(upbeat music) >> Hey everyone, welcome to this event: Build Your Cloud Center of Excellence. I'm your host, Lisa Martin, and I have two guests here with me today to talk about the hybrid cloud, the multi-cloud trends, and specifically the complexity. While we know these trends provide agility and flexibility for customers, they also bring in complexity. And this session is going to focus on exploring that with RBI and HitachiVantara. Please welcome my guests, Adithya Sastry the SVP of Digital Solutions at HitachiVantara and Werner Mayer, head of group core IT and head of group data at RBI International. Guys, welcome to the program. >> Thank you Lisa. Werner, nice to see you again. >> Great to see you both. >> And Werner, we're going to start with you. Talk about RBI. Tell the audience a little bit about what the business is and then we're going to get into your cloud transformation journey over the last couple of years. >> Yes, thank you. So Raiffeisen Bank International is international working banking groups. So our core markets are Central Eastern European, Central Eastern Europe and Austria. And we are serving around 50 million clients in this market. So we active in 13 markets. >> Got it. Talk to me, Werner about the cloud transformation journey that RBI has been on over the last couple of years and some of the complexities that you've experienced as you've launched it. >> Sure. Thank you for the question. So in 2020, we decided that we have to renew our IT strategy. And the aim of the strategy was to change the organization in a way that it can react and adapt fast to the future challenges. So one of the important pillars for us was that we are adapting fast also for new technologies. And this was core pillar in our strategy. So we're searching for technologies which are fit in to our HR transformation. And we found that the cloud and the public cloud environment fits to this venture. So we tested that. We are building up also the competent centers for that and also established the group cloud platform for that. Because our invoice to onboard our international group with the 13 units to this group cloud platform. So that means we have a lot to do to hardening the platforms in terms of security to put in. We have standard for that. We have to introduce large scale programs to train hundreds of engineers. We tested the approach, We convinced the top management and we implemented this, this program. So one of the highlights was, of course, also the the safeguarding of the Ukraine, let's say, banking environment. So we had to lift and shift the complete bank in three months. And it shows that let's say our platforms works. And let's say the approach is proven that we can scale it over the group. >> That's a big challenge. A lot of complexity especially with some of the global things going on. Adithya, these challenges are, are not unique to RBI. A lot of your customers are facing challenges with complexity around cloud management, cloud ops. What can you unpack was the real issue is here? >> Yeah, Lisa, absolutely. And you know, before I answer your question, I do want to, you know, just say a couple of things about Raiffeisen Bank. And you know, we've had the pleasure of working with them for about a year, a little bit more than a year now. And, and, and the way they approach the cloud transformation journey is - should be a template for a lot of the organizations in terms of the preparation in terms of understanding, you know. How other companies have done it and what are the pitfalls. What's worked, and really what's the recipe for their, you know, journey, right? Which is very unique because, you know, you look at you know, being present across 30 different countries within central and eastern Europe as Werner said. And the complexities of dealing with local regulations, GDPR and all these other issues that come with it, right? And not to mention the language variation from country to country. So, you know, phenomenal story there. The journey and the journey still goes, right Werner? It's not complete yet. But Lisa, to your question, you know. When we look at, you know, the complexities of this transformation, that most modern enterprises are going through. It's not very unique, right? What is unique for a Raiffeisen Bank is - has been the preparation. As you get into this journey of moving workloads to cloud, be it refactoring, modernizing, migrating, etc. One of the things that really is often overlooked is: "Are my applications and data workloads resilient on the cloud?" Meaning  how is the performance? Are they just running or are they performing with high availability to meet your customers goals? Is it scalable? And are my cost in line with what I projected when I moved prep. >> Because that's one of the areas we are seeing where you know, what enterprises projected from a cost savings to what they're realizing a year and a half into the journey is a pretty big delta, right? And, and, and a lot of it is dependent on are the cloud - are the applications and the workloads cloud, designed for the cloud? Or are they designed for on-prem which you just move to the cloud. >> So Werner, it sounds like what Adithya said is a compliment to, to you guys and the team at RBI in terms of this being a template for managing complexity. Give us, Werner, your perspective in terms of modern cloud ops. What's in? What's out? What is it that customers really need to be focusing on to be successful? >> Thanks for the compliment, Lisa. And I think this is a great relationship also in the journey. Topic is, is, is a - is a complex program where a lot of things have to fit together. But it was mentioning the resilience. The course, we call it finops, security operations and so on have to come together and have to work on spot. At the end, it's also, let's say, how we are able enabling our teams and how we are ramping out the skills of our teams to deal with these multidimensional, let's say environments. And this is something what we spend a lot of time in order to prepare, but also to bring up the people on a certain level that they can operate at. Because card guard handling is, is different than before. Because beforehand you have central operations team. They do everything for you. But in this world let's say we are also putting the responsibility of the run component of the absent to the - in the tribes and the application teams. And they have to do much more than before. On the other hand, we have first central rules. We have monitoring functions. We have support functions on that in order to best support them in their journey. So this is a hybrid between, let's say, what the teams have to do with the responsibility in the teams, but also with the central functions which are supporting them. And everything have to work together and goes hand in - right, to go hand-in-hand. >> Yeah. Yeah. And if, if I could just add Lisa really quick and and Werner hit the nail on the head, right? Because you cannot look at cloud operation the way we have traditionally looked at managed services. That's the key thing, right? You cannot, you know, traditional managed services you had L1, L2, L3 and then it goes into some sort of a vacuum and then all of a sudden somebody calls you at some point, right? >> Werner: Exactly. >> And it really has flipped, right? To, to Werner's point. And Werner hit that name on the head because you really have to understand. Bring an engineering led approach to make sure that the problems, you know, when you see an issue that you have some level of automation in terms of problem isolation. And then the problem is routed the right individual ie the application engineering team or the data engineering team for resolution in a rapid manner. Right? I think that the key - >> Yes. A very important point with that is said, yeah. So you cannot traditional transport let's say, the operation model what you have now into the cloud because this will not work, yeah. And finally at the end you will not benefit on the technology possibilities there. So super important point. My vision in the cloud and this is also something what we are working on is a sort of zero-ops environment, yeah? Because we're ultimately dealing with the automatization technologies and so on, you can that much - to much more compared to the traditional environment and the benefit of the cloud is: You can test it. You can give it feedback when it is not working, yeah? So it's a completely different operating model. What we try to establish in the cloud environment. >> So really what this seems like guys is is quite a delicate balance that you're solving for. Not the only delicate balance but Werner sticking with you. Talk to us about some of the challenges that you've had around cloud cost management in particular. Help us understand that. >> Thanks for the question. So in principle, we are doing very well on the cost side, surprisingly. And we also started the cloud journey that is said this is not the cost case. Because as I said before, let's say one of the pillars in the strategy strategy was the enablement of technology to the benefit of customer solutions to be adaptive, to be faster. But at the end it turned out that let's say with giving the responsibility of the operation to the dedicated team, they found they - they were working much closer to the cost, and let's say monitoring the cost, then we headed into traditional environments, yeah? I also saw some examples in the group where sort of gamification of the cost were going on. To say who can save more To say who can save more and make more much more out of that what you have in the cloud. And at the end we see that in minimum the cost are balance to the traditional environments in the data centers. But we also saw that let's say, the cost were brought down much more than before. So at the beginning we were relative conservative with the assumptions, yeah? But it turns out that we are really getting the benefit. The things are getting faster and also the costs are going down. And we see this in real cases. >> Yeah. And, and, and Lisa, if I could add something really quick, right? Because - There's been a mad rush to the cloud, right? Everybody kind of, it was, you know, the buzz the buzz was let's get to the cloud. We'll start to realize all these savings. And all of a sudden, everything kind of magically gets better, right? And what we have seen is also, you know, companies or customers or enterprises that have started this journey about 5, 6 years ago and are about, you know, a few years into it. What we are realizing is the cloud costs have increased significantly to what their projections were early on. And the way they're trying to address the cloud cost is by creating a FinOps organization that's looking at, you know, the cost of cloud from a structure standpoint and support as a reactive measure. Saying, "Hey if we move from Azure or one provider to another is there any benefit? If we move certain applications from the cloud back to on-prem, is there any benefit?" When in fact, one of the things that we have noticed really is: The problem needs to shift left to the engineering teams. Because if you are designing the applications and the systems the right way to begin with, then you can manage the data cost issues or the cost overruns, right? So you design for the cloud as opposed to designing and then looking at how do we optimize cloud. >> So Adithya, you talked about the RBI use case as really kind of a template but also some of the challenges with respect to hybrid and multi-cloud are kind of like a chicken and egg scenario. Talk to us kind of like overall about how Hitachi is really helping customers address these challenges and maximize the benefits to get the flexibility to get the agility so that they can deliver what their end user customers are expecting. >> Yeah, yeah. So, so one of the things we are doing, Lisa, when we work with customers, is really trying to understand, you know, look at their entire portfolio of applications, right? And, and look at what the intent of the applications is between customer facing, external customer, internal customer, high availability, production, etc., right? And then we go through a methodology called E3 which is envision, enable and execute. Which is really envision what the end stage should be regardless of what the environment is, right? And then we enable, which is really kind of go through a proof of value to move a few workloads, to modernize, rearchitect, replatform, etc. And look at the benefit of that application on its destination. If it's a cloud - if it's a cloud service provider or if it's another data center, whatever it may be, right? And finally, you know, once we've proven the value and the benefit and and say and kind of monetize the, you know realize the value of it from an agility, from a cost, from security and resilience, etc. Then we go through the execution, which was look we look at the entire portfolio, the entire landscape. And we go through a very disciplined manner working with our customers to roadmap it. And then we execute in a very deliberate manner where you can see value every 2-3 months. Because gone the days when you can do things as a science project that took 2-3 years, right? We, we - Everyone wants to see value, want to see - wants to see progress, and most importantly we want to see cost benefit and agility sooner than later. >> Those are incredibly important outcomes. You guys have done a great job explaining what you're doing together. This sounds like a great relationship. All right, so my last question to both of you is: "If I'm a customer and I'm planning a cloud transformation for my company, what are the two things you want me to remember and consider as I plan this? Werner, we'll start with you. >> I would pick up two things, yeah? The first one is: When you are organizing your company in HR way, then cloud is the HR technology for the HR transformation. Because HR teams needs HR technology. And the second important thing is, what I would say is: Cloud is a large scale and fast moving technology enabler to the company. So if your company is going forward to say: Technology is their enabler tool from a future business then cloud can support this journey. >> Excellent. I'm going to walk away with those. And Adithya, same question to you. I'm a, I'm a customer. I'm at an organization. I'm planning a cloud transformation. Top two things you want me to walk away with. >> Yeah. And I think Werner kind of actually touched on that in the second one, which is: it's not a tech, just an IT or a technology initiative. It is a business initiative, right? Because ultimately what you do from this cloud journey should drive, you know, should lead into business transformation or help your business grow top line or drive margin expansion, etc. So couple of things I would say, right? One is, you know, get Being and prioritize. Work with your business owners, with, you know with the cross-functional team not just the technology team. That's one. The second thing is: as the technology team or the IT team shepherds this journey, you know, keep everyone informed and engaged as you go through this journey. Because as you go through moving workloads modernizing workload, there is an impact to, you know receivables through omnichannel experiences the way customers interact and transact with you, right? And that comes with making making sure your businesses are aware your business stakeholders are aware. So in turn the end customers are aware. So you know, it's not a one and done from an engagement, it's a journey. And bring in the right experts. Talk to people who've done it, done this before, who have kind of stepped in all the pitfalls so you don't have to, right? That's the key. >> That's great advice. That's great advice for anything in life, I think. You talk about the collaboration, the importance of the business and the technology folks coming together. It really has to be - It's a delicate balance as we said before but it really has to be a holistic collaborative approach. Guys, thank you so much for joining me talking through what HitachiVantara and RBI are doing together. It sounds like you're well into this journey and it sounds like it's going quite well. We thank you so much for your insights and your perspectives. >> Thank you, Lisa. Werner, thank you again. >> Good stuff guys. For my guests, I'm Lisa Martin. Thank you so much for watching our event: Build Your Cloud Center of Excellence. (upbeat music)

(upbeat music) >> Hey everyone, welcome to this event: Build Your Cloud Center of Excellence. I'm your host, Lisa Martin, and I have two guests here with me today to talk about the hybrid cloud, the multi-cloud trends, and specifically the complexity. While we know these trends provide agility and flexibility for customers, they also bring in complexity. And this session is going to focus on exploring that with RBI and HitachiVantara. Please welcome my guests, Adithya Sastry the SVP of Digital Solutions at HitachiVantara and Werner Mayer, head of group core IT and head of group data at RBI International. Guys, welcome to the program. >> Thank you Lisa. Werner, nice to see you again. >> Great to see you both. >> And Werner, we're going to start with you. Talk about RBI. Tell the audience a little bit about what the business is and then we're going to get into your cloud transformation journey over the last couple of years. >> Yes, thank you. So Raiffeisen Bank International is international working banking groups. So our core markets are Central Eastern European, Central Eastern Europe and Austria. And we are serving around 50 million clients in this market. So we active in 13 markets. >> Got it. Talk to me, Werner about the cloud transformation journey that RBI has been on over the last couple of years and some of the complexities that you've experienced as you've launched it. >> Sure. Thank you for the question. So in 2020, we decided that we have to renew our IT strategy. And the aim of the strategy was to change the organization in a way that it can react and adapt fast to the future challenges. So one of the important pillars for us was that we are adapting fast also for new technologies. And this was core pillar in our strategy. So we're searching for technologies which are fit in to our HR transformation. And we found that the cloud and the public cloud environment fits to this venture. So we tested that. We are building up also the competent centers for that and also established the group cloud platform for that. Because our invoice to onboard our international group with the 13 units to this group cloud platform. So that means we have a lot to do to hardening the platforms in terms of security to put in. We have standard for that. We have to introduce large scale programs to train hundreds of engineers. We tested the approach, We convinced the top management and we implemented this, this program. So one of the highlights was, of course, also the the safeguarding of the Ukraine, let's say, banking environment. So we had to lift and shift the complete bank in three months. And it shows that let's say our platforms works. And let's say the approach is proven that we can scale it over the group. >> That's a big challenge. A lot of complexity especially with some of the global things going on. Adithya, these challenges are, are not unique to RBI. A lot of your customers are facing challenges with complexity around cloud management, cloud ops. What can you unpack was the real issue is here? >> Yeah, Lisa, absolutely. And you know, before I answer your question, I do want to, you know, just say a couple of things about Raiffeisen Bank. And you know, we've had the pleasure of working with them for about a year, a little bit more than a year now. And, and, and the way they approach the cloud transformation journey is - should be a template for a lot of the organizations in terms of the preparation in terms of understanding, you know. How other companies have done it and what are the pitfalls. What's worked, and really what's the recipe for their, you know, journey, right? Which is very unique because, you know, you look at you know, being present across 30 different countries within central and eastern Europe as Werner said. And the complexities of dealing with local regulations, GDPR and all these other issues that come with it, right? And not to mention the language variation from country to country. So, you know, phenomenal story there. The journey and the journey still goes, right Werner? It's not complete yet. But Lisa, to your question, you know. When we look at, you know, the complexities of this transformation, that most modern enterprises are going through. It's not very unique, right? What is unique for a Raiffeisen Bank is - has been the preparation. But as you get into this journey of moving workloads to cloud, be it refactoring, modernizing, migrating, etc. One of the things that really is often overlooked is: "Are my applications applications and data workloads resilient on, on the, on the cloud?" Meaning are they - How is the performance? Are they just running or are they performing with high availability to meet your customers goals? Is it scalable? And are my cost in line with what I projected when I moved prep, right? Because that's one of the areas we are seeing where you know, what enterprises projected from a cost savings to what they're realizing a year and a half into the journey is a pretty big delta, right? And, and, and a lot of it is dependent on are the cloud - are the applications and the workloads cloud, designed for the cloud? Or are they designed for on-prem which you just move to the cloud. >> So Werner, it sounds like what Adithya said is a compliment to, to you guys and the team at RBI in terms of this being a template for managing complexity. Give us, Werner, your perspective in terms of modern cloud ops. What's in? What's out? What is it that customers really need to be focusing on to be successful? >> Thanks for the compliment, Lisa. And I think this is a great relationship also in the journey. Topic is, is, is a - is a complex program where a lot of things have to fit together. But it was mentioning the resilience. The course, we call it finops, security operations and so on have to come together and have to work on spot. At the end, it's also, let's say, how we are able enabling our teams and how we are ramping out the skills of our teams to deal with these multidimensional, let's say environments. And this is something what we spend a lot of time in order to prepare, but also to bring up the people on a certain level that they can operate at. Because card guard handling is, is different than before. Because beforehand you have central operations team. They do everything for you. But in this world let's say we are also putting the responsibility of the run component of the absent to the - in the tribes and the application teams. And they have to do much more than before. On the other hand, we have first central rules. We have monitoring functions. We have support functions on that in order to best support them in their journey. So this is a hybrid between, let's say, what the teams have to do with the responsibility in the teams, but also with the central functions which are supporting them. And everything have to work together and goes hand in - right, to go hand-in-hand. >> Yeah. Yeah. And if, if I could just add Lisa really quick and and Werner hit the nail on the head, right? Because you cannot look at cloud operation the way we have traditionally looked at managed services. That's the key thing, right? You cannot, you know, traditional managed services you had L1, L2, L3 and then it goes into some sort of a vacuum and then all of a sudden somebody calls you at some point, right? >> Werner: Exactly. >> And it really has flipped, right? To, to Werner's point. And Werner hit that name on the head because you really have to understand. Bring an engineering led approach to make sure that the problems, you know, when you see an issue that you have some level of automation in terms of problem isolation. And then the problem is routed the right individual ie the application engineering team or the data engineering team for resolution in a rapid manner. Right? I think that the key - >> Yes. A very important point with that is said, yeah. So you cannot traditional transport let's say, the operation model what you have now into the cloud because this will not work, yeah. And finally at the end you will not benefit on the technology possibilities there. So super important point. My vision in the cloud and this is also something what we are working on is a sort of zero-ops environment, yeah? Because we're ultimately dealing with the automatization technologies and so on, you can that much - to much more compared to the traditional environment and the benefit of the cloud is: You can test it. You can give it feedback when it is not working, yeah? So it's a completely different operating model. What we try to establish in the cloud environment. >> So really what this seems like guys is is quite a delicate balance that you're solving for. Not the only delicate balance but Werner sticking with you. Talk to us about some of the challenges that you've had around cloud cost management in particular. Help us understand that. >> Thanks for the question. So in principle, we are doing very well on the cost side, surprisingly. And we also started the cloud journey that is said this is not the cost case. Because as I said before, let's say one of the pillars in the strategy strategy was the enablement of technology to the benefit of customer solutions to be adaptive, to be faster. But at the end it turned out that let's say with giving the responsibility of the operation to the dedicated team, they found they - they were working much closer to the cost, and let's say monitoring the cost, then we headed into traditional environments, yeah? I also saw some examples in the group where sort of gamification of the cost were going on. To say who can save more To say who can save more and make more much more out of that what you have in the cloud. And at the end we see that in minimum the cost are balance to the traditional environments in the data centers. But we also saw that let's say, the cost were brought down much more than before. So at the beginning we were relative conservative with the assumptions, yeah? But it turns out that we are really getting the benefit. The things are getting faster and also the costs are going down. And we see this in real cases. >> Yeah. And, and, and Lisa, if I could add something really quick, right? Because - You know, there's been a mad rush to the cloud, right? Everybody kind of, it was, you know, the buzz the buzz was let's get to the cloud. We'll start to realize all these savings. And all of a sudden, everything kind of magically gets better, right? And what we have seen is also, you know, companies or customers or enterprises that have started this journey about 5, 6 years ago and are about, you know, a few years into it. What we are realizing is the cloud costs have increased significantly to what their projections were early on. And the way they're trying to address the cloud cost is by creating a FinOps organization that's looking at, you know, the cost of cloud from a structure standpoint and support as a reactive measure. Saying, "Hey if we move from Azure or one provider to another is there any benefit? If we move certain applications from the cloud back to on-prem, is there any benefit?" When in fact, one of the things that we have noticed really is: The problem needs to shift left to the engineering teams. Because if you are designing the applications and the systems the right way to begin with, then you can manage the data cost issues or the cost overruns, right? So you design for the cloud as opposed to designing and then looking at how do we optimize cloud. >> So Adithya, you talked about the RBI use case as really kind of a template but also some of the challenges with respect to hybrid and multi-cloud are kind of like a chicken and egg scenario. Talk to us kind of like overall about how Hitachi is really helping customers address these challenges and maximize the benefits to get the flexibility to get the agility so that they can deliver what their end user customers are expecting. >> Yeah, yeah. So, so one of the things we are doing, Lisa, when we work with customers, is really trying to understand, you know, look at their entire portfolio of applications, right? And, and look at what the intent of the applications is between customer facing, external customer, internal customer, high availability, production, etc., right? And then we go through a methodology called E3 which is envision, enable and execute. Which is really envision what the end stage should be regardless of what the environment is, right? And then we enable, which is really kind of go through a proof of value to move a few workloads, to modernize, rearchitect, replatform, etc. And look at the benefit of that application on its destination. If it's a cloud - if it's a cloud service provider or if it's another data center, whatever it may be, right? And finally, you know, once we've proven the value and the benefit and and say and kind of monetize the, you know realize the value of it from an agility, from a cost, from security and resilience, etc. Then we go through the execution, which was look we look at the entire portfolio, the entire landscape. And we go through a very disciplined manner working with our customers to roadmap it. And then we execute in a very deliberate manner where you can see value every 2-3 months. Because gone the days when you can do things as a science project that took 2-3 years, right? We, we - Everyone wants to see value, want to see - wants to see progress, and most importantly we want to see cost benefit and agility sooner than later. >> Those are incredibly important outcomes. You guys have done a great job explaining what you're doing together. This sounds like a great relationship. All right, so my last question to both of you is: "If I'm a customer and I'm planning a cloud transformation for my company, what are the two things you want me to remember and consider as I plan this? Werner, we'll start with you. >> I would pick up two things, yeah? The first one is: When you are organizing your company in HR way, then cloud is the HR technology for the HR transformation. Because HR teams needs HR technology. And the second important thing is, what I would say is: Cloud is a large scale and fast moving technology enabler to the company. So if your company is going forward to say: Technology is their enabler tool from a future business then cloud can support this journey. >> Excellent. I'm going to walk away with those. And Adithya, same question to you. I'm a, I'm a customer. I'm at an organization. I'm planning a cloud transformation. Top two things you want me to walk away with. >> Yeah. And I think Werner kind of actually touched on that in the second one, which is: it's not a tech, just an IT or a technology initiative. It is a business initiative, right? Because ultimately what you do from this cloud journey should drive, you know, should lead into business transformation or help your business grow top line or drive margin expansion, etc. So couple of things I would say, right? One is, you know, get Being and prioritize. Work with your business owners, with, you know with the cross-functional team not just the technology team. That's one. The second thing is: as the technology team or the IT team shepherds this journey, you know, keep everyone informed and engaged as you go through this journey. Because as you go through moving workloads modernizing workload, there is an impact to, you know receivables through omnichannel experiences the way customers interact and transact with you, right? And that comes with making making sure your businesses are aware your business stakeholders are aware. So in turn the end customers are aware. So you know, it's not a one and done from an engagement, it's a journey. And bring in the right experts. Talk to people who've done it, done this before, who have kind of stepped in all the pitfalls so you don't have to, right? That's the key. >> That's great advice. That's great advice for anything in life, I think. You talk about the collaboration, the importance of the business and the technology folks coming together. It really has to be - It's a delicate balance as we said before but it really has to be a holistic collaborative approach. Guys, thank you so much for joining me talking through what HitachiVantara and RBI are doing together. It sounds like you're well into this journey and it sounds like it's going quite well. We thank you so much for your insights and your perspectives. >> Thank you, Lisa. Werner, thank you again. >> Good stuff guys. For my guests, I'm Lisa Martin. Thank you so much for watching our event: Build Your Cloud Center of Excellence. (upbeat music)

(upbeat music) >> Hey everyone, welcome to this event: Build Your Cloud Center of Excellence. I'm your host, Lisa Martin, and I have two guests here with me today to talk about the hybrid cloud, the multi-cloud trends, and specifically the complexity. While we know these trends provide agility and flexibility for customers, they also bring in complexity. And this session is going to focus on exploring that with RBI and HitachiVantara. Please welcome my guests, Adithya Sastry the SVP of Digital Solutions at HitachiVantara and Werner Mayer, head of group core IT and head of group data at RBI International. Guys, welcome to the program. >> Thank you Lisa. Werner, nice to see you again. >> Great to see you both. >> And Werner, we're going to start with you. Talk about RBI. Tell the audience a little bit about what the business is and then we're going to get into your cloud transformation journey over the last couple of years. >> Yes, thank you. So Raiffeisen Bank International is international working banking groups. So our core markets are Central Eastern European, Central Eastern Europe and Austria. And we are serving around 50 million clients in this market. So we active in 13 markets. >> Got it. Talk to me, Werner about the cloud transformation journey that RBI has been on over the last couple of years and some of the complexities that you've experienced as you've launched it. >> Sure. Thank you for the question. So in 2020, we decided that we have to renew our IT strategy. And the aim of the strategy was to change the organization in a way that it can react and adapt fast to the future challenges. So one of the important pillars for us was that we are adapting fast also for new technologies. And this was core pillar in our strategy. So we're searching for technologies which are fit in to our HR transformation. And we found that the cloud and the public cloud environment fits to this venture. So we tested that. We are building up also the competent centers for that and also established the group cloud platform for that. Because our invoice to onboard our international group with the 13 units to this group cloud platform. So that means we have a lot to do to hardening the platforms in terms of security to put in. We have standard for that. We have to introduce large scale programs to train hundreds of engineers. We tested the approach, We convinced the top management and we implemented this, this program. So one of the highlights was, of course, also the the safeguarding of the Ukraine, let's say, banking environment. So we had to lift and shift the complete bank in three months. And it shows that let's say our platforms works. And let's say the approach is proven that we can scale it over the group. >> That's a big challenge. A lot of complexity especially with some of the global things going on. Adithya, these challenges are, are not unique to RBI. A lot of your customers are facing challenges with complexity around cloud management, cloud ops. What can you unpack was the real issue is here? >> Yeah, Lisa, absolutely. And you know, before I answer your question, I do want to, you know, just say a couple of things about Raiffeisen Bank. And you know, we've had the pleasure of working with them for about a year, a little bit more than a year now. And, and, and the way they approach the cloud transformation journey is - should be a template for a lot of the organizations in terms of the preparation in terms of understanding, you know. How other companies have done it and what are the pitfalls. What's worked, and really what's the recipe for their, you know, journey, right? Which is very unique because, you know, you look at you know, being present across 30 different countries within central and eastern Europe as Werner said. And the complexities of dealing with local regulations, GDPR and all these other issues that come with it, right? And not to mention the language variation from country to country. So, you know, phenomenal story there. The journey and the journey still goes, right Werner? It's not complete yet. But Lisa, to your question, you know. When we look at, you know, the complexities of this transformation, that most modern enterprises are going through. It's not very unique, right? What is unique for a Raiffeisen Bank is - has been the preparation. But as you get into this journey of moving workloads to cloud, be it refactoring, modernizing, migrating, etc. One of the things that really is often overlooked is: "Are my applications applications and data workloads resilient on, on the, on the cloud?" Meaning are they - How is the performance? Are they just running or are they performing with high availability to meet your customers goals? Is it scalable? And are my cost in line with what I projected when I moved prep, right? Because that's one of the areas we are seeing where you know, what enterprises projected from a cost savings to what they're realizing a year and a half into the journey is a pretty big delta, right? And, and, and a lot of it is dependent on are the cloud - are the applications and the workloads cloud, designed for the cloud? Or are they designed for on-prem which you just move to the cloud. >> So Werner, it sounds like what Adithya said is a compliment to, to you guys and the team at RBI in terms of this being a template for managing complexity. Give us, Werner, your perspective in terms of modern cloud ops. What's in? What's out? What is it that customers really need to be focusing on to be successful? >> Thanks for the compliment, Lisa. And I think this is a great relationship also in the journey. Topic is, is, is a - is a complex program where a lot of things have to fit together. But it was mentioning the resilience. The course, we call it finops, security operations and so on have to come together and have to work on spot. At the end, it's also, let's say, how we are able enabling our teams and how we are ramping out the skills of our teams to deal with these multidimensional, let's say environments. And this is something what we spend a lot of time in order to prepare, but also to bring up the people on a certain level that they can operate at. Because card guard handling is, is different than before. Because beforehand you have central operations team. They do everything for you. But in this world let's say we are also putting the responsibility of the run component of the absent to the - in the tribes and the application teams. And they have to do much more than before. On the other hand, we have first central rules. We have monitoring functions. We have support functions on that in order to best support them in their journey. So this is a hybrid between, let's say, what the teams have to do with the responsibility in the teams, but also with the central functions which are supporting them. And everything have to work together and goes hand in - right, to go hand-in-hand. >> Yeah. Yeah. And if, if I could just add Lisa really quick and and Werner hit the nail on the head, right? Because you cannot look at cloud operation the way we have traditionally looked at managed services. That's the key thing, right? You cannot, you know, traditional managed services you had L1, L2, L3 and then it goes into some sort of a vacuum and then all of a sudden somebody calls you at some point, right? >> Werner: Exactly. >> And it really has flipped, right? To, to Werner's point. And Werner hit that name on the head because you really have to understand. Bring an engineering led approach to make sure that the problems, you know, when you see an issue that you have some level of automation in terms of problem isolation. And then the problem is routed the right individual ie the application engineering team or the data engineering team for resolution in a rapid manner. Right? I think that the key - >> Yes. A very important point with that is said, yeah. So you cannot traditional transport let's say, the operation model what you have now into the cloud because this will not work, yeah. And finally at the end you will not benefit on the technology possibilities there. So super important point. My vision in the cloud and this is also something what we are working on is a sort of zero-ops environment, yeah? Because we're ultimately dealing with the automatization technologies and so on, you can that much - to much more compared to the traditional environment and the benefit of the cloud is: You can test it. You can give it feedback when it is not working, yeah? So it's a completely different operating model. What we try to establish in the cloud environment. >> So really what this seems like guys is is quite a delicate balance that you're solving for. Not the only delicate balance but Werner sticking with you. Talk to us about some of the challenges that you've had around cloud cost management in particular. Help us understand that. >> Thanks for the question. So in principle, we are doing very well on the cost side, surprisingly. And we also started the cloud journey that is said this is not the cost case. Because as I said before, let's say one of the pillars in the strategy strategy was the enablement of technology to the benefit of customer solutions to be adaptive, to be faster. But at the end it turned out that let's say with giving the responsibility of the operation to the dedicated team, they found they - they were working much closer to the cost, and let's say monitoring the cost, then we headed into traditional environments, yeah? I also saw some examples in the group where sort of gamification of the cost were going on. To say who can save more To say who can save more and make more much more out of that what you have in the cloud. And at the end we see that in minimum the cost are balance to the traditional environments in the data centers. But we also saw that let's say, the cost were brought down much more than before. So at the beginning we were relative conservative with the assumptions, yeah? But it turns out that we are really getting the benefit. The things are getting faster and also the costs are going down. And we see this in real cases. >> Yeah. And, and, and Lisa, if I could add something really quick, right? Because - You know, there's been a mad rush to the cloud, right? Everybody kind of, it was, you know, the buzz the buzz was let's get to the cloud. We'll start to realize all these savings. And all of a sudden, everything kind of magically gets better, right? And what we have seen is also, you know, companies or customers or enterprises that have started this journey about 5, 6 years ago and are about, you know, a few years into it. What we are realizing is the cloud costs have increased significantly to what their projections were early on. And the way they're trying to address the cloud cost is by creating a FinOps organization that's looking at, you know, the cost of cloud from a structure standpoint and support as a reactive measure. Saying, "Hey if we move from Azure or one provider to another is there any benefit? If we move certain applications from the cloud back to on-prem, is there any benefit?" When in fact, one of the things that we have noticed really is: The problem needs to shift left to the engineering teams. Because if you are designing the applications and the systems the right way to begin with, then you can manage the data cost issues or the cost overruns, right? So you design for the cloud as opposed to designing and then looking at how do we optimize cloud. >> So Adithya, you talked about the RBI use case as really kind of a template but also some of the challenges with respect to hybrid and multi-cloud are kind of like a chicken and egg scenario. Talk to us kind of like overall about how Hitachi is really helping customers address these challenges and maximize the benefits to get the flexibility to get the agility so that they can deliver what their end user customers are expecting. >> Yeah, yeah. So, so one of the things we are doing, Lisa, when we work with customers, is really trying to understand, you know, look at their entire portfolio of applications, right? And, and look at what the intent of the applications is between customer facing, external customer, internal customer, high availability, production, etc., right? And then we go through a methodology called E3 which is envision, enable and execute. Which is really envision what the end stage should be regardless of what the environment is, right? And then we enable, which is really kind of go through a proof of value to move a few workloads, to modernize, rearchitect, replatform, etc. And look at the benefit of that application on its destination. If it's a cloud - if it's a cloud service provider or if it's another data center, whatever it may be, right? And finally, you know, once we've proven the value and the benefit and and say and kind of monetize the, you know realize the value of it from an agility, from a cost, from security and resilience, etc. Then we go through the execution, which was look we look at the entire portfolio, the entire landscape. And we go through a very disciplined manner working with our customers to roadmap it. And then we execute in a very deliberate manner where you can see value every 2-3 months. Because gone the days when you can do things as a science project that took 2-3 years, right? We, we - Everyone wants to see value, want to see - wants to see progress, and most importantly we want to see cost benefit and agility sooner than later. >> Those are incredibly important outcomes. You guys have done a great job explaining what you're doing together. This sounds like a great relationship. All right, so my last question to both of you is: "If I'm a customer and I'm planning a cloud transformation for my company, what are the two things you want me to remember and consider as I plan this? Werner, we'll start with you. >> I would pick up two things, yeah? The first one is: When you are organizing your company in HR way, then cloud is the HR technology for the HR transformation. Because HR teams needs HR technology. And the second important thing is, what I would say is: Cloud is a large scale and fast moving technology enabler to the company. So if your company is going forward to say: Technology is their enabler tool from a future business then cloud can support this journey. >> Excellent. I'm going to walk away with those. And Adithya, same question to you. I'm a, I'm a customer. I'm at an organization. I'm planning a cloud transformation. Top two things you want me to walk away with. >> Yeah. And I think Werner kind of actually touched on that in the second one, which is: it's not a tech, just an IT or a technology initiative. It is a business initiative, right? Because ultimately what you do from this cloud journey should drive, you know, should lead into business transformation or help your business grow top line or drive margin expansion, etc. So couple of things I would say, right? One is, you know, get Being and prioritize. Work with your business owners, with, you know with the cross-functional team not just the technology team. That's one. The second thing is: as the technology team or the IT team shepherds this journey, you know, keep everyone informed and engaged as you go through this journey. Because as you go through moving workloads modernizing workload, there is an impact to, you know receivables through omnichannel experiences the way customers interact and transact with you, right? And that comes with making making sure your businesses are aware your business stakeholders are aware. So in turn the end customers are aware. So you know, it's not a one and done from an engagement, it's a journey. And bring in the right experts. Talk to people who've done it, done this before, who have kind of stepped in all the pitfalls so you don't have to, right? That's the key. >> That's great advice. That's great advice for anything in life, I think. You talk about the collaboration, the importance of the business and the technology folks coming together. It really has to be - It's a delicate balance as we said before but it really has to be a holistic collaborative approach. Guys, thank you so much for joining me talking through what HitachiVantara and RBI are doing together. It sounds like you're well into this journey and it sounds like it's going quite well. We thank you so much for your insights and your perspectives. >> Thank you, Lisa. Werner, thank you again. >> Good stuff guys. For my guests, I'm Lisa Martin. Thank you so much for watching our event: Build Your Cloud Center of Excellence. (upbeat music)

>>Welcome back to the cubes. Continuing coverage of day, one of the snowflake summit 22 live from seizures forum in Las Vegas. I'm Lisa Martin. My co-host for the week is Dave ante, Dave and I are pleased to welcome Naga Raj Sastry to the program, the vice president of data and analytics at HCL technologies. Welcome. Great to have you. >>Same here. Thank you for inviting me here. >>Isn't it great to be back in person? >>Oh, love it. >>This the keynote this morning. I don't know if you had a chance to see it standing room only there was overflow rooms. People are ready for this, and it was a jam packed morning of announcements. >>Absolutely. >>Talk to us a little bit about the HCL snowflake partnership, but anybody in the audience who may not be familiar with HCL, give us a little bit of a background, vision, mission differentiation, and then that snowflake duo. >>Sure, sure. So let me first start off with, um, uh, talking about H at seal, we are 11.5 billion organization. Uh, we have three modes of working mode. One is everything to do with our infrastructure business and application services and maintenance mode. Two is anything that we do in the cutting edge, uh, ecosystem, whether it is cloud, whether it is application modernization, ERPs, uh, SA all of those put together is more to data. Analytics is part of our more to culture. Um, the whole ecosystem is called digital services business and, uh, within digital, uh, services, the one of the arms is data and analytics. We are about a billion dollars in terms of revenues from a data and analytics perspective, uh, of the 11 billion that I was talking to you about. And mode three is everything to do with our software services. So we have got our own software products, and that's a third of our business. So that's about HCL. So at C and, uh, snowflake relationship, we are a elite partner with snowflake. We are one of the fastest growing partners. We achieved the elite level within 18 months of us signing up as a snowflake partner. We're close to about 50 plus implementations worldwide, and, uh, about 800 people who are snowflake professionals within, within that CLE ecosystem, large customers that we serve. >>And how long have you been partners? >>Uh, about 18 to 20 months now. >>Okay. So, so the, during the last couple of tumultuous years, why snowflake, what was it about their vision, their strategy, their leadership that really to spoke to HCL as this is a partner for us? >>So, so one of the, uh, biggest things that we realized, uh, probably about four years ago was in terms of, you know, you had all the application databases or RDBMSs PPS, the huddle P ecosystems, which are getting expense systems, which were getting expensive, not in terms of the cost, but in terms of the pro processing times, the way the queries were getting created. And we knew that there was, there is something that is going to come and the people and the people. Yeah. >>And, uh, and we knew that, you know, there will be a hyperscaler that will come. And, uh, of course there was Azure was already there. AWS was there, Google was just picking it up. And at that point in time, we realized that, you know, there will be a cloud data warehouse because we had started reading about snowflake at that point in time. So fast forward a couple of years after that, and we realized that if we are to be in this business, you know, the, the right way of doing it is by getting partnering a partnering with the right tooling company. And snowflake brings that to table. We all know that now. And, uh, with, with what, what the keynote speakers were also saying, right, from 150 member team about five years ago in, uh, conference to about 12,000 people now. So you know that this is the right thing to do, and this is the right place to be at. So we, we devised a methodology in terms of saying that let's get into the partnership, let's get our resources trained and certified on the snowflake ecosystem. And let's take a point of view to our customers in terms of how data migrations and transformations have to be done in the snowflake arena. When >>You, when you think about your modes, you talked about modes one, two, and three. If I feel like snowflake touches on each of those, maybe not so much of the infrastructure and the apps, but although maybe going forward, it does increasingly. So, yeah, that's my question is where do you see snowflake vectoring into your modes? >>So it doesn in both in the first two modes, uh, and mode three also, uh, because, and I'll give you the reasons why mode one is predominantly because you can do application development on cloud yep. On the data cloud now, um, which basically means that I can have a qu application run on snowflake. Eventually that's the goal. Second is, uh, in, in more two, because it is a cloud data warehouse, it fits in exactly because the application data is in snowflake. I've got my, uh, regular data sets within snowflake. Both are talking to each other. There is zero, um, lapse time from a user perspective, >>It's a direct >>Tip. And then more three, the reason why I said more three was because software as a service or software services and products is because I can power by snowflake. I can implement that. So that's why it cuts across our entire ecosystem. >>The, the dig, the whole thing is called your dig business, correct? Yes. Is that right? So that's, this is the, the next wave of digital business that we're seeing here, cuz it's digital is data <laugh> right. That's really what it's about. It's about putting that data to work. >>So the president of our digital business, a BJA who was, who had done the, who had done a session in the, in the afternoon today, he says the D in the digital is data. >>There is right. >>And, uh, that's what we are seeing with our customers, large implementations that we do in this ecosystem. There is one other thing that we are focusing, uh, very heavily on is industrial solutions or industry led solutions. Like whether it is for healthcare, whether it's for retail or financial services, name, a vertical. And we have got our own capabilities around industrialized solutions that's fit that fit certain use cases. >>So in thinking about the D in, in digital is really data. If you think about the operating model for data, it's obviously evolved, you mentioned, had do, went to the cloud and all the data went to the cloud, but today it's, you've got an application development model, you got database, which is sort of hardened. And then you've got your data pipeline and your, your data stack and, and that's kind of the operating model. There's sort of siloed to a great degree. Mm-hmm <affirmative> how is that operating model changing as a result of, of data? So >>I answered it in two parts. Part is if you, if you realize over the years, what used to happen is you had a CIO in an organization or C more CIO, but, and then you had enterprise architecture teams, application development teams, support teams, and so on and so forth in the last 36 months. If you see there is an emergence of a new role, which is called the da chief data and analytics officer. So the data and analytics officer is a role that has been created. And the purpose of creating that role is to ensure that organizations will pull out our call out resources within the CIO organizations who are enterprise architects, who are data architects, who are application architects or security architects, and bring them under into the ecosystem of the data office from an operating model perspective. So that innovations can be driven. >>Data driven enterprises could be created and innovations can come through there. The other part of that is the use cases get prioritized when you start innovating. And then it is a factory model in terms of how those use cases get built, which is, which is, which is a no brainer in my mind, at least. But that is how the operating model is coming up from a people perspective, from a technology perspective. Also there is an operating model that is emerging. If you see all the hyperscalers that are there today, snowflake with its LA most latest and greatest announcements. If you see the way the industry is going, is everything will be housed into one ecosystem and the beauty of this entire thing. And if you, you are to, you'll be able to fathom it effectively, right? Because if you are, if I'm, multi-cloud kind of an environment and if I'm on snowflake, I don't care why, because I'm snowflake, which is, which can work around across the multi clouds. So my data is in one place >>Effectively. Yeah. It's interesting what you were saying about the chief data officer, the chief data officer, that role emerged out of the, the ashes, like a Phoenix of, of, you know, compliance data quality and, and healthcare and financial services and government, the highly regulated industries. And then it took a while, but it, it increasingly became, wow, this is a really front front of the board level role, if you will, you know, data, and now you're seeing it. It's it's, it is integrated with digital. >>Absolutely. And there is one other point, if you think about it, the emergence of the chief data officer came in because there were issues associated to data quality. Yeah. There were issues associated to data cataloging as to how data is cataloged. And there were issues in terms of trustability of the data. Now, the trustability of the data can be in two places. One is a data quality, Hey, bad data, garbage and garbage out. But then the other aspect of the trustability is in terms of, can I do the seven CS of data quality and say that, okay, I can hallmark this data platinum or gold or silver or bronze or UN hallmark data. And with snowflake, the advantage is if I, if you have a hallmark data set, that is a, say a platinum or a gold, and thanks to the virtual warehouse, the same data set gets penetrated across the enterprise. That's the beauty with which it comes. And then of course the metadata aspect of it, bringing in the technical metadata and the business metadata together for the purpose of creating the data catalogs is another key cool thing and enabled again by snowflake. >>What are some of it when you're in customer conversations, some of the myths or misconceptions that customers historically have typically been making when it comes to creating a data strategy, some of the misconceptions, and then what is your recommendation for those folks since every company, these days to be competitive has to be a data company. >>Yeah. So around data structures, the, the whole thought process has to be, uh, either do in the past, we used to go with, from source applications, we would gather requirements. Then we would figure out what sources are there, do a profiling of the data and then say, okay, the target data, data model should be this >>Too slow, >>Too slow right now, fast forward to the digital transformation. There is producers of data, which is basically that applications that are being modernized today are producers of data. They're actually telling you that I'm producing this kind of data. This is the kind of events that I'm producing. And this is my structure. Now the whole deal is I don't need to figure out what the requirements are. I know what the use case the application is going to be helping me with. So therefore the entire data model is supported. So, but at the same point in time, the newer generation applications that are getting created are not only created getting created in terms of the customer experience. Of course, that is very critical, but they're also taking into account aspects around metadata, the technical metadata associated within an application, the data quality rules or business rules that are implemented within an application, all of that is getting documented as a result, the whole timeline from source to profile to model, which used to be X number of days in the past is X minus, at least 20% now or 30% actually. So that is how the structures, uh, the data structures are coming into a play future futuristic thought process would be, there will be producers of data and there'll be consumers of data. Where is ETL then or ELT. Then there is not going to be any ETL or ELT because a producer is going to say that I'm producing the data for this. A consumer says that, okay, I wanna consume the data for this purpose. There, they meet through an API layer. So where is ETL eventually going to go away? >>Well, and those consumers of, if you think about the, the way it works today, the, the data operating model, if you will, the transaction systems and other systems draw off a bunch of exhaust, they gets thrown over the fence to the analytics system. They're not operation the data, the data pipeline, the data systems are not operationalized in a way that they need to be. And obviously Snowflake's trying to change that. >>So data >>That's a big change, please. >>Yeah. Sorry. Didn't mean to cut you off. My >>Apologies. No, no. I'm >>So data operations is a very, very critical aspect. And if you think about it holistically, we used to have ETL pipelines T pipelines. And then we used to have queries being written on top of metadata or PPS and HaLoop and all of that and reporting tools that would have number of reports that were created and certain self-service BI reports into the ecosystem. Now, when you think in terms of a cloud data warehouse, what is happening? Is this the way you are architecting your solution today in terms of data pipelines, those data pipelines are self manageable or self-healing do not need the number of people where there was no documentation in terms of what ETL pipelines were written in the past on certain ETL tools or why something is failing. Nobody knew why something was failing because these are age old code, but take it forward today. >>What happens is our organizations are migrating from on-prem to cloud and to the cloud data warehouse. And the overall cost of ownership is decreasing. The reason is the way we are implementing the data pipelines, the way the data operations are being done in terms of, you know, even before a pipeline is kicked, uh, or kicked in, then, you know, there is a check process to say whether the source application is ready or not ready. So such things, small, small things, which are part and parcel of the entire data operations lifecycle are taking the center stage as a result, self fueling mechanisms are coming in. And because of those self fueling mechanisms, metrics are being captured as a result, you know exactly where to focus on and where not to focus on as, as a result, the number of resources needed to support gets reduced. Cost of one service >>Is low, much higher trust self-service infrastructure, uh, data context in the hands of, of business users. Data is now more discoverable it's governed. So you can now create data products more quickly. So speed and scale become extremely important. >>Absolutely. And in fact, one of the things that, that, uh, that is changing is the way search is getting implemented here to in the past, you created an index and then, you know, the data is searchable, but now it is contextual search. Can I contextualize the entire search? Can I create a machine learning algorithm that will actually say that, okay, Nara as a persona was looking for this kind of data and then Nara as a person, or comes back again and looks for some different kind of data. Can the machine learning algorithm go and figure out, okay, what is, what is going on in a garage's mind? What is he trying to look at? And then, you know, improve the, the whole learnability of the, of the entire algorithm. That's how search is going to also take, get into a change kind of a scenario. >>Excellent NAAU garage. Thank you so much for joining us, talking about data modernization at speed, end scale HCL, what you're doing, what you're doing with snowflake, and the sounds like incredible power that you're enabling. And we're only just scratching the surface. I have a feeling there's a lot more under there that you guys are gonna uncover. >>Sure. So we have, we have a tool or an accelerator. We call it an accelerator in the HCL parlance, but just actually a tool. So when you think about data modernization onto snowflake, it is predominantly migrating the data set from your existing ecosystem onto snowflake. That is one aspect of it. The second aspect of it is the modernization of the ETL or E LT pipelines. The third aspect associated to the data that is there within this, these ecosystems is the reconciliation older application, uh, sorry, older legacy, uh, platform snowflake legacy platform gives me result. X does snowflake give me result X that kind of a reconciliation has to be done. Data reconciliation and testing. And then the third fourth layer associated is the reporting and visualization. So these four layers are part and parcel of something that we call as advantage. Migrate advantage migrate will convert your ter data, data, uh, model into a snowflake understandable data model automatically whether it's ter data, whether it is Oracle, extra data, green plum, <inaudible> you name a ecosystem. >>We have the mechanism to convert a data model from whatever it is into snowflake readable, understandable data model. The second aspect is the et L E L T pipeline. Whether you want to go from Informatica to DBT or Informatica to something else or data stage to something else doesn't matter. There is a, there is an algorithm, or there is a tool which is called the ETL pipeline. We call it gateway suit, gateway suit actually converts the code. It reads the code that is there on the left hand side, which is the legacy code, understands the logic, it reverse engineers and understands the logic. And then what it does is we use that understanding or that logic that has been called out into spark code or DBT or any other tool of your choice from a customer standpoint. That's the second layer. Third layer I talked about, which is basically data testing, automated data testing and data reconciliation and the last, but not the least is the reporting because older ways of reporting and visualization with, with current day reporting and visualization, which is more persona based, the art of visualization is something difficult or different in this, in this aspect, come over to our booth at 2 1, 1 4, and you'll see, uh, advantage migrate in the works >>Advantage. Migrate. There you go. Nero, thank you so much for joining us on the program and unpacking HCL, giving us really that technical dissection of what you guys are doing and together with snowflake. We appreciate your time. >>Thank you. My pleasure. Thank you >>For our guest and Dave ante. This is Lisa Martin live from the show floor of snowflake summit 22, Dave and I will be right back with our final guest of day one in just a minute.

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partner. (upbeat electronic music) >> Welcome back to The Cube. I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV, in downtown San Jose down the street from the Strata Data Conference. We're joined by a new guest to theCUBE, Sastry Malladi, the CTO Of FogHorn. Sastry, welcome to theCUBE. >> Thank you, thank you, Lisa. >> So FogHorn, cool name, what do you guys do, who are you? Tell us all that good stuff. >> Sure. We are a startup based in Silicon Valley right here in Mountain View. We started about three years ago, three plus years ago. We provide edge computing intelligence software for edge computing or fog computing. That's how our company name got started is FogHorn. For our particularly, for our IoT industrial sector. All of the industrial guys, whether it's transportation, manufacturing, oil and gas, smart cities, smart buildings, any of those different sectors, they use our software to predict failure conditions in real time, or do condition monitoring, or predictive maintenance, any of those use cases and successfully save a lot of money. Obviously in the process, you know, we get paid for what we do. >> So Sastry... GE populized this concept of IIoT and the analytics and, sort of the new business outcomes you could build on it, like Power by the Hour instead of selling a jet engine. >> Sastry: That's right. But there's... Actually we keep on, and David Floor did some pioneering research on how we're going to have to do a lot of analytics on the edge for latency and bandwidth. What's the FogHorn secret sauce that others would have difficulty with on the edge analytics? >> Okay, that's a great question. Before I directly answer the question, if you don't mind, I'll actually even describe why that's even important to do that, right? So a lot of these industrial customers, if you look at, because we work with a lot of them, the amount of data that's produced from all of these different machines is terabytes to petabytes of data, it's real. And it's not just the traditional digital sensors but there are video, audio, acoustic sensors out there. The amount of data is humongous, right? It's not even practical to send all of that to a Cloud environment and do data processing, for many reasons. One is obviously the connectivity, bandwidth issues, and all of that. But the two most important things are cyber security. None of these customers actually want to connect these highly expensive machines to the internet. That's one. The second is the lack of real-time decision making. What they want to know, when there is a problem, they want to know before it's too late. We want to notify them it is a problem that is occurring so that have a chance to go fix it and optimize their asset that is in question. Now, existing solutions do not work in this constrained environment. That's why FogHorn had to invent that solution. >> And tell us, actually, just to be specific, how constrained an environment you can operate in. >> We can run in about less than 100 to 150 megabytes of memory, single-core to dual-core of CPU, whether it's an ARM processor, an x86 Intel-based processor, almost literally no storage because we're a real-time processing engine. Optionally, you could have some storage if you wanted to store some of the results locally there but that's the kind of environment we're talking about. Now, when I say 100 megabytes of memory, it's like a quarter of Raspberry Pi, right? And even in that environment we have customers that run dozens of machinery models, right? And we're not talking -- >> George: Like an ensemble. >> Like an anomaly detection, a regression, a random forest, or a clustering, or a gamut, some of those. Now, if we get into more deep learning models, like image processing and neural net and all of that, you obviously need a little bit more memory. But what we have shown, we could still run, one of our largest smart city buildings customer, elevator company, runs in a raspberry Pi on millions of elevators, right? Dozens of machinery algorithms on top of that, right? So that's the kind of size we're talking about. >> Let me just follow up with one question on the other thing you said, with, besides we have to do the low-latency locally. You said a lot of customers don't want to connect these brown field, I guess, operations technology machines to the internet, and physically, I mean there was physical separation for security. So it's like security, Bill Joy used to say "Security by obscurity." Here it's security by -- >> Physical separation, absolutely. Tell me about it. I was actually coming from, if you don't mind, last week I was in Saudi Arabia. One of the oil and gas plants where we deployed our software, you have to go to five levels of security even to get to there, It's a multibillion dollar plant and refining the gas and all of that. Completely offline, no connectivity to the internet, and we installed, in their existing small box, our software, connected to their live video cameras that are actually measuring the stuff, doing the processing and detecting the specific conditions that we're looking for. >> That's my question, which was if they want to be monitoring. So there's like one low level, really low hardware low level, the sensor feeds. But you could actually have a richer feed, which is video and audio, but how much of that, then, are you doing the, sort of, inferencing locally? Or even retraining, and I assume that since it's not the OT device, and it's something that's looking at it, you might be more able to send it back up the Cloud if you needed to do retraining? >> That's exactly right. So the way the model works is particularly for image processing because you need, it's a more complex process to train than create a model. You could create a model offline, like in a GPU box, an FPGA box and whatnot. Import and bring the model back into this small little device that's running in the plant, and now the live video data is coming in, the model is inferencing the specific thing. Now there are two ways to update and revise the model: incremental revision of the model, you could do that if you want, or you can send the results to a central location. Not internet, they do have local, in this example for example a PIDB, an OSS PIDB, or some other local service out there, where you have an opportunity to gather the results from each of these different locations and then consolidate and retrain the model, put the model back again. >> Okay, the one part that I didn't follow completely is... If the model is running ultimately on the device, again and perhaps not even on a CPU, but a programmable logic controller. >> It could, even though a programmable controller also typically have some shape of CPU there as well. These days, most of the PLCs, programmable controllers, have either an RM-based processor or an x86-based processor. We can run either one of those too. >> So, okay, assume you've got the model deployed down there, for the, you know, local inferencing. Now, some retraining is going to go on in the Cloud, where you have, you're pulling in the richer perspective from many different devices. How does that model get back out to the device if it doesn't have the connectivity between the device and the Cloud? >> Right, so if there's strictly no connectivity, so what happens is once the model is regenerated or retrained, they put a model in a USB stick, it's a low attack. USB stick, bring it to the PLC device and upload the model. >> George: Oh, so this is sort of how we destroyed the Iranian centrifuges. >> That's exactly right, exactly right. But you know, some other environments, even though it's not connectivity to the Cloud environment, per se, but the devices have the ability to connect to the Cloud. Optionally, they say, "Look, I'm the device "that's coming up, do you have an upgraded model for me?" Then it can pull the model. So in some of the environments it's super strict where there are absolutely no way to connect this device, you put it in a USB stick and bring the model back here. Other environments, device can query the Cloud but Cloud cannot connect to the device. This is a very popular model these days because, in other words imagine this, an elevator sitting in a building, somebody from the Cloud cannot reach the elevator, but an elevator can reach the Cloud when it wants to. >> George: Sort of like a jet engine, you don't want the Cloud to reach the jet engine. >> That's exactly right. The jet engine can reach the Cloud it if wants to, when it wants to, but the Cloud cannot reach the jet engine. That's how we can pull the model. >> So Sastry, as a CTO you meet with customers often. You mentioned you were in Saudi Arabia last week. I'd love to understand how you're leveraging and gaging with customers to really help drive the development of FogHorn, in terms of being differentiated in the market. What are those, kind of bi-directional, symbiotic customer relationships like? And how are they helping FogHorn? >> Right, that's actually a great question. We learn a lot from customers because we started a long time ago. We did an initial version of the product. As we begin to talk to the customers, particularly that's part of my job, where I go talk to many of these customers, they give us feedback. Well, my problem is really that I can't even do, I can't even give you connectivity to the Cloud, to upgrade the model. I can't even give you sample data. How do you do that modeling, right? And sometimes they say, "You know what, "We are not technical people, help us express the problem, "the outcome, give me tools "that help me express that outcome." So we created a bunch of what we call OT tools, operational technology tools. How we distinguish ourselves in this process, from the traditional Cloud-based vendor, the traditional data science and data analytics companies, is that they think in terms of computer scientists, computer programmers, and expressions. We think in terms of industrial operators, what can they express, what do they know? They don't really necessarily care about, when you tell them, "I've got an anomaly detection "data science machine algorithm", they're going to look at you like, "What are you talking about? "I don't understand what you're talking about", right? You need to tell them, "Look, this machine is failing." What are the conditions in which the machine is failing? How do you express that? And then we translate that requirement, or that into the underlying models, underlying Vel expressions, Vel or CPU expression language. So we learned a ton from user interface, capabilities, latency issues, connectivity issues, different protocols, a number of things that we learn from customers. >> So I'm curious with... More of the big data vendors are recognizing data in motion and data coming from devices. And some, like Hortonworks DataFlow NiFi has a MiNiFi component written in C plus plus, really low resource footprint. But I assume that that's really just a transport. It's almost like a collector and that it doesn't have the analytics built in -- >> That's exactly right, NiFi has the transport, it has the real-time transport capability for sure. What it does not have is this notion of that CEP concept. How do you combine all of the streams, everything is a time series data for us, right, from the devices. Whether it's coming from a device or whether it's coming from another static source out there. How do you express a pattern, a recognition pattern definition, across these streams? That's where our CPU comes in the picture. A lot of these seemingly similar software capabilities that people talk about, don't quite exactly have, either the streaming capability, or the CPU capability, or the real-time, or the low footprint. What we have is a combination of all of that. >> And you talked about how everything's time series to you. Is there a need to have, sort of an equivalent time series database up in some central location? So that when you subset, when you determine what relevant subset of data to move up to the Cloud, or you know, on-prem central location, does it need to be the same database? >> No, it doesn't need to be the same database. It's optional. In fact, we do ship a local time series database at the edge itself. If you have a little bit of a local storage, you can down sample, take the results, and store it locally, and many customers actually do that. Some others, because they have their existing environment, they have some Cloud storage, whether it's Microsoft, it doesn't matter what they use, we have connectors from our software to send these results into their existing environments. >> So, you had also said something interesting about your, sort of, tool set, as being optimized for operations technology. So this is really important because back when we had the Net-Heads and the Bell-Heads, you know it was a cultural clash and they had different technologies. >> Sastry: They sure did, yeah. >> Tell us more about how selling to operations, not just selling, but supporting operations technology is different from IT technology and where does that boundary live? >> Right, so typical IT environment, right, you start with the boss who is the decision maker, you work with them and they approve the project and you go and execute that. In an industrial, in an OT environment, it doesn't quite work like that. Even if the boss says, "Go ahead and go do this project", if the operator on the floor doesn't understand what you're talking about, because that person is in charge of operating that machine, it doesn't quite work like that. So you need to work bottom up as well, to convincing them that you are indeed actually solving their pain point. So the way we start, where rather than trying to tell them what capabilities we have as a product, or what we're trying to do, the first thing we ask is what is their pain point? "What's your problem? What is the problem "you're trying to solve?" Some customers say, "Well I've got yield, a lot of scrap. "Help me reduce my scrap. "Help me to operate my equipment better. "Help me predict these failure conditions "before it's too late." That's how the problem starts. Then we start inquiring them, "Okay, what kind of data "do you have, what kind of sensors do you have? "Typically, do you have information about under what circumstances you have seen failures "versus not seeing failures out there?" So in the process of inauguration we begin to understand how they might actually use our software and then we tell them, "Well, here, use your software, "our software, to predict that." And, sorry, I want 30 more seconds on that. The other thing is that, typically in an IT environment, because I came from that too, I've been in this position for 30 plus years, IT, UT and all of that, where we don't right away talk about CEP, or expressions, or analytics, and we don't talk about that. We talk about, look, you have these bunch of sensors, we have OT tools here, drag and drop your sensors, express the outcome that you're trying to look for, what is the outcome you're trying to look for, and then we drive behind the scenes what it means. Is it analytics, is it machine learning, is it something else, and what is it? So that's kind of how we approach the problem. Of course, if, sometimes you do surprisingly occasionally run into very technical people. From those people we can right away talk about, "Hey, you need these analytics, you need to use machinery, "you need to use expressions" and all of that. That's kind of how we operate. >> One thing, you know, that's becoming clearer is I think this widespread recognition that's data intensive and low latency work to be done near the edge. But what goes on in the Cloud is actually closer to simulation and high-performance compute, if you want to optimize a model. So not just train it, but maybe have something that's prescriptive that says, you know, here's the actionable information. As more of your data is video and audio, how do you turn that into something where you can simulate a model, that tells you the optimal answer? >> Right, so this is actually a good question. From our experience, there are models that require a lot of data, for example, video and audio. There are some other models that do not require a lot of data for training. I'll give you an example of what customer use cases that we have. There's one customer in a manufacturing domain, where they've been seeing a lot of finished goods failures, there's a lot of scrap and the problem then was, "Hey, predict the failures, "reduce my scrap, save the money", right? Because they've been seeing a lot of failures every single day, we did not need a lot of data to train and create a model to that. So, in fact, we just needed one hour's worth of data. We created a model, put the thing, we have reduced, completely eliminated their scrap. There are other kinds of models, other kinds of models of video, where we can't do that in the edge, so we're required for example, some video files or simulated audio files, take it to an offline model, create the model, and see whether it's accurately predicting based on the real-time video coming in or not. So it's a mix of what we're seeing between those two. >> Well Sastry, thank you so much for stopping by theCUBE and sharing what it is that you guys at FogHorn are doing, what you're hearing from customers, how you're working together with them to solve some of these pretty significant challenges. >> Absolutely, it's been a pleasure. Hopefully this was helpful, and yeah. >> Definitely, very educational. We want to thank you for watching theCUBE, I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV in downtown San Jose. Come stop by Forager Tasting Room, hang out with us, learn as much as we are about all the layers of big data digital transformation and the opportunities. Stick around, we will be back after a short break. (upbeat electronic music)