world? And then we can also talk about how we have seen the evolution of extracting value from the data or adding value to the data. It's a great question. So first of all, data is everything in today's modern digitized world, right? Organizations have to collect and manage data in order to develop products, in order to provide a better experience for their customers, in order to be competitive in their industry. So data is everything to modern organizations today. There's a number of things that are floating in the marketplace today around things like data intelligence, data analytics, even artificial intelligence. And sometimes those terms and those terminologies get confused. Data intelligence in particular is all about understanding what data an organization has access to. How old is that data? When was that data created? How relevant is that data to the organization's business and mission? And the other two, data analytics and data and artificial intelligence are essentially leveraging the data to produce some result, some business outcome, some products, some technology, some capability. So those terms are interchangeable in some respects, but they're also very specific in terms of what they're defining. And as I mentioned before, in today's modern world, it's all about capturing data, preserving data, protecting data, and then being able to leverage data to drive business outcomes. Do you see that modern businesses, it also depends on what kind of businesses we are looking at. It can also depend on the age of businesses. There are a lot of companies who are still in kind of mid or early phase of their digital transformation because they have been around for hundreds of years. And then there are companies who are being born in the cloud native era, a cloud centric era. Do you feel that these companies, number one, are fully aware of data intelligence? They are getting the most out of it, or you feel that they are still scratching the surface. A lot of work is needed to be done to make them more data intelligence efficiency if we can use that term. Sure, no, that's a very good way to describe it. As you mentioned, there are organizations that have been around for hundreds of years that are in some form or process of their digital transformation. They're collecting information that could go back years and years to still be able to use that information in some respects, but in order to do that, it has to be digitized, it has to be collected, et cetera. And then there are organizations that are naturally data centric. They're born in the cloud, their business is all about data. They leverage data very effectively. And so that gamut or that spectrum of different organizations exists in the market today. I think there's a lot of very good tools. There's a lot of very good resources for organizations to help them, first of all, move in that direction of a digital transformation, but secondly, be able to analyze and understand the data that they have, sort of the data intelligence as I described before, but then also leverage that data to produce more results, to generate more outcomes, to build products, to build solutions, to build software, to build games, other things. There's a number of different ways that people can leverage data to actually create more and more items for their business. And so I think that transformation, as you talked about, as organizations move along that spectrum and get closer and closer to fully digitized, they can start to take advantage of these tools and these capabilities that exist in the market today. As we are talking about all the benefits and advantages of data intelligence and also depending on companies, how they are adopting, embracing it, can you also talk about, are there certain challenges that they face that might kind of affect their implementation of data intelligence? What kind of challenges are those? Are these technical challenges? Are these people and cultural challenges? Or these are challenges associated with not enough talent in the market? So that's a very good question. So there are a number of challenges that organizations face today as they're starting to leverage their data to provide outcomes. The first challenge is really understanding what data they have access to, whether that's data they created, whether that's data that's public data, a combination of those data assets, understanding what data they have access to is sort of the first challenge that organizations face. Secondly, it's a matter of coalescing that data or capturing that data in a way that it can be used and leveraged. Most organizations today are distributed in some nature. They have different locations, they have different offices, they have different facilities, they have different employees working in different locations, and therefore their data is likely separated as well. One of the key challenges that they face is how do I coalesce that data? How do I get that data sort of together so that I can then start to leverage and use that data? One of the ways that people are doing that is they're starting to move data into cloud repositories or cloud services, if you will, so that they can start to take advantage of some of the offerings that the cloud providers have. A third challenge, and this is really a big and important challenge, especially as we move forward into more artificial intelligence and data analytics, is making sure that the data that they're managing and the data that they're using is protected, it's secured, it's not exposed, say if someone's using artificial intelligence, their organization's specific data is not exposed to a large language model or some other repository so that others can get access to it that shouldn't have access to it. So securing and protecting that data is one of the most important challenges that organizations face today. If you look at these challenges, if you look at the opportunities benefit that you talked about earlier, how is Nassuni foot into the picture, how are you folks helping these organizations in respect of where they are in their data intelligence journey? We work a lot with organizations that design or build or generate either buildings or infrastructure, maybe they generate products or parts of products, they may generate software or games, as I mentioned before. We work with a lot of companies that design things and those are very data-centric companies. They capture a lot of data, they leverage that data in building and designing the parts or the pieces that they're building and offering to their customers and to the marketplace. And that data is very important for that process, whether it's a design process, an architecture process, or a building or manufacturing process, that data is very important to that process. They capture that data as part of their work effort and they want to leverage that data as part of maybe the next work effort or the next process that they're going after. Again, I mentioned we work with a lot of companies that design buildings and design infrastructure. Those companies are very project-oriented. They work on a particular project, they may work on another project that's similar, they may work on another project in another location that's very similar and they have some commonalities across those projects. But the data may be isolated, it may be in one location or another location. So we work with those customers to help them coalesce that data, get that data into the cloud with our hybrid cloud platform and then have the intelligence across that data to be able to find what they need, to be able to locate what they're looking for and then be able to leverage those data assets to maybe perhaps generate proposals for new projects. We had a very specific customer that works in the design infrastructure design space that was looking to propose for a new hydrology project but they wanted to leverage all of the work they'd done on previous projects in the hydrology space. They used the Nassuni platform to first of all, understand where all of that data existed and then they took the next step in terms of taking some of that hydrology data, incorporating it in with an AI-LLM and then using artificial intelligence and generative AI to produce proposals for the follow-on projects related to hydrology. So understanding what data you have is important for those organizations but then being able to leverage that data through pipelines to artificial intelligence was the key and what they were looking for to be able to be more productive going forward. What does your offering look like? Is it service? It is software, they run on the cloud, they run on the prem. Our offering is a software-defined offering. So we offer software to allow customers to manage their unstructured data in file format in a hybrid fashion. So as I mentioned before, the software is delivered to those organizations. They leverage both on-premises infrastructure but a small set of on-premises infrastructure as well as the cloud and storage in the cloud to coalesce their data, collect their data, consolidate their data into a repository in the cloud but then be able to have access to that data through this small set of infrastructure that's deployed locally. So it's a software-defined offering. It's a software offering, not a service but a software offering that our customers license from us on an annual subscription basis. So however much data they're managing under Nassuni, they pay us for that right to manage that data under Nassuni but they leverage infrastructure in their own physical premises as well as cloud services in order to build a hybrid solution that allows them to coalesce that data, consolidate that data in the cloud and then start to leverage that data for outcomes like I described. One more thing which is associated with data, it's the size of data. I mean, we are not only generating, we are also consuming massive amount of data. Next year, Apple is coming with GenPro, 4K, 3D, VR, no idea. How do you look at this whole movement of data? I don't want to get into the whole debate about data warehouse and data lake but let's just look at data being generated from one place and then value added or extracted and then it consume. I just want to understand the whole complexity that is associated with data, data intelligence and volume plus where it is stored. First of all, capturing data where it's generated is a key to first of all the Nassuni architecture and what customers are trying to use and to leverage in their businesses, in their organizations. I mentioned we work with a lot of manufacturers. They capture data from sensors on the manufacturing floor itself. They capture information about the widget that they're building so that they can go back and understand how well it was manufactured, how well it performs in the field from the manufacturing process, et cetera. So capturing the data at the edge where it's being generated is key to the architecture and Nassuni has a very, we call it a hub and spoke architecture. The consolidation or the hub is in the cloud where your data resides. The spokes are these edge locations where you're capturing that data and getting it consolidated into the cloud. So we offer that service and that capability to our customers. It's very important that they're able to capture that data locally. That they're able to transfer that data into a cloud system or cloud service so it can be used, it can be leveraged. It's also, by the way, protected, secured. It's protected from things like cyber attacks and inadvertent deletions, et cetera. So you can recover data if you need to in the event that you, for example, are hit by some sort of cyber attack. So we offer all those capabilities to our customers. The main thing that's relative to this conversation though is how do they then leverage that data? First of all, it's about understanding what that data is, where it was created, who created it, who has access to it, who's using it, who used it most recently, how long ago was that when the data was used, leveraging that data from an intelligence perspective and then being able to use that data for things like generating more data or more results or more products in their organization. So we offer that capability to our customers, but it's built on this architecture and this platform that is a hybrid platform in nature and gives them that benefit at the edge, but there's consolidation and collection of all your data in the cloud. Cloud is very important in the discussion because you mentioned scale, you mentioned capacity, you mentioned the challenges that people have in terms of managing all the data that they have. The good news about cloud is data storage and the access to storage is more or less ubiquitous. You have access to that data, you can grow as your business grows, the data consumption can grow as your business grows, you don't have to manage that physical infrastructure necessarily, you don't have to manage capacity, planning and other things, you just use the service that the cloud offers. So as organizations get larger and larger, they're seeing the benefit of consolidating that data in the cloud because they can continue to grow and scale as their business needs grow and scale. Are there any misconceptions or myths that are related to data intelligence that you have to go and fight when you engage with your customers and clients? We do run into some challenges that way as well. I mentioned one of them, one of them is that terms are somewhat used interchangeably and they're not necessarily the same. The fact that data intelligence is different from data analytics and that's different from artificial intelligence, they're all related and you have to have one in order to effectively use the other, but sometimes people use those terms interchangeably and they don't mean the same thing. Another thing that we run into a lot is that organizations think that artificial intelligence is eventually gonna replace the need for data intelligence and that's totally wrong. You need to understand as I mentioned before what data you have, you need to curate that data, you need to make sure that the data that you're passing to an artificial intelligence solution is the right data, it's appropriate, it's not replaced or overcome by other parts of data. So you need to do a process of curation of your data before you use and leverage artificial intelligence and data intelligence helps you with that process. One other misconception that we run into all the time is that organizations believe that they may need to have an army of data scientists in order to effectively use data intelligence and effectively use things like artificial intelligence and that's not necessarily true. You certainly need to educate your staff and the people that work in your organization about these concepts and how to leverage these tools and capabilities most effectively and you still have to have scientists to understand what is the right data and how is it being used but you can educate regular production workers and office workers, et cetera to be able to leverage the tools effectively. You don't necessarily have to go hire an army of data scientists in order to be effective with these technologies. And what advice would you have for organizations to have a very effective data intelligence strategy in place? First of all, you wanna define a specific set of objectives for your digital transformation or your data intelligence strategy. Be very clear, what are you trying to accomplish? What are your outcomes? What are your goals? Choose vendors and partners that can help you get there. Partners that have the experience, the skills, the knowledge to help you on this journey. You don't have to go it alone. You can work with companies like Nassuni and with other companies that have experience in this world and can help you in that transformation and in achieving those goals. Don't try to do too much all at once. Compartmentalize, divide and conquer, deliver short productive wins to the organization and build on that but also measure your progress as you go to your original objectives. And if you need to, course correct. Course correct quickly so that you can continue to make momentum and continue to make progress in your goals. Don't necessarily have to be locked into a specific way. Be flexible and be able to adapt as the organization's needs adapt and as well as the technology's adapt. Russ, thank you so much for taking time out today and of course talk about the company and the whole landscape here. And I would love to talk with you folks again. Thank you. Thank you very much Swapnil.