>>From around the globe. It's the cube with digital coverage of an AI ops virtual forum brought to you by Broadcom. >>Welcome to the AI ops virtual forum. Finally, some Artan extended to be talking with rich lane now, senior analyst, serving infrastructure and operations professionals at Forrester. Rich. It's great to have you today. >>Thank you for having me. I think it's going to be a really fun conversation to have today. >>It is. We're going to be setting the stage for, with Richard, for the it operations challenges and the need for AI ops. That's kind of our objective here in the next 15 minutes. So rich talk to us about some of the problems that enterprise it operations are facing now in this year, that is 2020 that are going to be continuing into the next year. >>Yeah, I mean, I think we've been on this path for a while, but certainly the last eight months has, uh, has accelerated, uh, this problem and, and brought a lot of things to light that, that people were, you know, they were going through the day to day firefighting as their goal way of life. Uh, it's just not sustainable anymore. You a highly distributed environment or in the need for digital services. And, you know, one of them has been building for a while really is in the digital age, you know, we're providing so many, uh, uh, the, the interactions with customers online. Um, we've, we've added these layers of complexity, um, to applications, to infrastructure, you know, or we're in the, in the cloud or a hybrid or multi-cloud, or do you know you name it using cloud native technologies? We're using legacy stuff. We still have mainframe out there. >>Uh, you know, the, just the, the vast amount of things we have to keep track of now and process and look at the data and signals from, it's just, it's a really untenable for, for humans to do that in silos now, uh, in, in, you know, when you add to that, you know, when companies are so heavily invested in gone on the digital transformation path, and it's accelerated so much in the last, uh, year or so that, you know, we're getting so much of our business in revenue derived from these services that they become core to the business. They're not afterthoughts anymore. It's not just about having a website presence. It's, it's about deriving core business value from the services you're providing to your, through your customers. And a lot of cases, customers you're never going to meet or see at that. So it's even more important to be vigilant. >>And on top of the quality of that service that you're giving them. And then when you think about just the staffing issues we have, there's just not enough bodies to go around it in operations anymore. Um, you know, we're not going to be able to hire, you know, like we did 10 years ago, even. Uh, so that's where we need the systems to be able to bring those operational efficiencies to bear. When we say operational efficiencies, we don't mean, you know, uh, lessening head count because we can't do that. That'd be foolish. What we mean is getting the head count. We have back to burping on and higher level things, you know, working on, uh, technology refreshes and project work that that brings better digital services to customers and get them out of doing these sort of, uh, low, uh, complexity, high volume tasks that they're spending at least 20%, if not more on our third day, each day. So I think that the more we can bring intelligence to bear and automation to take those things out of their hands, the better off we are going forward. >>And I'm sure those workers are wanting to be able to have the time to deliver more value, more strategic value to the organization, to their role. And as you're saying, you know, was the demand for digital services is spiking. It's not going to go down and as consumers, if w if we have another option and we're not satisfied, we're going to go somewhere else. So, so it's really about not just surviving this time right now, it's about how do I become a business that's going to thrive going forward and exceeding expectations that are now just growing and growing. So let's talk about AI ops as a facilitator of collaboration, across business folks, it folks developers, operations, how can it facilitate collaboration, which is even more important these days? >>Yeah. So one of the great things about it is now, you know, years ago, have I gone years, as they say, uh, we would buy a tool to fit each situation. And, you know, someone that worked in network and others who will somebody worked in infrastructure from a, you know, Linux standpoint, have their tool, somebody who's from storage would have their tool. And what we found was we would have an incident, a very high impact incident occur. Everybody would get on the phone, 24 people all be looking at their siloed tool, they're siloed pieces of data. And then we'd still have to try to like link point a to B to C together, you know, just to institutional knowledge. And, uh, there was just ended up being a lot of gaps there because we couldn't understand that a certain thing happening over here was related to an advantage over here. >>Um, now when we bring all that data into one umbrella, one data Lake, whatever we want to call it, a lot of smart analytics to that data, uh, and normalize that data in a way we can contextualize it from, you know, point a to point B all the way through the application infrastructure stack. Now, the conversation changes now, the conversation changes to here is the problem, how are we going to fix it? And we're getting there immediately versus three, four or five hours of, uh, you know, hunting and pecking and looking at things and trying to try to extrapolate what we're seeing across disparate systems. Um, and that's really valuable. And in what that does is now we can change the conversation for measuring things. And in server up time and data center, performance metrics as to how are we performing as a business? How are we overall in, in real time, how are businesses being impacted by service disruption? >>We know how much money losing per minute hour, or what have you, uh, and what that translate lights into brand damage and things along those lines, that people are very interested in that. And, you know, what is the effect of making decisions either brief from a product change side? You know, if we're, we're, we're always changing the mobile apps and we're always changing the website, but do we understand what value that brings us or what negative impact that has? We can measure that now and also sales, marketing, um, they run a campaign here's your, you know, coupon for 12% off today only, uh, what does that drive to us with user engagement? We can measure that now in real time, we don't have to wait for those answers anymore. And I think, you know, having all those data and understanding the cause and effect of things increases, it enhances these feedback loops of we're making decisions as a business, as a whole to make, bring better value to our customers. >>You know, how does that tie into ops and dev initiatives? How does everything that we do if I make a change to the underlying architectures that help move the needle forward, does that hinder things, uh, all these things factor into it. In fact, there into the customer experience, which is what we're trying to do at the end of the day, w w whether operations people like it or not, we are all in the customer experience business now. And we have to realize that and work closer than ever with our business and dev partners to make sure we're delivering the highest level of customer experience we can. >>Uh, customer experience is absolutely critical for a number of reasons. I always kind of think it's inextricably linked with employee experience, but let's talk about long-term value because as organizations and every industry has pivoted multiple times this year and will probably continue to do so for the foreseeable future, for them to be able to get immediate value that let's, let's not just stop the bleeding, but let's allow them to get a competitive advantage and be really become resilient. What are some of the, uh, applications that AI ops can deliver with respect to long-term value for an organization? >>Yeah, and I think that it's, you know, you touched upon this a very important point that there is a set of short term goals you want to achieve, but they're really going to be looking towards 12, 18 months down the road. What is it going to have done for you? And I think this helps framing out for you what's most important because it'd be different for every enterprise. Um, and it also shows the ROI of doing this because there is some, you know, change is going to be involved with things you're gonna have to do. But when you look at the, the, the longer time horizon of what it brings to your business as a whole, uh it's to me, at least it all seems, it seems like a no brainer to not do it. Um, you know, thinking about the basic things, like, you know, faster remediation of, of, uh, client impacting incidents, or maybe, maybe even predictive of sort of detection of these incidents that will affect clients. >>So now you're getting, you know, at scale, you know, it's very hard to do when you have hundreds of thousands of optics of the management that relate to each other, but now you're having letting the machines and the intelligence layer find out where that problem is. You know, it's not the red thing, it's the yellow thing. Go look at that. Um, it's reducing the amount of finger pointing and what have you like resolved between teams now, everybody's looking at the same data, the same sort of, uh, symptoms and like, Oh yeah, okay. This is telling us, you know, here's the root cause you should investigate this huge, huge thing. Um, and, and it's something we never thought we'd get to where, uh, this, this is where we smart enough to tell us these things, but this, again, this is the power of having all the data under one umbrella >>And the smart analytics. >>Um, and I think really, you know, it's a boat. Uh, if you look at where infrastructure and operations people are today, and especially, you know, eight months, nine months, whatever it is into the pandemic, uh, a lot of them are getting really burnt out with doing the same repetitive tasks over and over again. Um, just trying to keep the lights on, you know, we need, we need to extract those things for those people, uh, just because it just makes no sense to do something over and over again, the same remediation step, just we should automate those things. So getting that sort of, uh, you know, drudgery off their hands, if you will, and, and get them into, into all their important things they should be doing, you know, they're really hard to solve problems. That's where the human shine, um, and that's where, you know, having a, you know, really high level engineers, that's what they should be doing, you know, and just being able to do things I >>Think in a much faster, >>In a more efficient manner, when you think about an incident occurring, right. In, in a level, one technician picks that up and he goes and triaged that maybe run some tests. He has a script, >>Uh, or she, uh, and, >>You know, uh, they open a ticket and they enrich the ticket. They call it some log files. They can look up for the servers on it. You're in an hour and a half into an incident before anyone's even looked at it. If we could automate all of that, >>Why wouldn't we, that makes it easier for everyone. Um, >>Yeah. And I really think that's where the future is, is, is, is bringing this intelligent automation to bear, to take, knock down all the little things that consume the really, the most amount of time. When you think about it, if you aggregate it over the course of a quarter or a year, a great deal of your time is spent just doing that minutiae again, why don't we automate that? And we should. So I really think that's, that's where you get to look long-term. I think also the sense of we're going to be able to measure everything in the sense of business KPIs versus just IT-centric KPIs. That's really where we going to get to in the digital age. And I think we waited too long to do that. I think our operations models were all voted. I think, uh, you know, a lot of, a lot of the KPIs we look at today are completely outmoded. They don't really change if you think about it. When we look at the monthly reports over the course of a year, uh, so let's do something different. And now having all this data and the smart analytics, we can do something different. Absolutely. I'm glad >>That you brought up kind of looking at the impact that AI ops can make on, on minutiae and burnout. That's a really huge problem that so many of us are facing in any industry. And we know that there's some amount of this that's going to continue for a while longer. So let's get our let's leverage intelligent automation to your point, because we can to be able to allow our people to not just be more efficient, but to be making a bigger impact. And there's that mental component there that I think is absolutely critical. I do want to ask you what are some of these? So for those folks going, all right, we've got to do this. It makes sense. We see some short-term things that we need. We need short-term value. We need long-term value as you've just walked us through. What are some of the obstacles that you'd say, Hey, be on the lookout for this to wipe it out of the way. >>Yeah. I, I think there's, you know, when you think about the obstacles, I think people don't think about what are big changes for their organization, right? You know, they're, they're going to change process. They're going to change the way teams interact. They're they're going to change a lot of things, but they're all for the better. So what we're traditionally really bad in infrastructure and operations is communication, marketing, a new initiative, right? We don't go out and get our peers agreement to it where the product owner is, you know, and say, okay, this is what it gets you. This is where it changes. People just hear I'm losing something, I'm losing control over something. You're going to get rid of the tools that I have, but I love I've spent years building out perfecting, um, and that's threatening to people and understandably so because people think if I start losing tools, I start losing head count. >>And then, whereas my department at that point, um, but that's not what this is all about. Uh, this, this isn't a replacement for people. This isn't a replacement for teams. This isn't augmentation. This is getting them back to doing the things they should be doing and less of the stuff they shouldn't be doing. And frankly, it's, it's about providing better services. So when in the end, it's counterintuitive to be against it because it's gonna make it operations look better. It's gonna make us show us that we are the thought leaders in delivering digital services that we can, um, constantly be perfecting the way we're doing it. And Oh, by the way, we can help the business be better. Also at the same time. Uh, I think some of the mistakes people really don't make, uh, really do make, uh, is not looking at their processes today, trying to figure out what they're gonna look like tomorrow when we bring in advanced automation and intelligence, uh, but also being prepared for what the future state is, you know, in talking to one company, they were like, yeah, we're so excited for this. >>Uh, we, we got rid of our old 15 year old laundering system and the same day we stepped a new system. Uh, one problem we had though, was we weren't ready for the amount of incidents that had generated on day one. And it wasn't because we did anything wrong or the system was wrong or what have you. It did the right thing actually, almost too. Well, what it did is it uncovered a lot of really small incidents through advanced correlations. We didn't know we had, so there were things lying out there that were always like, huh, that's weird. That system acts strange sometimes, but we can never pin it down. We found all of those things, which is good. It goes, but it kind of made us all kind of sit back and think, and then our readership are these guys doing their job. Right? >>And then we had to go through an evolution of, you know, just explaining we were 15 years behind from a visibility standpoint to our environment, but technologies that we deployed in applications had moved ahead and modernized. So this is like a cautionary tale of falling too far behind from a sort of a monitoring and intelligence and automation standpoint. Um, so I thought that was a really good story for something like, think about as Eagle would deploy these modern systems. But I think if he really, you know, the marketing to people, so they're not threatened, I think thinking about your process and then what's, what's your day one and then look like, and then what's your six and 12 months after that looks like, I think settling all that stuff upfront just sets you up for success. >>All right. Rich, take us home here. Let's summarize. How can clients build a business case for AI ops? What do you recommend? >>Yeah. You know, I actually get that question a lot. It's usually, uh, almost always the number one, uh, question in, in, um, you know, webinars like this and conversations that, that the audience puts in. So I wouldn't be surprised, but if that was true, uh, going forward from this one, um, yeah, people are like, you know, Hey, we're all in. We want to do this. We know this is the way forward, but the guy who writes the checks, the CIO, the VP of ops is like, you know, I I've signed lots of checks over the years for tools wise is different. Um, and when I guide people to do is to sit back and, and start doing some hard math, right. Uh, one of the things that resonates with the leadership is dollars and cents. It's not percentages. So saying, you know, it's, it brings us a 63% reduction and MTTR is not going to resonate. >>Uh, Oh, even though it's a really good number, you know, uh, I think what it is, you have to put it in terms of avoid, if we could avoid that 63%. Right. You know, um, what does that mean for our, our digital services as far as revenue, right. We know that every hour system down, I think, uh, you know, typically in the market, you see is about $500,000 an hour for enterprise. We'll add that up over the course of the year. What are you losing in revenue? Add to that brand damage loss of customers, you know, uh, Forrester puts out a really big, uh, casino, um, uh, customer experience index every year that measures that if you're delivering good Udall services, bad digital services, if you could raise that up, what does that return to you in revenue? And that's a key thing. And then you just look at the, the, uh, hours of lost productivity. >>I call it, I might call it something else, but I think it's a catchy name. Meaning if a core internal system is down say, and you know, you have a customer service desk of a thousand customer service people, and they can't do that look up or fix that problem for clients for an hour. How much money does that lose you? And you multiply it out. You know, average customer service desk person makes X amount an hour times this much time. This many times it happens. Then you start seeing the real, sort of a power of AI ops for this incident avoidance, or at least lowering the impact of these incidents. And people have put out in graphs and spreadsheets and all this, and then I'm doing some research around this actually to, to, to put out something that people can use to say, the project funds itself in six to 12 months, it's paid for itself. And then after that it's returning money to the business. Why would you not do that? And when you start framing the conversation, that way, the little light bulb turn on for the people that sign the checks. For sure. >>That's great advice for folks to be thinking about. I loved how you talked about the 63% reduction in something. I think that's great. What does it impact? How does it impact the revenue for the organization? If we're avoiding costs here, how do we drive up revenue? So having that laser focus on revenue is great advice for folks in any industry, looking to build a business case for AI ops. I think you set the stage for that rich beautifully, and you were right. This was a fun conversation. Thank you for your time. Thank you. And thanks for watching >>From around the globe with digital coverage. >>Welcome back to the Broadcom AI ops, virtual forum, Lisa Martin here talking with Eastman Nasir global product management at Verizon. We spent welcome back. >>Hi. Hello. Uh, what a pleasure. >>So 2020 the year of that needs no explanation, right? The year of massive challenges and wanting to get your take on the challenges that organizations are facing this year as the demand to deliver digital products and services has never been higher. >>Yeah. So I think this is something it's so close to all the far far, right? It's, uh, it's something that's impacted the whole world equally. And I think regardless of which industry you rent, you have been impacted by this in one form or the other, and the ICT industry, the information and communication technology industry, you know, Verizon being really massive player in that whole arena. It has just been sort of struck with this massive consummation we have talked about for a long time, we have talked about these remote surgery capabilities whereby you've got patients in Kenya who are being treated by an expert sitting in London or New York, and also this whole consciousness about, you know, our carbon footprint and being environmentally conscious. This pandemic has taught us all of that and brought us to the forefront of organization priorities, right? The demand. I think that's, that's a very natural consequence of everybody sitting at home. >>And the only thing that can keep things still going is this data communication, right? But I wouldn't just say that that is, what's kind of at the heart of all of this. Just imagine if we are to realize any of these targets of the world is what leadership is setting for themselves. Hey, we have to be carbon neutral by X year as a country, as a geography, et cetera, et cetera. You know, all of these things require you to have this remote working capabilities, this remote interaction, not just between humans, but machine to machine interactions. And this there's a unique value chain, which is now getting created that you've got people who are communicating with other people or communicating with other machines, but the communication is much more. I wouldn't even use the term real time because we've used real time for voice and video, et cetera. >>We're talking low latency, microsecond decision-making that can either cut somebody's, you know, um, our trees or that could actually go and remove the tumor, that kind of stuff. So that has become a reality. Everybody's asking for it, remote learning, being an extremely massive requirement where, you know, we've had to enable these, uh, these virtual classrooms ensuring the type of connectivity, ensuring the type of type of privacy, which is just so, so critical. You can't just have everybody in a go on the internet and access a data source. You have to be concerned about the integrity and security of that data as the foremost. So I think all of these things, yes, we have not been caught off guard. We were pretty forward-looking in our plans and our evolution, but yes, it's fast track the journey that we would probably believe we would have taken in three years. It has brought that down to two quarters where we've had to execute them. >>Right. Massive acceleration. All right. So you articulated the challenges really well. And a lot of the realities that many of our viewers are facing. Let's talk now about motivations, AI ops as a tool, as a catalyst for helping organizations overcome those challenges. >>So yeah. Now on that I said, you can imagine, you know, it requires microsecond decision-making which human being on this planet can do microsecond decision-making on complex network infrastructure, which is impacting end user applications, which have multitudes of effect. You know, in real life, I use the example of a remote surgeon. Just imagine that, you know, even because of you just use your signal on the quality of that communication for that microsecond, it could be the difference between killing somebody in saving somebody's life. And it's not predictable. We talk about autonomous vehicles. Uh, we talk about this transition to electric vehicles, smart motorways, et cetera, et cetera, in federal environment, how is all of that going to work? You have so many different components coming in. You don't just have a network and security anymore. You have software defined networking. That's coming, becoming a part of that. >>You have mobile edge computing that is rented for the technologies. 5g enables we're talking augmented reality. We're talking virtual reality. All of these things require that resources and why being carbon conscious. We told them we just want to build a billion data centers on this planet, right? We, we have to make sure that resources are given on demand and the best way of resources can be given on demand and could be most efficient is that the thing is being made at million microsecond and those resources are accordingly being distributed, right? If you're relying on people, sipping their coffees, having teas, talking to somebody else, you know, just being away on holiday. I don't think we're going to be able to handle that one that we have already stepped into. Verizon's 5g has already started businesses on that transformational journey where they're talking about end user experience personalization. >>You're going to have events where people are going to go, and it's going to be three-dimensional experiences that are purely customized for you. How, how does that all happen without this intelligence sitting there and a network with all of these multiple layers? So spectrum, it doesn't just need to be intuitive. Hey, this is my private IP traffic. This is public traffic. You know, it has to not be in two, or this is an application that I have to prioritize over another task to be intuitive to the criticality and the context of those transactions. Again, that's surgeons. So be it's much more important than postman setting and playing a video game. >>I'm glad that you think that that's excellent. Let's go into some specific use cases. What are some of the examples that you gave? Let's kind of dig deeper into some of the, what you think are the lowest hanging fruit for organizations kind of pan industry to go after. >>Excellent. Brian, and I think this, this like different ways to look at the lowest hanging fruit, like for somebody like revising who is a managed services provider, you know, very comprehensive medicines, but we obviously have food timing, much lower potentially for some of our customers who want to go on that journey. Right? So for them to just go and try and harness the power of the foods might be a bit higher hanging, but for somebody like us, the immediate ones would be to reduce the number of alarms that are being generated by these overlay services. You've got your basic network, then you've got your whole software defined networking on top of that, you have your hybrid clouds, you have your edge computing coming on top of that. You know? So all of that means if there's an outage on one device on the network, I want to make this very real for everybody, right? >>It's like device and network does not stop all of those multiple applications or monitoring tools from raising and raising thousands of alarm and everyone, one capacity. If people are attending to those thousands of alarms, it's like you having a police force and there's a burglary in one time and the alarm goes off and 50 bags. How, how are you kind of make the best use of your police force? You're going to go investigate 50 bags or do you want to investigate where the problem is? So it's as real as that, I think that's the first wins where people can save so much cost, which is coming from being wasted and resources running around, trying to figure stuff out immediately. I'm tied this with network and security network and security is something which has you did even the most, you know, I mean single screens in our engineering, well, we took it to have network experts, separate people, security experts, separate people to look for different things, but there are security events that can impact the performance of a network. >>And then just drop the case on the side of et cetera, which could be falsely attributed to the metric. And then if you've got multiple parties, which are then the chapter clear stakeholders, you can imagine the blame game that goes on finding fingers, taking names, not taking responsibility that don't has all this happened. This is the only way to bring it all together to say, okay, this is what takes priority. If there's an event that has happened, what is its correlation to the other downstream systems, devices, components, and these are applications. And then subsequently, you know, like isolating it to the right cost where you can most effectively resolve that problem. Thirdly, I would say on demand, virtualized resource, virtualized resources, the heart and soul, the spirit of status that you can have them on demand. So you can automate the allocation of these resources based on customer's consumption their peaks, their cramps, all of that comes in. >>You see, Hey, typically on a Wednesday, the traffic was up significantly for this particular application, you know, going to this particular data center, you could have this automated system, uh, which is just providing those resources, you know, on demand. And so it is to have a much better commercial engagement with customers and just a much better service assurance model. And then one more thing on top of that, which is very critical is that as I was saying, giving that intelligence to the networks to start having context of the criticality of a transaction, that doesn't make sense to them. You can't have that because for that, you need to have this, you know, monkey their data. You need to have multi-cam system, which are monitoring and controlling different aspects of your overall end user application value chain to be communicating with each other. And, you know, that's the only way to sort of achieve that goal. And that only happens with AI. It's not possible >>So it was when you clearly articulated some obvious, low hanging fruit and use cases that organizations can go after. Let's talk now about some of the considerations, you talked about the importance of a network and AI ops, the approach I assume, needs to be modular support needs to be heterogeneous. Talk to us about some of those key considerations that you would recommend. >>Absolutely. So again, basically starting with the network, because if there's, if the metrics sitting at the middle of all of this is not working, then things can communicate with each other, right? And the cloud doesn't work, nothing metal. That's the hardest part of this, but that's the frequency. When you talk about machine to machine communication or IOT, it's just the biggest transformation of the span of every company is going for IOT now to drive those costs, efficiencies, and had, something's got some experience, the integrity of the topic karma, right? The security, integrity of that. How do you maintain integrity of your data beyond just a secure network components? That is true, right? That's where you're getting to the whole arena blockchain technologies, where you have to use digital signatures or barcodes that machine then, and then an intelligence system is automatically able to validate and verify the integrity of the data and the commands that are being executed by those end-user told them what I need to tell them that. >>So it's IOT machines, right? That is paramount. And if anybody is not keeping that into their equation, that in its own self is any system that is therefore maintaining the integrity of your commands and your hold that sits on those, those machines. Right? Second, you have your network. You need to have any else platform, which is able to restless all the fast network information, et cetera. And coupled with that data integrity piece, because for the management, ultimately they need to have a coherent view of the analytics, et cetera, et cetera. They need to know where the problems are again, right? So let's say if there's a problem with the integrity of the commands that are being executed by the machine, that's a much bigger problem than not being able to communicate with that machine and the best thing, because you'd rather not talk to the machine or have to do anything if it's going to start doing wrong things. >>So I think that's where it is. It's very intuitive. It's not true. You have to have subsequently if you have some kind of faith and let me use that use case self autonomous vehicles. Again, I think we're going to see in the next five years, because he's smart with the rates, et cetera, it won't separate autonomous cars. It's much more efficient, it's much more space, et cetera, et cetera. So within that equation, you're going to have systems which will be specialists in looking at aspects and transactions related to those systems. For example, in autonomous moving vehicles, brakes are much more important than the Vipers, right? So this kind of intelligence, it will be multiple systems who have to sit, N nobody has to, one person has to go in one of these systems. I think these systems should be open source enough that they, if you were able to integrate them, right, if something's sitting in the cloud, you were able to integrate for that with obviously the regard of the security and integrity of your data that has to traverse from one system to the other extremely important. >>So I'm going to borrow that integrity theme for a second, as we go into our last question, and that is this kind of take a macro look at the overall business impact that AI ops can help customers make. I'm thinking of, you know, the integrity of teams aligning business in it, which we probably can't talk about enough. We're helping organizations really effectively measure KPIs that deliver that digital experience that all of us demanding consumers expect. What's the overall impact. What would you say in summary fashion? >>So I think the overall impact is a lot of costs. That's customized and businesses gives the time to the time of enterprises. Defense was inevitable. It's something that for the first time, it will come to life. And it's something that is going to, you know, start driving cost efficiencies and consciousness and awareness within their own business, which is obviously going to have, you know, it domino kind of an effect. So one example being that, you know, you have problem isolation. I talked about network security, this multi-layers architecture, which enables this new world of 5g, um, at the heart of all of it, it has to identify the problem to the source, right? Not be bogged down by 15 different things that are going wrong. What is causing those 15 things to go wrong, right? That speed to isolation in its own sense can make millions and millions of dollars to organizations after we organize it. Next one is obviously overall impacted customer experience. Uh, 5g was given out of your customers, expecting experiences from you, even if you're not expecting to deliver them in 2021, 2022, it would have customers asking for those experience or walking away, if you do not provide those experience. So it's almost like a business can do nothing every year. They don't have to reinvest if they just want to die on the line, businesses want remain relevant. >>Businesses want to adopt the latest and greatest in technology, which enables them to, you know, have that superiority and continue it. So from that perspective that continue it, he will read that they write intelligence systems that tank rationalizing information and making decisions supervised by people, of course were previously making some of those. >>That was a great summary because you're right, you know, with how demanding consumers are. We don't get what we want quickly. We churn, right? We go somewhere else and we could find somebody that can meet those expectations. So it has been thanks for doing a great job of clarifying the impact and the value that AI ops can bring to organizations that sounds really now is we're in this even higher demand for digital products and services, which is not going away. It's probably going to only increase it's table stakes for any organization. Thank you so much for joining me today and giving us your thoughts. >>Pleasure. Thank you. We'll be right back with our next segment. >>Digital applications and services are more critical to a positive customer and employee experience than ever before. But the underlying infrastructure that supports these apps and services has become increasingly complex and expanding use of multiple clouds, mobile and microservices, along with modern and legacy infrastructure can make it difficult to pinpoint the root cause when problems occur, it can be even more difficult to determine the business impact your problems that occur and resolve them efficiently. AI ops from Broadcom can help first by providing 360 degree visibility, whether you have hybrid cloud or a cloud native AI ops from Broadcom provides a clear line of sight, including apt to infrastructure and network visibility across hybrid environments. Second, the solution gives you actionable insights by correlating an aggregating data and applying AI and machine learning to identify root causes and even predict problems before users are impacted. Third AI ops from Broadcom provides intelligent automation that identifies potential solutions when problems occur applied to the best one and learns from the effectiveness to improve response in case the problem occurs. Again, finally, the solution enables organizations to achieve digit with jelly by providing feedback loops across development and operations to allow for continuous improvements and innovation through these four capabilities. AI ops from Broadcom can help you reduce service outages, boost, operational efficiency, and effectiveness and improve customer and employee experience. To learn more about AI ops from Broadcom, go to broadcom.com/ai ops from around the globe. >>It's the cube with digital coverage of AI ops virtual forum brought to you by Broadcom. >>Welcome back to the AI ops virtual forum, Lisa Martin here with Srinivasan, Roger Rajagopal, the head of product and strategy at Broadcom. Raj, welcome here, Lisa. I'm excited for our conversation. So I wanted to dive right into a term that we hear all the time, operational excellence, right? We hear it everywhere in marketing, et cetera, but why is it so important to organizations as they head into 2021? And tell us how AI ops as a platform can help. >>Yeah. Well, thank you. First off. I wanna, uh, I want to welcome our viewers back and, uh, I'm very excited to, uh, to share, um, uh, more info on this topic. You know, uh, here's what we believe as we work with large organizations, we see all our organizations are poised to get out of the, uh, the pandemic and look for a brood for their own business and helping customers get through this tough time. So fiscal year 2021, we believe is going to be a combination of, uh, you know, resiliency and agility at the, at the same time. So operational excellence is critical because the business has become more digital, right? There are going to be three things that are going to be more sticky. Uh, you know, remote work is going to be more sticky, um, cost savings and efficiency is going to be an imperative for organizations and the continued acceleration of digital transformation of enterprises at scale is going to be in reality. So when you put all these three things together as a, as a team that is, uh, you know, that's working behind the scenes to help the businesses succeed, operational excellence is going to be, make or break for organizations, >>Right with that said, if we kind of strip it down to the key capabilities, what are those key capabilities that companies need to be looking for in an AI ops solution? >>Yeah, you know, so first and foremost, AI ops means many things to many, many folks. So let's take a moment to simply define it. The way we define AI ops is it's a system of intelligence, human augmented system that brings together full visibility across app infra and network elements that brings together disparate data sources and provides actionable intelligence and uniquely offers intelligent automation. Now, the, the analogy many folks draw is the self-driving car. I mean, we are in the world of Teslas, uh, but you know, uh, but self-driving data center is it's too far away, right? Autonomous systems are still far away. However, uh, you know, application of AI ML techniques to help deal with volume velocity, veracity of information, uh, is, is critical. So that's how we look at AI ops and some of the key capabilities that we, uh, that we, uh, that we work with our customers to help them on our own for eight years. >>Right? First one is eyes and ears. What we call full stack observability. If you do not know what is happening in your systems, uh, you know, that that serve up your business services. It's going to be pretty hard to do anything, uh, in terms of responsiveness, right? So from stack observability, the second piece is what we call actionable insights. So when you have disparate data sources, tools, sprawls data coming at you from, uh, you know, uh, from a database systems, it systems customer management systems, ticketing systems. How do you find the needle from the haystack? And how do you respond rapidly from a myriad of problems as CEO of red? The third area is what we call intelligent automation. Well, identifying the problem to act on is important, and then acting on automating that and creating, uh, a recommendation system where, uh, you know, you can be proactive about it is even more important. And finally, all of this focuses on efficiency. What about effectiveness? Effectiveness comes when you create a feedback loop, when what happens in production is related to your support systems and your developers so that they can respond rapidly. So we call that continuous feedback. So these are the four key capabilities that, uh, you know, uh, you should look for in an AI ops system. And that's what we offer as well. >>Russia, there's four key capabilities that businesses need to be looking for. I'm wondering how those help to align business. And it it's, again like operational excellence. It's something that we talk about a lot is the alignment of business. And it a lot more challenging, easier said than done, right. But I want you to explain how can AI ops help with that alignment and align it outputs to business outcomes? >>Yeah. So, you know, one of the things, uh, I'm going to say something that is, uh, that is, uh, that is simple, but, but, but this harder, but alignment is not on systems alignment is with people, right? So when people align, when organizations align, when cultures align, uh, dramatic things can happen. So in the context of AI ops VC, when, when SRE is aligned with the DevOps engineers and information architects and, uh, uh, you know, it operators, uh, you know, they enable organizations to reduce the gap between intent and outcome or output and outcome that said, uh, you know, these personas need mechanisms to help them better align, right. Help them better visualize, see the, you know, what we call single source of truth, right? So there are four key things that I want to call out. When we work with large enterprises, we find that customer journey alignment with the, you know, what we call it systems is critical. >>So how do you understand your business imperatives and your customer journey goals, whether it is car to a purchase or whether it is, uh, you know, bill shock scenarios and Swan alignment on customer journey to your it systems is one area that you can reduce the gap. The second area is how do you create a scenario where your teams can find problems before your customers do right outage scenarios and so on. So that's the second area of alignment. The third area of alignment is how can you measure business impact driven services? Right? There are several services that an organization offers versus an it system. Some services are more critical to the business than others, and these change in a dynamic environment. So how do you, how do you understand that? How do you measure that and how, how do you find the gaps there? So that's the third area of alignment that we, that we help and last but not least there are, there are things like NPS scores and others that, that help us understand alignment, but those are more long-term. But in the, in the context of, uh, you know, operating digitally, uh, you want to use customer experience and business, uh, you know, a single business outcome, uh, as a, as a key alignment factor, and then work with your systems of engagement and systems of interaction, along with your key personas to create that alignment. It's a people process technology challenge. >>So, whereas one of the things that you said there is that it's imperative for the business to find a problem before a customer does, and you talked about outages there, that's always a goal for businesses, right. To prevent those outages, how can AI ops help with that? Yeah, >>So, you know, outages, uh, talk, you know, go to resiliency of a system, right? And they also go to, uh, uh, agility of the same system, you know, if you're a customer and if you're whipping up your mobile app and it takes more than three milliseconds, uh, you know, you're probably losing that customer, right. So outages mean different things, you know, and there's an interesting website called down detector.com that actually tracks all the old pages of publicly available services, whether it's your bank or your, uh, you know, tele telecom service or a mobile service and so on and so forth. In fact, the key question around outages for, from, uh, from, uh, you know, executives are the question of, are you ready? Right? Are you ready to respond to the needs of your customers and your business? Are you ready to rapidly resolve an issue that is impacting customer experience and therefore satisfaction? >>Are you creating a digital trust system where customers can be, you know, um, uh, you know, customers can feel that their information is secure when they transact with you, all of these, getting into the notion of resiliency and outages. Now, you know, one of the things that, uh, that I, I often, uh, you know, work with customers around, you know, would that be find as the radius of impact is important when you deal with outages? What I mean by that is problems occur, right? How do you respond? How quickly do you take two seconds, two minutes, 20 minutes, two hours, 20 hours, right? To resolve the problem that radius of impact is important. That's where, you know, you have to bring a gain people, process technology together to solve that. And the key thing is you need a system of intelligence that can aid your teams, you know, look at the same set of parameters so that you can respond faster. That's the key here. >>We look at digital transformation at scale. Raj, how does AI ops help influence that? >>You know, um, I'm going to take a slightly long-winded way to answer this question. See when it comes to digital transformation at scale, the focus on business purpose and business outcome becomes extremely critical. And then the alignment of that to your digital supply chain, right, are the, are the, are the key factors that differentiate winners in the, in their digital transformation game? Really, what we have seen, uh, with, with winners is they operate very differently. Like for example, uh, you know, Nike matures, its digital business outcomes by shoes per second, right? Uh, Apple by I-phones per minute, Tesla by model threes per month, are you getting this, getting it right? I mean, you want to have a clear business outcome, which is a measure of your business, uh, in effect, I mean, ENC, right? Which, which, uh, um, my daughter use and I use very well. >>Right. Uh, you know, uh, they measure by revenue per hour, right? I mean, so these are key measures. And when you have a key business outcome measure like that, you can everything else, because you know what these measures, uh, you know, uh, for a bank, it may be deposits per month, right now, when you move money from checking account to savings account, or when you do direct deposits, those are, you know, banks need liquidity and so on and so forth. But, you know, the, the key thing is that single business outcome has a Starburst effect inside the it organization that touches a single money moment from checking a call to savings account can touch about 75 disparate systems internally. Right? So those think about it, right? I mean, all, all we're doing is moving money from checking account a savings account. Now that goats into a it production system, there are several applications. >>There is a database, there is, there are infrastructures, there are load balancers that are webs. You know, you know, the web server components, which then touches your, your middleware component, which is a queuing system, right. Which then touches your transactional system. Uh, and, uh, you know, which may be on your main frames, what we call mobile to mainframe scenario, right? And we are not done yet. Then you have a security and regulatory compliance system that you have to touch a fraud prevention system that you have to touch, right? A state department regulation that you may have to meet and on and on and on, right? This is the chat that it operations teams face. And when you have millions of customers transacting, right, suddenly this challenge cannot be managed by human beings alone. So therefore you need a system of intelligence that augments human intelligence and acts as your, you know, your, your eyes and ears in a way to, to point pinpoint where problems are. >>Right. So digital transformation at scale really requires a very well thought out AI ops system, a platform, an open extensible platform that, uh, you know, uh, that is heterogeneous in nature because there's tools, products in organizations. There is a lot of databases in systems. There are millions of, uh, uh, you know, customers and hundreds of partners and vendors, you know, making up that digital supply chain. So, you know, AI ops is at the center of an enabling an organization achieve digital op you know, transformation at scale last but not least. You need continuous feedback loop. Continuous feedback loop is the ability for a production system to inform your dev ops teams, your finance teams, your customer experience teams, your cost modeling teams about what is going on so that they can so that they can reduce the intent, come gap. >>All of this need to come together, what we call BizOps. >>That was a great example of how you talked about the Starburst effect. I actually never thought about it in that way, when you give the banking example, but what you should is the magnitude of systems. The fact that people alone really need help with that, and why intelligent automation and AI ops can be transformative and enable that scale. Raj, it's always a pleasure to talk with you. Thanks for joining me today. And we'll be right back with our next segment. Welcome back to the AI ops virtual forum. We've heard from our guests about the value of AI ops and why and how organizations are adopting AI ops platforms. But now let's see AI ops inaction and get a practical view of AI ops to deep Dante. The head of AI ops at Broadcom is now going to take you through a quick demo. >>Hello. So they've gotta head off AI ops and automation here. What I'm going to do today is talk through some of the key capabilities and differentiators of Broadcom's CII ops solution in this solution, which can be delivered on cloud or on-prem. We bring a variety of metric alarm log and applauded data from multiple sources, EPM, NetApps, and infrastructure monitoring tools to provide a single point of observability and control. Let me start where our users mostly stock key enterprises like FSI, telcos retailers, et cetera, do not manage infrastructure or applications without having a business context. At the end of the day, they offer business services governed by SLS service level objectives and SLI service level indicators are service analytics, which can scale to a few thousand services, lets our customers create and monitor the services as per their preference. They can create a hierarchy of services based on their business practice. >>For example, here, the sub services are created based on functional subsistence for certain enterprises. It could be based on location. Users can import these services from their favorite CMDB. What's important to note that not all services are born equal. If you are a modern bank, you may want to prioritize tickets coming from digital banking, for example, and this application lets you rank them as per the KPI of your choice. We can source the availability, not merely from the state of the infrastructure, whether they're running or not. But from the SLS that represent the state of the application, when it comes to triaging issues related to the service, it is important to have a complete view of the topology. The typology can show both east-west elements from mobile to mainframe or not South elements in a network flow. This is particularly relevant for a large enterprise who could be running the systems of engagement on the cloud and system of records on mainframe inside the firewall here, you can see that the issue is related to the mainframe kick server. >>You can expand to see the actual alarm, which is sourced from the mainframe operational intelligence. Similarly, clicking on network will give the hub and spoke view of the network devices, the Cisco switches and routers. I can click on the effected router and see all the details Broadcom's solution stores, the ontological model of the typology in the form of a journal graph where one can not only view the current state of the typology, but the past as well, talking of underlying data sources, the solution uses best of the pre data stores for structured and unstructured data. We have not only leveraged the power of open source, but have actively contributed back to the community. One of the key innovations is evident in our dashboarding framework because we have enhanced the open source Grafana technology to support these diverse data sources here. You can see a single dashboard representing applications to infrastructure, to mainframe again, sourcing a variety of data from these sources. >>When we talk to customers, one of the biggest challenges that they face today is related to alarms because of a proliferation of tools. They are currently drowning in an ocean of hundreds and thousands of alarms. This increases the Elmont support cost to tens of dollars per ticket, and also affects LTO efficiency leading to an average of five to six hours of meantime to resolution here is where we have the state of the art innovation utilizing the power of machine learning and ontology to arrive at the root cause we not only clusterize alarms based on text, but employ the technique of 41st. We look at the topology then at the time window duplicate text based on NLP. And lastly learn from continuous training of the model to deduce what we call situations. This is an example of a situation. As you can see, we provide a time-based evidence of how things unfolded and arrive at a root cause. >>Lastly, the solution provides a three 60 degree closed loop remediation either through a ticketing system or by direct invocation of automation actions instead of firing hard-coded automation runbooks for certain conditions, the tool leverage is machine learning to rank automation actions based on past heuristics. That's why we call it intelligent automation to summarize AI ops from Broadcom helps you achieve operational excellence through full stack observability, coupled with AIML that applies across modern hybrid cloud environments, as well as legacy ones uniquely. It ties these insights with intelligent automation to improve customer experience. Thank you for watching from around the globe. It's the cube with digital coverage of AI ops virtual forum brought to you by Broadcom. >>Welcome to our final segment today. So we've discussed today. The value that AI ops will bring to organizations in 2021, we'll discuss that through three different perspectives. And so now we want to bring those perspectives together and see if we can get a consensus on where AI ops needs to go for folks to be successful with it in the future. So bringing back some folks Richland is back with us. Senior analysts, serving infrastructure and operations professionals at Forrester smartness here is also back in global product management at Verizon and Srinivasan, Reggie Gopaul head of product and strategy at Broadcom guys. Great to have you back. So let's jump in and rich, we're going to, we're going to start with you, but we are going to get all three of you, a chance to answer the questions. So we've talked about why organizations should adopt AI ops, but what happens if they choose not to what challenges would they face? Basically what's the cost of organizations doing nothing >>Good question, because I think in operations for a number of years, we've kind of stand stood, Pat, where we are, where we're afraid change things sometimes, or we just don't think about a tooling as often. The last thing to change because we're spending so much time doing project work and modernization and fighting fires on a daily basis. >>Problem is going to get worse. If we do nothing, >>You know, we're building new architectures like containers and microservices, which means more things to mind and keep running. Um, we're building highly distributed systems. We're moving more and more into this hybrid world, a multi-cloud world, uh, it's become over-complicate and I'll give a short anecdote. I think, eliminate this. Um, when I go to conferences and give speeches, it's all infrastructure operations people. And I say, you know, how many people have three X, five X, you know, uh, things to monitor them. They had, you know, three years ago, two years ago, and everyone's saying how many people have hired more staff in that time period, zero hands go up. That's the gap we have to fill. And we have to fill that through better automation, more intelligent systems. It's the only way we're going to be able to fill back out. >>What's your perspective, uh, if organizations choose not to adopt AI ops. Yeah. So I'll do that. Yeah. So I think it's, I would just relate it to a couple of things that probably everybody >>Tired off lately and everybody can relate to. And this would resonate that we have 5g, which is all set to transform the world. As we know it, I don't have a lot of communication with these smart cities, smart communities, IOT, which is going to make us pivotal to the success of businesses. And as you've seen with this call with, you know, transformation of the world, that there's a, there's a much bigger cost consciousness out there. People are trying to become much more, forward-looking much more sustainable. And I think at the heart of all of this, that the necessity that you have intelligent systems, which are bastardizing more than enough information that previously could've been overlooked because if you don't measure engagement, not going right. People not being on the same page of this using two examples or hundreds of things, you know, that play a part in things, but not coming together in the best possible way. So I think it has an absolute necessity to drive those cost efficiencies rather than, you know, left right and center laying off people who are like 10 Mattel to your business and have a great tribal knowledge of your business. So to speak, you can drive these efficiencies through automating a lot of those tasks that previously were being very manually intensive or resource intensive. And you could allocate those resources towards doing much better things, which let's be very honest going into 20, 21 after what we've seen with 2020, it's going to be mandate treat. >>And so Raj, I saw you shaking your head there when he was mom was sharing his thoughts. What are your thoughts about that sounds like you agree. Yeah. I mean, uh, you know, uh, to put things in perspective, right? I mean we're firmly in the digital economy, right? Digital economy, according to the Bureau of economic analysis is 9% of the U S GDP. Just, you know, think about it in, in, in, in, in the context of the GDP, right? It's only ranked lower, slightly lower than manufacturing, which is at 11.3% GDP and slightly about finance and insurance, which is about seven and a half percent GDP. So the digital economy is firmly in our lives, right. And as Huisman was talking about it, you know, software eats the world and digital, operational excellence is critical for customers, uh, to, uh, you know, to, uh, to drive profitability and growth, uh, in the digital economy. >>It's almost, you know, the key is digital at scale. So when, uh, when rich talks about some of the challenges and when Huseman highlights 5g as an example, those are the things that, that, that come to mind. So to me, what is the cost or perils of doing nothing? You know, uh, it's not an option. I think, you know, more often than not, uh, you know, C-level execs are asking head of it and they are key influencers, a single question, are you ready? Are you ready in the context of addressing spikes in networks because of the pandemic scenario, are you ready in the context of automating away toil? Are you ready to respond rapidly to the needs of the digital business? I think AI ops is critical. >>That's a great point. Roger, where does stick with you? So we got kind of consensus there, as you said, wrapping it up. This is basically a, not an option. This is a must to go forward for organizations to be successful. So let's talk about some quick wins, or as you talked about, you know, organizations and sea levels asking, are you ready? What are some quick wins that that organizations can achieve when they're adopting AI? >>You know, um, immediate value. I think I would start with a question. How often do your customers find problems in your digital experience before you do think about that? Right. You know, if you, if you, you know, there's an interesting web, uh, website, um, uh, you know, down detector.com, right? I think, uh, in, in Europe there is an equal amount of that as well. It ha you know, people post their digital services that are down, whether it's a bank that, uh, you know, customers are trying to move money from checking account, the savings account and the digital services are down and so on and so forth. So some and many times customers tend to find problems before it operations teams do. So a quick win is to be proactive and immediate value is visibility. If you do not know what is happening in your complex systems that make up your digital supply chain, it's going to be hard to be responsive. So I would start there >>Visibility this same question over to you from Verizon's perspective, quick wins. >>Yeah. So I think first of all, there's a need to ingest this multi-care spectrum data, which I don't think is humanly possible. You don't have people having expertise, you know, all the seven layers of the OSI model and then across network and security and at the application level. So I think you need systems which are now able to get that data. It shouldn't just be wasted reports that you're paying for on a monthly basis. It's about time that you started making the most of those in the form of identifying what are the efficiencies within your ecosystem. First of all, what are the things, you know, which could be better utilized subsequently you have the >>Opportunity to reduce the noise of a trouble tickets handling. It sounds pretty trivial, but >>An average you can imagine every trouble tickets has the cost in dollars, right? >>So, and there's so many tickets and there's art >>That get created on a network and across an end user application value, >>We're talking thousands, you know, across and end user >>Application value chain could be million in >>A year. So, and so many of those are not really, >>He, you know, a cause of concern because the problem is something. >>So I think that whole triage is an immediate cost saving and the bigger your network, the bigger >>There's a cost of things, whether you're a provider, whether you're, you know, the end customer at the end of the day, not having to deal with problems, which nobody can resolve, which are not meant to be dealt with. There's so many of those situations, right, where service has just been adopted, >>Which is just coordinate quality, et cetera, et cetera. So many reasons. So those are the, >>So there's some of the immediate cost saving them. They are really, really significant. >>Secondly, I would say Raj mentioned something about, you know, the user, >>Your application value chain, and an understanding of that, especially with this hybrid cloud environment, >>Et cetera, et cetera, right? The time it takes to identify a problem in an end user application value chain across the seven layers that I mentioned with the OSI reference model across network and security and the application environment. It's something that >>In its own self has massive cost to business, >>Right? That could be >>No sale transactions that could be obstructed because of this. There could be, and I'm going to use a really interesting example. >>We talk about IOT. The integrity of the IOT machine is exciting. >>Family is pivotal in this new world that we're stepping into. >>You could be running commands, >>Super efficient. He has, everything is being told to the machine really fast with sending yeah. >>Everything there. What if it's hacked? And if that's okay, >>Robotic arm starts to involve the things you don't want it to do. >>So there's so much of that. That becomes a part of this naturally. And I believe, yes, this is not just like from a cost >>standpoint, but anything going wrong with that code base, et cetera, et cetera. These are massive costs to the business in the form of the revenue. They have lost the perception in the market as a result, the fed, >>You know, all that stuff. So >>These are a couple of very immediate problems, but then you also have the whole player virtualized resources where you can automate the allocation, you know, the quantification of an orchestration of those virtualized resources, rather than a person having to, you know, see something and then say, Oh yeah, I need to increase capacity over here, because then it's going to have this particular application. You have systems doing this stuff and to, you know, Roger's point your customer should not be identifying your problems before you, because this digital is where it's all about perception. >>Absolutely. We definitely don't want the customers finding it before. So rich, let's wrap this particular question up with you from that senior analyst perspective, how can companies use make big impact quickly with AI ops? Yeah, >>Yeah, I think, you know, and it was been really summed up some really great use cases there. I think with the, uh, you know, one of the biggest struggles we've always had in operations is isn't, you know, the mean time to resolve. We're pretty good at resolving the things. We just have to find the thing we have to resolve. That's always been the problem and using these advanced analytics and machine learning algorithms now across all machine and application data, our tendency is humans is to look at the console and say, what's flashing red. That must be what we have to fix, but it could be something that's yellow, somewhere else, six services away. And we have made things so complicated. And I think this is what it was when I was saying that we can't get there anymore on our own. We need help to get there in all of this stuff that the outline. >>So, so well builds up to a higher level thing of what is the customer experience about what is the customer journey? And we've struggled for years in the digital world and measuring that a day-to-day thing. We know an online retail. If you're having a bad experience at one retailer, you just want your thing. You're going to go to another retailer, brand loyalty. Isn't one of like it, wasn't a brick and mortal world where you had a department store near you. So you were loyal to that because it was in your neighborhood, um, online that doesn't exist anymore. So we need to be able to understand the customer from that first moment, they touch a digital service all the way from their, their journey through that digital service, the lowest layer, whether it be a database or the network, what have you, and then back to them again, and we're not understanding, is that a good experience? >>We gave them. How does that compare to last week's experience? What should we be doing to improve that next week? Uh, and I think companies are starting and then the pandemic certainly, you know, push this timeline. If you listened to the, the, the CEO of Microsoft, he's like, you know, 10 years of digital transformation written down. And the first several months of this, um, in banks and in financial institutions, I talked to insurance companies, aren't slowing down. They're trying to speed up. In fact, what they've discovered is that they're, you know, obviously when we were on lockdown or what have you, they use of digital servers is spiked very high. What they've learned is they're never going to go back down. They're never going to return to pretend endemic levels. So now they're stuck with this new reality. Well, how do we service those customers and how do we make sure we keep them loyal to our brand? >>Uh, so, you know, they're looking for modernization opportunities. A lot of that that's things have been exposed. And I think Raj touched upon this very early in the conversation is visibility gaps. Now that we're on the outside, looking in at the data center, we know we architect things in a very way. Uh, we better ways of making these correlations across the Sparrow technologies to understand where the problems lies. We can give better services to our customers. And I think that's really what we're going to see a lot of the innovation and the people really clamoring for these new ways of doing things that starting, you know, now, I mean, I've seen it in customers, but I think really the push through the end of this year to next year when, you know, economy and things like that straightened out a little bit more, I think it really, people are gonna take a hard look of where they are and is, you know, AI ops the way forward for them. And I think they'll find it. The answer is yes, for sure. >>So we've, we've come to a consensus that, of what the parallels are of organizations, basically the cost of doing nothing. You guys have given some great advice on where some of those quick wins are. Let's talk about something Raj touched on earlier is organizations, are they really ready for truly automated AI? Raj, I want to start with you readiness factor. What are your thoughts? >>Uh, you know, uh, I think so, you know, we place our, her lives on automated systems all the time, right? In our, in our day-to-day lives, in the, in the digital world. I think, uh, you know, our, uh, at least the customers that I talk to our customers are, uh, are, uh, you know, uh, have a sophisticated systems. Like for example, advanced automation is a reality. If you look at social media, AI and ML and automation are used to automate away, uh, misinformation, right? If you look at financial institutions, AI and ML are used to automate away a fraud, right? So I want to ask our customers why can't we automate await oil in it, operation systems, right? And that's where our customers are. Then the, you know, uh, I'm a glass half full, uh, cleanup person, right? Uh, this pandemic has been harder on many of our customers, but I think what we have learned from our customers is they've Rose to the occasion. >>They've used digital as a key needs, right? At scale. That's what we see with, you know, when, when Huseman and his team talk about, uh, you know, network operational intelligence, right. That's what it means to us. So I think they are ready, the intersection of customer experience it and OT, operational technology is ripe for automation. Uh, and, uh, you know, I, I wanna, I wanna sort of give a shout out to three key personas in this mix. It's about people, right? One is the SRE persona, you know, site, reliability engineer. The other is the information security persona. And the third one is the it operator automation engineer persona. These folks in organizations are building a system of intelligence that can respond rapidly to the needs of their digital business. We at Broadcom, we are in the business of helping them construct a system of intelligence that will create a human augmented solution for them. Right. So when I see, when I interact with large enterprise customers, I think they, they, you know, they, they want to achieve what I would call advanced automation and AI ML solutions. And that's squarely, very I ops is, you know, is going as it, you know, when I talk to rich and what, everything that rich says, you know, that's where it's going and that's what we want to help our customers to. So, which about your perspective of organizations being ready for truly automated AI? >>I think, you know, the conversation has shifted a lot in the last, in, in pre pandemic. Uh, I'd say at the end of last year, we're, you know, two years ago, people I'd go to conferences and people come up and ask me like, this is all smoke and mirrors, right? These systems can't do this because it is such a leap forward for them, for where they are today. Right. We we've sort of, you know, in software and other systems, we iterate and we move forward slowly. So it's not a big shock. And this is for a lot of organizations that big, big leap forward where they're, they're running their operations teams today. Um, but now they've come around and say, you know what? We want to do this. We want all the automations. We want my staff not doing the low complexity, repetitive tasks over and over again. >>Um, you know, and we have a lot of those kinds of legacy systems. We're not going to rebuild. Um, but they need certain care and feeding. So why are we having operations? People do those tasks? Why aren't we automating those out? I think the other piece is, and I'll, I'll, I'll send this out to any of the operations teams that are thinking about going down this path is that you have to understand that the operations models that we're operating under in, in INO and have been for the last 25 years are super outdated and they're fundamentally broken for the digital age. We have to start thinking about different ways of doing things and how do we do that? Well, it's, it's people, organization, people are going to work together differently in an AI ops world, um, for the better. Um, but you know, there's going to be the, the age of the 40 person bridge call thing. >>Troubleshooting is going away. It's going to be three, four, five focused engineers that need to be there for that particular incident. Um, a lot of process mailer process we have in our level, one level, two engineering. What have you running of tickets, gathering of artifacts, uh, during an incident is going to be automated. That's a good thing. We should be doing those, those things by hand anymore. So I'd say that the, to people's like start thinking about what this means to your organization. Start thinking about the great things we can do by automating things away from people, having to do them over and over again. And what that means for them, getting them matched to what they want to be doing is high level engineering tasks. They want to be doing monitorization, working with new tools and technologies. Um, these are all good things that help the organization perform better as a whole great advice and great kind of some of the thoughts that you shared rich for what the audience needs to be on the lookout. For one, I want to go over to you, give me your thoughts on what the audience that should be on the lookout for, or put on your agendas in the next 12 months. >>So there's like a couple of ways to answer that question. One thing would be in the form of, you know, what are some of the things they have to be concerned about in terms of implementing this solution or harnessing its power. The other one could be, you know, what are the perhaps advantages they should look to see? So if I was to talk about the first one, let's say that, what are some of the things I have to watch out for like possible pitfalls that everybody has data, right? So yeah, there's one strategy we say, okay, you've got the data, let's see what we can do with them. But then there's the exact opposite side, which has to be considered when you're doing that analysis. What are the use cases that you're looking to drive? Right. But then use cases you have to understand, are you taking a reactive use case approach? >>Are you taking active use cases, right? Or, yeah, that's a very, very important concentration. Then you have to be very cognizant of where does this data that you have, where does it reside? What are the systems and where does it need to go to in order for this AI function to happen and subsequently if there needs to be any backward communication with all of that data in a process manner. So I think these are some of the very critical points because you can have an AI solution, which is sitting in a customer data center. It could be in a managed services provider data center, like, right, right. It could be in a cloud data center, like an AWS or something, or you could have hybrid views, et cetera, all of that stuff. So you have to be very mindful of where you're going to get the data from is going to go to what are the use cases you're trying to get out to do a bit of backward forward. >>Okay, we've got this data thing and I think it's a journey. Nobody can come in and say, Hey, you've built this fantastic thing. It's like Terminator two. I think it's a journey where we built starting with the network. My personal focus always comes down to the network and with 5g so much, so much more right with 5g, you're talking low latency communication. That's like the true power of 5g, right? It's low latency, it's ultra high bandwidth, but what's the point of that low latency. If then subsequently the actions that need to be taken to prevent any problems in application, IOT applications, remote surgeries, uh, self driving vehicles, et cetera, et cetera. What if that's where people are sitting and sipping their coffees and trying to take action that needs to be in low latency as well. Right? So these are, I think some of the fundamental things that you have to know your data, your use cases, that location, where it needs to be exchanged, what are the parameters around that for extending that data? >>And I think from that point at one word, it's all about realizing, you know, sense of business outcomes. Unless AI comes in as a digital labor that shows you, I have, I have reduced your this amount of time and that's a result of big problems or identified problems for anything. Or I have saved you this much resource in a month, in a year or whatever timeline that people want to see it. So I think those are some of the initial starting points, and then it all starts coming together. But the key is it's not one system that can do everything. You have to have a way where, you know, you can share data once you've caught all of that data into one system. Maybe you can send it to another system at make more, take more advantage, right? That system might be an AI and IOT system, which is just looking at all of your street and make it sure that Hey parents. So it's still off just to be more carbon neutral and all that great stuff, et cetera, et cetera, >>Stuff for the audience to can cigarette rush, take us time from here. What are some of the takeaways that you think the audience really needs to be laser focused on as we move forward into the next year? You know, one thing that, uh, I think a key takeaway is, um, uh, you know, as we embark on 2021, closing the gap between intent and outcome and outputs and outcome will become critical, is critical. Uh, you know, especially for, uh, you know, uh, digital transformation at scale for organizations context in the, you know, for customer experience becomes even more critical as who Swan Huseman was talking, uh, you know, being network network aware network availability is, is a necessary condition, but not sufficient condition anymore. Right? The what, what, what customers have to go towards is going from network availability to network agility with high security, uh, what we call app aware networks, right? How do you differentiate between a trade, a million dollar trade that's happening between, uh, you know, London and New York, uh, uh, versus a YouTube video training that an employee is going through? Worse is a YouTube video that millions of customers are, are >>Watching, right? Three different context, three different customer scenarios, right? That is going to be critical. And last but not least feedback loop, uh, you know, responsiveness is all about feedback loop. You cannot predict everything, but you can respond to things faster. I think these are sort of the three, three things that, uh, that, uh, you know, customers aren't going to have to have to really think about. And that's also where I believe AI ops, by the way, AI ops and I I'm. Yeah. You know, one of the points that was smart and shout out to what he was saying was heterogeneity is key, right? There is no homogeneous tool in the world that can solve problems. So you want an open extensible system of intelligence that, that can harness data from disparate data sources provide that visualization, the actionable insight and the human augmented recommendation systems that are so needed for, uh, you know, it operators to be successful. I think that's where it's going. >>Amazing. You guys just provided so much content context recommendations for the audience. I think we accomplished our goal on this. I'll call it power panel of not only getting to a consensus of what, where AI ops needs to go in the future, but great recommendations for what businesses in any industry need to be on the lookout for rich Huisman Raj, thank you for joining me today. We want to thank you for watching. This was such a rich session. You probably want to watch it again. Thanks for your time. Thanks so much for attending and participating in the AI OBS virtual forum. We really appreciate your time and we hope you really clearly understand the value that AI ops platforms can deliver to many types of organizations. I'm Lisa Martin, and I want to thank our speakers today for joining. We have rich lane from Forrester who's fund here from Verizon and Raj from Broadcom. Thanks everyone. Stay safe..

>>From around the globe with digital coverage brought to you by Broadcom. >>So our final segment today, so we've discussed today, the value that AI ops will bring to organizations in 2021, we'll discuss that through three different perspectives. And so now we want to bring those perspectives together and see if we can get a consensus on where AI ops needs to go for folks to be successful with it in the future. So bringing back some folks Richland is back with us. Senior analysts, serving infrastructure and operations professionals at Forester with smartness here is also back global product management at Verizon and Srinivasan, Reggie Gopaul head of product and strategy at Broadcom guys. Great to have you back. So let's jump in and Richard, we're going to, we're going to start with you, but we are going to get all three of you, a chance to answer the questions. So rich, we've talked about why organizations should adopt AI ops, but what happens if they choose not to what challenges would they face? Basically what's the cost of organizations doing nothing. >>Yeah. So it's a really good question because I think in operations for a number of reviews, we've kind of stand, uh, stood Pat, where we are, where we're afraid change things sometimes. Or we just don't think about a tooling is often the last thing to change because we're spending so much time doing project work and modernization and fighting fires on a daily basis. Uh, that problem is going to get worse if we do nothing. Um, you know, we're building new architectures like containers and microservices, which means more things to mind and keep running. Um, we're building highly distributed systems where you got moving more and more into this hybrid world, the multicloud world, uh, it's become over-complicate and I'll give a short anecdote. I think, eliminate this. Um, when I go to conferences and give speeches, it's all infrastructure operations people. And I say, you know, how many people have three X, five X, you know, uh, things to monitor them. They had, you know, three years ago, two years ago, and everyone's hand goes up, say how many people have hired more staff in that time period. Zero hands go up. That's the gap we have to fill. And we have to fill that through better automation, more intelligent systems. It's the only way we're going to be able to feel back out. >>What's your perspective, uh, if organizations choose not to adopt AI ops. >>Yeah. >>That's pretty good. So I'll do that. >>Yeah. So I think it said, I say it's related to a couple of things that probably everybody tired off lately and everybody can relate to. And this would resonate that we'd have 5g, which is old set to transform the one that we know it, of communication with these smart cities, smart communities, IOT, which is going to become pivotal to the success of businesses. And as we seen with this, COVID, you know, transformation of the world that there's a, there's a much bigger cost consciousness out there. People are trying to become much more, forward-looking much more sustainable. And I think at the heart of all of this, that the necessity that you have intelligent systems, which are bastardizing more than enough information that previous equipment overlooked, because if you don't measure engagement, not going right. People love being on the same page of this using two examples for hundreds of things that play a part in things not coming together in the best possible way. So I think it has an absolute necessity to grind those cost efficiencies rather than, you know, left right and center laying off people who are like pit Mattel to your business and have a great tribal knowledge of your business. So to speak, you can drive these efficiencies through automating a lot of those tasks that previously were being very manually intensive or resource intensive. And you could allocate those resources towards doing much better things, which let's be very honest going into 20, 21, after what we've seen with 2020, it's going to be mandate >>Shaking your head there when you, his mom was sharing his thoughts. What are your thoughts about this sounds like you agree. Yeah. I mean, uh, you know, uh, to put things in perspective, right? I mean, we are firmly in the digital economy, right? Digital economy, according to the Bureau of economic analysis is 9% of the us GDP. Just, you know, think about it in, in, in, in, in the context of the GDP, right? It's only ranked lower, slightly lower than manufacturing, which is at 11.3% GDP and slightly about finance and insurance, which is about seven and a half percent GDP. So G the digital economy is firmly in our lives, right? And so someone was talking about it, you know, software eats the world and digital, operational excellence is critical for customers, uh, to, uh, you know, to, uh, to drive profitability and growth, uh, in the digital economy. >>It's almost, you know, the key is digital at scale. So when, uh, when rich talks about some of the challenges and when newsman highlights 5g, as an example, those are the things that, that, that come to mind. So to me, what is the cost or perils of doing nothing? You know, uh, it's not an option. I think, you know, more often than not, uh, you know, C-level execs are asking their head of it and they are key influencers, a single question, are you ready? Are you ready in the context of addressing spikes in networks because of, uh, the pandemic scenario, are you ready in the context of automating away toil? Are you ready to respond rapidly to the needs of the digital business? I think AI ops is critical. >>That's a great point, Roger, where gonna stick with you. So we got kind of consensus there, as you said, wrapping it up. This is basically a, not an option. This is a must to go forward for organizations to be successful. So let's talk about some quick wins. So as you talked about, you know, organizations and C-levels asking, are you ready? What are some quick wins that that organizations can achieve when they're adopting AI? >>You know, um, immediate value. I think I would start with a question. How often do your customers find problems in your digital experience before you think about that? Right. You know, if you, if you, you know, there's an interesting web, uh, website, um, uh, you know, down detector.com, right? I think, uh, in, in Europe, there is an equal amount of that as well. It ha you know, people post their digital services that are down, whether it's a bank that, uh, you know, customers are trying to move money from checking account, the savings account and the digital services are down and so on and so forth. So some and many times customers tend to find problems before it operation teams do. So. A quick win is to be proactive and immediate value is visibility. If you do not know what is happening in your complex systems that make up your digital supply chain, it's going to be hard to be responsive. So I would start there >>Vice visibility. There's some question over to you from Verizon's perspective, quick wins. >>Yeah. So I think first of all, there's a need to ingest this multi-layered monetize spectrum data, which I don't think is humanly possible. You don't have people having expertise, you know, all seven layers of the OSI model and then across network and security and at the application of it. So I think you need systems which are now able to get that data. It shouldn't just be wasted reports that you're paying for on a monthly basis. It's about time that you started making the most of those in the form of identifying what are the efficiencies within your ecosystem. First of all, what are the things, you know, which could be better utilized subsequently you have the opportunity to reduce the noise of a troubled tickets handle. It sounds pretty trivial, but as an average, you can imagine every shop is tickets has the cost in dollars, right? >>So, and there's so many tickets and there's desserts that get on a network and across an end-user application value chain, we're talking thousands, you know, across and end user application value chain could be million in a year. So, and so many of those are not really, you know, cause of concern because the problem is somewhere else. So I think that whole triage is an immediate cost saving and the bigger your network, the bigger the cost of whether you're a provider, whether you're, you know, the end customer at the end of the day, not having to deal with problems, which nobody can resolve, which are not meant to be dealt with. If so many of those situations, right, where service has just been adopted, which is coordinate quality, et cetera, et cetera. So many reasons. So those are the, those are some of the immediate cost savings. >>They are really, really significant. Secondly, I would say Raj mentioned something about, you know, the end user application value chain and an understanding of that, especially with this hybrid cloud environment, et cetera, et cetera, right? The time it takes to identify a problem in an end-user application value chain across the seven layers that I mentioned with the OSI reference model across network and security and the application environment, it's something that in its own self has a massive cost to business, right? They could be point of sale transactions that could be obstructed because of this. There could be, and I'm going to use a very interesting example. When we talk IOT, the integrity of the IOT machine is extremely pivotal in this new world that we're stepping into. You could be running commands, which are super efficient. He has, everything is being told to the machine really fast. >>We're sending everything there. What if it's hacked? And if that robotic arm starts to involve the things you don't want it to do. So there's so much of that. That becomes a part of this naturally. And I believe, yes, this is not just like from a cost saving standpoint, but anything going wrong with that code base, et cetera, et cetera. These are massive costs to the business in the form of the revenue. They have lost the perception in the market as a result, the fed, you know, all that stuff. So these are a couple of very immediate funds, but then you also have the whole player virtualized resources where you can automate the allocation, you know, the quantification of an orchestration of those virtualized resources, rather than a person having to, you know, see something and then say, Oh yeah, I need to increase capacity over here, because then it's going to have this particular application. You have systems doing this stuff to, you know, Roger's point your customer should not be identifying your problems before you, because this digital where it's all about perception. >>Absolutely. We definitely don't want the customers finding it before. So rich, let's wrap this particular question up with you from that analyst perspective, how can companies use make big impact quickly with AI? >>Yeah, I think, you know, and it has been really summed up some really great use cases there. I think with the, uh, you know, one of the biggest struggles we've always had in operations is isn't, you know, the mean time to resolve. We're pretty good at resolving the things. We just have to find the thing we have to resolve. That's always been the problem and using these advanced analytics and machine learning algorithms now across all machine and application data, our tendency as humans is to look at the console and say, what's flashing red. That must be what we have to fix, but it could be something that's yellow, somewhere else, six services away. And we have made things so complicated. And I think this is what it was. One was saying that we can't get there anymore on our own. We need help to get there in all of this stuff that the outline. >>So, so well builds up to a higher level thing of what is the customer experience about what is the customer journey? And we've struggled for years in the digital world and measuring that a day-to-day thing. We know an online retail. If you're having a bad experience at one retailer, you just want your thing. You're going to go to another retailer, brand loyalty. Isn't one of the light. It wasn't the brick and mortal world where you had a department store near you. So you were loyal to that cause it was in your neighborhood, um, online that doesn't exist anymore. So we need to be able to understand the customer from that first moment, they touch a digital service all the way from their, their journey through that digital service, the lowest layer, whether it be a database or the network, what have you, and then back to them again, and we not understand, is that a good experience? >>We gave them. How does that compare to last week's experience? What should we be doing to improve that next week? And I think companies are starting and then the pandemic, certainly you push this timeline. If you listen to the, the, the CEO of Microsoft, he's like, you know, 10 years of digital transformation written down. And the first several months of this, um, in banks and in financial institutions, I talked to insurance companies, aren't slowing. Now they're trying to speed up. In fact, what they've discovered is that there, obviously when we were on lockdown or what have you, the use of digital services spiked very high. What they've learned is they're never going to go back down. They're never going to return to pretend levels. So now they're stuck with this new reality. Well, how do we service those customers and how do we make sure we keep them loyal to our brand? >>Uh, so, you know, they're looking for modernization opportunities. A lot of that, that things have been exposed. And I think Raj touched upon this very early in the conversation is visibility gaps. Now that we're on the outside, looking in at the data center, we know we architect things in a very specific way. Uh, we better ways of making these correlations across the Sparrow technologies to understand where the problems lies. We can give better services to our customers. And I think that's really what we're going to see a lot of the, the innovation and the people really for these new ways of doing things starting, you know, w now, I mean, I think I've seen it in customers, but I think really the push through the end of this year to next year when, you know, economy and things like that, straighten out a little bit more. I think it really, people are going to take a hard look of where they are is, you know, AI ops the way forward for them. And I think they'll find it. The answer is yes, for sure. >>So we've, we've come to a consensus that, of what the parallels are of organizations, basically the cost of doing nothing. You guys have given some great advice on where some of those quick wins are. Let's talk about something Raj touched on earlier, is organizations, are they really ready for truly automated AI? Raj, I want to start with you readiness factor. What are your thoughts? >>Uh, you know, uh, I think so, you know, we place our, her lives on automated systems all the time, right? In our, in our day-to-day lives, in the, in the digital world. I think, uh, you know, our, uh, at least the customers that I talked to our customers are, uh, are, uh, you know, uh, have a sophisticated systems, like for example, advanced automation is a reality. If you look at social media, AI and ML and automation are used to automate away, uh, misinformation, right? If you look at financial institutions, AI and ML are used to automate away a fraud, right? So I want to ask our customers why can't we automate await oil in it, operation systems, right? And that's where our customers are. Then, you know, uh, I'm a glass half full, uh, clinical person, right? Uh, this pandemic has been harder on many of our customers, but I think what we have learned from our customers is they've Rose to the occasion. >>They've used digital as a key moons, right? At scale. That's what we see with, you know, when, when Huseman and his team talk about, uh, you know, network operational intelligence, right. That's what it means to us. So I think they are ready, the intersection of customer experience it and OT, operational technology is ripe for automation. Uh, and, uh, you know, I, I wanna, I wanna sort of give a shout out to three key personas in, in this mix. It's somewhat right. One is the SRE persona, you know, site, reliability engineer. The other is the information security persona. And the third one is the it operator automation engineer persona. These folks in organizations are building a system of intelligence that can respond rapidly to the needs of their digital business. We at Broadcom, we are in the business of helping them construct a system of intelligence that will create a human augmented solution for them. Right. So when I see, when I interact with large enterprise customers, I think they, they, you know, they, they want to achieve what I would call advanced automation and AI ML solutions. And that's squarely, very I ops is, you know, is going as an it, you know, when I talked to rich and what, everything that rich says, you know, that's where it's going. And that's what we want to help our customers to. >>So rich, talk to us about your perspective of organizations being ready for truly automated AI. >>I think, you know, the conversation has shifted a lot in the last, in, in pre pandemic. Uh, I'd say at the end of last year, we're, you know, two years ago, people I'd go to conferences and people come up and ask me like, this is all smoke and mirrors, right? These systems can't do this because it is such a leap forward for them, for where they are today. Right. We we've sort of, you know, in software and other systems, we iterate and we move forward slowly. So it's not a big shock. And this is for a lot of organizations that big, big leap forward in the way that they're running their operations teams today. Um, but now they've come around and say, you know what? We want to do this. We want all the automations. We want my staff not doing the low complexity, repetitive tasks over and over again. >>Um, you know, and we have a lot of those kinds of legacy systems. We're not going to rebuild. Um, but they need certain care and feeding. So why are we having operations? People do those tasks? Why aren't we automating those out? I think the other piece is, and I'll, I'll, I'll send this out to any of the operations teams that are thinking about going down this path is that you have to understand that the operations models that we're operating under in INO and have been for the last 25 years are super outdated and they're fundamentally broken for the digital age. We have to start thinking about different ways of doing things and how do we do that? Well, it's, it's people, organization, people are going to work together differently in an AI ops world, um, for the better, um, but you know, there's going to be the, the age of the 40 person bridge call thing. >>Troubleshooting is going away. It's going to be three, four, five focused engineers that need to be there for that particular incident. Um, a lot of process mailer process we have for now level one level, two engineering. What have you running of tickets, gathering of artifacts, uh, during an incident is going to be automated. That's a good thing. We shouldn't be doing those, those things by hand anymore. So I'd say that the, to people's like start thinking about what this means to your organization. Start thinking about the great things we can do by automating things away from people, having to do them over and over again. And what that means for them, getting them matched to what they want to be doing is high level engineering tasks. They want to be doing monitorization, working with new tools and technologies. Um, these are all good things that help the organization perform better as a whole >>Great advice and great kind of some of the thoughts that you shared rich for what the audience needs to be on the, for going on. I want to go over to you, give me your thoughts on what the audience should be on the lookout for, or put on your agendas in the next 12 months. >>So there's like a couple of ways to answer that question. One thing would be in the form of, you know, what are some of the things they have to be concerned about in terms of implementing this solution or harnessing its power. The other one could be, you know, what are the perhaps advantages they should look to see? So if I was to talk about the first one, let's say that, what are some of the things you have to watch out for like possible pitfalls that everybody has data, right? So yeah, that's one strategy, we'd say, okay, you've got the data, let's see what we can do with them. But then there's the exact opposite side, which has to be considered when you're doing that analysis that, Hey, what are the use cases that you're looking to drive, right? But then use cases you have to understand, are you taking a reactive use case approach? >>Are you taking quite active use cases, right? Or that that's a very, very important consideration. Then you have to be very cognizant of where does this data that you have vision, it reside, what are the systems and where does it need to go to in order for this AI function to happen and subsequently if there needs to be any, you know, backward communication with all of that data in a process better. So I think these are some of the very critical points because you can have an AI solution, which is sitting in a customer data center. It could be in a managed services provider data center, like, right, right. It could be in a cloud data center, like an AWS or something, or you could have hybrid scenarios, et cetera, all of that stuff. So you have to be very mindful of where you're going to get the data from is going to go to what are the use cases you're trying to, you have to do a bit of backward forward. >>Okay. We've got this data cases and I think it's the judgment. Nobody can come in and say, Hey, you've built this fantastic thing. It's like Terminator two. I think it's a journey where we built starting with the network. My personal focus always comes down to the network and with 5g so much, so much more right with 5g, you're talking low latency communication. That's like the two power of 5g, right? It's low latency, it's ultra high bandwidth, but what's the point of that low latency. If then subsequently the actions that need to be taken to prevent any problems in critical applications, IOT applications, remote surgeries, uh, test driving vehicles, et cetera, et cetera. What if that's where people are sitting and sipping their coffees and trying to take action that needs to be in low latency as well. Right? So these are, I think some of the fundamental things that you have to know your data, your use cases and location, where it needs to be exchanged, what are the parameters around that for extending that data? >>And I think from that point onward, it's all about realizing, you know, in terms of business outcomes, unless AI comes in as a digital labor, that shows you, I have, I have reduced your, this amount of, you know, time, and that's a result of big problems or identified problems for anything. Or I have saved you this much resource right in a month, in a year, or whatever, the timeline that people want to see it. So I think those are some of the initial starting points, and then it all starts coming together. But the key is it's not one system that can do everything. You have to have a way where, you know, you can share data once you've got all of that data into one system, maybe you can send it to another system and make more, take more advantage, right? That system might be an AI and IOT system, which is just looking at all of your streetlights and making sure that Hey, parent switched off just to be more carbon neutral and all that great stuff, et cetera, et cetera >>For the audience, you can take her Raj, take us time from here. What are some of the takeaways that you think the audience really needs to be laser focused on as we move forward into the next year? You know, one thing that, uh, I think a key takeaway is, um, uh, you know, as we embark on 2021, closing the gap between intent and outcome and outputs and outcome will become critical, is critical. Uh, you know, especially for, uh, uh, you know, uh, digital transformation at scale for organizations context in the, you know, for customer experience becomes even more critical as Swan Huseman was talking, uh, you know, being network network aware network availability is, is a necessary condition, but not sufficient condition anymore. Right? The what, what, what customers have to go towards is going from network availability to network agility with high security, uh, what we call app aware networks, right? >>How do you differentiate between a trade, a million dollar trade that's happening between, uh, you know, London and New York, uh, versus a YouTube video training that an employee is going through? Worse is a YouTube video that millions of customers are, are watching, right? Three different context, three different customer scenarios, right? That is going to be critical. And last but not least feedback loop, uh, you know, responsiveness is all about feedback loop. You cannot predict everything, but you can respond to things faster. I think these are sort of the three, uh, three things that, uh, that, uh, you know, customers are going to have to, uh, have to really think about. And that's also where I believe AI ops, by the way, AI ops and I I'm. Yeah. You know, one of the points that was smart, shout out to what he was saying was heterogeneity is key, right? There is no homogeneous tool in the world that can solve problems. So you want an open extensible system of intelligence that, that can harness data from disparate data sources provide that visualization, the actionable insight and the human augmented recommendation systems that are so needed for, uh, you know, it operators to be successful. I think that's where it's going. >>Amazing. You guys just provided so much content context recommendations for the audience. I think we accomplished our goal on this. I'll call it power panel of not only getting to a consensus of what, where AI ops needs to go in the future, but great recommendations for what businesses in any industry need to be on the lookout for rich Huisman Raj, thank you for joining me today. >>Pleasure. Thank you. Thank you. >>We want to thank you for watching. This was such a rich session. You probably want to watch it again. Thanks for your time.

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat welcome back this is the cubes coverage of Red Hat summit 2020 of course this year instead of all gathering together in San Francisco we're getting to talk to red hat executives their partners and their customers where they are around the globe I'm your host Stu minimun and happy to welcome to the program Nick Barr said who is the senior director of Technology Strategy at Red Hat he happens to be on a boat in the Bahamas so Nick thanks so much for joining us hey thank you for inviting me it's a great pleasure to be here and it's a great pleasure to work for a company that has always dealt with remote people so it's really easy for us to kind of thing yeah Nick you know it's interesting I've been saying probably for the last 10 years that the challenge of our time is really distributed systems you know from a software standpoint that's what we talked about and even more so today and number one of course the current situation with the global plan global pandemic but number two the topic we're gonna talk to you about is edge and 5g it's obviously gotten a lot of hype so before we get into that - training Nick you know you came into Red Hat through an acquisition so give us a little bit about your background and what you work on Baretta about five years ago company I was working for involves got acquired by read at and I've been very lucky in that acquisition where I found a perfect home to express my talent I've been free software advocate for the past 20-some years always been working in free software for the past 20 years and Red Hat is really wonderful for that yeah it's addressing me ok yeah I remember back the early days we used to talk about free software now we don't talk free open-source is what we talk about you know dream is a piece of what we're doing but yeah let's talk about you know Ino Vaughn's I absolutely remember the they were a partner of Red Hat talked to them a lot at some of the OpenStack goes so I I'm guessing when we're talking about edge these are kind of the pieces coming together of what red had done for years with OpenStack and with NFB so what what what's the solution set you're talking about Ferguson side how you're helping your customers with these blue well clearly the solution we are trying to put together as to combine what people already have with where they want to go our vision for the future is a vision where openshift is delivering a common service on any platform including hardware at the far edge on a model where both viens and containers can be hosted on the same machine however there is a long road to get there and until we can fulfill all the needs we are going to be using combination of openshift OpenStack and many other product that we have in our portfolio to fulfill the needs of our customer we've seen for example a Verizon starting with OpenStack quite a few years ago now going with us with openshift that they're going to place on up of OpenStack or directly on bare metal we've seen other big telcos use tag in very successful to deploy their party networks there is great capabilities in the existing portfolio we are just expanding that simplifying it because when we are talking about the edge we are talking about managing thousands if not millions of device and simplicity is key if you do not want to have your management box in Crete excellent so you talked a lot about the service providers obviously 5g as a big wave coming a lot of promise as what it will enable both for the service providers as well as the end-users help us understand where that is today and what we should expect to see in the coming years though so in respect of 5g there is two reason why 5g is important one it is B it is important in terms of ad strategy because any person deploying 5g will need to deploy computer resources much closer to the antenna if they want to be able to deliver the promise of 5g and the promise of very low latency the second reason it is important is because it allows to build a network of things which do not need to be interconnected other than through a 5g connection and this simplifies a lot some of the edge application that we are going to see where sensors needs to provide data in a way where you're not necessarily always connected to a physical network and maintaining a Wi-Fi connection is really complex and costly yeah Nick a lot of pieces that sometimes get confused or conflated I want you to help us connect the dots between what you're talking about for edge and what's happening the telcos and the the broader conversation about hybrid cloud or red hat calls at the O the open hybrid cloud because you know there were some articles that were like you know edge is going to kill the cloud I think we all know an IP nothing ever dies everything is all additive so how do these pieces all go together so for us at reddit it's very important to build edge as an extension of our open hybrid cloud strategy clearly what we are trying to build is an environment where developers can develop workloads once and then can the administrator that needs to deploy a workload or the business mode that means to deploy a workload can do it on any footprint and the edge is just one of these footprint as is the cloud as is a private environment so really having a single way to administer all these footprints having a single way to define the workloads running on it is really what we are achieving today and making better and better in the years to come um the the reality of [Music] who process the data as close as possible to where the data is being consumed or generated so you have new footprints - let's say summarize or simplify or analyze the data where it is being used and then you can limit the traffic to a more central site to only the essential of it is clear that we've the current growth of data there won't be enough capacity to have all the data going directly to the central part and this is what the edge is about making sure we have intermediary of points of processing yeah absolutely so Nikki you talked about OpenStack and OpenShift of course there's open source project with with OpenStack openshift the big piece of that is is kubernetes when it comes to edge are there other open source project the parts of the foundations out there that we should highlight when looking at these that's Luke oh there is a tremendous amount of projects that are pertaining to the edge read ad carry's many of these projects in its portfolio the middleware components for example Quercus or our amq mechanism caki are very important components we've got storage solutions that are super important also when you're talking about storing or handling data you've got in our management portfolio two very key tool one called ansible that allows to configure remotely confidence that that is super handy when you need to reconfigure firewall in Mass you've got another tool that he's a central piece of our strategy which is called a CM read at forgot the name of the product now we are using the acronym all the time which is our central management mechanism just delivered to us through IBM so this is a portfolio wide we are making and I forgot the important one which is real that Enterprise Linux which is delivering very soon a new version that is going to enable easier management at the edge yeah well of course we know that well is you know the core foundational piece with most of the solution in a portfolio that's really interesting how you laid that out though as you know some people on the outside look and say ok Red Hat's got a really big portfolio how does it all fit together you just discussed that all of these pieces become really important when when they come together for the edge so maybe uh you know one of the things when we get together summit of course we get to hear a lot from your your your customer so any customers you can talk about that might be a good proof point for these solutions that you're talking about today so right now most of the proof points are in the telco industry because these are the first one that have made the investment in it and when we are talking about their eyes and we are talking about a very large investment that is reinforced in their strategy we've got customers in telco all over the world that are starting to use our products to deploy their 5g networks and we've got lots of customer starting to work with us on creating their tragedy for in other vertical particularly in the industrial and manufacturing sector which is our necks and ever after telco yet yeah well absolutely Verizon a customer I'm well familiar with when it comes to what they've been used with Red Hat I'd interviewed them it opens back few years back when they talked about that those nmv type solutions you brought a manufacturing so that brings up one of the concerns when you talk about edge or specifically about IOT environment when we did some original research looking at the industrial Internet the boundaries between the IT group and the OT which heavily lives lives in manufacturing wouldn't they did they don't necessarily talk or work together so Houser had had to help to make sure that customers you know go through these transitions Plus through those silos and can take advantage of these sorts of new technologies well obviously you you have to look at a problem in entirety you've got to look at the change management aspect and for this you need to understand how people interact together if you intend on modifying the way they work together you also need to ensure that the requirements of one are not impeding the yeah other the man an environment of a manufacturer is really important especially when we are talking about dealing with IOT sensors which have very limited security capability so you need to add in the appropriate security layers to make what is not secure secure and if you don't do that you're going to introduce a friction and you also need to ensure that you can delegate administration of the component to the right people you cannot say Oh from now on all of what you used to be controlling on a manufacturing floor is now controlled centrally and you have to go through this form in order to have anything modified so having the flexibility in our tooling to enable respect of the existing organization and handle a change management the appropriate way is our way to answer this problem right Nick last thing for you obviously this is a maturing space lots of age happening so gives a little bit of a look forward as to what users should be affecting and you know what what what pieces will the industry and RedHat be working on that bring full value out of the edge and find a solution so as always any such changes are driven by the application and what we are seeing is in terms of application a very large predominance of requirements for AI ml and data processing capability so reinforcing all the components around this environment is one of our key addition and that we are making as we speak you can see Chris keynote which is going to demonstrate how we are enabling a manufacturer to process the signal sent from multiple sensors through an AI and during early failure detection you can also expect us to enable more and more complex use case in terms of footprint right now we can do very small data center that are residing on three machine tomorrow we'll be able to handle remote worker nodes that are on a single machine further along we'll be able to deal with disconnected node a single machine acting as a cluster all these are elements that are going to allow us to go further and further in the complication of the use cases it's not the same thing when you have to connect a manufacturer that is on solid grounds with fiber access or when you have to connect the Norway for example or a vote and talk about that too Nick thank you so much for all the updates no there's some really good breakouts I'm sure there's lots on the Red Hat website find out more about edge in five B's the Nick bark set thanks so much for joining us thank you very much for having me all right back with lots more covered from Red Hat summit 2020 I'm stoom in a man and thanks though we for watching the queue [Music]

>>From our studios in the heart of Silicon Valley. Palo Alto, California myth is a cute conversation. >>Hello and welcome to the cube studios in Palo Alto, California. For another cube conversation. We go in depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. It's a well known fact of life at this point in time. We're going to the cloud in some manner, way, shape or form. Every business that intends to undertake a digital transformation is going to find themselves in a situation where they are using cloud resources to build new classes of applications and accelerate their opportunities to create new markets that are more profitable. What folks haven't fully internalized yet though is what it means to govern those activities. What does it mean to use data that is in the cloud in a compliant and reliable way? What does it mean to allow rapid innovation while at the same time ensuring that our businesses are not compromised by new classes of risk, new classes of compliance issues as a result of making certain liberties, uh, with how we handle governance. So that's what we're going to talk about today and we've got a great conversation for you. Toby Knapp is a co founder and CTO of day two IQ and Charlie Betts is the principal analyst at Forrester. Toby. Charlie, welcome to the cube theater. All right, so Charlie, I'm going to start with you. I kind of outline the overall nature of the problem, but let's get it very specific. What is the problem that enterprises face today as they try to accelerate their use of technology in a way that doesn't compromise the risk and compliance concerns? >>Well, we are hearing the same story over and over again. Peter, uh, companies are starting on the cloud native journey and perhaps a dev ops journey. You know, there's some similarities there. You know, one leads to the other in many cases and they S they do a proof of concept and they do a pilot and they like the results. But both of those efforts had what from monopoly, we would call it a get out of jail free card. You know, they had a pass to bypass certain regulatory or governance or compliance controls. Now they want to scale it. They want to roll it out across the enterprise and you can't give every team a get out of jail free card. >>Well, let me dig into this because is it that the speed with which we're trying to create new things, is that the key issue? Is it that the new technologies like Coobernetti's lend themselves to new style that doesn't necessarily bring good governance along with it? What is, what are those factors that are driving this problem? >>I think the central factor, Peter, is the movement from stage gated governance to governance of continuous flow. We could unpack this in various ways, but really if you look at so many governance models and people ship them to us and we comb through them and it's getting, you know, doing a lot of out lately, what we see is over and over again, this idea that delivery pauses experts come in from their perspective with a checklist they go through, they check the delivery against the checklist, and then the Greenlight is given to move on. And this is how we've run digital systems for a long time now. But now we're moving towards continuous flow, continuous iteration, >>agile, agile, DevOps, >>dev ops, all the rest. And these methods are well suited to be supported by architectures like Coobernetti's. And there are certain things you can do with automation that are very beneficial in cloud native systems, but you're up against, you know, decades of policy that assume this older model is based on older guidance like ITIL and PIM, Bach and, and COBIT and all the rest. COBIT 2019 is still based on a plan build run model, >>which is not, is not necessarily a bad thing in the grand scheme of things, but it doesn't fit into a month long sprint. >>It doesn't fit. And more and more what we're seeing when I say stage Gates are going away, what we're seeing is that the life cycle becomes internalized to the team. You still plan, build, run. But it's not something that you can put controls >>on at the high level. And so the solution seems to be is that we need to be able to foster this kind of speedy acceleration that encourages the use of agile, uh, leads to a dev ops orientation. And somehow fold good solid governance practices right into the mix. What do you think the, let's take a look at 2025, what's it going to look like? And uh, even if we're not ready for it yet? >>Well, I think you were going to govern a lot more at the level of the outcome. You're going to govern what not how as much, but there are a lot of things that still are essential and just basic solid good practice such as not having 15 different ways or a hundred different ways to configure major pieces of infrastructure. You know, there's a, in the, some of the reports, uh, the state of DevOps report that came out, there was a, uh, a note in there or a finding in there that it was best to let the developers have a lot of choice. And I understand that developer autonomy is very important, but every time a development team chooses a new technology or a new way to configure an ex, an existing technology, that's an expansion of attack surface. And I'm very concerned about that, especially as we see things like Equifax with the, uh, the struts exploit, you know, we, we have to keep our environment secure, well patched up to date. And if you only have one or two ways that things are configured, that means your staff are more likely to do the right thing as opposed to, you know, infinite levels of variation, you know, on a hundred different ways of configuring. Coobernetti's >>well, presumably we don't want the infinite levels of variation to be revealed at the business level and not down at the infrastructure level. I think one of the things that folks mean or folks aren't intending or hope to be able to do with digital business you're alluding to this is creating a digital asset, a software based asset because ultimately it's going to be more integratable, but you lose the opportunity to integrate those things if you're increasing the transaction costs by introducing a plethora of discordant governance models. Is that what you're seeing as well, Toby? >>Absolutely. And I think, uh, you know, some aspects of cloud native that make this problem a lot bigger is actually, you know, cloud native encourages sort of a self service model for infrastructure. And also we're seeing our shift, um, off, uh, power and decision making towards developers, right? So you have developers introducing a lot of these new stacks, often in a very, you know, sort of bottoms up, um, organic way. So very quickly and enterprise finds themselves with, you know, 10, 15 different ways to provision infrastructure to provision communities, clusters. Um, and often, you know, the teams that are in charge of governance aren't even aware of these things, right? Yes. So, uh, I think it starts actually with that and you know, how can we find, uh, this balance of giving developers the flexibility they want, uh, you know, having them leverage the benefits of cloud native, but at the same time making the folks that are in charge of governance, uh, aware of what's going on in, in their enterprise, uh, making them aware of the different stacks that are provisioned. Uh, and then finding the right balance between that flexibility and enforcing governance. Uh, there's ways to do that. Um, you know, there what we see a lot is, is, uh, waste, uh, people building one stack on cloud provider, a different stack on cloud provider B, a third stack, you know, at the edge or in their data center. And so when it comes to patching, security issues, upgrading versions, you know, you, you're doing three, five times the, the amount of work. >>Well, let me ask you a question because we can see that the problem is this explosion in innovation at the digital level, uh, that is running into this, uh, the, the stricture of historical practices. And as a result, people are in running governance. What is it, I mean, if I think about this, it sounds to me like the developer tooling is getting better, faster than the governance tooling. Where are we in the marketplace in terms of thinking about technologies that can improve the productivity on the governance side so that we can bring governance models to the developers so they don't have to make decisions at that level? >>Right. I think where we are in the market is, um, so obviously cloud native and Kubernetes specifically has seen rapid adoption Indiana price, right? And I think, um, you know, the governance and tools are just now catching up. Right? Right. Um, so the typical journey we see is, uh, you know, folks try out Kubernetes, they try out cloud native technologies to have a very good first experience. It's easy. And so they kind of, uh, you know, forget some of the best practices that we've learned over the years for how to secure a production stack, how to make it upgradable, maintainable, how to govern workloads and versions, um, because they'll still, schools just simply didn't exist. Uh, so far we're now seeing these tools emerge. Um, and, and really it's the same approaches that have worked for us in the past for, for running these types of infrastructure. It's, um, you're having a central pane of class for visibility. What versions am I running? Uh, you know, first being aware of what's out there and then you'll centralizing management of these, of these stacks. Um, how do I, you know, lifecycle manage my, my Kubernetes clusters and all the related technologies. Those are the tools that are just now showing up in the market, >>but it's also got to be, I presume that, uh, a degree of, uh, presuming that the tooling itself does bring forward good governance practices into a modern world. If I got that right. >>Yeah, absolutely. I think this is one of the key things that the updated INO team, uh, the infrastructure and operations and our, our view is that these become platform teams. So we've maybe relieved the INO term behind we go with the platform teams. This is one thing that they should be doing is creating reference implementations. You know, the, you know, here's your hello world stack and it's perfectly compliant. Go solve your business problem and leave the undifferentiated heavy lifting to us. You know, and this is I think, uh, going should be a welcome message. Uh, assuming that the stack is providing all the services that the developer expects. >>Well it certainly suggests that there is a reasonable and rational separation of duties and function within a business. So the people that are close to the business of building the function that the business needs are out there doing it. Meanwhile, we've got infrastructure developers that are capable of building a platform that serves as multitude of purposes with the specificity required for each workload and in compliance with the overall organization. >>There's a key message that I want to reinforce with the audience as we think about the future of INO. I, we've been thinking a lot about it at Forester. What is the future of the traditional INO organization? If I say infrastructure that implies application and I'm talking about a stack that doesn't go away, you know, there will always be a stack, a layered architecture. What is being challenged is, when I say operations, that implies dev and I'm talking now about a life cycle. That's what's merging together. And so well, the life cycle becomes something that is held internally within your feature or component team and is no longer a suitable topic of governance. Absolutely. In terms of the layered infrastructure, this is where we, it's still a thing, you know, because yes, we will platform teams, component teams, feature teams facing the business or the end user. >>Well, it's all back to the idea that a resource is a reasonably well bound, but nonetheless with the appropriate separation, uh, of, of function that delivers some business outcome. And that's gonna include both infrastructure at a software level, an application at a software level. So look, we, you spent a lot of time talking to customers about these issues when they come back to you. Uh, where are you seeing successes most obviously and why? >>Yeah, so where we see successes is where, um, you know, organizations, um, figure out a way to give developers what they want, which is in the cloud native spaces. Every development team wants to own their own communities cluster. They want to, it is their sandbox. They want to install their own applications on there. They don't want to talk to different team when they install applications. So how can you give them that while at the same time enforcing the standards that you need to, right? How do you make sure those clusters follow a certain blueprint that have the right access control rules? Um, you know, sensitive information like, like credentials are distributed in the right way. The right versions of workloads are available. Organizations that figure out how to do that, uh, they are successful at this. So the government from a central place, they have um, you know, essential pane of glass. >>Um, you know, like our product commander where they essentially set up blueprints for teams. Um, each individual team can have their own cluster. It gets provisioned with this blueprint. And then from the central place I can say, all right, here is what my production clusters should look like. Right? Here are the secrets that should be available. Here are the access control rules that need to be set. And so you find the right balance that way, right? You can enforce your governance standards while at the same time giving developers their individual clusters that development their staging of production clusters. >>And here's the options and what is an edible option and what is not. Right. Yeah. So it seems to me as if I, I mentioned this earlier, if I think about digital business, it's the opportunity to not only turn process, we're increasingly digitized process, but the real promise also is to then find ways of bringing these things together, integrate the business in response to new opportunities or new, uh, competitive factors or regulatory factors, whatever else it might be, and literally reconfigure the business quickly. That has to be more difficult if we have a wide array of, of governance models and operational principles. Trolley is, you think about customer success, uh, what does it mean for the future to be able to foster innovation with governance so that the whole thing can come together when it needs to come together? >>Well, I think that we need to move to governing again, as I said earlier, governing >>what not. How uh, >>I believe that, uh, you know, teams should be, should be making certain promises and there's a whole idea of the theory that's out there. A guy named Mark Burgess who is, you know, well known in certain certain infrastructure as code circles. So what are the promises that the team makes within the larger construct of the team of teams and is that team being accountable to those promises? And I think this is the basis of some of the new operating models we're seeing like Holacracy and teal. I think we're in very early days of looking at this. But you know, yeah, you will be held accountable for you know, objectives and key results. But how you get there, you have more degrees of freedom and yet at an infrastructure level, this is also bounded by the fact that if this is a solved problem, if this is not interesting to the business, you shouldn't be burning brain power on solving it. You know, and maybe it was interesting, you know, a couple of years ago and there was a need to explore new technologies, but really the effort should be spent solving the customer's problems. Charlie Betts, principal analyst at Forrester, Toby not co founder and CTO of D to IQ. Thanks very much for being on the cube. Thank you. Thank you, Peter, and thank you for joining us for another cube conversation. Once again, I'm Peter Burris. See you next time..

>>Live from Atlanta, Georgia. It's the cube covering Ansible Fest 2019. Brought to you by red hat. >>Welcome back everyone. Live cube coverage here in Atlanta. This is the keeps coverage of Ansible Fest. This is red hat and suppose two days of live coverage. They had a contributor day yesterday before the conference all being covered by the cube. I'm John furrier, Miko Stu Miniman. Our next guest is Chris Gardner, principal analyst at Forrester Gardner. Welcome to the cube. Thanks. See you. Good to talk to you. Hey, analyzing the players in this space is really challenging. You've got a new wave that came out a few months ago. Yep. Laying it all out. Um, certainly the world changed. You go back eight years. Cloud was just hitting the scene on premises. Look good. Data's Stanley was rocking. You're doing network management, you're doing some configuration management now you've got observability, you've got automation apps. The world's changing big time. What's your take? What's this? I mean, it's interesting because the prior versions of that wave focused entirely on configuration management and the feedback I got was, um, the world's a lot bigger than that, right? >>And we have to talk about platforms and you heard it this morning during the keynote about Redhat moving towards an platform and automation platform. And my definition of a platform is things like configuration management, hybrid cloud management, all the various types of automation and orchestration need to be there. But you also need compliance. You need governance, you need the ability to hopefully make a call as to what is actually occurring and have some intelligence behind the automation. And obviously you need the integrations. It's not a situation to simply have as many people as possible, although that's nice as many vendors you work with. But to have real relationships, if you have Microsoft working on automation code with you, if, if Amazon working on automation code with you, that makes a true platform, right? It's John said earlier day a platform needs to be an enabler. And we've even said, if you can't build on top of this, like the collections that Ansible announced here seems like it might fit under that definition. >>And there's an old joke that everything becomes a platform eventually. Right? Um, but I think that, I think it bears it. There's some merit in this one. Um, the other thing is that I'm seeing a lot of folks want a holistic automation solution and the only way you're going to do that is to have a platform that you can build things on top of it and connect the pieces and provide the proper governance. So, um, I'm mostly in agreement with the definition that's been described here and I think you could tackle different ways. Uh, and all the vendors in the space are certainly doing that. Definitely platform thinking is different. Um, you know, the easy way to look at it and the old big data space do, we'll use to cover that was a tool versus a platform, you know, tools, a hammer, everything looks like a nail, did great things. >>One thing great are a few things. Good platform is more of a systems thinking. Yes, yes. And you've got glue layers, you've got data. So it's really more of that systems thinking that separates the winners from the losers, at least at our opinion. Absolutely. I mean, when you looked at who was the leaders in my wave, it wasn't the basics of automating or orchestration and configuration management, they all had that. The, the ones that were winners, where can I do compliance in a different way? Can I actually have people come into the system that aren't it people and make a call on some of these things? Can I apply AI and machine learning to some of this? Can I make some recommendations and hopefully direct people in the right, you know, the way they should go. And you know, the folks that were able to do that Rose to the top, the folks that weren't were average and below. >>Yeah. Chris bring us inside to some of the competitive dynamics here. We understand that, you know, there's a lot of open source here and therefore everybody holds hands and things can buy y'all. But, you know, there's, you know, product tools, there's the public clouds and what they do. And then, you know, Ansible, uh, you know, fit, fits in a lot of different places. Yeah. It's, it's a bit ironic because, uh, you know, this is one of those waves where, and it's very rare that everyone was sitting was, was at least preaching kumbaya. They are all saying that they were friendly with one another. And, and, uh, quite frankly, I, I tend to believe it. We're in a situation right now where you can't get by, especially in a hybrid cloud world. We are going to have resources that live in multiple, you know, AWS and Azure, but also on premises and at the edge. You need to have these integrations. You need to be able to talk to one another. So, um, that said, there's certainly a lot of coopertition going on where people are saying, if I can integrate these tools better, if I could provide a better governance layer, if I can again, hand things off to the enterprise in a way that has not been handed off before that I don't even have to go through an INO group and infrastructure operations group, those are willing, could be the ones that truly succeed in this space. >>Software defined data center, software defined cloud, everything software defined. Yep. These abstraction layers, data and software. We had a guest on the cube a week ago saying, data's the new software I get. Okay, it's nice, nice gimmick. But if you think about it, this abstraction layer, it's like a control plan. Everyone wants to go for these control planes, which is a feature of platform. As this automation platform becomes ultimately the AI platform, how do you see it evolving and expanding? Because you see organic growth, you see certainly key positions, 6 million stars on get hub. I mean, it's running the plumbing. I mean, come on. Like it's not, it's not like it's just some corner case. >>Yeah, yeah. Infrastructure. Yeah. I mean, you know, in an idealistic way, I'd like to see, we us resolve on singular holistic platforms for enterprises. The reality is that's not not the way you can do it today. What I do try to help clients do is at least rationalize their portfolio. If they have 12 different automation products they're running, chances are that's not the best idea. Um, I've actually had situations where someone will say to me, um, I'm running Ansible in one portion of my organization and chef and another, and I say, well, it's some, they do similar things. And the reason for it was because they were stood up organically. Each group kind of figured out the things along the way. And I have to at least guide them and say, you know, where are the similarities? Where can you potentially, you know, move some stuff from there. >>But the cloud discussion, you know, always debate upon, you know, multi-cloud, Seoul cloud, ultimately the workload needs something underneath. And I think workload definition dictates kind of what might be underneath. So it might be okay to have a couple, you know, automation platforms or it could be great to have one. I mean, this is really the eye of the beholder. Beauty is in the eye of the, >>yeah, in my view. Um, I, I've been an analyst for a couple of years before that I was doing this stuff for a living. I have the worst scars and in my view it's, it's not even a matter of how many tools you use. It's putting the workload where it belongs, that matters. And if you could do that with fewer tools, obviously that from an operational level that makes life a lot easier. Um, but I'm not going to say to somebody, you know, completely dismantle your entire automation and orchestration workflow just because I think this one tool is better. Let's talk about how we can, >>that's the worst case scenario because if you have to dictate workloads based on what tool you have, that's supposed to be the other way around. >>Yes. Setting up a nuclear bomb in the data center or in the cloud has never worked. Note to self, don't do that. Yes. One of the interesting conversations we've already been having here at the show is that the tool is actually helping to drive some of the cultural change in collaboration. So, you know, what are you finding in your research? How is that, you know, kind of this admin role and you know, to the cloud in applications. You know, it's interesting. I, we continued to beat the drum that these folks are becoming developers, but we've been beating that drum for a decade now and quite frankly we had to continue to beat it. But what I think is more even more interesting is we have groups starting to pop up in our research that are separate from it, that focus on automation in a way that no one has done before. >>Some we went into it saying, Oh, that's a center of excellence, right? And the teams that we talked to said no, do not call us a center of excellence. A two reasons. One is that term is tainted. Uh, but secondly, we're not one team. There's multiple automation teams. So we're actually starting to call these groups, strike teams that come in and standardize and say, okay, I have a lead architect, a lead robot architects say it's around infrastructure automation. I'm going to standardize across the board and when other groups need to come on board, I have the principles already laid out. I have the, the process is already laid out. I come in, I accelerate that, I set it up and then I back off. I don't own the process and I'm not part of it either. I T's got operations of its own that's got to worry about. >>I'm going between the two and when we talk to especially the fortune 100 they are setting these groups up. Now when I ask them what do you called them? They don't have a name yet, so I think strike team sounds sexy, but ultimately this is not like a, a section of it that's been severed off and becomes this role. It's a completely true committee. I yeah. Oh yeah. I want our falls slow process. Exactly, exactly. And it better fits what the role is. The role is to come in, nail the process, get it automated and the get out. It's not to stand there and be a standards body forever. Um, there's certainly some groups that in some types of automation like RPA where you want them to stick around because you may want them to manage the bots. There's a whole role called bot masters, which is specifically for that role. But most of the time you want them to be part of that process and then you know, hand it back off. >>Yeah. We've seen some interesting patterns. I want to get your thoughts on this as a little bit of a non-sequitur. Want to bring it in, but in the security space you seeing a CSOs chief information security officers building their own stacks internally, they're picking one cloud, Amazon or Azure and they're building all in maybe some hedge with some people working on some backup cloud, but they don't want to fork their talent all on one cloud and they cause they need to be bad ass responsive strike teams for security pressure. Yeah, yeah, absolutely. Not as critical with the security side with automation, but certainly relevance. Is that the same thing going on here with this development Durham, this being continued to be as much more around core competency and building internally stacks and building some standards? >>I I, I think it is, and you know what's interesting too is that I work with, I'm on the infrastructure and operations team at Forrester. I talk with INO people all day long, but I work alongside the security team and I said to them a couple of years ago, um, you guys are going to have to get your hands dirty with this stuff that I cover. You guys have to know infrastructure, automation, API APIs, you need to know how to code these things. And I said, are you comfortable telling your sec ops folks, your clients that they go, no, by all means they have to be part of this. So they're okay with them talking to me, talking to them and saying that you need to be part of the infrastructure design process and need to be part of this decision making process. Right. Um, which is different than their sec ops role used to be. So my point is, is that these worlds are not that dissimilar as some people might think they are sec dev ops or whatever we're going to call it. We keep tacking letters onto this thing, uhm, is a actual discipline. And it is a reality in most organizations I talked to the people should. >>So a system has all of these things as data across the system. They have high blood subsystem you're talking about and yet it's this holistic system security and data. Yeah. >>And we're in a world now, especially around things like edge computing where data gravity matters. So all these pieces, you know, it's, if you go back to the old school kind of computer science folks from the, you know, 50 sixties and seventies, they're like, this is not new. We've been thinking systems thinking for awhile, but I think we're finally at a place where we're actually now breaking down the silos that we've been championing to do. So for, >>I got to ask you the analyst questions since you're watching the landscape. Sue wants to jump in, but I want to get this out. So observability became a category at a network management. I mean, network management was like this boring kind of plotting along white space. I mean, super important. People need to do network management. Then in comes the cloud becomes a data problem. Whether it's observability you get to microservices, you got security signal FX, all these companies going public. Um, well a lot of M and a activities basically large segment, a lot of frothiness automation feels like it's growing to be big. Is there startup opportunities here? If, if platforms are becoming being a combination of things, is there room for startups and if so, what would you say? Um, those stars would look like? There are, I think >>what we're seeing is, and it speaks to the observer, observe the word you just said. Um, uh, I can, I can S I can know what it is, but I can't say it. Um, we're seeing the APM vendors move down the stack. We're seeing the infrastructure monitoring vendors move up the stack and in the middle we're seeing them both try to automate the same things. Um, you cannot pull off some of the infrastructure as code automation that we need to pull off without observability, but you can't get that observability unless you are able to pull it from the top of the stack. Um, what we're going to see is consolidation and we're already starting to see it, um, where you're gonna have different groups come together and say, why did have to tools to do this? Why not do one? Um, the reason why you do multiple tools today is because no one is truly strong at the entire stack. >>A lot of the folks that are going down the stack to say that they're not quite infrastructure automation players just yet, but watch this space, they will eventually, Oh, this change happening. Absolutely. Startups getting funded. Do you think there's opportunity to take some territory down? If there's any opportunity? And, and I'm, I'm pushing for this, it's in the AI AI ops space when it comes to these things is actually going beyond where we stand today. So I want to be clear that, um, AI ops is a great concept. The reality of is that we're still a ways away from being practical. I'd like to see not just recommendations from these tools that the startups are providing, but actually trust in them to make the changes necessary. So Chris, it sounds like the antibody automation platform announcement today fits with what you've been saying for the last couple of years. >>So the question is, what's next? Where does the Ansible need to mature and expand and you know, what, what are users asking for that Ansible is not doing today? So a couple things. Um, they did okay, but not fantastic at infrastructure modeling. Ansible. They did okay, but not amazing at what we call comprehension, which is making a call as to, you know, using AI and machine learning to make a call and what the infrastructure layers should look like. To be Frank, no one did really well in that one. So not too, not too bad on that. Um, and the other thing is they need to improve slightly. Is there integration story? They actually have a really good one. You see all the folks that are here. Um, it's just, it's, it's just as hair away from being the best. They're not quite there yet. So, and when, again, when I mean integrations, I don't mean having a laundry list of vendors you work with. >>I mean actually working with them to build code and you saw that this morning where there's the best, uh, right now surprisingly is VMware, but for you Morris built that relationship off for a long time. Um, they work right alongside Microsoft and Google and all these folks to build the code together in the industry. Uh, I think the darkest source of all is probably, and it remains to be seen if they can actually do something that is HashiCorp. Um, Terraform is an interesting player in this entire space. I actually included them in our wave on infrastructure automation platforms and you can argue is it even an automation platform? Quite frankly. Um, uh, I think HashiCorp itself was trying to figure out exactly what it is. But the bottom line is it's got tremendous Mindshare and it works well. So I think that if you watch, if you see the strategy going forward and look at, you know, what they're putting their investments into, they could become a really serious damaging player in this space. Chris Gardner, thanks for coming on the cube, sharing your insights and your research at Forrester forced wave. Check it out. Just came out a couple of months ago. Uh, infrastructure automation platforms. Q three 2019. Chris Gardner, the author here in the Q, breaking it down. I'm John furrier. There's too many men. We'll be back with more after the short break. Thank you.