>> [Announcer] Live, from Silicon Valley, it's The Cube Covering Mobile World Congress 2017. Brought to you by Intel. >> [John] Welcome back, everyone. We are here live in Palo Alto, California for a special two days of Mobile World Congress. We're on day two of wall to wall coverage from eight a.m. to six p.m. Really breaking down what's happening in studio and going to our reporters and analysts in the field. We'll have Pete Injerich coming up next and we're going to get on the ground analysis from the current analysis, now with global data. But next we have a segment where, I had a chance this morning early in the morning my time top of the morning Tuesday in Barcelona which was hours ago, I had a chance to speak with Caroline Chan and Dan Rodriguez. I wanted to get their opinion on what's happening and I asked Caroline Chan, "What's the biggest story coming out of Mobile World Congress?" This is what she had to say: >> [Caroline Chan] So last year this time, the people coming in asked a lot of questions about 5G technology. Is it real? Can we really pull it off? You know, 3G, 4G, it's a little bit ho-hum. But this year, I would say when I look around, not just in Apple, everybody else is good. I'm also hoping to, people talk about it as a faithful, I went to a panel last night with Orange, and AT&T, and Telefonica. I think the conversation switched from will there be a 5G to solutions. So, I look around in our booth and next door in Verizon there's a lot of cars, autonomous driving. We had network 5G enable smart city, it's in our homes, It becomes from technology to solution, and then in the last discussion about this iteration of 5G, there was an announcement about the 5G in our loan, Whole bunch of talk about acceleration. It's really becoming how can we quickly get out there. And then the other thing I've read is about AI. How does AI now because 5G becomes an enigma. AI and the cloud, there's all these analytics, so 5G can actually now be able to bring that into the cloud. So AI becomes a buzzword. I just read the SAT CTO Was all NWC live TV at the venue, I talked about AI and 5G transforming the mobile industry, so it really becomes much more of a solution oriented. >> [Dan] No, I can't agree with Caroline more there. Tremendous amount of excitement around 5G as well as network transformation in the show and the two things are really becoming linked. So Caroline mentioned a few of the use cases out there on 5G, so again, lots of autonomous driving, lots of smart home, lots of smart city. I personally had a great time hanging out in our smart home demonstration earlier, but I think the key linkage of all those use cases is that the network needs to become more intelligent, more flexible, and definitely more agile to be able to support this wide variety of use cases. And we're seeing it being really echoed back by not only operators, but a lot of the OES and telecommunication equipment factors, really rallying behind NSE and truly the path to 5G. >> [John] Take a minute, guys, to explain the 5G revolution and why it's not just an evolution from 4G. What's the difference? What is the key enabler of 5G and what is Intel have that's different now than it was before. >> [Caroline] So you imagined 3G is all about getting better voice and also a little bit of SMS, and 4G is a literal 3G on steroids. Now 4G has all these, you can go on the internet and download all kind of things. 5G takes that to the next level. So 2G, 3G, and 4G is about network building for the masses If you think about it it's like a general network. So when you build it and somebody vertical says I want to make this my private network for my enterprise it's a best effort basis, so either too hot or too cold. So what that means is it operates under a wirenut either giving you way too much, unable to recuperate your investments or if it gives you not enough, you wind up with a bad user experience. 5G fundamentally changes this. Why does it change in the standard itself that's undergoing in the 3G PP. As you have a different type of schedule with them, you must predict the different use cases. For example, if you're doing a mission cryptic IOG versus a massive connector IOG, you get a different protocol. You strip out some of the heavy amount of signaling that is typically needed for mission critical for something that's just there like smart city, like traffic light changes, that kind of information you don't need that to generate a whole bunch of bandwidth. So you see something with a different, natively different in the protocol itself so that's a fundamental shift from the mindset that we always had. So that is technology enabled. And the second thing is that the network today, thanks to all the network transformation journey that everybody is on, it's much softer and flexible, it moves away from a single part purse, a belt, power to something that is much more flexible, such that you can enable something like the network driving So a prize for enhanced mobile program for ARPR would be different from something for autonomous driving. So it makes the network fundamentally different, the interface itself is much more flexible for different types of applications, and then not to mention that we have different types of spectrums on the traditional 3 GHz to 6 And now two millimeter waves we open up a whole swathe of the spectrum to allow for a much, much bigger bandwidth and things like camera applications. It really changed the game. >> [Dan] Thanks, Caroline. So I think at a high level, what Caroline was pointing out is that the wide variety of use cases with 5G will stretch and pull the network in all sorts of directions. Essentially, there will be different use cases that require blatant fact network speed, but maximum amounts of bandwidth, but some use cases also require very low latency. So when you think about all the variety of use cases, the best way to truly insure you're meeting the user experience and also delivering the right economic value for the industry is to move to more intelligent and a flexible network. And as Caroline mentioned, it is going to be software-defined. And when you think about some of the products that we're investing in, and the status in our group for networking of course you think about our Intel Xeon processors. These processors can be found in a number of servers around the globe, and customers are using these for a variety of virtual network functions, really everything ranging from the core network to the access network to newer use cases such as virtual TV. In this bit, we did announce some additional products that will be made available later in the year. This is the Atom C3000 series as well as the Xeon D1500 network series. Both of these are SoC, and when you think about 5G, you do think about the mix of centralized and distributed to plan it, and you think about that network edge becoming smarter, so these types of SoCs are very critical because they provide excellent performance density at the right power level so you can have a very intelligent edge of your network. >> [John] Great point. Just to follow up on that, it's interesting, we had a conversation yesterday in The Cube around millimeter waves, CBMA, all the different types of wireless, and I think what's interesting is you have some use cases where you have a lot of density and some cases where you need low latency, but you also have an internet of things. A car, for example, you could say, we were discussing a car is essentially going to become a data center on wheels, where mobility is going to be very important and might not need precise bandwidth per se, but in more mobility in some cases you'll need more bandwidth. And also as an internet of things comes on, whether they're industrial devices that the notion of a phone being provisioned once and then being used is not the same use case as, say, IOT, which you could have anything connected to a network, these devices are going to come on and offline all the time, so there's a real need for dynamic networks. What is Intel's approach here, because this seems to be the conversation that most people are talking about that's happening under the hood, that's the true enabler around bringing out the real mobile edge. >> [Caroline] The couple things that we're doing, number one we use a concept called flex term, flex core which is a server-based platform that works on a variety of technologies applied to it lots of these real time visualizations, dynamic resource sharing and reconfigurations, we're able to support what you just described and provide a flex support team for different types of scenarios. And then the other thing that builds into the 5G support network Splicing allows you to splice up to the pairs of light resources for a variety of cases, Including the coarse part of it, so for example, HP here in this room is demonstrating what looks a server, walks like a server and is a server and it has the RAM, virtual PC, it has orchestration, it has mobile edge computing, it's really become a network in a box. So the fact is the ultimate freedom to support the service providers and enterprises and to apply all the 5G to different scenarios. >> [John] The final question, guys, is market readiness through partners and collaboration. Intel obviously is the leader, Intel Inside who was the main story we've been hearing at Mobile World Congresses end to end, fortunately a great piece with Intel CEO talking about the end to end value in the underlying architecture, it all runs on Intel, it works better, it brings up the notion of market readiness in the ecosystem. What are you guys doing to make the ecosystem robust and vibrant, because Intel can't do it alone, you're going to need partners. Thoughts on how you guys are accelerating it, and really the market readiness for 5G and just timing in your mind when all the fruit comes off the 5G tree, if you will. >> [Caroline] We started with the trials this year, so 2017 we're going to be able, we're working closely with partners, like Ericsson, Nokia, and Cisco and we should be seeing early performance coming up and I really think the wide spread of commercial publicly is more like 2019, 2020 timeframe because of some of the standardization, would you say? >> [Dan] Yeah, so that's a great summary, Caroline. I think the key thing that we're really seeing at Mobile Congress and things that we're investing in, diverse as you mentioned. It definitely takes a village to pull off this network transformation and the movement to 5G, and I think the great thing is about the network size is the network is becoming much more pliable, more software to find, more resilient, more agile, and it's out there to find. You can really invest in many of these innovations we've been discussing today now. So we're seeing a lot of folks start investing in Flex-Core, Network in a Box, mobilized computing, et cetera, so you transform your network now, utilizing network function virtualization, and then you have a sturdy foundation when all the 5G use cases come online in the next years. >> [John] Guys, final question. What power demos are you showing? You guys usually have great demos on the floor, Mobile World Congress, lot of glam, lot of flair at the show. >> [Dan] Great question. We have a number of super demos here, we have a smart and connected home, which showcases all sorts of intel, wireless technology out of the gateway as well as other devices we're showing a smart city, as you know, with 5G, and its lightening fast speeds to pass the lower latencies. It's truly going to change the urban landscape. And we're also showing augmented virtual reality in a few different demonstrations and one definitely caught my eye and I was pretty excited about it. In our Flex Ren demo, we were showcasing augmented virtual reality, actually viewing a skier going downhill and it was pretty exciting. I had a great time, I can't wait to when, in a few years when 5G is out there and I can use augmented virtual reality to watch a number of sporting events ranging from college football to my favorite sport, which is surfing. >> [John] What's next for 5G? How are you guys going to roll this out, what's the big plans post Mobile World Congress? >> [Caroline] Like I mentioned, we have trial plans with our partners through 2017, and then we're also participating in the Winter Olympics showcase, again through our customers. There's activities happening in China now, so I think we can be in a lot of places. You can see us in 5G. >> [John] Winter Olympics, expect to get the downloads and all the video in real time on 4K screens, thank you very much. (laughs) We expect to see some good bandwidth on the Olympics, I'm sure. >> [Dan] Hey thanks, John, this was great. >> [Caroline] Thanks, bye! >> [John] Thank you. Caroline Chan and Dan Rodriguez, from Barcelona, calling in with all the details, I'm John Furrier, we'll be back with more live coverage from the Mobile World Congress after this short break.

(upbeat music) >> Hi, I'm Joe Rodriguez, Managing Director of Financial Services at Cloudera. Welcome to the Fight Fraud with Data session. At Cloudera we believe that fighting fraud begins with data. So financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks. Four out of the five top stock exchanges, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see, most financial services institutions utilize Cloudera for data analytics and machine learning. We also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations that our customers are coming up with. Criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost to purchase your account data is well worth the price to fraudsters. According to Experian, credit and a debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110 if it comes with all the bank information, including your name, social security number, date of birth, complete account numbers, and other personal data. Our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is huge and growing exponentially. All this data needs to be evaluated in real time. There are new sources of streaming data that need to be integrated with existing legacy data sources. This includes biometrics data and enhanced authentication video surveillance, call center data, and of course all that needs to be integrated with existing legacy data sources. There is an analytics Arms Race between the banks and the criminals, and the criminal networks never stop innovating. They also have to deal with disjointed security and governance. Security and governance policies are often set per data source or application requiring redundant work across workloads. And they have to deal with siloed environments. The specialized nature of platforms and people results in disparate data sources and data management processes. This duplicates efforts and divides the business risk and crime teams, limiting collaboration opportunities between them. CDP enhances financial crime solutions to be holistic by eliminating data gaps between siloed solutions, with an enterprise data approach, advanced data analytics and machine learning. By deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning, you tighten up the loop between detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning. Cloudera stands rather than replaces your existing fraud modeling applications. So Oracle, SAS, Actimize, to name a few, integrate with an enterprise data hub to scale the data, increase speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like Enhanced KYC and Customer 360 for example. I just wanted to highlight a couple of our partners in financial crime prevention, Simudyne and Quantexa. So Simudyne provides fraud simulation using agent-based modeling machine learning techniques to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective GDPR-compliant virtual environment to significantly improve financial crime detection systems. Simudyne identifies future fraud topologies for millions of simulations that can be used to dynamically train new machine learning algorithms for enhanced identification. And Quantexa connects the dots within your data using dynamic entity resolution, and advanced network analytics to create context around your customers. This enables you to see the bigger picture and automatically assesses potential criminal behavior. Now let's go over some of our customers and how they're using Cloudera. First, we'll talk about United Overseas Bank or UOB. UOB is a leading full service bank in Asia with a network of more than 500 offices in 19 countries and territories, in Asia Pacific, Western Europe and North America. UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. UOB set up it's big data analytics center in 2017. It was Singapore's first centralized big data unit within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance the bank's performance. Essential to this work was implementing a platform that could cost efficiently bring together data from dozens of separate systems and incorporate a range of unstructured data, including voice and text. Using Cloudera CDP and machine learning, UOB gained a richer understanding of its customer preferences to help make their banking experience simpler, safer, and more reliable. Working with Cloudera, UOB has a big data platform that gives business staff and data scientists, faster access to relevant and quality data for self-service analytics, machine learning and emerging artificial intelligence solutions. With new self-service analytics and machine learning driven insights, UOB has realized improvements in digital banking, asset management, compliance, AML, and more. Advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and high risk individuals with Cloudera and machine learning technologies, UOB was able to enhance AML detection and reduce the time to identify new links from months to three weeks. Next, let's speak about MasterCard. So MasterCard's principle business is to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases. MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure, delivering deep insights and best practices and big data security and compliance. Next, let's speak about Bank Rakyat in Indonesia or BRI. BRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. It's headquartered in Jakarta, Indonesia. BRI is well-known for its focus on microfinancing initiatives and serves over 75 million customers through its more than 11,000 offices and rural service outposts. BRI required better insight to understand customer activity and identify fraudulent transactions. The bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. BRI used Cloudera Enterprise data platform to build an agile and reliable, predictive augmented intelligence solution to enhance its credit scoring system. And to address the rising concern around data security from regulators and customers, BRI developed a real-time fraud detection service powered by Cloudera and Kafka, BRI's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, loan transactions, deposits, payroll and other financial real-time data. This led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a new digital microfinancing product. With the enablement of real-time fraud detection, BRI was able to reduce the rate of fraud by 40%. It improved relationship manager productivity by two and a half fold. It improved the credit scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with data focus if you haven't already. It offers a quick return on investment and it's a focused area that's not too entrenched across the company. To learn more about fraud prevention, go to www.cloudera.com, and you should schedule a meeting with Cloudera to learn even more. And with that, thank you for listening and thank you for your time. (upbeat music)

>>Good morning or good afternoon or good evening, depending on where you are and welcome to this session, reduce claims, fraud, we're data, very excited to have you all here. My name is Winnie castling and I'm Cloudera as managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time. Collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance health, and, um, life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge Glomar conglomerates in the world, you are still perfectly fine with us. >>So >>Why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last descending year or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and in society at large around cuvettes, uh, both regulators, as well as companies have enabled digital processing and the digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the cloud and the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the, um, the, the time periods that it took to settle on a claim. However, um, the more digital you go, it, it opened up more access points for fraud, illicit activities. So unfortunately we saw indicators of fraud and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already >>Are. >>And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization, um, around, in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data affords pretty much start around data warehousing and we eliminate analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing data to understand better what we know already. Now, when we move to the middle blue collar, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. >>And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this light, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a TPA data, policy verification, um, claims file staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of instructor texts there, and we do a use case around that later. >>And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around. In our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud, use cases, optimally. >>Now how we do that and how we look at at a Cloudera is actually not as complicated as, as this slight might want to, um, to, to give you an impression. So let's start at the left side at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities who are inside gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance, uh, environment. Because if not the most regulated industry in the world, insurance is awfully close. >>And if it's not the most regulated one, it's a close second. So it's critically important that insurers know, um, where the data is, who has access to it for Rodriguez, uh, what is being used for so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience. So it goes over the whole Cloudera platform and every application or tool or experience you use would include Dao. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims, fraud management. So over the last year or so, we've seen a lot of use cases around upcoding people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti money laundering. So those are the types of use cases on the right side that we are supporting, um, on the platform, uh, around, um, claims fraud. >>And this is an example of how that actually looks like now, this is a one that it's actually a live one of, uh, a company that had, um, claims that dealt with health situations and being killers. So that obviously is relevant for health insurers, but you also see it in, um, in auto claims and counterclaims, right, you know, accidents. There are a lot of different claims scenarios that have health risks associated with it. And what we did in this one is we joined tables in a complex schema. So we have to look at the claimant, the physician, the hospital, all the providers that are involved procedures that are being deployed. Medically medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim and one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that is member. >>This claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever that classically it's a very complicated and complex, um, the and costly data operation. So nowadays that tends to be done by graph databases, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in batch, you can see that in this case, that is a member that was shopping around for being killers and went through different systems and different providers to get, um, multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. >>So I want to share some customer success stories and recent, uh, AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail, um, about them because we have some time to spend on one of them immediately after this. But one of them for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, uh, the voice records they got from the customer service people to try to predict which one were potentially fraud list. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used certain trigger words, but they also were looking at tone of voice pitch of voice, uh, speed of talking. >>So they try to see trends there and hear trends that would, um, that would bring them for a potential bad situation. Now good and bad news of this proof of concept was it's. We learned that it's very difficult just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand life and health situations tend to come with emotions, or so people either got very sad or they got very angry or so the proof of concept didn't really get us to a firm understanding of potential driverless situation, but it did get us to a much better understanding of workflow around, um, claims escalation, um, in customer service to route people, to the right person, depending on what they need. >>And that specific time, another really interesting one was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries, uh, for anti money laundering scams, because there were some plots out there that networks of criminals would all buy the low value policies, surrendered them a couple of years later. And in that way, God criminal money into the regular amount of monetary system whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together, um, with the actions, with the policies to figure out where potential pain points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things, no, but most, you know, exciting. >>I think that we see happening at the moment and we, we, you know, our partner, if analytics just went live with this with a large insurer, is that by looking at different types that insurers already have, um, unstructured data, um, um, their claims nodes, um, repour its claims, filings, um, statements, voice records, augmented with information that they have access to, but that's not their ours such as geo information obituary, social media Boyd on the cloud. And we can analyze claims much more effectively and efficiently for fraud and litigation and alpha before. And the first results over the last year or two showcasing a significant degree is significant degrees in claims expenses and, um, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswami, the CEO of infinite Lytics, the opportunity to walk you through this use case and actually show you how this looks like in real life. So Sheree, here >>You go. So >>Insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority. Majority of it is unstructured data. Can AI analyze all of this historically and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at infill lyrics to create the industries where very first pre-trained and prebuilt insights engine called Charlie, Charlie basically summarizes all of the data structured and unstructured. And when I say unstructured, I go back to what money basically traded. You know, it is including documents, reports, third-party, um, it reports and investigation, uh, interviews, statements, claim notes included as well at any third party enrichment that we can legally get our hands on anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlie does is takes all of this data and very neatly summarizes all of this. After the analysis into insights within our dashboard, our proprietary naturally language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights >>Actually. So >>Let's just get into, um, standing what these steps are and how Charlie can help, um, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, uh, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlie basically does is crunches all, all of this data removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines in the next step. >>In the next step, we are basically utilizing Charlie's built-in proprietary, natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dash. Cool. And if you look at what has been presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn from not only from what the system can provide, but also from the historic data can help understand and uncover some of these patterns in the newer claims that are coming in so important to learn from the historic learnings and apply those learnings in the new claims that are coming in. >>Let's just take a very quick example of what this is going to look like a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, I'm experiencing a very large, um, average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlie basically pulls together all these topics and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. >>And this is where the managers can adjust their workflows based on what we can predict using those patterns that we have learned and predict the new claims, the operations team can also leverage Charlie's deep level insights, claim level insights, uh, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations and the operations team can mitigate the claims much more effectively and proactively using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlie and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >>Thank you very much for you. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, uh, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud batter, right? So to close this session out as a next step, we would really urge you to a Sasha available data sources and advanced or predictive fraud prevention capabilities aligned with your digital initiatives to digital initiatives that we all embarked on over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more at one to learn more about Cloudera and our insurance work and our insurance efforts, um, you to call me, uh, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet when that's possible again, and schedule a meeting with us, and again, we love insurance. We'll gladly talk to anyone until they say in parts of the United States, the cows come home about it. And we're dad. I want to thank you all for attending this session and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day. >>Good afternoon, I'm wanting or evening depending on where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is goonie castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurance, it's maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry in there. >>So >>Recently what we have been seeing, and it's actually accelerated as a result of the recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customers with lotta be a commercial clients or a personal person, personal insurance risk in a dynamic and a B spoke way. And what I mean with that is in a dynamic, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and the changing per person they're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in that capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendors, global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help, uh, gone for that. >>And this is a bit of a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress in make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing sorts and events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, watch next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fraud or lists or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. >>Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. So you will see that on the reporting side, in the beginning, we start with rates, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as a descriptions, move into some sort of an unstructured data environment, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the other side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. >>Um, but I'm not Whitely integrated yet. So I will walk through some use cases around these specifically. So think about sensors, wearables, you know, sensors on people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or risk, risk inspector or engineer to every building, you know, be insurers now, fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies out there. Now that pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different data. There are, some of them are managed and controlled by you. Some orders you get from third parties, and we'll talk about Della medics in a little bit. It's one of the use cases. They move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it either in an operational environment for processing or in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close, and it will come in as a, a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on the old, the different regulations that, that we are subject to. So we do that in the cloud era Sharon's data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrids, most insurers run hybrid models, which are part of the data on premise and part of the data and use cases and workloads in the clouds. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you to some of the use cases that we've seen in action recently that showcases how this work in real life. >>First one >>Is to seize that group plus Cloudera, um, uh, full disclosure. This is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be dodged is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any insurer, customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need it in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs and results. The other one that I wanted to quickly mention is Octo. As most of you know, Optune is a very, very large telemedics provider, telematics data provider globally. It's been with Cloudera for quite some time. >>This one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points. And really what they're doing is the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance, telematics programs made to pay as you drive pay when you drive, pay, how you drive. And this whole telemedics part of insurance is actually growing very fast too, in, in, still in sort of a proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. So they become preventative and predictive too. So now you got to the program staff being me as a driver saying, Monique, you're hopping in the car for two hours. >>Now, maybe it's time you take a break. Um, we see that there's a Starbucks coming up on the ride or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 cents discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. That's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial, commercial, um, enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are. And, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly moved to other use cases or deployed into other use cases to help price risks, betters better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance risk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other any, any insurable entity, right? The please physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as a last thing, I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

>>Uh, hi, I'm Joe Rodriguez, managing director of financial services at Cloudera. Uh, welcome to the fight fraud with a data session, uh, at Cloudera, we believe that fighting fraud with, uh, uh, begins with data. Um, so financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top, uh, stock exchanges, uh, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see most financial services institutions, uh, utilize Cloudera for data analytics and machine learning, uh, we also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations, uh, that our customers are coming up with. Uh, criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost of, uh, to purchase a, your account data is well worth the price to fraudsters. Uh, according to Experian credit and debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110. If it comes with all the bank information, including your name, social security number, date of birth, uh, complete account numbers and, and other personal data. >>Um, our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is, is huge and growing exponentially. Uh, all this data needs to be evaluated in real time. Uh, there is, uh, there are new sources of, of streaming data that need to be integrated with existing, uh, legacy data sources. This includes, um, biometrics data and enhanced, uh, authentication, uh, video surveillance call center data. And of course all that needs to be integrated with existing legacy data sources. Um, there is an analytics arms race between the banks and the criminals and the criminal networks never stop innovating. They also we'll have to deal with, uh, disjointed security and governance, security and governance policies are often set per data source, uh, or application requiring redundant work, work across workloads. And, and they have to deal with siloed environments, um, the specialized nature of platforms and people results in disparate data sources and data management processes, uh, this duplicates efforts and, uh, divides the, the business risk and crime teams, limiting collaboration opportunities between CDP enhances financial crime solutions, uh, to be holistic by eliminating data gaps between siloed solutions with, uh, an enterprise data approach, uh, advanced, uh, data analytics and machine learning, uh, by deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning. >>Uh, you tighten up the loop between, uh, detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning cloud Derrick stands rather than replaces your existing fraud modeling applications. So Oracle SAS Actimize to, to name a few, uh, integrate with an enterprise data hub to scale the data increased speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like enhanced KYC and a customer 360 4 example. >>I just, I wanted to highlight a couple of our partners in financial crime prevention, uh, semi dine, and Quintex, uh, uh, so send me nine provides fraud simulation using agent-based modeling, uh, machine learning techniques, uh, to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective, uh, GDPR compliant, virtual environment, significantly improved financial crime detection systems, semi dine identifies future fraud topologies, uh, from millions of, of simulations that can be used to dynamically train, uh, new machine learning algorithms for enhanced fraud identification and context, um, uh, connects the dots within your data, using dynamic entity resolution, and advanced network analytics to create context around your customers. Um, this enables you to see the bigger picture and automatically assesses potential criminal beads behavior. >>Now let's go some of our, uh, customers, uh, and how they're using cloud caldera. Uh, first we'll talk about, uh, United overseas bank, or you will be, um, you'll be, is a leading full service bank in, uh, in Asia. It, uh, with, uh, a network of more than 500 offices in, in 19 countries and territories in Asia, Pacific, Western Europe and north America UA, um, UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. Um, you'll be set up, uh, set up it's big data analytics center in 2017. Uh, it was Singapore's first centralized big data unit, uh, within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance, uh, the banks, uh, uh, performance essential to this work was implementing a platform that could cost efficiently, bring together data from dozens of separate systems and incorporate a range of unstructured data, including, uh, voice and text, um, using Cloudera CDP and machine learning. >>UOB gained a richer understanding of its customer preferences, uh, to help make their, their banking experience simpler, safer, and more reliable. Working with Cloudera UOB has a big data platform that gives business staff and data scientists faster access to relevant and quality data for, for self-service analytics, machine learning and, uh, emerging artificial intelligence solutions. Um, with new self-service analytics and machine learning driven insights, you'll be, uh, has realized improvements in, in digital banking, asset management, compliance, AML, and more, uh, advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and, uh, high risk individuals, uh, with, uh, Cloudera and machine learning, uh, technologies. You you'll be, uh, was able to enhance AML detection and reduce the time to identify new links from months 2, 3, 3 weeks. >>Excellent mass let's speak about MasterCard. So MasterCard's principle businesses to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases MasterCard chose Cloudera enterprise for fraud detection, and to optimize their DW infrastructure, delivering deepens insights and best practices in big data security and compliance. Uh, next let's speak about, uh, bank Rakka yet, uh, in Indonesia or Bri. Um, it, VRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. Uh, it's headquartered in Jakarta Indonesia. Uh, Bri is well known for its focus on financing initiatives and serves over 75 million customers through it's more than 11,000 offices and rural service outposts. Uh, Bri required better insight to understand customer activity and identify fraudulent transactions. Uh, the bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. >>Uh, Bri used, uh, Cloudera enterprise data platform to build an agile and reliable, predictive augmented intelligence solution, uh, to enhance its credit scoring system and to address the rising concern around data security from regulators, uh, and customers, uh, Bri developed a real-time fraud detection service, uh, powered by Cloudera and Kafka. Uh, Bri's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, uh, loan transactions, deposits, payroll and other financial, um, uh, real-time time data. Uh, this led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a, of a new digital microfinancing product, uh, with the enablement of real-time fraud detection, VRI was able to reduce the rate of fraud by 40%. Uh, it improved, uh, relationship manager productivity by two and a half fold. Uh, it improved the credit score scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with a data focus. If you haven't already, it offers a quick return on investment, uh, and it's a focused area. That's not too entrenched across the company, uh, to learn more about fraud prevention, uh, go to kroger.com and to schedule, and you should schedule a meeting with Cloudera, uh, to learn even more. Uh, and with that, thank you for listening and thank you for your time. >>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data, um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming up with. >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized offers and advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive services to get it better, better understanding of your customers, use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory advice. It's so different organizations are going to be at different points in their data analytics and AI journey. >>Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time. And, uh, real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. Uh, they start to ingest near real-time streaming data that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is a customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions, or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, the Cloudera data platform, um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want, uh, to take their data through, to enable the end to end data journey. >>If you ask our customers, they want different types of analytics, uh, for their diverse user bases to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage, uh, within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real time and transactional type type data. Examples include, uh, click stream data, web logs, um, machine generated, data chat bots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhanced them, personalized customer experience, um, customer journey analytics next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. >>Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and churn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco suntan there. Um, is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiaries, something there UK wanted to revolutionize the customer experience with the use of real time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometime there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes, processing processing, 10 million transactions daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And there has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash, on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabobank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement, Rabobank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer experience bank level Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real time streaming data. >>They were able to quickly create a new data lake that allowed for faster queries of both historical and real time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation in this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, long repayments and are able to generate accurate, uh, current real liquidity. I refuse, uh, next, uh, let's uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via, uh, ultra personalized marketing, uh, using the Cloudera stack. The team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, PR is a well-known, uh, for its, uh, focused on micro-financing initiative initiatives and serves over 75 million customers through more than 11,000 offices and rural outposts, um, Bri needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze the vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. >>Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of the net profit lost, uh, this resulted in improved credit, improved credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to, to two minutes and, uh, enhanced fraud detection. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider and assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

>>From around the globe with digital coverage of IBM, think 2021 brought to you by IBM. Welcome to the cubes coverage of IBM. Think 2021. I am Lisa Martin today. I have Savio and Rodriguez here with me, the VP of integration and application platform Savio. It's great to have you on the program, >>Lisa, really great to be here. Thanks for having me >>Talk about automation integration. But one of the things that we're going to kind of break down versus is hyper automation. Gardner announced that about a year and a half ago was one of the top 10 things. It was the top 10 strategic technology trends of 2020. Well, here we are in 2021. Before we talk about automating integrations, getting IBM's perspective on hyper automation and what did we see in 2020? Like reality? >>Yeah, no great, great question. So, and IBM, we believe that the next tidal wave to hit organizations will be really the task, but frankly, the opportunity to automate the entire enterprise. And by that, I really do mean everything in the enterprise. So Gartner, when they talk about hyper automation, they're absolutely right, because they're focusing on automating business tasks, but IBM's point of view is broader than that. And so we want to think about the work that business professionals, it developers that it staff security, focus, administrators, all of that work. And we think that the real differentiation is going to come to organizations that attack the task of automating work for all three labor types, business developers, and it, so hyper automation focuses on the first labor type IBM's approach is looking at all three labor types. Now you should pick automation projects that are specific to one labor type to begin, right. Instead of saying let's automate everything, but the latter is the strategic statement. The former is tactical. Um, and, and w w we're seeing clients automating specific business processes, like order to cash, and then others are automating work of, uh, it had been such as reducing the number of security vulnerabilities found in production, and then others are automating the work of developers by automating the approach that they take to the integration life cycle. And that's what I'd like to talk to the audience about today. >>All right. So look how you talked about it in terms of prioritization. Cause that's one thing I think that businesses can struggle with in terms of making automation and eventually hyper automation successful is where do we start? Let's talk though about this application sprawl that every organization pretty much is living in. We saw this massive adoption of SAS applications and 2020, which we, a lot of businesses were dependent on to even facilitate just collaboration, but talk to us about the relationship between integration automation, applications. >>Another great question. So I spend most of my day thinking about integration, um, but I also know that most of my clients and probably the audience here thinks about automation first and then thinks about integration as a means, not the ends. The ultimate goal is digital transformation. I E delivering new apps faster with higher quality, if that's the case. And you think about what's an application today versus what will an application 20 years ago. So today there's definitely some business logic and code that you're writing, but the majority is actually integration logic. So you have to connect to a SAS service like Workday to get data, connect to an app that's running on AWS, get other data that's running on IBM cloud to transform it, put it into a different database that's running on Azure. So there's a little bit of application logic and a ton of integration logic. So if you're a line of business owner that controls 50% or more of it budgets, or you're a CIO, that's beholden to that line of business, um, and you want applications faster than ever before, and you don't want to sacrifice quality. How are you going to do that? Well, the way you do that is by focusing on the integration tier, because applications are really driven by integration today. So if you want a faster applications with higher quality, you really need to think about delivering integrations faster with higher quality. >>An integration is absolutely critical as we look at the hybrid cloud, the advance of AI organizations that are in this multi-hybrid cloud world, what are some of the challenges that they face with respect to integrating those applications? So to your point, you know, they can pull down data for Workday, align it with data in AWS, for example, to make business decisions in real time, >>One of the biggest challenges is manual effort, right? So we started the conversation thinking about automation and when we're coming back to it, because we believe that you have to automate your integrations and the way you do so is through AI. So you can of course use rules-based, um, automations. And that helps to some degree, but things get really interesting when you apply AI and the automation is driven by real world data. That's specific to your organization in a continuous feedback loop. We like to call closed loop and that's continuously driving efficiency. So if you think about the integration life cycle, you've got to create an integration, test it, socialize, it operated governance. That's what we mean by automating integrations, that whole life cycle. So for instance, if you can create an integration flow and do a field mapping based on AI, best practices, you reduce manual effort, you reduce coding, you reduce the need for integration experts, or if you're a business user, and you're able to describe your intent and you have your integration software handle, um, converting that intent into university that's required. >>So for instance, if you could say, generate a lead score and wrote the leads based on location, um, to your sales team, well, you know, what, what you're trying to achieve, why not get the software to do that for you based on AI, under the covers, or if you're doing testing, um, how about letting the AI generate hundreds of new tests for your integrations that reflect real world usage behavior at your specific company. And these tests are based on other APIs that are running at your company. So we take the operational data. We know what's, uh, which parts of the APR are being exercised. We know what data is going through your system. So things that are, for instance, personally, identifiable shouldn't be used as test data, right. Or if you're operating your integrations and wouldn't it be great if your AI could uncover optimizations in the integration flow, such as adding, adding in, um, maybe buffering to a message queue so that it prevents you from, uh, overages on your Salesforce account and having that happen without needing a human in front of a dashboard. I E the AI under the covers is doing this for you. So for AI to really drive that integration automation, you need the operational data, um, from your specific company and using that in a closed loop fashion. So you're continuously improving, not just your current integrations, but your future integration. >>I can only imagine how much more important this has been become in the last year as businesses and industry we're pivoting multiple times to survive. And then ultimately thriving. When I think of integrations, I think of customers that I've spoken to, who you get the right example with perspective sales, they've got a CRM, they're got an ERP and they're not in sync and not integrated so that I can't, there's no one system of record. I can only imagine how much more important having that system of record has been in the last year for supply chains, even for demanding consumers going, can I get some toilet paper? And if so, where can I find it? >>I absolutely. And this is where that notion of a closed loop, um, approach to integration and the automation via AI comes in, right? So we strongly feel that today, this is the time the clients need to rethink their integration strategy. And we do agree with some of the other analysts and vendors that are talking about automating the integration work, and that's part of what we've discussed earlier. And that's definitely necessary, but it's not sufficient. Right. Go ahead. Sorry. Sorry. Well, yeah. So our feeling here is that you also have to be thinking about evolving those integrations in a closed loop fashion. So you're continuously making those integrations better, uh, with AI that's powered by your operational data, that's specific to your company. And then finally that the, you know, the old approach that integration vendors used to have in terms of this style of integration fits all problems, is the wrong approach. And instead, what we start seeing today is that customers are using multiple forms of integration to solve a specific business problems. So they're using CAFCA API APIs messaging iPad. So from an IBM standpoint, we feel that every integration must be automated closed loop. And Multistyle with AI, that's informed by your company specific data to continue to improve so that you end up getting integrations faster, but that, they're also better >>When, when companies have that spectrum of different integration processes, as you just mentioned, one of the things that I kind of think is as we look forward, and you mentioned this a minute ago, wanting to have the foundation so that not only are applications integrated today and communicating well and sharing data, but in the future. So talk to me about this closed loop system that you mentioned, and how does that enable an organization to establish that now, but be able to take on applications that are not even created yet, >>But that's really a foundational aspect that clients need to be thinking about, right? Because the closed loop nature of thinking of your integrations means that you're always looking at operational data and using that operational data and feeding it into your AI to improve your business processes, your integrations today, but also the ones that you're going to be delivering in the future. Right? So I'm sure your listeners are sitting here thinking, you know, where should I get started? Um, and frankly for me, I turn it around and say, you probably should ask your integration vendor of choice, how effectively their solutions can provide an automated closed loop and multi-step approach to integration. And if the answer that they give you, isn't very detailed, but I hope you'll ask IBM. And when you ask us this question, what you're going to hear about is IBM's cloud pack for integration, which is our, uh, our complete platform for automated closed loop. >>And multicell integrations. It's optimized for deployment across clouds, with red hat OpenShift. And with IBM, you'll be able to use natural language powered integration flows, uh, AI powered flow and field mapping, RPA conductivity, things that really take the manual effort of integration out and replace it with AI driven, um, automation. Um, second, you want to think about the data that's feeding the AI, right? So this is where the operational, um, closed loop aspect comes into play. Sometimes the other vendors in the space are taking, um, operation data from hundreds of, of, um, customers and putting it together and coming out with the average and using that to train the AI. We don't think that's the right approach because your most important, uh, integration processes are shared by no other customer, right? So you want your operational data to feed the AI. That's providing things like field mapping, flow creation, creating the API tests automatically, or the uncovering, the inefficiencies that are running in your, um, your production environment. >>Um, and then finally, would I be able to tell you is we've got the broadest set of integration capabilities of a Multistyle integration capabilities, all delivered with a common UI and shared reuse and governance with unified management across clouds. And that's exactly what clients need, because if you think about where are you deploying applications today, the composers are running on multiple clouds, so you have to integrate across clouds. And then finally, what you hear from us is that IBM provides a proven hybrid and DMC ready security gateway. That's never been hacked in 15 years, over 30,000 TPS for second, but the performance and security that frankly clients need for their applications today. So automated closed loop. Multistyle, you'll hear me repeat those over and over because we feel that's absolutely necessary for, for, um, listeners when they think about their next generation applications and the integrations that we required for them. >>Excellent. Well, Sophia, I wish we had more time, but thank you for sharing. What's going on with audit, uh, in automating integrations, AI, what hyper automation means kind of where it is. Now we look forward to hearing more about this and I'm sure the guests will be excited to see what comes at IBM. Think we thank you for your time. >>Thank you very much >>For Savio Rodriguez. I'm Lisa Martin. You're watching the cubes coverage by IBM. Think 2021.

(upbeat music) >> Advertiser: From around the globe, It's theCUBE, With digital coverage of IBM think 2021, brought to you by IBM. >> Welcome to theCUBES coverage of IBM think 2021, I am Lisa Martin. Today I have Savio Rodriguez here with me, the VP of integration and application platform. Savio, It's great to have you on the program. >> Lisa, really great to be here. Thanks for having me. >> We're going to talk about Automation Integration, but one of the things that we're going to kind of break down first is hyperautomation. Gartner announced that about a year and a half ago, was one of the top 10 top, 10 strategic technology trends of 2020, and here we are in 2021. Before we talk about Automating Integrations, give me IBM's perspective on hyperautomation. And what did we see in 2020? Like, reality? >> Yeah, no, great question. So, an IBM, we believe that the next tidal wave to hit organizations will be really the task, but frankly the opportunity to automate the entire enterprise. And by that, I really do mean everything in the enterprise. So, Gartner, when they talk about hyperautomation, they're absolutely right because they're focusing on automating business tasks. But IBM's point of view is broader than that. And so we want to think about the work that business professionals, IT developers, that IT staff, security focus, administrators, all of that work. And we think that the real differentiation is going to come to organizations that attack the task of automating work for all three labor types; business, developers and IT. So hyperautomation, focuses on the first labour type. IBM's approach is looking at all three labour types. Now, you should pick automation projects that are specific to one labour type to begin, right? Instead of saying, "let's automate everything." but the latter is a strategic statement, the former is tactical. And we're seeing clients automating specific business processes like, order the cash. And then others are automating work of IBM, such as, reducing the number of security vulnerabilities, found in production. And then others are automating the work of developers by automating the approach that they take to be integration life cycle. And that's what I'd like to talk to the audience about today. >> Alright, so look how you talked about it in terms of prioritization, cause that's one thing I think that, businesses can struggle with in terms of making automation and eventually hyper automation successful, As where do we start? Let's talk though, about this application sprawl, that every organization pretty much is living in. We saw this massive adoption as SaaS applications in 2020, which a lot of businesses were dependent on to even facilitate just collaboration. But talk to us about, the relationship between integration, automation, applications. >> Another great question. So, I spend most of my day thinking about integration, but I also know that most of my clients and probably the audience too, thinks about automation first, and then thinks about integration as a means, not the ends, right? The ultimate goal, is digital transformation I.e., delivering new apps faster with higher quality. If that's the case, and you think about what's an application today? Versus, what were an application 20 years ago? So, today, there's definitely some business logic in code that you're writing but the majority is actually integration logic. So you have to connect to a SaaS service like, Workday to get data, connect to an app that's running on AWS, get other data that's running on IBM Cloud to transform it, put it into different database that's running on Azure. So, there's a little bit of application logic and a tone of integration logic. So if you're a line of business owner, that controls 50% or more of IT budgets, or you're a CIO that, beholden to that line of business, and you want applications faster, than ever before, and you don't want to sacrifice quality, How are you going to do that? Well, the way you do that, is by focusing on the integration tier because, applications are really driven by integration today. So if you want, a faster applications with higher quality, you really need to think about delivering integrations faster with higher quality. >> An integration is absolutely critical. As we look at the hybrid cloud, the advance of AI, organizations that are in this multi-hybrid cloud world, what are some of the challenges that they face, with respect to integrating these applications at your point? You know, they can pull down data from Workday, align it with data in AWS, for example, to make business decisions in real time. >> One of the biggest challenges is, manual effort, right? So, we started the conversation thinking about automation and we're coming back to it, because we believe that, you have to automate your integrations and the way you do so, is through AI. So you can of course use, the rules-based automations. And that helps to some degree. But things get really interesting, when you apply AI, and the automation is driven by real world data. that's specific to your organization in a continuous feedback loop, we like to call, closed loop and that's continuously driving efficiency. So, if you think about the integration life cycle, you've got to create an integration, test it, socialize it, operate it, govern it. That's what we mean by, Automating Integrations, the whole life cycle. So, for instance, if you can create an integration flow, and do field mapping based on AI best practices, you reduce manual effort, you reduce coding you reduce the need for integration experts or if you're a business user, and you're able to describe your intent, and you have your integration software, handle, converting that intent into universal that's required. So for instance, if you could say, generate a lead score, and wrote the leads based on location to your sales team. Well, you know what you're trying to achieve, when I get the software to do that for you, based on AI under the covers or if you're doing testing, how about letting the AI generate hundreds of new tests for your integrations, that reflect real world usage behavior, at your specific company. And these tests, are based on, other API that are running at your company. So we take the operational data, we know which parts of the API are being exercised, We know what data is going through your system. So things that are for instance, personally identifiable, shouldn't be used to test data, right? Or if you're operating your integrations, and wouldn't it be great if your AI could uncover optimizations in the integration flow? such as, adding in maybe buffering to a message queue so that, it prevents you from overages on your Salesforce account and having that happen, without needing a human in front of a dashboard I.e., the AI under the covers is doing this for you. So, for AI to really drive that Integration Automation, you need the operational data, from your specific company and using that in a closed loop fashion you're continuously improving, not just your current integrations, but your future integration. >> I can only imagine how much more important this has been become, in the last year as businesses and every industry were pivoting, multiple times to survive and then ultimately thriving. When I think of integrations, I think of customers that I've spoken to who you get the right example with respect to sales. They've got a CRM, they're got an ERP and they're not in sync and not integrated so that I can't... There's no one system of record. I can only imagine how much more important having that system of record, has been in the last year of supply chains, even for demanding consumers going, "can I get some toilet paper?" And if so, "where can I find it?" >> Absolutely. And this is where that notion of a closed loop, approach to integration and the automation via AI comes in, right? So we strongly feel that, today, this is the time, the client needs to rethink their integration strategy. And we do agree with some of the other analysts and vendors that are talking about automating the integration work, and that's part of what we've discussed earlier. And that's definitely necessary, but it's not sufficient. >> Go ahead, Sorry. >> Sorry, well, yes, so our feeling here is that, you also have to be thinking about, evolving those integrations in a closed loop fashion. So you're continuously making those integrations better with AI, that's powered by your operational data, that's specific to your company. And then finally, that you know, the old approach that, integration vendors used to have in terms of this style of integration fits all problems, is the long approach. And instead, what we start seeing today, is like, customers are using multiple forms of integration to solve a specific business problem. So they're using CAFCA APIs, messaging, iPad. So, from an IBM standpoint, we feel that every integration must be automated, closed loop and Multistyle, with AI that's informed by your company specific data to continue to improve, so that you end up getting integrations faster but they're also better. >> When companies have that spectrum of different integration, process, as you just mentioned, one of the things that I kind of think is, as we look forward, and you mentioned this a minute ago wanting to have, the foundation so that, not only are applications integrated today, communicating well and sharing data, but in the future. So, talk to me about this closed loop system that you mentioned. And how does that, unable an organization to establish that now, but be able to take on applications that are not even created yet? >> That's really a foundation aspect that the clients need to be thinking about, right? Because the closed loop nature of thinking of your integrations, means that, you're always looking at operational data and using that operational data and feeding it into your AI to improve your business processes, your integrations today but also the ones that you're going to be delivering in the future, right? So, I'm sure your listeners are sitting here thinking you know, where should I get started? And frankly for me, I turn around and say, you probably should ask your integration vendor of choice, how effectively their solutions can provide an automated, closed loop and Multistyle approach to integration. And if the answer that they give you, isn't very detailed, but I hope you ask IBM. And when you ask us those questions, what you going to hear about is, IBM's cloud pack for integration, which is our complete platform for automated closed loop and multi-style integrations. It's optimized for deployment across clouds with Red Hat OpenShift. And with IBM, you'll be able to use natural language powered integration flows, AI powered flow and field mapping, RPA conductivity. Things that really take the manual effort of integration out and replace it with AI driven automation. Second, you want to think about the data that's feeding the AI, right? This is where the operational closed loop aspect comes into play. Sometimes the other vendors in the space are taking operation data from hundreds of customers and putting it together and coming out with the average and using that to train the AI. We don't think that's the right approach because your most important integration processes, are shared by no other customer, right? So, you want your operational data to feed the AI. That's providing things like, field mapping, flow creation, creating the API tests automatically, or the uncovering the inefficiencies that are running in your production environment. And then finally, would I be able to tell you we've got the broadest set of integration capabilities of a multistyle integration capabilities, all delivered with a common UI and shared reuse in governance with unified management across clouds. And that's exactly what clients need. Because if you think about in where are you deploying applications today? The composers are running on multiple clouds, so you have to integrate across clouds. And then finally, what you hear from us is that, IBM provides a proven hybrid and DMC ready Security Gateway. That never been hacked in 15 years, over 30,000 TPS for second but the performance, and security that, frankly clients need for their applications today. So automated, closed loop, multistyle, you'll hear me repeat those over and over because we feel that's absolutely necessary for listeners when they think about, the next generation applications and the integrations that will be required for that. >> Excellent, well, Savio, I wish we had more time but thank you. for sharing what's going on with Automating Integrations, AI what hyperautomation means, kind of where it is now. We look forward to hearing more about this and I'm sure the guests will be excited to see what comes at IBM think. We thank you for your time. >> Thank you very much. >> Savio Rodriguez, I'm Lisa Martin, you're watching theCUBES coverage, via IBM think 2021. (upbeat music)

>> Narrator: From New York, it's the CUBE. Covering Blockchain Week. Now here's John Furrier. >> Hello everyone, welcome back. This is the CUBE here in New York City on the ground for Consensus 2018. Part of Blockchain Week New York City. I'm John Furrier your cohost of the CUBE and Enrique Rodriguez is here with me. He's a blockchain guru and he's part of the Crypto Consulting Group. Welcome to the CUBE. >> Nice to be here. Thanks for having me. >> So love that big coin little thing there. >> Yeah. >> Come on are you holding som bitcoin right now? >> Yeah yeah. >> So tell me about your project says in the hallways here and checking in on what's going on. You're working with Andrew Prell the alumni. >> Yeah. So. >> On a cool project, so explain what that is. >> So the project with Andre or what we do? >> What you guys do first. >> Yeah so essentially you know there's a big problem right now with people trying to get into the space. There's a lot of pitfalls new comers fall victim to and there's not a lot of education out there. It's really fragmented across the internet. So what we're really trying to do is provide you know really great resources to people that are looking to get into the space. We essentially want to be the on ramp for people looking to get into the crypto space. >> Where you located? >> Louisville, Kentucky. Yeah so it's a different location. I think that's why we stand out quite a bit cause we're trying to bring such a new and disruptive technology to a place that's not so on the leading edge of technology sometimes. >> And you know it's cool about it too is I live in Silicon Valley. It's good to be the epicenter, everyone's got to go to Silicon Valley. The blockchain phenomenon and crypto in general is a global thing. >> It is. >> It is not one place. You can be anywhere. >> Absolutely. >> What are you doing, what are you working on with people? What are some of the things that your projects attacking. >> Yeah so right now we're really working on our educational events. We're really putting together just great content for people to come and join us and really just learn about the tech. We're also working with Andrew Prell from Silica Nexus project. He's having ICO soon and one of the things we're doing for them is really auditing the accounts that they have their tokens in. So they have in their tokenomics they have funds set aside for the team, for the advisors. All these different things and they also have ten investment funds that they're going to be using to essentially get more developers to develop on their project. So we'll be auditing those transactions that they send out just to ensure the transparency and that people know the investors that are putting their money into this project. Know where those funds are going. >> So basically it's an audit trail but it's not code review. So when you do smart contracts, there's one aspect which is code review. >> Yeah. >> And the other side of this, the coin so to speak is the transactional efficiency and affectiveness. >> Yeah no absolutely so if out of this wallet they send ten thousand droids to this developer or this project. We are essentially going to be putting together reports for that. So it's all about auditing and the transparency available. >> So you're automating his system end to end so he can manage it. >> Absolutely. >> Cause alternative is what? What's his alternative. Andrew's in particular. >> Yeah I think he went to the big four and they really didn't know. I guess display enough knowledge about the blockchain, the blockchain explorers and all those things and really came at a high price and so instead of do it themselves. It's something that we do on a regular basis. You know blockchain exploring, just looking up transaction. Second nature to us so I mean it's really good fit and it's an industry first. So really could be a break through for ICOs to come so we're hoping it works out well. >> Enrique how did you get here? What's your journey and tell your story. >> It has been awhile so. So I'm 23 years old, around the age of 20 I started hearing about bitcoin and blockchain. I worked at UPS in the international department in Louisville which if you're not familiar. We have the world port, the biggest automated hub in the world but we were having a lot of problems with the supply chain. You know packages going missing, invoices being fraudulent. A lot of manual paperwork. So really just looking into some of these problems and trying to find a solution. Stumbled into blockchain and really went down the rabbit hole and haven't came up since. I started telling people about it, meeting with people. >> So you became an enthusiast, evangelist. >> Yeah and so I mean it's really grown from me meeting people in restaurants, coffee shops and now we have office. We have eight consultants working with us and really trying to make a national network of people that can just educate. You know investors and individuals on the technology. >> Are you happy you made the move? >> Oh so happy, you know I work for myself now. It's really the happiest I've ever been. I'm passionate about something that could potentially change the world. And so I love the space I'm in. Just being here with so many like minded individuals you know from so many different backgrounds. It really is a beautiful thing that CoinDesk was able to put together here. >> And it's also cool, a lot of new people are coming in. Both old and young. I mean old guys like me and so Dan Bates on just before. We're kindred spirits, we're the old dogs. He's doing real business but the young guns are making it happen too. >> Absolutely. >> So it's not about ageism. Lot of us old system guys know this is all one big operating system. >> Even with our clients, we have people as young as 15 coming in like hey how do I figure this out and 85 people that don't even have email set up. You know want to get involved in this space. I mean we have a wide spectrum of people. >> If you got an AOL account we're ignoring you. Although I just try to turn my on that instead have the throwback. >> That's what it is. >> I got to ask you because one of the things I've really been apart of in my whole life in computer science is open source. Even when I was renegade back in the old days now it's tier one. Open source, cloud computing, has really and open source particular. Really built the idea of a community. >> Absolutely. >> The blockchain community is very small still young tight knit and growing. So as people come in, what's your advice to people entering the community. How thy should align, what should they do? >> Yeah this is something that we have to deal with a lot and so whenever because a lot of the headlines that go around. You know the bitcoin bubble all the crazy gains the lambos. People come in with this mindset that it's a get rick quick thing. You know they want to dump money into the newest ICO or the next big bitcoin and well you really have to educate them on is that this is a long term play. We're still very early in this space. Never invest anything that you're not willing to lose and so a lot of these. We call them the commandments actually just in a podcast episode on them. So there's a lot of just base level things that we try and enlighten our newcomers in. It's been a really great because a lot of people whenever they learn about this technology under the surface. It's just enlightening and so it's been great the community grows. >> A lot of businesses are growing into the community. A lot of people are joining the community but also a big trend is that big business and small medium sized businesses are looking at as an opportunity. So I got to ask you the question right which is I see a lot of people out there that are passing themselves off as code gurus because they bought bitcoin in 2013. >> Oh absolutely. >> They don't, but they haven't actually built anything. >> Yeah. >> So a lot of people are hiring fraudsters. So I'm not saying, there's nothing wrong with trading bitcoin and being involved in the currency. >> Absolutely. >> But the difference between someone who buys currency and builds the next generation with the community. How does someone vet that person? How does some a business owner how do you figure out the pretenders from the players? >> Yeah I think it's really about getting to know the person that you're talking to about this. Seeing how transparent they are, their ideologies, why they're in this space. Why they bought bitcoin a lot of these fundamental questions that you could tell a lot about a person from their answers. Because we've come across that a lot. Whenever reason I started this company is because you know over the past three years or so it's been a lot of trail and error really trying to figure this stuff out. >> I always ask too, what have you built. >> Yeah no absolutely and so we're currently actually in the beta version of a platform that we want to build that's essentially going to allow us to connect these consultants as well as a portfolio tracker but. >> I got to ask you the question. What's the coolest thing you've done? >> The coolest thing I've done, probably getting my pilots license a month after my drivers license in high school. Just in general you'll be able to leave school and go fly planes. All of my best friends were in a class. You know it was really, it was amazing. >> Surreal, Enrique great chatting with you. >> You as well. >> Awesome voice. So glad to have you on the CUBE and good luck with your venture with Andrew Prell. That's cool project and on the things you work on. Best success to you. Enrique Rodriguez here on the CUBE breaking it down. Lot of new action going on, lot of great voices. Lot of talent coming into the community of course it is a community. It's tight knit, it's early growing super fast and as the crypto action. This is the CUBE bringing it all to you. I'm John Furrier we're watching after this short break. We'll be right back.

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS Reinvent 2017, presented by AWS, intel, and our ecosystem of partners. >> Well, welcome back. We're live here in Las Vegas at Reinvent. AWS putting on it's annual show, and you might notice the volume's gone up a little bit around here. Well, it's 5 o'clock reception time here, so the show floor has a little different vibe to it, you might say, right now. Justin Warren, John Walls, you kind of feel it, don't you, right now? >> Oh yeah, there is an energy just sort of vibrating around. I can feel the energy lifting as the booze starts to flow some more. >> Energy's a good way to put it. >> Yeah. >> Right. We're with W. Curtis Preston, who is the chief technical architect at Druva, and Curtis, thanks for being with us. >> Glad to be here. >> Do you feel a vibe, too? >> I feel the vibe. I feel the vibe standing out in the big line to get in here. >> Yeah, in here. >> And now we're in here, it's yeah, it's a lot of people. >> By the way, for those of you at home not familiar, you were named, this year, on the Deloitte Technology Fast 500 list, 175. >> Hey. >> Quite an honor. >> I assume you're talking about Druva, not me personally. >> Well, yeah, Druva, not you. Although maybe you did, I don't know. >> Yeah, I don't think. >> But that's quite an accomplishment, though. And quite an honor for the company. I mean, tell a little bit about that, about that process, and what do you think that means? What's that stamp of approval for what you guys are doing? >> Well, I think it's just, you know, like a lot of those lists, it's a recognition of the position that we're holding, right? I mean, Druva historically is really well known for their protection of endpoints and SaaS applications. They're expanding into data center and Cloud protection, but I think they're absolutely recognized as the leader in the protection of endpoints. >> Okay, so characterize the Cloud work you guys are doing. Like you said, this is a new move for you, I mean relatively new move, but the market's driving that way, right? >> Yeah. >> People starting to nod their head, and they're thinking, yep, this is where we need to be. >> Yeah, yep. >> So, what has been your strategy then, as far as facilitating what's no longer a trend, it's a way of life. >> Yeah, so I'd say first off, we are definitely unlike a lot of other players. We are a Cloud first company, in that, it's not a strategy, it's a way of life, so our entire application is built in and for the cloud, and by that I mean that it takes advantage of everything that the Cloud offers, right? And when you look at specifically AWS, a lot of backup software products use, well, they all use some kind of database, some kind of catalog to keep track of all the backups. And all of those catalogs, all of those databases, whether it's SQL Server or Db2 or Oracle, they all have scalability limits. We chose to use DynamoDB, which is an incredibly scalable no-SQL database. It's built and available in Amazon as a service, and then all of our products all run in Amazon, right? And so, we can scale both up and down to meet the requirements of a customer. So if we get a new customer. We had a customer that I can't mention by name, but they're a large company that started out with what we consider a small installation of about 10,000 laptops. And that was nice. And then it went well. And then there was a ransomware scare, and so they said, you know what, we're gonna go ahead and do everything. And so suddenly we needed to do 10 times as many laptops. Well, because of the way AWS is, we could scale both the database, the compute, and the storage all instantly to meet the demands of that client. And then once that's done, scale it back down to get back to a state of normal, right? So, for us the Cloud is sort of the core of who we are, and then the only expansion for us is actually protecting the Cloud. So, we've always used the Cloud as our destination, but now our newest offering, Apollo, actually is designed to protect starting with AWS and then expanding into the rest of the Amazon, well, I should say starting with EC2, and then eventually expanding into the rest of the AWS world. >> All right, so, with the tradition of endpoint protection and... >> You're gonna have to speak up, it's really loud in here. >> It's really loud, I'll make sure I'm yelling. So with the heritage that you've got of backing up endpoints and being able to protect endpoints, and now you're moving to protect Cloud workloads, as you say, you've got this Cloud heritage, but you're now looking at protecting workloads that live in the Cloud, what are some of the things that Druva's bringing from that endpoint knowledge that applies to those Cloud type workloads? >> Well I think the idea is that, you know, one of the things about the Cloud, people sort of view, I think there's steps of people using AWS, right? They sort of experiment, and they try out this and that, but once somebody really understands like we did, the things you can do when you can scale your VMs instantly and limitlessly, and your storage and your compute and your databases, once they go down that route, I think the fact that we, it's not necessarily the history of the endpoint itself, but the infrastructure that we built in order to protect those endpoints is already totally scalable and ready to meet the needs of however big of a workload that you'll put in AWS. >> Yeah, I often like to say that Cloud is a state of mind, so if you've already got that state of mind that I want to run my workloads in a Cloud-like way, well I want to be able to protect them in a Cloud-like way, and it sounds like that's really what you're trying to nail there. >> Yeah, and it's a big, because any like, I can look out and see multiple backup products available, and there's a lot of good backup products here. And any of them can run in the cloud, right? You can create a Linux VM or a Windows VM and install your backup software, but it's not going to magically become more scalable because you're running it in Amazon, right? So, designing the product for Amazon and that scalable way of doing things, that's why we talk about being Cloud native. >> Yeah, so how are you attracting customers who would have traditionally thought of you as an endpoint company. It's like, now you're actually saying, look we have these different offerings. So how are you starting to talk to those different kind of customers. How are you finding them and what is it that you're finding resonates with them as compared to some of the other options that they might have? >> Yeah, so as you probably know, I've been in the backup space now for, quarter of a century... (clearing throat) >> Literally wrote the book. >> Literally wrote the book, right? It's on O'Reilly. (laughing) Oreilly.com. >> We'll give you a plug later. >> Don't worry. >> Yeah, yeah, yeah. >> Literally wrote the book. >> Yeah, one thing I can say, there's a couple of things I can say about backups in general, in the average data center. One is, everybody hates their backup software. Right? Like, nobody likes it because it's so hard, right? It's so hard to configure, and using disc as a mechanism instead of tape as a primary mechanism, it's made things better, but it hasn't really solved it, right? It's still this really difficult to manage. There's this massive amount of infrastructure that has to be put in place to do all of that. And because that's so hard and it's so error prone and you're invisible or you're in trouble. No one cares about the millions of backups you get right, only the one restore you got wrong. And so what that translates into is the other truth, which is nobody wants to be the backup guy, right? >> I mean the way I got my first job in backups 24 years ago was a guy named Ron Rodriguez did not want to be the backup guy. >> Curtis, you're it. >> Yeah, you're it. And I within two months, had my first major failure as the backup guy for a 35 billion dollar company, and I thought I was done, I thought I was fired, like so many other backup people, and somehow just accidentally I ended up staying, and so what happens is, it's so hard. So, to go to your question, well what if it was simple, right? The situation is, the current system's not scalable. You're always buying another media server, you're always buying another tape driver, you're always buying another dedupe box. You know, you're always out of something, right? I remember having to go to my boss and being out of tapes. This is, you know, back when tapes were a thing. And I remember saying, "hey, I'm out of tapes." and she was like, we don't have budget." She's like, "what are our choices?" and I go, well, I can stop the backups. She's like, "that's not funny." I'm like, "that's our choice." Right? >> I have so much capacity here. >> These are our choices, right? And so she gave me the tapes that I needed, right? And so it's not scalable, the current system. You're always in need of some piece. It's also super expensive, right? And it's super hard. So we try to be the opposite of that. We try to be scalable, simple, and you know save people money. Right? I know we have a 4S thing. >> It's right there on the tip of your tongue. >> It's right there on the tip of my tongue. But basically we try to be the opposite of everything that backups are. So the big thing is, it's way easier. Just put a piece of software and magic happens, right? And if you're large enough data center that you need to do what we call seeding, where you have to use sneakernet to get the data to us, we have a system for that. If you have a large enough system where the RTO is not going to be able to be met by a copy that's on the other side of the internet, then we have a caching appliance that goes onsite to provide fast recovery. So it's like it's super simple, way less expensive. And I do mean way less expensive. I've seen some TCOs where we compete against other companies, we're even less expensive than renewing what you have, let alone going and buying. >> John: Replacing. >> And replacing it with something, because that happens all the times. Because people are always swapping their backup software, because the problem has got to be the backup software. Right? And I think in the end, it is, right? But, it's because that core architecture, that core way we've done backups, has essentially stayed the same since before I started. All we did was we changed tape to disc, right? And we introduced dedupe, which was great, but there's this technology that we call dedupe, that is really hard when you do it on the backend. You know, there's a company here who makes a lot of money on selling those appliances, right? Except it's really hard to do that, and so it's really expensive to do that. And then you gotta pay for one here and you gotta pay for one over there. With us, you don't buy that. You just go straight to us, and then because we're in AWS, it's already in three locations, right? And it's already offsite. >> Well Curtis, they said 24 years ago it wasn't gonna last, but it did. You made it, congratulations. >> Thanks. >> We appreciate the time here. >> Thanks. >> Thanks for being here with us on theCUBE and onto 175th. Next year who knows where you're going, right? >> Who knows where we're going. >> Excellent, Curtis Preston, joining us here from Druva. Back with more live from Las Vegas. We are at Reinvent at AWS. Back with more in a bit.

>> Narrator: Live from Silicon Valley, it's theCUBE. Covering mobile world congress 2017. Brought to you by Intel. >> Okay, welcome back everyone, we're here live in Palo Alto for day two of two days of Mobile World Congress special coverage here in Palo Alto, where we're bringing all the folks in Silicon Valley here in the studio to analyze all the news and commentary of which we've been watching heavily on the ground in Barcelona. We have reporters, we have analysts, and we have friends there, of course, Intel is there as well as SAP, and a variety of other companies we've been talking to on the phone and all those interviews are on YouTube.com/siliconANGLE. And we're here with Chuck Tato, who's the marketing director of the data center of communications with Intel around the FPGA, which is the programmable chips, formerly with the Alterra Group, now a part of Intel, welcome to theCUBE, and thanks for coming on. >> Thank you for having me. So, actually all the rage Mobile World Congress Intel, big splash, and you guys have been, I mean, Intel has always bene the bellweather. I was saying this earlier, Intel plays the long game. You have to in the chips games. You got to build the factories, build fabs. Most of all, have been the heartbeat of the industry, but now doing more of less chips, Most of all, making them smaller, faster, cheaper, or less expensive and just more power. The cloud does that. So you're in the cloud data center group. Take a second to talk about what you guys do within Intel, and why that's important for folks to understand. >> Sure. I'm part of the programmable solutions group. So the programmable solutions group primarily focuses on field programmable gate array technology that was acquired through the Alterra acquisition at Intel. So our focus in my particular group is around data center and Coms infrastructure. So there, what we're doing is we're taking the FPGAs and we're applying them to the data center as well as carrier infrastructure to accelerate things, make them faster, make them more repeatable, or more terministic in nature. >> And so, that how it works, as you were explaining beforehand, kind of, you can set stream of bits at it and it changes the functionality of the chip. >> Yes. So essentially, an FPGA, think of it as a malleable set of resources. When I say that, you know, you can create, it's basically a fabric with many resources in an array. So through the use of a bit stream, you can actually program that fabric to interconnect the different elements of the chip to create any function that you would like, for the most part. So think of it as you can create a switch, you can create a classification engine, and things like that. >> Any why would someone want that functionality versus just a purpose-built chip. >> Perfect question. So if you look at, there's two areas. So in the data center, as well as in carrier infrastructure, the workloads are changing constantly. And there's two problems. Number one you could create infrastructure that becomes stranded. You know, you think you're going to have so much traffic of a certain type and you don't. So you end up buying a lot of purpose-built equipment that's just wrong for what you need going forward. So by building infrastructure that is common, so it kind of COTS, you know, on servers, but adding FPGAs to the mix allows you to reconfigure the networking within the cloud, to allow you to address workloads that you care about at any given time. >> Adaptability seems to be the key thing. You know kind of trends based upon certain things, and certainly the first time you see things, you've got to figure it out. But this gives a lot of flexibility, it sounds like. >> Exactly. Adaptability is the key, as well as bandwidth, and determinism, right? So when you get a high bandwidth coming into the network, and you want to something very rapidly and consistently to provide a certain service level agreement you need to have circuits that are actually very, very deterministic in nature. >> Chuck, I want to get your thoughts on one of the key things. I talked with Sandra Reddy, Sandra Rivera, sorry, she was, I interviewed her this morning, as well as Dan Rodriguez, and Caroline Chan, Lyn Comp as well. Lot of different perspectives. I see 5G as big on one hand, have the devices out there announcing on Sunday. But what was missing, and I think Fortune was the really, the only one I saw pick up on this besides SiliconANGLE, on terms of the coverage was, there's a real end-to-end discussion here around not just the 5G as the connectivity piece that the carriers care about, but there's the under-the-hood work that's changing in the Data Center. And the car's a data center now, right? >> Yeah. >> So you have all these new things happening, IOT, people with sensors on them, and devices, and then you've got the cloud-ready compute available, right? And we love what's happening with cloud. Infinite compute is there and makes data work much better. How does the end-to-end story with Intel, and the group that you're in, impact that and what are some of the use cases that seem to be popping up in that area. >> Okay, so that's a great question, and I guess some of the examples that I could give of where we're creating end-to-end solutions would be in wireless infrastructure, as you just mentioned. As you move on to 5G infrastructure, the goal is to increase the bandwidth by 100X and reduce the latency by orders of magnitude. It's a very, very significant challenge. To do that is quite difficult, to do it just in software. FPGA is a perfect complement to a software-based solution to achieve these goals. For example, virtual switching. It's a significant load on the processors. By offloading virtual switching in an FPGA, you an create the virtual switch that you need for the particular workload that you need. Workloads change, depending on what type of services you're offering in a given area. So you can tailor it to exactly what you need. You may or may not need6 high levels of security, so things like IPsec, yo6u know, at full line rate, are the kind of things that FPGAs allow you to add ad hoc. You can add them where you need them, when you need them, and change them as the services change. >> It sounds like, I'd never thought about that, but it sounds like this is a real architectural advantage, because I'd never thought about offloading the processor, and we all know we all open up or build our PCs know that the heat syncs only get bigger and bigger, so that people want that horsepower for very processor-intensive things. >> Absolutely. So we do two things. One is we do create this flexible infrastructure, the second thing is we offload the processor for things that you know, free up cores to do more value-added things. >> Like gaming for, my kids love to see that gaming. >> Yes. There's gaming, virtual reality, augmented virtual reality, all of those things are very CPU intensive, but there's also a compute-intensive aspect. >> Okay, so I've got to get your take on this. This is kind of a cool conversation because that's, the virtual reality and augmented reality really are relevant. That is a key part of Mobile World Congress, beside the IOT, which I think is the biggest story this year, is IOT, and all the security aspects of it around, and all that good stuff. And that's really where the meat is, but the real sex appeal is the virtual reality and augmented reality. That's an example of the new things that have popped out of the woodwork, so the question for you is for all these new-use cases that I have found that emerge, there will be new things that pop out of the woodwork. "Oh, my God, I don't have to write software for that, There's an app for that now." So the new apps are going to start coming in, whether it's something new and cool on a car, Something new and cool on a sensor, something new and cool in the data center. How adaptive are you guys and how do you guys kind of fit into that kind of preparing for this unknown future. >> Well, that's a great question, too. I like to think about new services coming forward as being a unique blend of storage, compute, and networking, and depending on the application and the moment in that application, you may have to change that mix in a very flexible way. So again, the FPGA provides you the ability to change all of those to match the application needs. I'm surprised as we dig into applications, you know, how many different sets of needs there are. So each time you do that, you can envision, reprogramming your FPGA. So just like a processor, it's completely reprogrammable. You're not going to reprogram it in the same instantaneous way that you do in software, but you can reprogram it on the fly, whatever you would like. >> So, I'm kind of a neophyte here, so I want to ask some dumb questions, probably be dumb to you, but common to me, but would be like, okay, who writes bits? Is it the coders or is it someone on the firmware side, I'm trying to understand where the line is between that hardened top of kind of Intel goodness that goes on algorithmically or automatically, or what programmers do. So think full-stack developer, or a composer, a more artisan type who's maybe writing an app. Are there both access points to the coding, or is it, where's the coding come from? >> So there's multiple ways that this is happening. The traditional way of programming FPGA is the same way that you would design any ASIC in the industry, right? Somebody sits down and they write RTL, they're very specialized programmers However, going forward, there's multiple ways you an access it. For one, we're creating libraries of solutions that you can access through APIs that are built into DPDK, for example on Xeon. So you can very easily access accelerated applications and inline applications that are being developed by ourselves as well as third parties. So there's a rich eco system. >> So you guys are writing hooks that go beyond being the ASIC special type, specialist programming. >> Absolutely. So this makes it very accessible to programmers. The acceleration that's there from a library and purpose-built. >> Give me an example, if you can. >> Sure, virtual switch. So in our platform for NFE, we're building in a virtual switch solution, and you can program that just like you know, totally in software through DPDK. >> One of the things that coming up with NFE that's interesting, I don't know if this y6our wheelhouse or not, but I want to throw it out there because it's come up in multiple interviews and in the industry. You're seeing very cool ideas and solutions roll out, and I'll give, you know, I'll make one up off the top of my head, Openstack. Openstack is a great, great vision, but it's a lot of fumbling in the execution of it and the cost of ownership goes through the roof because there's a lot of operation, I'm overgeneralizing certain use-case, not all Openstack, but in generally speaking, I do have the same problem with big data where, great solution-- >> Uh-huh. >> But when you lay out the architect and then deploy it there's a lot of cost of ownership overhead in terms of resources. So is this kind of an area that you guys can help simplify, 'cause that seems to be a sticking point for people who want to stand up some infrastructure and do dev ops and then get into this API-like framework. >> Yes, from a hardware perspective, we're actually creating a platform, which includes a lot of software to tie into Openstack. So that's all preintegrated for you, if you will. So at least from a hardware interface perspective, I can say that that part of the equation gets neutralized. In terms of the rest of the ownership part, I'm not really qualified to answer that question. >> That's good media training, right there. Chuck just came back from Intel media training, which is good. We got you fresh. Network transformation, and at the, also points to some really cool exciting areas that are going on that are really important. The network layer you see, EDFE, and SDN, for instance, that's really important areas that people are innovating on, and they're super important because, again, this is where the action is. You have virtualization, you have new capabilities, you've got some security things going down lower in the stack. What's the impact there from an Intel perspective, helping this end-to-end architecture be seamless? >> Sure. So what we are doing right now is creating a layer on top of our FPGA-based SmartNIC solutions, which ties together all of that into a single platform, and it cuts across multiple Intel products. We have, you know, Xeon processors integrated with FPGAs, we have discreet FPGAs built onto cards that we are in the process of developing. So from a SmartNIC through to a fully-integrated FPGA plus Xeon processor is one common framework. One common way of programming the FPGA, so IP can move from one to the other. So there's a lot of very neat end-to-end and seamless capabilities. >> So the final question is the customer environment. I would say you guys have a lot of customers out there. The edge computing is a huge thing right now. We're seeing that as a big part of this, kind of, the clarity coming out of Mobile World Congress, at least from the telco standpoints, it's kind of not new in the data center area. The edge now is redefined. Certainly with IOT-- >> Yes. >> And IOTP, which we're calling IOTP app for people having devices. What are the customer challenges right now, that you are addressing. Specifically, what's the pain points and what's the current state-of-the-art relative to the customer's expectations now, that they're focused on that you guys are solving. >> Yeah, that's a great question, too. We have a lot of customers now that are taking transmission equipment, for example, mobile backhaul types of equipment, and they want to add mobile edge computing and NFE-type capabilities to that equipment. The beauty of what we're doing is that the same solution that we have for the cloud works just as well in that same piece of equipment. FPGAs come in all different sizes, so you can fit within your power envelope or processors come in all different sizes. So you can tailor your solution-- >> That's super important on the telco side. I mean, power is huge. >> Yes, yes, and FPGAs allow you to tailor the power equation as much as possible. >> So the question, I think is the next question is, does this make it cloud-ready, because that's term that we've been hearing a lot of. Cloud-ready. Cause that sounds like what you're offering is the ability to kind of tie into the same stuff that the cloud has, or the data center. >> Yes, exactly. In fact, you know, there's been very high profile press around the use of FPGAs in cloud infrastructure. So we're seeing a huge uptick there. So it is getting cloud-ready. I wouldn't say it's perfectly there, but we're getting very close. >> Well the thing that's exciting to me, I think, is the cloud native movement really talks about again, you know, these abstractions with micro services, and you mentioned the APIs, really fits well into some of the agilenesss that needs to happen at the network layer, to be more dynamic. I mean, just think about the provisioning of IOT. >> Chuck: Yeah. >> I mean, I'm a telco, I got to provision a phone, that's get a phone number, connect on the network, and then have sessions go to the base station, and then back to the cloud. Imagine having to provision up and down zillions of times those devices that may get provision once and go away in an hour. >> Right. >> That's still challenging, give you the network fabric. >> Yes. It is going to be a challenge, but I think as common as we can make the physical infrastructure, the better and the easier that's going to be, and as we create more common-- >> Chuck, final question, what's your take from Mobile World Congress? What are you hearing, what's your analysis, commentary, any kind of input you've heard? Obviously, Intel's got a big presence there, your thoughts on what's happening at Mobile World Congress. >> Well, see I'm not at Mobile World Congress, I'm here in Silicon Valley right now, but-- >> John: What have you heard? >> Things are very exciting. I'm mostly focused on the NFE world myself, and there's been just lots and lots of-- >> It's been high profile. >> Yes, and there's been lots of activity, and you know, we've been doing demos and really cool stuff in that area. We haven't announced much of that on the FPGA side, but I think you'll be seeing more-- >> But you're involved, so what's the coolest thing in NFE that you're seeing, because it seems to be crunch time for NFE right now. This is a catalyst point where at least, from my covering NFE, and looking at it, the iterations of it, it's primetime right now for NFE, true? >> Yeah, it's perfect timing, and it's actually perfect timing for FPGA. I'm not trying to just give it a plug. When you look at it, trials have gone on, very significant, lots of learnings from those trials. What we've done is we've identified the bottlenecks, and my group has been working very hard to resolve those bottlenecks, so we can scale and roll out in the next couple of years, and be ready for 5G when it comes. >> Software definer, Chuck Tato, here from Intel, inside theCUBE, breaking down the coverage from Mobile World Congress, as we wind down our day in California, the folks in Spain are just going out. It should be like at 12:00 o'clock at night there, and are going to bed, depending on how beat they are. Again, it's in Barcelona, Spain, it's where it's at. We're covering from here and also talking to folks in Barcelona. We'll have more commentary here in Silicon Valley on the Mobile World Congress after this short break. (techno music)