(upbeat music) >> Okay, we're now going to try and stretch our minds a little bit and stretch Supercloud to the edge. Supercloud, as we've been discussing today and reporting through various breaking analyses, is a term we use to describe a continuous experience across clouds, or even on-prem, that adds new value on top of hyperscale infrastructure. Priya Rajagopal is the director of product management at Couchbase. She's a developer, a software architect, co-creator on a number of patents as well as being an expert on edge, IoT, and mobile computing technologies. And we're going to talk about edge requirements. Priya, you've been around software engineering and mobile and edge technologies your entire career, and now you're responsible for bringing enterprise class database technology to the edge and IoT environments, synchronizing. So, when you think about the edge, the near edge, the far edge, what are the fundamental assumptions that you have to make with regards to things like connectivity, bandwidth, security, and any other technical considerations when you think about software architecture for these environments? >> Sure, sure. First off, Dave, thanks for having me here. It's really exciting to be here again, my second time. And thank you for that kind introduction. So, quickly to get back to your question. When it comes to architecting for the edge our principle is prepare for the worst and hope for the best. Because, really, when it comes to edge computing, it's sort of the edge cases that come to bite you. You mentioned connectivity, bandwidth, security. I have a few more. Starting with connectivity, as you import on low network connectivity, think offshore oil rigs, cruise ships, or even retail settings, when you want to have business continuity, most of the time you've got an internet connection, but then when there is disruption, then you lose business continuity. Then when it comes to bandwidth, the notion or the approach we take is that bandwidth is always limited or it's at a premium. Data plans can go up through the roof, depending on the volume of data. Think medical clinics in rural areas. When it comes to security, edge poses unique challenges because you're moving away from this world garden, central cloud-based environment, and now everything is accessible over the internet. And the internet really is inherently untrustworthy. Every bit of data that is written or read by an application needs to be authenticated, needs to be authorized. The entire path needs to be secured end-to-end. It needs to be encrypted. That's confidentiality. Also the persistence of data itself. It needs to be encrypted on disk. Now, one of the advantages of edge computing or distributing data is that the impacted edge environment can be isolated away without impacting the other edge location. Looking at the classic retail architecture, if you've got retail use case, if you've got a a retail store where there's a security breach, you need to have a provision of isolating that store so that you don't bring down services for the other stores. When it comes to edge computing, you have to think about those aspects of security. Any of these locations could be breached. And if one of them is breached, how do you control that? So, that's to answer those three key topics that you brought up. But there are other considerations. One is data governance. That's a huge challenge. Because we are a database company at Couchbase, we think of database, data governance, compliance, privacy. All that is very paramount to our customers. It's not just about enforcing policies right now. We are talking about not enforcing policies in a central location, but you have to do it in a distributed fashion because one of the benefits of edge computing is, as you probably very well know, is the benefits it brings when it comes to data privacy, governance policies. You can enforce that at a granular scale because data doesn't have to ever leave the edge. But again, I talked about this in the context of security, there needs to be a way to control this data at the edge. You have to govern the data when it is at the edge remotely. Some of the other challenges when thinking about the edge is, of course, volume, scale, think IoT, mobile devices, classic far edge type scenarios. And I think the other criteria that we have to keep in mind when we are architecting a platform for this kind of computing paradigm is the heterogeneity of the edge itself. It's no longer a uniform set of compute and storage resources that are available at your disposal. You've got a variety of IoT devices. You've got mobile devices, different processing capabilities, different storage capabilities. When it comes to edge data centers, it's not uniform in terms of what services are available. Do they have a load balancer? Do they have a firewall? Can I deploy a firewall? These are all some key architectural considerations when it comes to actually architecting a solution for the edge. >> Great. Thank you for that awesome setup. Talking about stretching to the edge this idea of Supercloud that connote that single logical layer that spans across multiple clouds. It can include on on-prem, but a critical criterion is that the developer, and, of course, the user experience, is identical or substantially similar. Let's say identical. Let's say identical, irrespective of physical location. Priya, is that vision technically achievable today in the world of database. And if so, can you describe the architectural elements that make it possible to perform well and have low latency and the security and other criteria that you just mentioned? What's the technical enablers? Is it just good software. Is it architecture? Help us understand that. >> Sure. You brought up two aspects. You mentioned user experience, and then you mentioned from a developer standpoint, what does it take? And I'd like to address the two separately. They are very tightly related, but I'd like to address them separately. Just focusing on the easier of the two when it comes to user experience, what are the factors that impact user experience? You're talking about reliability of service. Always on, always available applications. It doesn't matter where the data is coming from. Whether the data is coming from my device, it's sourced from an on-prem data center, or if it is from the edge of the cloud, it's from a central cloud data center, from an end-user perspective, all they care about is that their application is available. The next is, of course, responsiveness. Users are getting increasingly impatient. Do you want to reduce wait times to service? You want something which is extremely fast. They're looking for immersive applications or immersive experiences, AR, VR, mixed reality use cases. Then something which is very critical, and what you just touched upon, is this sort of seamless experience. Like this omnichannel, as we talk about in the context of retail kind of experience, Or what I like to refer to as park and pick up reference. You park, you start your application, running your application, you start a transaction on one device, you park it, pick it up on another device. Or in case of retail, you walk into a store, you pick it up from there. So, there's a park and pick up. Seamless mobility of data is extremely critical. In the context of a database, when we talk about responsiveness, two key, the KPIs are latency, bandwidth. And latency is really the round trip time from the time it takes to make a request for data, and the response comes back. The factors that impact latency are, of course, the type of the network itself, but also the proximity of the data source to the point of consumption. And so the more number of hubs that the data packets have to take to reach from the source to its destination, then you're going to incur a lot of latency. And when it comes to bandwidth, we are talking about the capacity of the network. How much data can be shot through the pipe? And, of course, when edge computing, large number of clients. I talked about scale, the volume of devices. And when you're talking about all of them concurrently connected, then you're going to have network congestion which impacts bandwidth which, in turn, impacts performance. And so when it comes to how do you architect a solution for that, if you completely remove the reliance on network to the extent possible, then you get the highest guarantees when it comes to responsiveness, availability, reliability. Because your application is always going to be on. In order to do that, if you have the database and the data processing components co-located with the application that needs it, that would give you the best experience. But, of course, you want to bring it as close. A lot of times, it's not possible to end with that data within your application itself. And that's where you have options of your an on-prem data center, the edge of the cloud, max end and so on. So the closer you bring the data, you're going to get the better experience. Now, that's all great. But then when it comes to something to achieve a vision of Supercloud, when we talked about, "Hey, one way from a developer standpoint, I have one API to set up this connection to a server, but then behind the scenes, my data could be resident anywhere." How do you achieve something like that? And so, a critical aspect of the solution is data synchronization. I talked about data storage as a database, data storage database, that's a critical aspect of what database is really where the data is persisted, data processing, the APIs to access and query the data. But another really critical aspect of distributing a database is the data synchronization technology. And so once all the islands of data, whether it is on the device, whether it's an on-prem data center, whether it's the edge of the cloud, or whether it is a regional data center, once all those databases are kept in sync, then it's a question of when connectivity to one of those data centers goes down, then there needs to be a seamless switch to another data center. And today, at least when it comes to Couchbase, a lot of our customers do employ global load balancers which can automatically detect. So, from a perspective of an application, it's just one URL end point. But then when one of those services goes down or data centers goes down, we have active failover and standby. And so the load balance automatically redirects all the traffic to the backup data center. And of course, for that to happen, those two data centers need to be in sync. And that's critical. Did that answer your question? >> Yeah, let me jump in here. Thank you again for that. I want to unpack some of those, and I want use the example of Couchbase Light, which, as the name implies, a mobile version of Couchbase. I'm interested in a number of things that you said. You talked about, in some cases, you want to get data from the most proximate location. Is there a some kind of metadata intelligence that you have access to? I'm interested in how you do the synchronization. How do you deal with conflict resolution and recovery if something goes wrong? You're talking about distributed database challenges. How do you approach all that? >> Wow, great question. And probably one that I could occupy the entire session for, but I'll try and keep it brief and try and answer most of the points that you touched upon. So, we talked about distributed database and data sync. But here's the other challenge. A lot of these distributed locations can actually be disconnected. So, we've just exacerbated this whole notion of data sync. And that's what we call offline first, not just we call, what is typically referred to as offline first sync. But the ability for an application to run in a completely disconnected mode, but then when there is network connectivity, the data is synced back to the backend data servers. In order for this to happen, you need a sync protocol (indistinct). Since you asked in the context of Couchbase, our sync protocol, it's a web sockets, extremely lightweight data synchronization protocol that's resilient to network disruption. So, what this means is I could have hundreds of thousands of clients that are connected to a data center, and they could be at various stages of disconnect. And you have a field application, and then you are veering in and out of pockets of network connectivity, so network is disrupted, and then network connectivity is restored. Our sync protocol has got a built-in checkpoint mechanism that allows the two replicating points to have a handshake of what is the previous sync point, and only data from that previous sync point is sent to that specific client. And in order to achieve that you mentioned Couchbase Light, which is, of course, our embedded database for mobile, desktop and any embedded platform. But the one that handles the data synchronization is our Sync Gateway. So, we got a component, Sync Gateway, that sits with our Couchbase server, and that's responsible for securely syncing the data and implementing this protocol with Couchbase Light. You talked about conflict resolution. And it's great that you mentioned that. Because when it comes to data sync, a lot of times folks think, "Oh well, how hard can that be?" I mean, you request for some data, and you pull down the a data, and that's great. And that's the happy path. When all of the clients are connected, when there is reliable network connectivity, that's great. But we are, of course, talking about unreliable network connectivity and resiliency to network disruptions. And also the fact that you have lots of concurrently connected clients, all of them potentially updating the same piece of data. That's when you have a conflict, When two or more clients are updating the same, clients or writers. You could have the writes coming in from the clients. You could have the writes coming in from the backend systems. Either way, multiple writers do the same piece of data. That's when you have conflicts. Now, when it comes to, so, a little bit to explain how conflict resolution is handled within our data sync protocol in Couchbase, it would help to understand a little bit about what kind of database we are, how is data itself stored within our database. So, Couchbase Light is a NoSql JSON document store, which means everything is stored as JSON documents. And so every time there is a write, an update to a document, let's say you start with an initial version of the document, the document is created. Every time there is a mutation to a document, you have a new revision to that document. So, as you build in more rights or more mutations to that document, you build out what's called a revision tree. And so when does a conflict happen? Conflict happens when there is a branch in the tree. So, you've got two writers, writing to the same revision, then you get a branch, and that's what is a conflict. We have a way of detecting those conflicts automatically. That's conflict detection. So, now we know there's a conflict, but we have to resolve it. And within Couchbase, you have two options. You don't have to do anything about it. The system has built-in automatic conflict resolution heuristics built in. So, it's going to check, pick a winning revision. And so we use a bunch of criteria, and we pick a winning revision. So, if two writers are updating the same revision of the document, version of the document, we pick a winner. But then that seemed to work from our experience, 80% of the use cases. But then for the remaining 20%, applications would like to have more control over how the winner of the conflict is picked. And for that, applications can implement a custom conflict resolver. So, we'll automatically detect the conflicting revisions and send these conflicting revisions over to the application via a callback, and the application has access to the entire document body of the two revisions and can use whatever criteria needs to merge >> So, that's policy based in that example? >> Yes. >> Yeah, yeah, okay. >> So you can have user policy based, or you can have the automatic heuristics. >> Okay, I got to wrap because we're out of time, but I want to run this scenario by you. One of the risks to the Supercloud Nirvana that we always talk about is this notion of a new architecture emerging at the edge, far edge really, 'cause they're highly-distributed environments. They're low power, tons of data. And this idea of AI inferencing at the edge, a lot of the AI today is done in modeling in the cloud. You think about ARM processors in these new low-cost devices and massive processing power eventually overwhelming the economics. And then that's seeping back into the enterprise and disrupting it. Now, you still get the problem of federated governance and security, and that's probably going to be more centralized slash federated. But, in one minute, do you see that AI inferencing real-time taking off at the edge? Where is that on the S-curve? >> Oh, absolutely right. When it comes to IoT applications, it's all about massive volumes of data generated at the edge. You talked about the economics doesn't add up. Now you need to actually, the data needs to be actioned at some point. And if you have to transfer all of that over the internet for analysis, the responsiveness, you're going to lose that. You're not going to get that real-time responsiveness and availability. The edge is the perfect location. And a lot of this data is temporal in nature. So, you don't want that to be sent back to the cloud for long-term persistence, but instead you want that to be actioned close as possible to the source itself. And when you talk about, there are, of course, the really small microcontrollers and so on. Even there, you can actually have some local processing done, like tiny ML models, but then mobile devices, when you talk about those, as you're very well aware, these are extremely capable. They're capable of running neural, they have neural network processors. And so they can do a lot of processing locally itself. But then when you want to have an aggregated view within the edge, you want to process that data in an IoT gateway and only send the aggregated data back to the cloud for long-term analytics and persistence. >> Yeah, this is something we're watching, and I think could be highly disruptive, and it's hard to predict. Priya, I got to go. Thanks so much for coming on the "theCube." Really appreciate your time. >> Yeah, thank you. >> All right, you're watching "Supercloud 22." We'll be right back right after this short break. (upbeat music)

>> Welcome to the Cubes coverage of Couchbase connect online 2021. I'm Lisa Martin. I have a first timer here on the cube Priya Rajgopal, the director of product management from Couchbase joins me next. Priya, welcome to the program. >> Thank you, Lisa. Thanks for having me here and glad to be here. First timer. So really excited. >> Yeah. Well, we'll make sure that you're going to have fun. We're going to talk about edge computing and what I'd love to get is your perspectives on what's going on and the evolution in the last 18 months. I'm sure so much has changed, but talk to me about edge computing what's going on >> Sure. >> Sure. there's 6 lot of literature on there and different there's a lot of literature on there and different interpretations and the way we see it at Couchbase, it's a distributed computing paradigm, that brings compute and storage to the edge. And what is the edge? The edge is the location where data is generated or consumed. And so the edge, again, the taxonomy is varied, but it's really a continuum. So it's not a thing, right? So it's a location. So it could be a single device or it could actually be a data center. And so it's getting a lot of traction with the proliferation of a lot of applications around AR, VR, IOT, and mobile devices and mobile applications. Because it delivers on the promise of ultra low latency access to data because you know, the edges where the data is generated and consumed, data privacy, governance, residency to network disruptions, low bandwidth usage. So to your question on how does mobile fit into the space of edge computing? In my view, mobile application, mobile devices are a classic example of edge computing because think about mobile devices, right, they're generating data, they're processing data, applications are processing data right there on the devices. You can store data in offline mode on those devices. So it is a classic edge device. And of course, the data doesn't have to be generated on the device itself. There's mobile applications could sort of be gateways to other external like variables for instance, and other IOT devices, which can connect to these mobile applications. And these mobile applications could process that data. >> Got it. So thank you for sharing Couchbase's definition. And it's a good point to do that as so many times, there's so many different terms and solutions and technologies that can be interpreted and explained many different ways. Let's now go through Couchbase's role in edge computing. Help the audience understand where you fit into that. >> Sure. So if you recap the definition, right? edge computing is all about storage and compute to the edge. So clearly a database has a key role to play in this model, Right? Or in this paradigm, because when you think about it, a classic application architecture, you've got three tiers, you've got an application tier, it includes your business logic, some of the UI elements, that's optional. You've got your database tier, which drives the application, Does the obligation needs data? It's driven by the database tier. And then you've got the infrastructure tier, that includes your network storage compute. Now, when you're talking about an edge computing architecture, you're talking about distributing all these three tiers. Your application tier, a database tier, as well as your infrastructure tier and a Couchbase is a fully distributed, no sequel database solution. So it fits in right into this paradigm of edge computing. Now, when we are talking about distributing our storage, that's just one aspect of it, right? You have to distribute it to these edge devices. You may have to distribute it to edge data centers, You need to be able to sync or move data between these You need to be able to sync or move data between these distributed cloud environments, right? So data synchronization is a key component of the tier of of edge computing architectures. And then finally, there's data management. That's all about enforcement of policies, when it comes to data privacy, you know what, data needs to be resident at the edge, what data needs to be filtered, what needs to be aggregated? what data needs to be filtered, what needs to be aggregated? So you need a solution that can provide those hooks that allows you to enforce those policies. So, a database like Couchbase has a critical role to play solution that can be deployed in the cloud, or it can be deployed at the edge. And again, or it can be deployed at the edge. And again, the edge could be a data center or it could be a device. So what about device? We have an embedded database solution for mobile desktop and embedded platforms. And then of course, data movement, comprehensive data synchronization technology. comprehensive data synchronization technology. >> Let's go through specifically some of the database capabilities that are required for businesses in any industry to be successful in edge computing. >> Sure, absolutely. Right. to do sort of reiterate or reinforce the three concepts, right? Data storage, data movement, data management, right? And Couchbase technology because that the stack consists of couchbase server, our flagship fully distributed, no sequel data platform. It can be containerized. It can be deployed in any public or private cloud. It could be deployed at the edge cloud. And then you've a Couchbase lite. Again, no sequel embedded database full featured, right? Anything that you can do with a standalone database, you can do it with the embedded database. Now you can embed that within your mobile applications, within your other embedded applications or desktop applications. And that's great, right? That's the data storage part of it. And, and that's one part of it, but what about the data movement? And that's where you got a data synchronization technology where we facilitate a high throughput, high performance, highly scalable data synchronization, between the edge and the cloud. And of course, as I mentioned, data management is a critical aspect of all this, right? And so the synchronization technology has got components that allow you to set filters, access control policies. And there's a lot of hooks when it comes to data governance. So for instance, if an edge goes out of commission, or if there's a security breach, for instance, you want to isolate the edge, you can do that. The data that was previously synchronized to that edge, you want to be able to poach that data. So we have options the automatically poach the data, if the device is no longer in the hands of the right recipient for instance. those are the critical aspects. Of course, the overarching theme is security, right? And, that goes hand-in-hand with encryption of the data at rest, encryption of the data in motion, then authentication, authorization, access control. >> Security is even more important in given the events of the last 18 months where we've seen a massive rise in ransom, where we've seen a huge rise in DDoS attacks. Let's, double-click more on the security aspect of what Couchbase is delivering. >> Sure, absolutely. So when it comes to security of data at rest, right, even when the Couchbase lite, which is our embedded device, your entire database is encrypted AES-256 data encryption, and then data, when it leaves the device through our data synchronization protocol, everything is encrypted. And of course, when it goes to a sync gateway, the sync gateway is sort of, as I mentioned, the middle tier component, that is responsible for data synchronization between the embedded devices and Couchbase server. That entity is responsible for enforcement of access control policies. So you are guaranteed that only users who should have access to those documents or data are granted access to that. And in fact, we are NoSQL Json database. So which means, everything is modeled in the form of documents, Json documents. And so when we're talking about read, write access control, read access is at the granularity of a document, and write access can be enforced at the granularity of a property within the document. So you may have access to an entire document, but you may only be allowed to update a certain property within the document. So, as I mentioned, when it comes to distributed computing architectures like edge computing, security is even more paramount, right? You have devices going offline, coming back online and, you might have a breach point at one edge environment, whether it is a data center or an edge device, you need to be able to ensure that you have isolated all the other edge components from that breach. And as I mentioned, when it comes to data governance and so on, data retention, for instance, even if it is not a security breach, let's say you do have, for some reason, the owner of a device should no longer have access to that content. You know, their role has changed, they have transitioned to a different company for instance. Then you will have a way of automatically purging all that data that was previously synchronized to the user's device. >> Got it. Okay. Let's continue talking about the events of the last year and a half. Because we saw this massive scatter, 18 months ago of an explosion at the edge when a lot of people went from the office to this work from home, work from any anywhere environment in which we're still in. So how has the pandemic and the events that related to it changed mobile apps and edge computing and what are some of the new requirements that customers have? >> Sure. Well, as you rightly said, right? In fact, if anything, the relevance of mobile devices and applications has just grown in significance through the pandemic. And it's kind of interesting, there are some surveys that have suggested that through the pandemic people have been using their mobile devices as their primary communication device for accessing the internet. And it's kind of interesting because you think, well, everyone is cooped up in their homes. They probably will have access to other forms of data consumption, but no, it's mobile devices. That's what they have primarily been using. So with that, there is also a new range of use cases and applications, which are driven in large part by the events of the pandemic. But I think that's just made things much more efficient. Customer satisfaction, user experience is paramount, is number one. And I think a lot of that is here to stay even following or post pandemic because it's just made things a lot more efficient. And we've seen that through different industries, right? Healthcare, there was always telemedicine medicine, but now for non-essential care, it's always telemedicine, Of course, specific to the pandemic. there was the, tracking, the contact tracing application, right? That's enabled through technologies like Bluetooth and GPS, so they track the whereabouts of infected persons. But then even if you arrive at your doctor's office, right, you wait in your car and you get notified when the doctor's ready to check you in. And then retail sector, E-commerce right? Of course everything was going online, but everything is overwhelming People are shopping online through their mobile devices, than the traditional web based applications. And you order on your phone, you pick up at the store, right? So curbside pickup, you pull into the store, the store clerk is notified of your arrival. They come out to the curb with your order. And here's the interesting bit, you know, it's kind of intuitive that it's going to be e-commerce applications. They got a huge boost through the pandemic. But interestingly, even the experience when it comes to retail in store, that's undergone a transformation because it was all about how do we make the process very efficient. So customers are in and out of the store really quick, right? there was the reason for that. But now we can translate to making the whole shopping experience much more easy. So you walk into the store, you meet a sales associate who can bring up information about the catalog inventory right there on the iPad. And so if you have any questions, whether it's something is available in the store or an access for you're looking for, they can give answers to you immediately. Right? And of course there are companies like Walmart, they have been rolling out applications. Mobile scan and go sort of applications, which is all about, you know, you scan items as you walk through the aisle, do a self checkout, totally contact-less experience. And, the list goes on, right? We talked about healthcare retail, same thing in, in a restaurant, right? A curbside that delivery and pickup, you can now track your delivery order because now it's just a huge surge in order deliveries. And then the same pickup concept, curbside pickup concept, you arrive in your car, the kitchen is notified of your arrival and they come out with your order, very streamlined drive-through. You've got people now coming to your car, taking the order, right there from the car on their tablets, that synced in real time to the backend kitchen, your order. And you get notified when your order is ready. So I think all this is about making things a lot more efficient. It's about customer experience, and edge computing has a big role to play in that. And so I think, if anything is just propelled the growth of mobile applications and use cases. >> Yeah. That that propulsion is something that we've been hearing a lot about the acceleration in the last year and a half. You did a great job there of painting a picture of some of the positives that come out of this accelerating the efficiencies that we all as consumers and in our business life expect to have. And this explosion at the edge that's really become even more of a lifeline for millions and millions and billions of people globally. I got to ask you that from a connectivity perspective, that's another area where we had this expectation as again, consumers or in our business lives we have connectivity. Where does all that talk about 5G; What does 5G fit into edge computing? >> Sure. That's a good question. Because 5G and edge computing sort of go hand in hand so much so that they are being used synonymously in some cases and that's inaccurate. Okay? So because every time people talk about edge computing, there are folks talking about 5G in the same breath, right? But really 5G, as we all know, is a cellular next generation cellular technology, promises, ultra low latency, very high bandwidth. Now we talked about this huge surge, right in mobile applications and new sort of use cases where a lot of the data is generated at the edge. IOT applications are just data intensive applications, right gaming apps and so on. And all of these applications, they demand ultra low latency, right? And they're generating a lot of this data and all that data needs to be processed in real time. So if you have to send all of that data back to the cloud, and then you get the responses, that's a really bad experience. So that's what 5G is here to solve, right? I mean, it's like low latency, high bandwidth, high concurrency. Now that's all great. But then the coverage of 5G, it terminates at the edge of the mobile operator network. So you have all these massive influx of devices generating all that data. And all that stuff is transmitted under a very low latency conditions over a 5G network. But then if all that data from the mobile operator network has to be back hauled to the internet, to the backend servers, then you kind of defeat the whole purpose of ultra low latency applications. So that's where edge computing comes into play because edge computing is really an architecture, right? It's a distributed architecture. So now what mobile operators are doing is deploying what they refer to as NDCs, but it's effectively micro data centers at the edge of the mobile operator network. So you have all this data coming in over the 5G network. Great. They get analyzed, they get processed locally at the edge of the mobile operator network and you get real-time responses. And of course, as needed that data in aggregated or filter form goes back to the cloud. And so that's where the two relate. So in my view, I think edge computing architectures are important to deliver on the promise of 5G, but 5G has propelled the relevance or importance of edge computing as a solution, as a deployment architecture. So very interrelated. >> Very interrelated, very symbiotic. And of course the need for real time data real-time analytics in every industry became very prominent in the last year and a half. We had this expectation that we're going to be able to understand things in real time. And that's often a huge differentiator for businesses. We're out of time, but I want to ask you one more question Priya, and that is where can customers go to get started with Couchbase? >> Oh, absolutely. So Couchbase servers and gateway, you can deploy that, it's available as software. You can download it from our website. Couchbased lite is available for all your mobile applications. So it is available as a download, but you also have the classic package management systems through which you can download Couchbase Lite. And then of course, as I said, you can deploy this standalone, but you can also deploy it in the cloud. So we have marketplace offerings for both Couchbase server and sync gateway. So if you want to deploy it on AWS, as your Google, you can do that as well. >> Excellent. Priya, thank you so much for joining me on the program, talking about Couchbase the evolution, the changes, the opportunities with edge computing and mobile and how Couchbase is involved. I appreciate your time. >> Thank you very much. And thanks for having me. >> For Priya Rajgopal, I'm Lisa Martin. You're watching the Cubes coverage of Couchbase connect, online 2021.

>>From around the globe. It's the cube with digital coverage of an AI ops virtual forum brought to you by Broadcom. >>Welcome to this preview of Broadcom's AI ops virtual forum on your host, Lisa Martin. And joining me to give you a sneak peek of this event. That's on December 3rd is streaming of Boston Rajagopal or Raj, the head of AI ops at Broadcom. Raj. This has meant it's coming up in a couple of weeks. Excited, >>Good to be here. I am excited, Lisa, uh, you know, um, customers are poised for growth, uh, in 2021. And, uh, they, uh, we believe they all, they will also come out of the pandemic to grow their business and serve, uh, their customers. Uh, you know, well, they have two key challenges. How do you grow at the same time, operate with efficiency, right? These two challenges are decision-makers are struggling with every day at scale. That is why they do digital transformation at scale. And our key influencers like it, operators and SRE personas are helping our decision-makers in our customers to drive the efficiency. They are trying to, uh, focus on converting outputs to outcomes. That's what AI ops is all about. And you're going to hear from us. >>Yeah. And we've got a panel of experts here. Rich lane, senior research analyst for Forrester is going to be joining us as well as nastier, the global product management at Verizon. And of course, Raj, you're going to be hearing some of the latest trends for AI ops. Why now is the time Raj, what are some of the key takeaways that you think those key influencers and those decision makers are going to walk away from this event? >>So the, you know, our decision makers and key influencers have a single question in mind when they deal with enterprise large enterprise scenarios, the questions that they get asked by their skill level execs are, are you ready? Are you ready? When remote work is the norm, are you ready when you have to optimize your investments? And are you ready when you have to accelerate your transformation at scale to operate as a digital enterprise, all of this requires them to think and act differently from people process technology. And how do you bring all of this together? Under the ages of what we call AI ops is what they're going to learn about. >>Another thing too, is you're going to hear the latest industry trends on AI ops from Raj and the panel of experts that mentioned a minute ago, how organizations like yours are finding value from AI ops and something that Raj talked about a minute ago is understanding why now is the time to be ready for AI ops. So Raj and I look forward to you joining us along with our other panelists, December 3rd, register for the Broadcom AI ops virtual forum today.

>>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 Rajagopal, the head of products and strategy at Broadcom Raj Welcome. >>Good to be 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, etcetera. But why is it so important to organizations as they head into 2021 tell us how ai ops as a platform can help? >>Yeah. Thank you. First off, I wanna I wanna welcome our viewers back and I'm very excited Toe share more info on this topic. You know, here's what we believe. As we work with large organizations, we see all our organizations are poised toe get out off the pandemic and look for growth for their own business and helping customers get through this tough time. So fiscal year 2021 we believe, is going to be a combination off, you know, resiliency and agility at the at the same time. So operational excellence is critical because the business has become mawr digital, right? There are gonna be three things that are gonna be more sticky. You know, remote work is gonna be more sticky. Um, cost savings and efficiency is going to be an imperative for organizations. And the continued acceleration of digital transformation off enterprises at scale is going to be in reality. So when you put all these three things together as a team, that is, you know, that's working behind the scenes toe help the businesses succeed. Operational excellence is going to be make or break for organizations. >>Russia 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 foremost AI ops means many things to many, many folks. So let's take a moment to simply define it. The way we defined AI ops is it's a system off intelligence human augmented system that brings together full visibility across app, infra and network elements that brings together despite of data sources on provides actionable intelligence and uniquely offers intelligent automation. Now the technology many folks draw is the self driving car, right? I mean, we are in the world of Tesla's, but, you know, but self driving data center is is too far away, right? Autonomous systems are still far away. However, you know, application off the I M l techniques toe help deal with volume velocity, veracity of information. Eyes is critical. So that's how we look at AI ops and some of the key capabilities that we that we that we work with our customers to help them around 48 years. Right? First one is eyes and years. What we call full stack, observe ability. If you do not know what is happening in your systems, you know that that serve up your business services, it's gonna be pretty hard to do anything in terms of responsiveness, right? So from stack of their ability, the second piece is what we call actionable insights. So when you have disparaged data sources, tool sprawls, data coming at you from, you know from database systems, I T systems, customer management systems, ticketing systems, how do you find the needle from the haystack? And how do you respond rapidly from a myriad off problems? A sea off read The third area is what we call intelligent automation. Well, Identifying the problem toe Act on is important and then acting on. Automating that and creating a recommendation system where you know you could be proactive about that 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 you know you should look for in an AI ops system. And that's what we offer us. >>Alright, Russia. There's four key capabilities that businesses need to be looking for. I'm wondering how those help to align business and i t. It's again like operational excellence. It's something that we talk about a lot is the alignment of business and I t a lot more challenging. Is your something done right? But I want you to explain how can a iob help with that alignment and align? I t outputs to business outcomes. >>So you know, one of the things I'm going to say something that this, that is that is simple. But it's harder. Alignment is not on systems. Alignment is with people, right? So when people align when organizations aligned, when cultures align, dramatic things can happen. So in the context off AI ops, we see when when saris aligned with the develops engineers and information architects. And, uh, you know, I t operators, you know, they enable organizations to reduce the gap between intent and outcome or output an outcome that said, you know, these personas need mechanisms toe help them better align, right, help them Better visual. I see the you know what we call single source of truth, right? So there are four key things that I wanna call out when we work with large enterprises. We find that customer journey alignment with the you know what we call I T systems is critical. So how do you understand your business imperatives and your customer journey goals? Whether it is card toe purchase or whether it is, you know, Bill shock scenarios and swan alignment on customer journey to your I T 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 out. It's scenarios and so on. So that's the second area off alignment. The third area off alignment is how can you measure business impact driven services right? There are several services that an organization off course as the 19 system. Some services are more critical to the business. Well, then, others and thes 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 that's the 3rd 80 off 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 off, you know, operating digitally. You want to use customer experience and, you know single business outcome as 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, actually. >>So where is one of the things that you said there is that it's imperative for the business toe. 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, out they just talk, you know, go to resiliency off a system, right? And they also goto have, you know, agility off the same system. You know, if you are a customer and if you're ripping up your mobile happened, it takes more than you know, three milliseconds. You know, you're probably losing that customer, right? So I would just mean different things, you know? And there's an interesting website called don't detector dot com that actually tracks all the outages of publicly available services, whether it's your bank or your, you know, telecom service or mobile service and so on and so forth. In fact, the key question around outages for from from you know, executives are the question of Are you ready? Right? Are you ready to respond to the needs off your customers and your business? Are you ready toe rapidly to solve an issue that is impacting customer experience and therefore satisfaction. Are you creating a digital trust system where customers can be, You know, you know, customers can feel that their information is secure when they transact with you. All of these getting toe the notion of resiliency and outages. Now, you know, one of the things that I often you know work with customers around, you know, that we find is the radius off. 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 that problem. That radius of impact is important. That's where you know you have to bring again. People process technology together to solve that. And the key thing is, you need a system of intelligence that can aid you your teams, you know, look at the same set of parameters so that you can respond faster. That's the key here. >>But as we look at digital transformation at scale, Raj, how does a apps help influence that? >>You know, I'm gonna 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 off that to your digital supply chain right are the are the are the key factors that differentiate vintners in the in their digital transformation game. Really? What we have seen with with winners is they operate very differently. Like, for example, you know, 19 assures its digital business outcomes by shoes per second, right apple buy iPhones per per minute. Tesla by model threes per month. Are you getting getting it right? I mean, you wanna have, ah, clear business outcome, which is a measure off your business. In effect, I mean, easy right, which which my daughter use. And I use very well, right? You know, they measured 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 align everything else because you know what these measures you know, 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 key thing is that single business outcome has a starburst effect inside the I T. Organization that touches a single money movement from checking account to savings account can touch about 75 disparage systems internally. Right? So those think about right. I mean, all we're doing is moving money from checking accounts savings account. Now that goats in tow, a IittIe production system, there are several applications. There is a database there is there are infrastructures, their load balancers, that our webs, 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 on. Do you know which may be on your mainframes what we call mobile toe mainframe scenario, right? And we're 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 challenge that I t operation teams phase. And when you have millions of customers transacting right? Certainly this challenge cannot be, you know, managed by, you know, human beings alone. So therefore, you need a system off intelligence that augments human intelligence and acts as you, you know, your your eyes and ears in of a toe point pinpoint. Their problems are right. So digital transformation at scale really requires a well thought out ai ops system a platform and open extensible platform that you know, that is heterogeneous in nature because their stools problems in organizations. There is, uh, you know, a lot of data bases in systems. There are million's off, you know, customers and hundreds off partners and vendors, you know, making up that digital supply chain. So, you know, AI ops is at the center off, enabling an organization achieved digital up, you know, transformation at scale. Last but not least, you need continuous feedback loop. Continuous feedback loop is the ability for a production system toe. Inform your develops teams your finance teams, your customer experience teams your cost Modeling teams about what is going on say that they can so that they can reduce the intent outcome gap. All of this need to come together. What we call biz obs for ideal abs. >>That was a great example of how you talked about the Starburst effect. 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 air ops could be transformative and enable that scale. Raj, it's always a pleasure to talk with you. Thanks for joining me today. Yeah, >>great to be here >>and we'll be right back with our next segment.

>>from around the globe. It's the Cube with digital coverage of AI ops. Virtual Forum Brought to You by Broadcom >>Welcome to this preview of Broadcom's AI Ops Virtual Forum on your host Lisa Martin, and joining me to give you a sneak peek of this event that's on December 3rd is Srinivasan, Rajagopal or Raj, the head of a I Ops at Broadcom Raj, this event is coming up in a couple of weeks. Excited. >>Good to be here. I am excited, Lisa. You know, um, customers are poised for growth in 2021 and, uh, they are we believe they all. They will also come out off the pandemic toe, grow their business and serve their customers, you know? Well, they have to key challenges. How do you grow at the same time, operate with efficiency, right. These two challenges our decision makers are struggling with every day at scale. That is why they do digital transformation at scale. And our key influencers like I t operators and SRE personas are helping our decision makers in our customers to drive the efficiency they are trying toe focus on converting outputs to outcomes. That's what the eye ops is all about and you're gonna hear it from us. >>Yeah, We've got a panel of experts here. Rich Lane, senior research analyst for Forrester, is going to be joining us as well as Guzman nastier the global product management at Verizon. And, of course, Raj, you're gonna be hearing some of the latest trends for AI ops. Why, now is the time, Raj, What are some of the key takeaways that you think those key influencers and those decision makers are gonna walk away from this event empowered with >>So the You know, our decision makers and a key influencers have a single question in mind when they deal with enterprise large enterprise scenarios, the questions that they get asked by their C level execs are Are you ready? Are you ready when remote work is the norm? Are you ready when you have to optimize your investments and are you ready when you have to accelerate your transformation at scale toe operate as a digital enterprise? All of this requires them to think and act differently from people process technology. And how do you bring all of this together under the ages off what we call a I ops is what they're gonna learn about. >>Another thing, too, is you're going to hear the latest industry trends on AI ops from Raj and the panel of experts that we mentioned a minute ago. How organizations like yours are finding value from a I ops and something that Raj talked about a minute ago is understanding why Now is the time to be ready for I also Raj and I look forward to you joining us along with our other Panelists. December 3rd register for the Broadcom AI Ops Virtual form today.

>>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..