>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security, by providing encrypted computation on sensitive data and isolating data, and apps that are fenced off enclave during processing. The concept of, I got to start over. I fucked that up, I'm sorry. That's not right, what I said was not right. On Dave in five, four, three. Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data, isolating data from apps and a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space, where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show. But before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing, I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data in transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system, ARM, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now, the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images, updates, different services and the entire code flow aren't directly addressed by memory encryption. Rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Bronco, sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign from memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the consortium is seen as limiting by AWS. This is my guess, not AWS' words. But I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got to lead with this Annapurna acquisition. It was way ahead with ARM integration, and so it's probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names, including Aem, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic. Nelly Porter is Head of Product for GCP Confidential Computing and Encryption and Dr. Patricia Florissi is the Technical Director for the Office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again, security or infrastructure securities that I usually own. And we are talking about encryption, end-to-end encryption, and confidential computing is a part of portfolio. Additional areas that I contribute to get with my team to Google and our customers is secure software supply chain because you need to trust your software. Is it operate in your confidential environment to have end-to-end security, about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay, Patricia? >> Well, I am a Technical Director in the Office of the CTO, OCTO for short in Google Cloud. And we are a global team, we include former CTOs like myself and senior technologies from large corporations, institutions and a lot of success for startups as well. And we have two main goals, first, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we advice Google and Google Cloud Engineering, product management on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool and one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they run them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end-to-end protection of our customer's data when they bring the workloads and data to cloud thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain? Do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential computing matters because at the end of the day, it reduces more and more the customer's thrush boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now, we are also encrypting data while in the use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused but very beneficial for highly regulated industries, it applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting and I want to understand that a little bit more but I got to push you a little bit on this, Nellie if I can, because there's a narrative out there that says confidential computing is a marketing ploy I talked about this up front, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine Dave, with this statement. But the most importantly is we mixing a multiple concepts I guess, and exactly as Patricia said, we need to look at the end-to-end story, not again, is a mechanism. How confidential computing trying to execute and protect customer's data and why it's so critically important. Because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud offering to offer additional stronger isolation, they called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants running on the same host but also us because they don't need to worry about against rats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers to tenants from us. We also writing code, we also software providers, we also make mistakes or have some zero days. Sometimes again us introduce, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and among those tenants, we really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together with very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. You know, operator access. Yeah, maybe I trust my cloud's provider, but if I can fence off your access even better, I'll sleep better at night separating a code from the data. Everybody's ARM, Intel, AMD, Nvidia and others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift though, no changing the apps and performing and having very, very, very low latency and scale as any cloud can, some things that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done, and as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine within the whole entire host has integrity guarantee, means nobody changing my code on the most low level of system, and we introduce this in 2017 called Titan. So our specific ASIC, specific inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing included, but for confidential computing is what we have to change, we bring in AMD or future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate intelligent not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD Secure Processor, it's special ASIC best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop spark capability. We offer all of that and those keys are not available to us. It's the best case ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, "Where's the key? Who will have access to the key?" because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing why it's so revolutionary technology, us cloud providers who don't have access to the keys, they're sitting in the hardware and they fed to memory controller. And it means when hypervisors that also know about this wonderful things saying I need to get access to the memories, that this particular VM I'm trying to get access to. They do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but most importantly in hardware not exportable. And it means now you will be able to have this very interesting world that customers or cloud providers will not be able to get access to your memory. And what we do, again as you can see, our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you've running in VM, you actually see your memory clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box, no, no, no, no, no, you will now be able to do it. Now, you'll see cyber test and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified and OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine Dave, that's increasing and it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is in addition to, let's go pre-confidential computing days, what are the sort of new guarantees that these hardware based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret. The code is actually looking at the data, only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tempered with. So the application, the workload as we call it, that is processing the data is also has not been tempered and preserves integrity. I would also say that this is all verifiable, so you have attestation and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call sealing, this idea that the secrets have been preserved and not tempered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications is transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before, I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way, and it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate and open. So again our operating system, we working this operating system repository OS is OS vendors to ensure that all capabilities that we need is part of the kernels are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors kernel, host kernel to support this capability and it means working this community to ensure that all of those pages are there. We also worked with every single silicon vendor as you've seen, and it's what I probably feel that Google contributed quite a bit in this world. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is following the lead and also announcing a trusted domain extension, very similar architecture and no surprise, it's a lot of work done with our partners to convince work with them and make this capability available. The same with ARM this year, actually last year, ARM announced future design for confidential computing, it's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at Station Sig, the community-based systems that we want to build, and influence, and work with ARM and every other cloud providers to ensure that they can interop. And it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers really. And to do it, we need to continue what we are doing, working open and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem in different regions and then of course data sovereignty comes up, typically public policy, lags, the technology industry and sometimes it's problematic. I know there's a lot of discussions about exceptions but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove the data is deleted with a hundred percent certainty, you got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it at all, that's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty, where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the cloud and that you can use open source. Now, let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing need to typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection, we want to ensure the confidentiality, and integrity, and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data, and this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and logging accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty, we care about whether it resides, who is operating on the data, but the moment that the data is being processed, I need to trust that the processing of the data we abide by user's control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now, the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is in cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user's control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year-end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post, so I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it will become utility, it will become TLS. As of freakin' 10 years ago, we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heeding and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you. And Patricia, what's your prediction? >> I would double that and say, hey, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations, and for organizations that want to collaborate with each other, confidential computing will become the norm, it will become the default, if I say mode of operation. I like to compare that today is inconceivable if we talk to the young technologists, it's inconceivable to think that at some point in history and I happen to be alive, that we had data at rest that was non-encrypted, data in transit that was not encrypted. And I think that we'll be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis, there's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much, yeah. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition in our view will moderate price hikes and at the end of the day, this is under-the-covers technology that essentially will come for free, so we'll take it. I want to thank our guests today, Nelly and Patricia from Google. And thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters, and Rob Hoof is our editor-in-chief over at siliconangle.com, does some great editing for us. Thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or DM me at D Vellante, and you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (subtle music)

>> Live from Las Vegas it's theCUBE covering Adobe Summit 2019. Brought to you by Accenture Interactive. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Vegas at the Adobe Summit 2019. I think there's about 17,000 people. The first time we've been here but we've been excited to be here. There's a crazy good buzz and energy and actually a ton of CUBE alumni here at Adobe. We're greeting old friends but it's always great to meet new friends. And coming off of great mention in the keynote this morning we're excited to have Santiago Aldana. He is the CDO and CTO of Avianca. Welcome. >> Thank you, John. >> So I was surprised this morning, we were watching the keynote and there's Satya Nadella and he has a shout-out for you guys. >> It was quite a surprise. It was very engaging and I'm happy to hear that. >> Yeah, congratulations. >> Thank you. >> And a little fact, you guys are the second oldest commercial airline, he said. I had no idea. >> That's right, we've been flying for almost 100 years. It's our 100th anniversary this year. >> Awesome. >> So great, great. >> Well, congratulations. >> Thank you. >> So air travel's a really interesting industry because it's growing like crazy in terms of the total number of passenger miles, right? More people are flying all the time. But it's got to be super competitive. You got to worry about fuel costs. A seat mile is a seat mile. So there's all kinds of interesting ways to compete. You guys are really into it, you've been successful for 100 years, so how do you differentiate what you guys are doing and continue to evolve and be successful? >> So there's several things. If you look 10 years back we used to be a domestic airline. We used to have around 30 planes, now we're around 170 planes. We're the second largest airline in Latin America. That has been a huge growth. >> Wait, how long did you do that? >> That's for the last 10 years. >> 10 years you went from 30 planes to 170. >> To 170, 180. >> And domestic to international. >> To a Latin American airline. >> That's a big move. >> That's a big move but we're shifting our emphasis, going more, rather than growth, going to profitability. And to make that profitability we have to make the strong transformation to make that happen. >> So for profitability there's all kinds of things that go in there, there's higher utilization, there's hopefully everybody buys Teslas so the gas doesn't cost as much for the airplanes. How are you focusing on profitability? 'Cause here we're at Adobe, all the talk's about experience, experience, experience. If I'm flying on your plane, I want to get a good deal and keep everything good but I'm not necessarily that worried about your profitability. >> So let me tell you a little bit about that. If you think about an airline we're just the distance between our customers and their dreams. We're just the distance. So the customer doesn't want to go to security. The customer doesn't want to go to the whole hassle of planning the trip. Our purpose is reducing that distance, reducing that effort, and when we reduce that effort we're going to self-service, we're going to personalize, to make life easier for our customers. That's the basic challenge. And that has to do with three main areas. One, knowing our customer. The other one is, making sure that the value proposal for that customer journey is proper. So that's operational work. And the other one is providing our employees with enough information to make that happen. All of those are working along data, and data to be able to provide a real value proposal to making that happen. The customer has to be in the center of our strategy and that's where we have to be working all the time. And when you do this, it's not about technology, it's about the customer. And being that, about the customer, the strongest challenge is not technology but people, making people change so we that can provide the value proposals to our customers. >> So what are some of the things that you did to enable the experience of my engagement, whether it's electronic or whether it's when I'm talking to that person at the counter, checking in or getting on my flight, how have you helped them provide me a better experience? >> You talk as if it was part of the past. To be honest, it's a journey, we're still working on that. There are several things that we did last year, a whole bunch of things. We changed our app, we changed our website, we changed our interaction with our customers with data. And regarding Adobe, we're here at Adobe, we implemented a whole set of tools. So AEM, the website is a new thing. Regarding Microsoft we implemented a CRM to know about our customers. We changed our app, and the app is like a platform with which we're transforming the customer journey. What we have to do at the end of the game is changing those touch points so that those require less effort from our customer, they're more seamless and we are able to personalize and know in advance what the customer is looking for to provide alternatives. And that makes it more seamless. So we're in that process of doing data-centered decision making to reduce that effort from our customers and make things happen. >> So as you've gone through this journey to date what are some of the surprising things that came up that you just didn't expect at all, on a positive side? And then what were some of the negative things that you didn't know, that were so negative that now you've kind of removed? >> Okay, so I've been here in this business and Avianca just for the last two years, so if you talk about surprises this is my first time in airlines. I wasn't expecting this to be so challenging. >> Well, it's good to come at it from a fresh point of view, absolutely. >> I've been in banking, I've been in telcos. Believe me, there's a huge technical depth, there's a lot of complexity, and bringing this customer information up to the table, it's been challenging. Lots of things going together. Surprises, yeah, we have to work with our employees. We have to transform that culture. We have to move towards a more testing ... Having experiments iterating and learning from that process. And that takes time and that requires a lot change of culture. The other one is being more agile and that's more easily said than done. So making the teams be more collaborative. And working with partners. We decided to choose a handful of partners to make this transformation work. And those partners, that's not one thing that you just plug and play, you have to make it work and that requires a lot of effort. Even if it's big, strong, world-wide, world-level sponsors and partners, it requires engaging and making them work together. At the end it's about people in every part. And making people work together, that's a challenge. That's a challenge. >> You've got the whole gamut too, 'cause you've got the front line people that are directly engaged with the client, whether again it be at the gate agent or on the telephone or processing those things all the way back to the senior management and the operations which I'm sure are not only regulated and very very finely detailed for safety and everything else. So that's got to be hard to try to drive transformation in what was probably a pretty rigid situation. >> It is, that's why you have to choose what to do. And probably you don't know how to do it at the beginning but you know what you want to achieve. And that requires a more iteration way of learning, experimenting and finding a way. That's regarding agility. And that's where you work with partners to also leapfrog and move faster forward unto this. That's where we choose partners as Accenture, Adobe, Microsoft, SAP, and Amadeus. And they're moving us forward unto that. >> So what are some of the ways that you're trying to measure success? What are some of the things you're tracking as you go through this transformation? >> Well, several of them. Let me talk just about a couple of them. One of the things that we have to do is make the buying process easier. We're starting way behind, strong technical depth, and we have to decouple our systems to make those steps that our customer has to do, make them fewer, easier, and changing the whole booking flow. But to do that, we don't have the answer. First we have to decouple the system, the legacy systems, and then we have to learn from our customers. We have to do a lot of A/B testing to see what works better. Test and see if the process is better accepted by our customers, learn from that, probably fail, do it again, iterate it and do it again. And that process we have to engage unto that. The other one is ... So that's one of the areas. But the other one is, how can we make sure that the operational value proposal takes place? Since we have been growing for the last 10 years so much, we started from a local airline to the second biggest airline in Latin America, but that growth is a little bit disorganized and we have to set things up to make it happen. We have to provide a lot more data and connectivity to all our employees at the airports, at the counters, at the call center, and providing them with more customer information to make it happen. >> Right, so you're on that process, so you're starting to deliver new data to the gate agents and the people on the front line? >> Sure. >> So how are they reacting to that? Do they like to be empowered, are they afraid of being empowered, are they saying, "Ah, finally I have the information "in front of me that I can take care of this traveler?" >> So there's not one answer for that. In some cases we empower them and they enjoy a lot and they say, "Hey, finally we got this." For example, we are giving our ... This is a recent project that we launched at the airport. We're providing them data through mobility, making the turnaround of our planes faster, and we're giving them much more data. Before then, they had to call everywhere to find what was happening. Now they have it at their hands, and that's different. So that changes the whole thing and they look forward to that. At other times, we sometimes do mistakes also. We provided more information through the apps to our pilots. They were finding that awesome. But then some of the information that they used to have, we didn't get it. So we have to iterate it and give it and then they start loving it. Regarding our customers, which is the other side, it's not internal employees, we do some things in which we test and sometimes they say, "Oh, that was not what we were expecting." So we have to learn from that. I mean, it's not about making a huge waterfall project. It's about learning in the process, failing, and iterating and making it happen again and again. It's a whole journey. >> We just had our last guest, he talked about trying to move this stuff to the cloud. It's like, first time didn't work, second time didn't work, third time, hey, now it's working. So you don't know until you know, right? And what we hear over and over is as you start this top-level transformation project you uncover a bunch of stuff under the covers that has to be reworked to support what you're trying to do on the front end. >> That's right. >> I assume it's a lot of the same thing that you found? >> You're exactly right, there's a lot of things in that way. On all three areas. Customer, on customer we didn't have customer information, we didn't even have a CRM. So we implemented our CRM at a huge fast pace that we did it, in a year we already had it. The app and the website, we have to totally remake it, and getting more information from that and getting personalized information regarding that. That's technical depth, I was not expecting that to be there. >> So I'm just curious, what was the catalyst of this transformation and this growth? Were you trying to put in systems to support the growth that you did from going from a relatively small domestic airline to an international, or are you trying to set the table for continued growth, to continue on that growth path? That's a pretty aggressive growth path. >> It's a little bit more simple than that and I'm going to be blunt here. Three years ago the board at Avianca was doing a search for a new CEO. That's my boss right now. He came over three years ago. He used to be the president for Microsoft in Latin America. In the interviews they told him a lot of things. And after he was questioned and doing the interview, he said, "Okay, let me say this now. "Are you asking me to make Avianca "a digital company flying airplanes?" And they said, "Yeah, that's exactly right. "That's what we want." So that was the initial pace. That was three years ago. I joined the team two years ago. There was already a vision, and that vision is making things easier and effortless for the customer. That's part of what we're trying to build. And that is before, during, and after the trip. If we are able to do that we're reducing costs, we're making it simpler. The whole process is about being simpler, taking away complexity, making sure that our operations are better, and that's taking away complexity. You can do that through technology also. But again, the biggest challenge is probably not technology. It's a cultural change and it's the leadership required to move on and make our employees, our customers, take advantage of it. >> Bold move by the board and a bold move by the CEO but we hear it all the time. Everybody's a digital company now, it's just what product or service do you happen to wrap it around? So what a great story. >> Thank you. And yeah, again, we got to go more data-centered, we have to know our customer better. If we want to do something personalized the only way is through the data. We have to know in advance what our customers are requesting and trying to make it easier for all of them, and that's the data. >> Well Santiago, thanks for sharing your story. And again congratulations on the keynote shout-out. >> Thank you, thanks a lot. >> All right. He's Santiago, I'm Jeff, you're watching theCUBE. We're at Adobe Summit 2019 in Las Vegas. Thanks for watching, we'll see you next time. (lively electronic music)

>> Announcer: Live from Midtown Manhattan, it's theCUBE, covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsor. >> Okay, welcome back, everyone, to our live special CUBE coverage in New York City in Manhattan, we're here in Hell's Kitchen for theCUBE's exclusive coverage of our Big Data NYC event and Strata Data, which used to be called Strata Hadoop, used to be Hadoop World, but our event, Big Data NYC, is our fifth year where we gather every year to see what's going on in big data world and also produce all of our great research. I'm John Furrier, the co-host of theCUBE, with Peter Burris, head of research. Our next guest, Itamar Ankorion, who's the Chief Marketing Officer at Attunity. Welcome back to theCUBE, good to see you. >> Thank you very much. It's good to be back. >> We've been covering Attunity for many, many years. We've had many conversations, you guys have had great success in big data, so congratulations on that. But the world is changing, and we're seeing data integration, we've been calling this for multiple years, that's not going away, people need to integrate more. But with cloud, there's been a real focus on accelerating the scale component with an emphasis on ease of use, data sovereignty, data governance, so all these things are coming together, the cloud has amplified. What's going on in the big data world, and it's like, listen, get movin' or you're out of business has pretty much been the mandate we've been seeing. A lot of people have been reacting. What's your response at Attunity these days because you have successful piece parts with your product offering? What's the big update for you guys with respect to this big growth area? >> Thank you. First of all, the cloud data lakes have been a major force, changing the data landscape and data management landscape for enterprises. For the past few years, I've been working closely with some of the world's leading organizations across different industries as they deploy the first and then the second and third iteration of the data lake and big data architectures. And one of the things, of course, we're all seeing is the move to cloud, whether we're seeing enterprises move completely to the cloud, kind of move the data lakes, that's where they build them, or actually have a hybrid environment where part of the data lake and data works analytics environment is on prem and part of it is in the cloud. The other thing we're seeing is that the enterprises are starting to mix more of the traditional data lake, the cloud is the platform, and streaming technologies is the way to enable all the modern data analytics that they need, and that's what we have been focusing on on enabling them to use data across all these different technologies where and when they need it. >> So, the sum of the parts is worth more if it's integrated together seems to be the positioning, which is great, it's what customers want, make it easier. What is the hard news that you guys have, 'cause you have some big news? Let's get to the news real quick. >> Thank you very much. We did, today, we have announced, we're very excited about it, we have announced a new big release of our data integration platform. Our modern platform brings together Attunity Replicate, Attunity Compose for Hive, and Attunity Enterprise Manager, or AEM. These are products that we've evolved significantly, invested a lot over the last few years to enable organizations to use data, make data available, and available in the real time across all these different platforms, and then, turn this data to be ready for analytics, especially in Hive and Hadoop environments on prem and now also in the cloud. Today, we've announced a major release with a lot of enhancements across the entire product line. >> Some people might know you guys for the Replicate piece. I know that this announcement was 6.0, but as you guys have the other piece part to this, really it's about modernization of kind of old-school techniques. That's really been the driver of your success. What specifically in this announcement makes it, you know, really work well for people who move in real time, they want to have good data access. What's the big aha for the customers out there with Attunity on this announcement? >> That's a great question, thank you. First of all is that we're bringing it all together. As you mentioned, over the past few years, Attunity Replicate has emerged as the choice of many Fortune 100 and other companies who are building modern architectures and moving data across different platforms, to the cloud, to their lakes, and they're doing it in a very efficient way. One of the things we've seen is that they needed the flexibility to adapt as they go through their journey, to adapt different platforms, and what we give them with Replicate was the flexibility to do so. We give them the flexibility, we give them the performance to get the data and efficiency to move only the change of the data as they happen and to do that in a real-time fashion. Now, that's all great, but once the data gets to the data lake, how do you then turn it into valuable information? That's when we introduced Compose for Hive, which we talked about in our last session a few month ago, which basically takes the next stage in the pipeline picking up incremental, continuous data that is fed into the data lake and turning those into operational data store, historical data stores, data store that's basically ready for analytics. What we've done with this release that we're really excited about is putting all of these together in a more integrated fashion, putting Attunity Enterprise Manager on top of it to help manage larger scale environments so customers can move faster in deploying these solutions. >> As you think about the role that Attunity's going to play over time, though, it's going to end up being part of a broader solution for how you handle your data. Imagine for a second the patterns that your customers are deploying. What is Attunity typically being deployed with? >> That's a great question. First of all, we're definitely part of a large ecosystem for building the new data architecture, new data management with data integration being more than ever a key part of that bigger ecosystem because as all they actually have today is more islands with more places where the data needs to go, and to your point, more patterns in which the data moves. One of those patterns that we've seen significantly increase in demand and deployment is streaming. Where data used to be batch, now we're all talking about streaming. Kafka has emerged as a very common platform, but not only Kafka. If you're on Amazon Web Services, you're using Kinesis. If you're in Azure, you're using Azure Event Hubs. You have different streaming technologies. That's part of how this has evolved. >> How is that challenge? 'Cause you just bring up a good point. I mean, with the big trend that customers want is they want either the same code basis on prem and that they have the hybrid, which means the gateway, if you will, to the public cloud. They want to have the same code base, or move workloads between different clouds, multi-cloud, it seems to be the Holy Grail, we've identified it. We are taking the position that we think multi-cloud will be the preferred architecture going forward. Not necessarily this year, but it's going to get there. But as a customer, I don't want to have to rebuild employees and get skill development and retraining on Amazon, Azure, Google. I mean, each one has its own different path, you mentioned it. How do you talk to customers about that because they might be like, whoa, I want it, but how do I work in that environment? You guys have a solution for that? >> We do, and in fact, one of the things we've seen, to your point, we've seen the adoption of multiple clouds, and even if that adoption is staged, what we're seeing is more and more customers that are actually referring to the term lock-in in respect to the cloud. Do we put all the eggs in one cloud, or do we allow ourselves the flexibility to move around and use different clouds, and also mitigate our risk in that respect? What we've done from that perspective is first of all, when you use the Attunity platform, we take away all the development complexity. In the Attunity platform, it is very easy to set up. Your data flow is your data pipelines, and it's all common and consistent. Whether you're working on prem, whether you work on Amazon Web Services, on Azure, or on Google or other platforms, it all looks and feels the same. First of all, and you solve the issue of the diversity, but also the complexity, because what we've done is, this is one of the big things that Attunity is focused on was reducing the complexity, allowing to configure these data pipelines without development efforts and resources. >> One of the challenges, or one of the things you typically do to take complexity out is you do a better job of design up front. And I know that Attunity's got a tool set that starts to address some of of these things. Take us a little bit through how your customers are starting to think in terms of designing flows as opposed to just cobbling together things in a bespoke way. How is that starting to change as customers gain experience with large data sets, the ability, the need to aggregate them, the ability to present them to developers in different ways? >> That's a great point, and again, one of the things we've focused on is to make the process of developing or configuring these different data flows easy and modular. First, while in Attunity you can set up different flows in different patterns, and you can then make them available to others for consumption. Some create the data ingestion, or some create the data ingestion and then create a data transformation with Compose for Hive, and with Attunity Enterprise Manager, we've now also introduced APIs that allow you to create your own microservices, consuming and using the services enabled by the platform, so we provide more flexibility to put all these different solutions together. >> What's the biggest thing that you see from a customer standpoint, from a problem that you solve? If you had to kind of lay it out, you know the classic, hey, what problem do you solve? 'Cause there are many, so take us through the key problem, and then, if there's any secondary issues that you guys can address customers, that seems the way conversation starts. What are key problems that you solve? >> I think one of the major problems that we solve is scale. Our customers that are deploying data lakes are trying to deploy and use data that is coming, not from five or 10 or even 50 data sources, we work at hundreds going on thousands of data sources now. That in itself represents a major challenge to our customers, and we're addressing it by dramatically simplifying and making the process of setting those up very repeatable, very easy, and then providing the management facility because when you have hundreds or thousands, management becomes a bigger issue to operationalize it. We invested a lot in a management facility for those, from a monitoring, control, security, how do you secure it? The data lake is used by many different groups, so how do we allow each group to see and work only on what belongs to that group? That's part it, too. So again, the scale is the major thing there. The other one is real timeliness. We talked about the move to streaming, and a lot of it is in order to enable streaming analytics, real-time analytics. That's only as good as your data, so you need to capture data in real time. And that of course has been our claim to fame for a long time, being the leading independent provider of CDC, change data capture technology. What we've done now, and also expanded significantly with the new release, version six, is creating universal database streaming. >> What is that? >> We take databases, we take databases, all the enterprise databases, and we turn them into live streams. When you think, by the way, by the most common way that people have used, customers have used to bring data into the lake from a database, it was Scoop. And Scoop is a great, easy software to use from an open source perspective, but it's scripting and batch. So, you're building your new modern architecture with the two are effectively scripting and batch. What we do with CDC is we enable to take a database, and instead of the database being something you come to periodically to read it, we actually turn it into a live feed, so as the data changes in the database, we stream it, we make it available across all these different platforms. >> Changes the definition of what live streaming is. We're live streaming theCUBE, we're data. We're data streaming, and you get great data. So, here's the question for you. This is a good topic, I love this topic. Pete and I talk about this all the time, and it's been addressed in the big data world, but it's kind of, you can see the pattern going mainstream in society globally, geopolitically and also in society. Batch processing and data in motion are real time. Streaming brings up this use case to the end customer, which is this is the way they've done it before, certainly store things in data lakes, that's not going to go away, you're going to store stuff, but the real gain is in motion. >> Itamar: Correct. >> How do you describe that to a customer when you go out and say, hey, you know, you've been living in a batch world, but wake up to the real world called real time. How do you get to them to align with it? Some people get it right away, I see that, some people don't. How do you talk about that because that seems to be a real cultural thing going on right now, or operational readiness from the customer standpoint? Can you just talk through your feeling on that? >> First of all, this often gets lost in translation, and we see quite a few companies and even IT departments that when you talk, when they refer to real time, or their business tells them we need real time, what they understand from it is when you ask for the data, the response will be immediate. You get real time access to the data, but the data is from last week. So, we get real time access, but for last week's data. And that's what we try to do is to basically say, wait a second, when you mean real time, what does real time mean? And we start to understand what is the meaning of using last week's data versus, or yesterday's data, over the real time data, and that makes a big difference. We actually see that today the access, the availability, the availability to act on the real time data, that's the frontier of competitive differentiation. That's what makes a customer experience better, that's what makes the business more operationally efficient than the competition. >> It's the data, not so much the process of what they used to do. They're version of real time is I responded to you pretty quickly. >> Exactly, the other thing that's interesting is because we see it with, again, change of the capture becoming a critical component of the modern data architecture. Traditionally, we used to talk about different type of tools and technology, now CDC itself is becoming a critical part of it, and the reason is that it serves and it answers a lot of fundamental needs that are now becoming critical. One is the need for real-time data. The other one is efficiency. If you're moving to the cloud, and we talked about this earlier, if you're data lake is going to be in the cloud, there's no way you're going to reload all your data because the bandwidth is going to get in the way. So, you have to move only the delta. You need the ability to capture and move only the delta, so CDC becomes fundamental both in enabling the real time as well the efficient, the low-impact data integration. >> You guys have a lot of partners, technology partners, global SIs, resellers, a bunch of different partnership levels. The question I have for you, love to get your reaction and share your insight into is, okay, as the relationship to the customer who has the problem, what's in it for me? I want to move my business forward, I want to do digital business, I need to get up my real-time data as it's happening. Whether it's near real time or real time, that's evolution, but ultimately, they have to move their developers down a certain path. They'll usually hire a partner. The relationship between partners and you, the supplier to the customer, has changed recently. >> That's correct. >> How is that evolving? >> First of all, it's evolving in several ways. We've invested on our part to make sure that we're building Attunity as a leading vendor in the ecosystem of they system integration consulting companies. We work with pretty much all the major global system integrators as well as regional ones, boutique ones, that focus on the emerging technologies as well as get the modern analytic-type platforms. We work a lot with plenty of them on major corporate data center-level migrations to the cloud. So again, the motivations are different, but we invest-- >> More specialized, are you seeing more specialty, what's the trend? >> We've been a technology partner of choice to both Amazon and Microsoft for enabling, facilitating the data migration to the cloud. They of course, their select or preferred group of partners they work with, so we all come together to create these solutions. >> Itamar, what's the goals for Attunity as we wrap up here? I give you the last word, as you guys have this big announcement, you're bringing it all together. Integrating is key, it's always been your ethos in the company. Where is this next level, what's the next milestone for you guys? What do you guys see going forward? >> First of all, we're going to continue to modernize. We're really excited about the new announcement we did today, Replicate six, AEM six, a new version of Compose for Hive that now also supports small data lakes, Aldermore, Scaldera, EMR, and a key point for us was expanding AEM to also enable analytics on the data we generate as data flows through it. The whole point is modernizing data integration, providing more intelligence in the process, reducing the complexity, and facilitating the automation end-to-end. We're going to continue to solve, >> Automation big, big time. >> Automation is a big thing for us, and the point is, you need to scale. In order to scale, we want to generate things for you so you don't to develop for every piece. We automate the automation, okay. The whole point is to deliver the solution faster, and the way we're going to do it is to continue to enhance each one of the products in its own space, if it's replication across systems, Compose for Hive for transformations in pipeline automation, and AEM for management, but also to create integration between them. Again, for us it's to create a platform that for our customers they get more than the sum of the parts, they get the unique capabilities that we bring together in this platform. >> Itamar, thanks for coming onto theCUBE, appreciate it, congratulations to Attunity. And you guys bringing it all together, congratulations. >> Thank you very much. >> This theCUBE live coverage, bringing it down here to New York City, Manhattan. I'm John Furrier, Peter Burris. Be right back with more after this short break. (upbeat electronic music)

>> Narrator: Live from Boston, Massachusetts, it's the Cube. Covering Red Hat Summit 2017, brought to you by Red Hat. (techno music) >> Welcome back to Boston, everybody. And welcome back to Red Hat Summit. This is the Cube, the leader in live tech coverage. My name is Dave Vellante, and I'm here with my co-host, Stu Miniman. Stu, we were saying this is your 100th Red Hat Summit, so congratulations on reaching that milestone. Joe Dickman is here. He's the senior vice president of Vizuri. Cool name, love it. And Michael Quintero, or Quintero if you prefer, of LogistiCare. He's an enterprise solutions architect. Gentlemen, welcome to the Cube. >> Thank you. It's a pleasure to be here. >> So Vizuri. Love the name. It strikes a visualization. It's (mumbles) trendy. Tell us about Vizuri, and tell us about your relationship with LogistiCare, and we'll get into it. >> Vizuri is the private division of a company called AEM Corporation. We created the brand to serve the commercial market for research and development. We became partners with JBoss before Red Hat's acquisition, so we jumped into open source in like 2003. And since then, we've built a business around open source technologies, and market leading technologies that bring value. We found LogistiCare because they solicited us for some work to help them transform their organization. And it's worked out well. I mean, Michael and I have been working together for about 18 months. >> So, tell us a little bit about LogistiCare. >> So LogistiCare is the world's largest provider of non-emergency medical transportation. So, we service the health market around people have benefits. The insurance companies don't provide transportation, and the members come to us and we broker the transportation for them. Been in business for quite some time. Do about 70 million trips a year, a little bit more. And we have roughly 80% of that market. And we just want to stay on top of, and be recognized as the world leader in that capability with the best services and the care for our members. >> So JBoss of course was like the second pillar for Red Hat after Red Hat (mumbles) Rob Bearden, who was a CEO at the time, and Cube alum and friend. But so, how did you utilize that capability, the sort of whole middleware, and how does that affect your digital transformation? And where did you guys all fit together? >> So, well digital transformation is a business strategy, not a technology. So, we looked at our need to be more flexible, and dynamic, and innovate. Our legacy, our what we call classic internally, software stack is limiting. It's not service oriented. It's not extensible. It's a compiled, executable, distributed -- serves the business very well. In fact, we're still using it today in some aspects. We haven't fully replaced it. But it's long in the tooth, and it's difficult for us to reach that new business requirement and test and deliver it scale. So, I joined the company to help modernize that architecture. Very quickly recognized that in order to get to scale, and loosely coupling, and massive customization, that microservices was a good solution for us. And when we surveyed the market for a partner that could help take us there, software wise, Red Hat has the most complete stack. They offer everything we need to do, and then they have the things we think we're going to do in the future. So, we looked around for somebody who could help us get to the Red Hat, enable to that, with Docker, and get to an auto-scaling kind of solution so we have infrastructure on demand. And we found Vizuri as a partner. They were able to help us enable the technology and teach us how to do things that we weren't presently doing. Because we didn't have any kind of scale solution in-house, it was just put more web servers out there. >> We started small, it started with a Business Process Management System. If you think about all the logistics that are necessary for coordinating medical transport, "I'm a dialysis patient. I'm somebody that is home-bound. I need to get to a physician appointment." We took that domain knowledge, that's part of one of the pillars of digital transformation. It's infrastructure, it's integration, and it's knowledge management. We started with knowledge management. Think about all the complex business rules for manage care organizations, reimbursement, right? Which is what LogistiCare does. Quickly after we solved that problem, we looked at integration, and we said, "Well now we have all these trading partners." So we guided LogistiCare into their next purchase which was Fuse. So now we had an API strategy for publicly linking them to other consumer providers, because they are a logistics organization for reimbursement. And as Michael said, we started building data centers. Or LogistiCare did. But guess what? Containers and OpenShift came in and we started provisioning our development environments to Amazon Web Services. And when they saw the cost-savings, they abandoned building out on-prem data centers, and went Cloud-native. >> So there's also a revenue drive, or component, as well, right? >> It is. It is. It's an OpEx (mumbles) and the CapEx cost-savings. >> Let's unpack both of those. >> Joe: Sure. >> Where do you want to start? Cost or the telephone numbers? (laughs) >> So, we're mostly a call center based company in history. Right? We have 20-something call centers around the country. We service most of the U.S. And we have a variety of contracts with medical care providers, like Aetna, and Wellpoint, and Blue Cross, and those type people. And then the managed care organizations come in. So, we look to reduce our OpEx by diminishing the number and the interfaces that we have with our call centers. People don't have to call in to the call centers to do business with us. You know, something like one-minute reduction in call-time is about a six or seven million dollar a year benefit for us. And there's a lot of things that people can do for themselves. I mean, you can call in and cancel a trip that they've had scheduled. We figured that about 30% of the cancellation rate, if we could get that done through a service interface, through an IVR, where you can come in and say "I'm not going to go." and cancel it. That's a five or six million dollar savings for us right there. Just in 30%. >> Michael, I'm curious. Was there any hesitancy inside to say, "Okay. I'm going to kill data centers, going to go to a public Cloud." You know, how did that transition go? And anything, you know, kind of the good, the bad, and the ugly that you could share. >> So, well, we're a healthcare company. HIPA and HITRUST certified coming. And there's a certain amount of fear on Cloud migration. So we had to demonstrate the knowledge, skills, and abilities around getting secure, scalable solutions out to the Cloud. And this is our core application. If we don't do this well, we could become Blockbuster and go away. Right? So we don't want that. So, we had Vizuri come to the table and help us understand just how secure we can be, how OpenShift is helping us make sure our information is never violated. There's great integrity in it. And then we did prototyping, and we actually evaluated it, and we have third parties that come in and take a look at our solution and say, "Can I penetrate that? Can I get into your information?" So, and, we also are subject to audit, not only by the federal government, but by all of our payer partners. So we have to be above the line in every criteria, and we think that we are. >> The other thing that you mention was, when we talk about OpEx, right? That's human capital. He talked about the minute per time on a call. We also reduce tribal knowledge. Think about all these new managed care organizations in health care. Is it the call center representative, is it our responsibility to train them on this car, and this company requires a car service, this company requires an ambulance. That knowledge, if we could eliminate that and put that in the middle tier. Now what we do is we have given them a business scale. Now they have a business strategy for taking on new managed health care organizations. Do you have different compliance rules? Do you have different knowledge? It is no longer us having to go back out to those 20 call centers and re-train everybody, because you never know where the consumers are coming from. So, what they do is they answer the phone, they put their information into the system, and the system makes the deterministic call as to what car service, when, and how it's reimbursed. >> So, you say you automated essentially that tribal knowledge. >> Joe: We did. >> Eliminated it. >> And we reduced it so it not only reduced the calls per time frame, but it sped up our time of getting a call center agent from three weeks of training down to basically one. >> Yes, and we have the ability now to support all of our contracts from any call center. So if there's disaster recovery models, or, you know, Phoenix for instance is one of our larger call centers and they get heavy downpours of rain there. There are times when people can't get to work, or they have outages. We can't afford for that function to be offline. So those skills are very easily moved to another call center to support the members that would call in there. Just route the calls. And there's no local knowledge about, you know, my contract in Arizona does a certain thing, or in the Southwest, so it's very simple to support our population from any call center. That gives us the benefit of providing very high quality service, 'cause people when they call in, they expect us to service them. >> Joe, I want to follow up. We were talking about kind of, you know, hesitancy, healthcare tends to be a little bit conservative. I hear things like microservices, and containers. You know, these are still relatively new things. Is (mumbles) -- sorry, OpenShift the solution that allows you to deliver that with confidence to your customers? >> Yes. OpenShift. (laughs) >> Yeah, sorry about that. (laughs) >> No worries. (laughs) OpenShift does. What happens is the Docker container format enables us to pre-configure those servers and those workloads, and we talked about microservices. We wanted to reduce the business decisions or the integrations into the smallest component. What we also wanted to do was provide some taxonomy with them. These are for billing, these are for scheduling, these are for a different aspect of the business. By that, we can change, and we can change often. >> Mhm. >> How long did it take before if we wanted to make a change to some of the infrastructure? >> So. >> Weeks? Months? >> Well, even longer. I mean infrastructure is hard to acquire. And you only talk about CapEx expense. It's very easy, I mean there's a refresh cycle for equipment that you get. So even when you have it, you have to pay attention to maintenance and keeping that thing going forward. As you add scale to your business, you got to go acquire more storage. And it's not a dynamic thing. You have to plan -- the planning cycle is very difficult. We moved to the Cloud. Now we have infrastructure on demand. There's a myriad of choices of platforms and solutions that we can apply to our business model. Things we hadn't even thought of before. We're actually looking now at potentially moving our call centers away from our in-house standard, and moving to an Amazon provided call center solution. Because it can scale. And we can consolidate. And we can provide service from anywhere in the world. That's a big benefit to us. >> It is. So call center as a service, essentially. >> Michael: Yes. >> Is something you're evaluating. >> Think about how big they are. 80 million rides, right. What they didn't want to do is be disintermediated by the newcomers. Right? The Uber's, the Lyft's. They had a large footprint. So, he used the word Blockbuster before, and that's what they use a lot internally. >> Dave: There's one left, in Alaska, I heard. (laughs) >> Who remembers Blockbuster? And then they remember how Blockbuster was no longer in business. So what they wanted to do is to ensure that -- they agilely transformed not only the software engineering discipline, but their firm beliefs. So, everybody from business analysis through implementation has this new agile approach. And one of the features that we developed, we used to send people home after four hours of dialysis in taxi cabs. So, an executive, or team, at LogistiCare said, "We need dependency. We need certified drivers." They actually entered into a business relationship with Lyft. And you want to talk about an agile enterprise? We developed a custom interface into Lyft with a scheduling service that never existed, within five weeks. >> Michael: That's right. >> We would never have been able to do that. And we moved our first ride after five weeks, and since then, we're currently up to about five or six thousand. But it's going to scale to thousands. And the goal is to, again, as Michael said, let people interface with LogistiCare by their device of choice. If we don't have to have people call in to cancel rides, or call in to schedule, then the business scales, and it scales without human capital. >> And the enablers there, (mumbles) we always talk about it, people, process, and technology. So the technology behind that was, what, you're living this API economy that everybody talks about. >> Michael And Joe: We are. >> Joe: That is exactly what we did. >> And then you've got underneath that, OpenShift, what else is sort of there that you're leveraging? >> BPMS, BRMS. So, Business Process Management System. Business Rules Management System. JBoss fused for an integration strategy and Camel Routes. And then Openshift, and then we do Ansible for doing server provisioning. >> And I have to ask you about the security question again. Stu was (mumbles) poking at it before. We've heard from a lot of practitioners that the security in the Cloud is just fine, it is great actually. The challenge is, it doesn't necessarily exactly map the edicts of our organization. So, is that, did you find that? And did you have to maybe change the way in which you plugged into AWS, or was it just sort of out of the box for you? >> So, you have to understand the shared responsibility model when you move to the Cloud, right? I mean they're very good at the security in the Cloud, or of the Cloud, and you have to be good at the security in the Cloud. You can choose bad technology at Amazon and be insecure. But they have a published, HIPA standard, that if you use these technologies, then you can be HIPA certified. We applied our HITRUST certification standards to our choices. We're making very solid -- and this isn't willy nilly. I mean I've been in a HIPA solution for 20 years. So it's not like I don't know what is required, and what the auditors are going to ask us. So, but I do want to redress one point that we can't go past. Is that (mumbles) Our customers are getting better service from all this we're doing. >> Joe: I agree. >> When somebody calls us and says, "I'm ready to go home from the doctor." and they didn't know what time they were going to go home when they scheduled their ride to the doctor, we can get somebody there in 10 minutes now to come and get them and take them home. >> Dave: Wow. >> That's a great satisfier. Rather than having to wait 90 minutes for us to find somebody that can go pick them up. That world has changed, right? And that's a great customer satisfier and that is why they're going to love continuing to do business with us. >> Great business outcome from something that you probably couldn't have done, you know, five years ago? Even maybe two years ago. >> They're a social caring organization. One of the largest rides that they do is for kidney dialysis. And those people, I mean, I've never had it, but somebody sitting there after four hours of dialysis, the last thing you want to do is wait 90 minutes for a cab. You want to go home. You also want to have an authoritative source that the drivers are credentialed drivers. And that's something that we're working on so that not only do these older generations, right? And think about the baby boomers, which I'm actually part of. >> Michael: Me too. (laughs) >> The age population is growing. So the need for these types of services is growing too. And we become accustomed and we get set in our ways. And people might be fearful. Any taxi showing up, versus now, a Lyft shows up, you know who the driver is. You see the car, you see that. There's a high degree of confidence that LogistiCare has the best interests of their constituents. So they manage that type of business. So it's not just technology, it really is a caring and methodical organization. >> But we have the ability to follow patterns that are already established. We look at how Netflix handles their widely distributed kinds of interface devices. You know, how do they figure out what kind of data-stream to send back to what he's got in his hand versus what I have. We're following the same kind of model, and we're using the technology platform to our best advantage to make sure that we're talking to someone who's got a flip-phone differently than we are talking to someone who's got a (mumbles) Plus, right? (Dave laughs) Because the payload can't be the same, but the backend services don't need to know that. We built a solution here that can examine the request and return the right data-stream. So, "Where's my ride?" Might be "Just around the corner." or it might be a map with a breadcrumb trail and a picture of the driver and all of that. Like you get with a Lyft or an Uber. So, you know, we're building it. >> Great case study, gentlemen. Thanks very much for coming to the Cube and sharing it. >> Well, thank you very much for having, we enjoyed the time. >> Alright, keep it right there everybody. We'll be right back with our next guests. This is the Cube. We're live from Red Hat Summit in Boston. Be right back. (electronic music)