>>Hi today, I'm going to talk about network analytics and what that means for, for telecommunications as we go forward. Um, thinking about, uh, 5g, what the impact that's likely to have on, on network analytics and the data requirement, not just to run the network and to understand the network a little bit better. Um, but also to, to inform the rest of the operation of the telecommunications business. Um, so as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure, uh, to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, uh, in the, in the initial, uh, telecommunications networks were designed, there were management systems that were built in, um, eventually fault management systems, uh, assurance systems, provisioning systems, and so on were abstracted away. >>So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whatever it happened to be. You could just look at your fault management system, understand where false, what happened as we got into the last sort of 10, 15 years or so. Telecommunication service providers become became more sophisticated in terms of their approach to data analytics and specifically network analytics, and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a, as a bit of an independent function was born and over time, more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. >>As all telecommunications carriers are migrating as many it workloads as possible, um, to the cloud. So what are the things that are happening as we migrate to the cloud that drive, uh, uh, enhancements in use cases and enhancements and scale, uh, in telecommunications network analytics? Well, 5g is the big thing, right? So 5g, uh, it's not just another G in that sense. I mean, in some cases, in some senses, it is 5g means greater bandwidth, lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5g is really about the enterprise and enterprise services. Transformation, 5g is more secure, kind of a network, but 5g is also a more pervasive network 5g, a fundamentally different network topology than previous generations. So there's going to be more masts and that means that you can have more pervasive connectivity. >>Uh, so things like IOT and edge applications, autonomous cars, smart cities, these kinds of things, um, are all much better served because you've got more masks that of course means that you're going to have a lot more data as well. And we'll get to that. The second piece is immersive digital services. So with more masks, with more connectivity, with lower latency with higher man, the potential, uh, is, is, is, is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential. Um, but we, we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5g is phenomenal. Um, it certainly means that we're going to have a lot more, uh, edge devices, um, uh, and that again is going to lead to an increase in the amount of data that we have available. >>And then the idea of pervasive connectivity when it comes to smart, smart cities, uh, autonomous, autonomous currents, um, uh, integrated traffic management systems, um, all of this kind of stuff, those of those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent, uh, connection to the network. Um, again, that's going to drive, um, um, uh, more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in, in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first is more physical elements. So we said already that 5g networks are going to have a different apology. 5g networks will have more devices, more and more masks. >>Um, and so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood when, so that we can, um, have a better understanding as to why things happened the way they do, why the network behaves in which they do in, in, in, in ways that it does and why devices that are connected to the network. And ultimately of course, consumers, whether they be enterprises or retail customers, um, behave in the way they do in relation to their interaction within our edge nodes and devices, we're going to have a, uh, an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and, uh, and, and sensors that are hanging off the edge of the network, whether it's to make buildings smarter car smarter, or people smarter, um, in, in terms of having the, the, the measurements and the connectivity and all that sort of stuff. >>So the numbers of devices on the agent beyond the age, um, are going to be phenomenal. One of the things that we've been trying to with as an industry over the last few years is where does the telco network end, and where does the enterprise, or even the consumer network begin. You used to be very clear that, you know, the telco network ended at the router. Um, but now it's not, it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, um, you start to see end to end network services being deployed. Um, uh, and so are they being those services in some instances are being managed by the service provider themselves, and in some cases by the enterprise client, um, again, the line between where the telco network ends and where the enterprise or the consumer network begins, uh, is not clear. >>Uh, so, so those edge, the, the, the proliferation of devices at the age, um, uh, in terms of, um, you know, what those devices are, what the data yield is and what the policies are, their need to govern those devices, um, in terms of security and privacy, things like that, um, that's all going to be really, really important virtualized services. We just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of it operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure, and that has two major impacts. First of all, it means that you've got the agility and all of the scale, um, uh, benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff. But arguably more importantly for the telco, it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. >>So if you're selling to a government department, for example, is looking to stand up a system for certification of, of, you know, export certification, something like that. Um, you can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could sentence, you could offer them in theory, an entire end-to-end communications network, um, and with 5g network slicing, they can even have their own little piece of the 5g bandwidth that's been allocated against the carrier, um, uh, and, and have a complete end to end environment. So the kinds of services that can be offered by telcos, um, given virtualize network infrastructure, uh, are, are many and varied. And it's a, it's a, it's a, um, uh, an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. >>That means the amount of data that we're getting on, uh, informing us as to how those network elements are behaving, how they're performing, um, uh, is, is, is going to go up as well. And then finally, AI complexity. So on the demand side, um, while historically, uh, um, network analytics, big data, uh, has been, has been driven by, um, returns in terms of data monetization, uh, whether that's through cost avoidance, um, or service assurance, uh, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry, the potential for autonomous operations, uh, is extremely attractive. And so understanding how the end-to-end telecommunication service delivering delivery infrastructure works, uh, is essential, uh, as a training ground for AI models that can help to automate a huge amount of telecommunications operating, um, processes. So the AI demand for data is just going through the roof. >>And so all of these things combined to mean big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily, um, and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the, in the data center. Now you're looking at, um, uh, migrating a workload, but also expanding the use cases in that work and bear in mind, many of those, those are going to need to remain on prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy a regulatory jurisdictional requirements. So let's talk about an example. >>So LGU plus is a Finastra fantastic service provider in Korea. Um, huge growth in that business over the last, uh, over the last 10, 15 years or so. Um, and obviously most people will be familiar with LG, the electronics brand, maybe less so with, uh, with LG plus, but they've been doing phenomenal work. And we're the first, uh, business in the world who launch commercial 5g in 2019. And so a huge milestone that they achieved. And at the same time they deploy the network real-time analytics platform or in rep, uh, from a combination of Cloudera and our partner calmer. Now, um, there were a number of things that were driving, uh, the requirement for it, for the, for the analytics platform at the time. Um, clearly the 5g launch was that was the big thing that they had in mind, but there were other things that re so within the 5g launch, um, uh, they were looking for, for visibility of services, um, and service assurance and service quality. >>So, you know, what services have been launched? How are they being taken up? What are the issues that are arising, where are the faults happening? Um, where are the problems? Because clearly when you launch a new service, but then you want to understand and be on top of the issues as they arise. Um, so that was really, really important. The second piece was, and, you know, this is not a new story to any telco in the world, right. But there are silos in operation. Uh, and so, um, taking advantage of, um, or eliminating redundancies through the process, um, of, of digital transformation, it was really important. And so particular, the two silos between wired and the wireless sides of the business come together so that there would be an integrated network management system, um, for, uh, for LGU plus, as they rolled out 5g. So eliminating redundancy and driving cost savings through the, the integration of the silos is really, really important. >>And that's a process and the people thing every bit, as much as it is a systems and a data thing. So, um, another big driver and the fourth one, you know, we've talked a little bit about some of these things, right? 5g brings huge opportunity for enterprise services, innovation. So industry 4.0 digital experience, these kinds of use cases, um, are very important in the south Korean marketing and in the, um, in the business of LGU plus. And so, uh, um, looking at AI and how can you apply AI to network management? Uh, again, there's a number of use cases, really, really exciting use cases that have gone live now, um, in LG plus since, uh, since we did this initial deployment and they're making fantastic strides there, um, big data analytics for users across LGU plus, right? So it's not just for, um, uh, it's not just for the immediate application of 5g or the support or the 5g network. >>Um, but also for other data analysts and data scientists across the LGU plus business network analytics, while primarily it's primary it's primary use case is around network management, um, LGU plus, or, or network analytics, um, has applications across the entire business, right? So, um, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform, um, it was very important. And then finally, as I mentioned, LG group is much bigger than just LG plus it's because the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019, and still being a part of that was, well, some of them, the problems that they were looking to address. >>Um, so first of all, the integration of wired and wireless data service data sources, and so getting your assurance data sources, your network, data sources, uh, and so on integrated with is really, really important scale was massive for them. Um, you know, they're talking about billions of transactions in under a minute, uh, being processed, um, and hundreds of terabytes per day. So, uh, you know, phenomenal scale, uh, that needed to be available out of the box as it were, um, real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, w to make, make it to meet certain criteria, certain standards, um, customer specific, real time analysis of 5g, particularly for the launch root cause analysis, an AI based prediction on service, uh, anomalies and service service issues was, was, was a core use case. Um, as I talked about already the provision of service of data services across the organization, and then support for 5g, uh, served the business service, uh, impact, uh, was extremely important. >>So it's not just understand well, you know, that you have an outage in a particular network element, but what is the impact on the business of LGU plus, but also what is the impact on the business of the customer, uh, from an outage or an anomaly or a problem on, on, on the network. So being able to answer those kinds of questions really, really important, too. And as I said, between Cloudera and Kamarck, uh, uh, and LGU plus, uh, really themselves an intrinsic part of the solution, um, uh, this is, this is what we, we ended up building. So a big complicated architecture space. I really don't want to go into too much detail here. Um, uh, you can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources, um, you have all of your wireless network information, other data sources. >>This is really important because sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well, southbound and northbound interfaces. So we get our data from the network and so on, um, and network management applications through file interfaces. CAFCA no fire important technologies. And also the RDBMS systems that, uh, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, um, you know, we spoke already about me making network analytics services available across the enterprise. Um, so, uh, you know, uh, having both the file and the API interface available, um, for other systems and other consumers across the enterprise is very important. Um, lots of stuff going on then in the platform itself to petabytes and persistent storage, um, Cloudera HDFS, 300 nodes for the, the raw data storage, um, uh, and then, uh, could do for real time storage for real-time indicator analysis, alarm generation, um, uh, and other real time, um, processes. >>Uh, so there, that was the, the core of the solution, uh, spark processes for ETL key quality indicators and alarming, um, and also a bunch of work done around, um, data preparation, data generation for transferal to, to third party systems, um, through the northbound interfaces, um, uh, Impala, API queries, um, for real-time systems, uh, there on the right hand side, and then, um, a whole bunch of clustering classification, prediction jobs, um, through the, uh, the, the, the, the ML processes, the machine learning processes, uh, again, another key use case, and we've done a bunch of work on that. And, um, I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Um, so this is some pretty cool stuff. Um, and then finally, just the upstream services, some of these there's lots more than, than, than simply these ones, but service assurance is really, really important. So SQM cm and SED grade. So the service quality management customer experience, autonomous controllers, uh, really, really important consumers of, of the, of the real-time analytics platform, uh, and your conventional service assurance, um, functions like faulted performance management. Uh, these things are as much consumers of the information and the network analytics platform as they are providers of data to the network, uh, analytics >>Platform. >>Um, so some of the specific use cases, uh, that, uh, have been, have been stood up and that are delivering value to this day and lots of more episodes, but these are just three that we pulled out. Um, so first of all, um, uh, sort of specific monitoring and customer quality analysis, Karen response. So again, growing from the initial 5g launch and then broadening into broader services, um, understanding where there are the, where there are issues so that when people complaining, when people have an issue, um, that, um, uh, that we can answer the, the concerns of the client, um, in a substantive way, um, uh, AI functions around root cause analysis or understanding why things went wrong when they went wrong. Um, uh, and also making recommendations as to how to avoid those occurrences in the future. Uh, so we know what preventative measures can be taken. Um, and then finally the, uh, the collaboration function across LGU plus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira through file interfaces and other things, and through interface integrations with, uh, with upstream systems. >>So, um, that's kind of the, the, uh, real quick run through of LGU plus the numbers are just stave staggering. Um, you know, we've seen, uh, upwards of a billion transactions in under 40 seconds being, um, uh, being tested. Um, and, and we've gone beyond those thresholds now, already, um, and we're started and, and, and, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So, um, with those things that I mentioned earlier with the proliferation of, of, um, of network infrastructure, uh, in the 5g context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the, uh, the, the, the network analytics platform. So phenomenal scale. Um, this is just one example we work with, with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. >>They use Cloudera in the network, and we're seeing those customers, all migrating legacy cloud platforms now onto CDP onto the Cloudera data platform. Um, they're increasing the, the, the jobs that they do. So it's not just warehousing, not just ingestion ETL, and moving into things like machine learning. Um, and also looking at new data sources from places like NWTF the network data analytics function in 5g, or the management and orchestration layer in, in software defined networks, network, function, virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time growth in, in, in, in the application scope from, as we say, from edge to AI. Um, and so it's, it's really exciting to see how the, the, the, the footprint is growing and how, uh, the applications in telecommunications are really making a difference in, in facilitating, um, network transformation. And that's covering that. That's me covered for today. I hope you found that helpful, um, by all means, please reach out, uh, there's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page, reach out to me directly at Cloudera. I'd love to answer your questions, um, uh, and, uh, and talk to you about how big data is transforming networks, uh, and how network transformation is, is accelerating telcos, uh, throughout >>Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about the legal data, where basically a Silicon valley startup, uh, started in 2014, and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services, and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places, uh, I'd like to call your attention to the, uh, the middle really of the screen there. So here is where we have done some partnership with Cloudera. >>So if you look at that and you can see we're in Holland and Jamaica, and then a lot to throughout Africa as well. Now, the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called a flare, which we'll talk about in a minute below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over 10 up. So flare now, flare is a piece of that legal data IP. The rest is there. And what flare does is that basically pulls in data, integrates it to an event streaming platform. It's, uh, it is the engine behind the data fabric. >>Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it, and we're able to alert are, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a CA picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. >>So that's pretty much for us at legal data, not really to set the context of where we are. So this is a traditional telco environments. So you see the systems of record, you see the cloud, you see OSS and BSS data. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tools that pulls in the data. So it provides even streaming capabilities. It preserves entity states, so that you can go back and look at it state at any time. >>It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like the graph database, where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is succinct to run out of is ongoing, and an offer can be sent to him right on the fly. And that's set up by the business user as opposed to programmers, uh, data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data, scientists like that, uh, and, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. >>Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid tools can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. >>And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So teleco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talked about in a minute monetization. So this helps teleco providers monetize their specific data, but monetize it in. Okay, how to, how do they make money off of it, but also how might you leverage this data to engage with another client? So for instance, in some where it's allowed a DPI is used, and the fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing on the, on the four or 5g. And, uh, the, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Do they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's signs of, and we spoke briefly about Sanchez before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. A lot of those offers to be just basically not taken and, and not, not, uh, audited. Uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the envy and, oh, the challenge this, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system and gather further information about that. All of these tools, again, we're running on top of the Cloudera data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, no increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Okay. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12, uh, opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformations, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer in the business semantics layer again, are, are sitting on top of the Cloudera data platform. You see the Kafka CLU, uh, Q1, the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And, and by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, a use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for 24 million plus that a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at, uh, again, 183%. This is something that we find consistently. And these figures come from our, uh, our actual end client. How do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working at the junction on this, and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>No, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straight away. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jamie, and thank you for your part. Sure. Um, that's a, that's, that's our story for today. Like Jamie says, um, please do flee, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better. So thank you.