(upbeat music) >> Today more than ever before, organizations are striving to gain a competitive advantage, deliver more value to customers, reduce risk, and respond more quickly to the needs of businesses. Now, to achieve these goals, organizations need easy access to a single view of accurate, consistent and very importantly, trusted data. If it's not trusted, nobody's going to use it and all in near real time. However, the growing volumes and complexities of data make this difficult to achieve in practice. Not to mention the organizational challenges that have evolved as data becomes increasingly important to winning in the marketplace. Specifically as data grows, so does the prevalence of data silos, making, integrating and leveraging data from internal and external sources a real challenge. Now, in this final segment, we'll hear from Joe Lichtenberg who's the global head of product and industry marketing, and he's going to discuss how smart data fabrics can be applied to different industries. And by way of these use cases, we'll probe Joe's vast knowledge base and ask him to highlight how InterSystems, which touts a next gen approach to Customer 360, how the company leverages a smart data fabric to provide organizations of varying sizes and sectors in financial services, supply chain, logistics and healthcare with a better, faster and easier way to deliver value to the business. Joe welcome, great to have you here. >> Thank you, it's great to be here. That was some intro. I could not have said it better myself, so thank you for that. >> Thank you. Well, we're happy to have you on this show now. I understand- >> It's great to be here. >> You you've made a career helping large businesses with technology solutions, small businesses, and then scale those solutions to meet whatever needs they had. And of course, you're a vocal advocate as is your company of data fabrics. We talked to Scott earlier about data fabrics, how it relates to data mesh big discussions in the industry. So tell us more about your perspective. >> Sure, so first I would say that I have been in this industry for a very long time so I've been like you, I'm sure, for decades working with customers and with technology, really to solve these same kinds of challenges. So for decades, companies have been working with lots and lots of data and trying to get business value to solve all sorts of different challenges. And I will tell you that I've seen many different approaches and different technologies over the years. So, early on, point to point connections with custom coding, and I've worked with integration platforms 20 years ago with the advent of web services and service-oriented architectures and exposing endpoints with wisdom and getting access to disparate data from across the organization. And more recently, obviously with data warehouses and data lakes and now moving workloads to the cloud with cloud-based data marts and data warehouses. Lots of approaches that I've seen over the years but yet still challenges remain in terms of getting access to a single trusted real-time view of data. And so, recently, we ran a survey of more than 500 different business users across different industries and 86% told us that they still lack confidence in using their data to make decisions. That's a huge number, right? And if you think about all of the work and all of the technology and approaches over the years, that is a surprising number and drilling into why that is, there were three main reasons. One is latency. So the amount of time that it takes to access the data and process the data and make it fit for purpose by the time the business has access to the data and the information that they need, the opportunity has passed. >> Elapsed time, not speed a light, right? But that too maybe. >> But it takes a long time if you think about these processes and you have to take the data and copy it and run ETL processes and prepare it. So that's one, one is just the amount of data that's disparate in data silos. So still struggling with data that is dispersed across different systems in different formats. And the third, is data democratization. So the business really wants to have access to the data so that they can drill into the data and ask ad hoc questions and the next question and drill into the information and see where it leads them rather than having sort of pre-structured data and pre-structured queries and having to go back to IT and put the request back on the queue again and waiting. >> So it takes too long, the data's too hard to get to 'cause it's in silos and the data lacks context because it's technical people that are serving up the data to the business people. >> Exactly. >> And there's a mismatch. >> Exactly right. So they call that data democratization or giving the business access to the data and the tools that they need to get the answers that they need in the moment. >> So the skeptic in me, 'cause you're right I have seen this story before and the problems seem like they keep coming up, year after year, decade after decade. But I'm an optimist and so. >> As am I. >> And so I sometimes say, okay, same wine new bottle, but it feels like it's different this time around with data fabrics. You guys talk about smart data fabrics from your perspective, what's different? >> Yeah, it's very exciting and it's a fundamentally different approach. So if you think about all of these prior approaches, and by the way, all of these prior approaches have added value, right? It's not like they were bad, but there's still limitations and the business still isn't getting access to all the data that they need in the moment, right? So data warehouses are terrific if you know the questions that you want answered and you take the data and you structure the data in advance. And so now you're serving the business with sort of pre-planned answers to pre-planned queries, right? The data fabric, what we call a smart data fabric is fundamentally different. It's a fundamentally different approach in that rather than sort of in batch mode, taking the data and making it fit for purpose with all the complexity and delays associated with it, with a data fabric where accessing the data on demand as it's needed, as it's requested, either by the business or by applications or by the data scientists directly from the source systems. >> So you're not copying it necessarily to that to make that you're not FTPing it, for instance. I've got it, you take it, you're basically using the same source. >> You're pulling the data on demand as it's being requested by the consumers. And then all of the data management processes that need to be applied for integration and transformation to get the data into a consistent format and business rules and analytic queries. And with Jess showed with machine learning, predictive prescriptive analytics all sorts of powerful capabilities are built into the fabric so that as you're pulling the data on demand, right, all of these processes are being applied and the net result is you're addressing these limitations around latency and silos that we've seen in the past. >> Okay, so you've talked about you have a lot of customers, InterSystems does in different industries supply chain, financial services, manufacturing. We heard from just healthcare. What are you seeing in terms of applications of smart data fabrics in the real world? >> Yeah, so we see it in every industry. So InterSystems, as you know, has been around now for 43 years, and we have tens of thousands of customers in every industry. And this architectural pattern now is providing value for really critical use cases in every industry. So I'm happy to talk to you about some that we're seeing. I could actually spend like three hours here and there but I'm very passionate about working with customers and there's all sorts of exciting. >> What are some of your favorites? >> So, obviously supply chain right now is going through a very challenging time. So the combination of what's happening with the pandemic and disruptions and now I understand eggs are difficult to come by I just heard on NPR. >> Yeah and it's in part a data problem and a big part of data problem, is that fair? >> Yeah and so, in supply chain, first there's supply chain visibility. So organizations want a real time or near real time expansive view of what's happening across the entire supply chain from a supply all the way through distribution, right? So that's only part of the issue but that's a huge sort of real-time data silos problem. So if you think about your extended supply chain, it's complicated enough with all the systems and silos inside your firewall, before all of your suppliers even just thinking about your tier one suppliers let alone tier two and tier three. And then building on top of real-time visibility is what the industry calls a control tower, what we call the ultimate control tower. And so it's built in analytics to be able to sense disruptions and exceptions as they occur and predict the likelihood of these disruptions occurring. And then having data driven and analytics driven guidance in terms of the best way to deal with these disruptions. So for example, an order is missing line items or a cargo ship is stuck off port somewhere. What do you do about it? Do you reroute a different cargo ship, right? Do you take an order that's en route to a different client and reroute that? What's the cost associated? What's the impact associated with it? So that's a huge issue right now around control towers for supply chain. So that's one. >> Can I ask you a question about that? Because you and I have both seen a lot but we've never seen, at least I haven't the economy completely shut down like it was in March of 2020, and now we're seeing this sort of slingshot effect almost like you're driving on the highway sometimes you don't know why, but all of a sudden you slow down and then you speed up, you think it's okay then you slow down again. Do you feel like you guys can help get a handle on that product because it goes on both sides. Sometimes you can't get the product, sometimes there's too much of a product as well and that's not good for business. >> Yeah, absolutely. You want to smooth out the peaks and valleys. >> Yeah. >> And that's a big business goal, business challenge for supply chain executives, right? So you want to make sure that you can respond to demand but you don't want to overstock because there's cost associated with that as well. So how do you optimize the supply chains and it's very much a data silo and a real time challenge. So it's a perfect fit for this new architectural pattern. >> All right, what else? >> So if we look at financial services, we have many, many customers in financial services and that's another industry where they have many different sources of data that all have information that organizations can use to really move the needle if they could just get to that single source of truth in real time. So we sort of bucket many different implementations and use cases that we do around what we call Business 360 and Customer 360. So Business 360, there's all sorts of ways to add business value in terms of having a real-time operational view across all of the different GOs and parts of the business, especially in these very large global financial services institutions like capital markets and investment firms and so forth. So around Business 360, having a realtime view of risk, operational performance regulatory compliance, things like that. Customer 360, there's a whole set of use cases around Customer 360 around hyper-personalization of customers and in realtime next best action looking to see how you can sell more increase share of wallet, cross-sell, upsell to customers. We also do a lot in terms of predicting customer churn. So if you have all the historical data and what's the likelihood of customers churning to be able to proactively intercede, right? It's much more cost effective to keep assets under management and keep clients rather than going and getting new clients to come to the firm. A very interesting use case from one of our customers in Latin America, so Banco do Brasil largest bank in all of Latin America and they have a very innovative CTO who's always looking for new ways to move the needle for the bank. And so one of their ideas and we're working with them to do this is how can they generate net new revenue streams by bringing in new business to the bank? And so they identified a large percentage of the population in Latin America that does no banking. So they have no banking history not only with Banco do Brasil, but with any bank. So there's a fair amount of risk associated with offering services to this segment of the population that's not associated with any banks or financial institutions. >> There is no historical data on them, there's no. >> So it's a data challenge. And so, they're bringing in data from a variety of different sources, social media, open source data that they find online and so forth. And with us running risk models to identify which are the citizens that there's acceptable risk to offer their services. >> It's going to be huge market of unbanked people in vision Latin America. >> Wow, that's interesting. >> Yeah, yeah, totally vision. >> And if you can lower the risk and you could tap that market and be first >> And they are, yeah. >> Yeah. >> So very exciting. Manufacturing, we know industry 4.0 which is about taking the OT data, so the data from the MES systems and the streaming data, real-time streaming data from the machine controllers and integrating it with the IT data, so your data warehouses and your ERP systems and so forth to have not only a real-time view of manufacturing from supply and source all the way through demand but also predictive maintenance and things like that. So that's very big right now in manufacturing. >> Kind of cool to hear these use cases beyond your healthcare, which is obviously, your wheelhouse, Scott defined this term of smart data fabrics, different than data fabrics, I guess. So when we think about these use cases what's the value add of so-called smart data fabrics? >> Yeah, it's a great question. So we did not define the term data fabric or enterprise data fabric. The analysts now are all over it. They're all saying it's the future of data management. It's a fundamentally different approach this architectural approach to be able to access the data on demand. The canonical definition of a data fabric is to access the data where it lies and apply a set of data management processes, but it does not include analytics, interestingly. And so we firmly believe that most of these use cases gain value from having analytics built directly into the fabric. So whether that's business rules or predictive analytics to predict the likelihood of a customer churn or a machine on the shop floor failing or prescriptive analytics. So if there's a problem in the supply chain, what's the guidance for the supply chain managers to take the best action, right? Prescriptive analytics based on data. So rather than taking the data and the data fabric and moving it to another environment to run those analytics where you have complexity and latency, having tall of those analytics capabilities built directly into the fabric, which is why we call it a smart data fabric, brings a lot of value to our customers. >> So simplifies the whole data lifecycle, data pipelining, the hyper-specialized roles that you have to have, you can really just focus on one platform, is that? >> Exactly, basically, yeah. And it's a simplicity of architecture and faster speed to production. So a big differentiator for our technology, for InterSystems, Iris, is most if not all of the capabilities that are needed are built into one engine, right? So you don't need to stitch together 10 or 15 or 20 different data management services for relational database in a non-relational database and a caching layer and a data warehouse and security and so forth. And so you can do that. There's many ways to build this data fabric architecture, right? InterSystems is not the only way. >> Right? >> But if you can speed and simplify the implementation of the fabric by having most of what you need in one engine, one product that gets you to where you need to go much, much faster. >> Joe, how can people learn more about smart data Fabric some of the use cases that you've presented here? >> Yeah, come to our website, intersystems.com. If you go to intersystems.com/smartdatafabric that'll take you there. >> I know that you have like probably dozens more examples but it would be cool- >> I do. >> If people reach out to you, how can they get in touch? >> Oh, I would love that. So feel free to reach out to me on LinkedIn. It's Joe Lichtenberg I think it's linkedin.com/joeLichtenberg and I'd love to connect. >> Awesome. Joe, thanks so much for your time. Really appreciate it. >> It was great to be here. Thank you, Dave. >> All right, I hope you've enjoyed our program today. You know, we heard Scott now he helped us understand this notion of data fabrics and smart data fabrics and how they can address the data challenges faced by the vast majority of organizations today. Jess Jody's demo was awesome. It was really a highlight of the program where she showed the smart data fabrics inaction and Joe Lichtenberg, we just heard from him dug in to some of the prominent use cases and proof points. We hope this content was educational and inspires you to action. Now, don't forget all these videos are available on Demand to watch, rewatch and share. Go to theCUBE.net, check out siliconangle.com for all the news and analysis and we'll summarize the highlights of this program and go to intersystems.com because there are a ton of resources there. In particular, there's a knowledge hub where you'll find some excellent educational content and online learning courses. There's a resource library with analyst reports, technical documentation videos, some great freebies. So check it out. This is Dave Vellante. On behalf of theCUBE and our supporter, InterSystems, thanks for watching and we'll see you next time. (upbeat music)