[Music] does hardware still matter the attractiveness of software-defined models and services that are running in the cloud really make you wonder don't they but the reality is that software has to run on something and that something is hardware and history in the it business shows that the hardware that you purchase today is going to be up against the price performance of new systems in short order and these new systems will be far superior from a price performance standpoint within a couple of years so when it's time to purchase a new system look at whether it's a laptop a mainframe or a server configuring a leading edge product is going to give you the longest useful life of that new system now when i say a system what makes up a system well there's a lot of underlying technology components of course you have the processor you got memories you got storage devices there's networking like network interface cards there's interconnects and the bus architecture like pcie gen4 or whatever these components are constantly in a leapfrog mode like clock speeds and more cores and faster memories and ssds versus spinning disks and faster network cards the whole gamut so you see a constant advancement of the system components it's like it's a perpetual and sometimes chaotic improvement of the piece parts now i say chaotic because balancing these different components such that you're not wasting resources and that you're ensuring consistent application performance is a critical aspect of architecting systems so it becomes a game of like whack-a-mole meaning you're going to find the bottlenecks and you got to stamp them out it's a constant chase for locating the constraints designing systems that address these constraints without breaking the bank and optimizing all these components in a harmonious way hello everyone this is dave vellante of the cube and while these issues may not capture all the headlines except for maybe tom's hardware blog they're part of an important topic that we want to explore more deeply and to do so we're going to go inside some new benchmarking tests with our good friend kim lenar who's principal performance architect at broadcom kim always great to see you thanks so much for coming back on the cube hi there dave good to see you too thanks for having me on you bet hey so last time we met we talked about the importance of designing these balance systems i talked about that in my open and how solid state threw everything out of whack because the system was designed around spinning disk and we talked about nvme and we're here today with some new data an independent performance lab prowess consulting conducted some initial tests i've seen their their white papers on this stuff it compared the current generation of dell servers with previous models to quantify the performance impact of these new technologies and so before we get into that kim tell us a little about your background and your performance chops sure sure so i started my career about 22 years ago back when the ultra 160 scuzzy was out and just could only do about 20 megabytes a second um but i felt my experience really studying that relationship between the file systems and the application the os and storage layers as well as the hardware interaction i was absolutely just amazed with how you know touching one really affects the other and you have to understand that in order to be a good performance architect so i've authored dozens of performance white papers and i've worked with thousands of customers over the years designing and optimizing and debugging storage and trying to build mathematical models like project that next generation product where we really need to land but honestly i've just been blessed to work with really brilliant um and some of the most talented minds in the industry yeah well that's why i love having you on you you can go go really deep and so like i said we've got these these new white papers uh new test results on these dell servers what's the role people might be wondering what's the role broadcom plays inside these systems well we've been working alongside dell for for decades trying to design some of the industry's best uh storage and it's been a team effort in fact i've been working with some of these people for for you know multiple decades i know their their birthdays and their travel plans and where they vacation so it's been a really great relationship between broadcom and dell over the years we've been with them through the sata to the sas to the ssd kind of revolution now we're working from all the way back at that series five to their latest series 11 products that support nvme so it's been it's been really great but it's not just about you know gluing together the latest host or the latest disk interface you know we work with them to try and understand and characterize their customers and our customers applications the way that they're deployed security features management optimizing the i o path and making sure that when a failure happens we can get those raid volumes back optimal so it's been a really really great um you know role between between broadcom and dell got it okay let's get into the tested framework let's keep it at high level and then we're going to get into some of the data but but what did prowess test what was the workload what can you tell us about you know what they were trying to measure well the first thing is you have to kind of have an objective so what we had done was um we had them benchmark on one of the previous dell poweredge our 740xd servers and then we had them compare that to the rs750 and not just one r 750 there was two different configurations of the rs750 so we get to see kind of you know what gen 3 to gen 4 looks like um and upgrading the processor so we kind of got from like a gold system to maybe a platinum system we've added more controllers we add more drives um and then we said you know let's go ahead and let's do some sql transactional benchmarking on it and i'd like to go into why we chose that but you know microsoft sql server is one of the most popular database management platforms in the world and you know there are two kinds ones at oltp which processes records and business transactions and then there's kind of a an oltp which does analytical analytical processing and does a lot of complex queries and you know together these two things they drive the business operations and help kind of improve productivity it's a real critical part for the decision makers in a uh you know for for all of our companies so before we get in share the actual test results what specifically did prowess measure what were some of the metrics that we're going to see here we focused on the transactional workloads so we did something called a tpcc like and let me be really clear we did not execute a tpcc benchmark but it was a tpcc like benchmark and tpcc is one of the most mature standardized industry database benchmarks in the world and what it does is it simulates a sales model of a wholesale supplier so we can all kind of agree that you know handling payments and orders and status and deliveries and things like that those are those are really critical parts to running a business and ultimately what this results in is something called a new order so somebody might go on they'll log on they'll say hey is this available let me pay you um and then once that transaction is done it's called a new to order so they come up with something called a tpmc which is the new order transactions per minute now the neat thing is it's not just a one-size-fits-all kind of benchmark so you get to scale that in the way you scale the database you scale the size and the capacity of the database by adding more warehouses in our case we actually decided to choose 1400 warehouses which is a pretty standardized size and then you can also test the concurrency so you could start from one thread which kind of simulates a user all the way up to however many threads you want we decided to settle on 100 threads now this is very different from the generic benchmarking we're actually doing real work we're not just doing random reads and random rights which those are great they're critical they tell us how well we're performing but this is more like a paced workload it really executes sql i o transactions uh and you know those in order operations um are very different you do a read and then a write and then another read and those have to be executed in order it's very different from just setting up a q depth and a workers and it also provides very realistic and objective measurements that exercises not just the storage but the entire server all right let's get into some of the results so the first graphic we're going to show you is that what you were just talking about new orders per minute how should we interpret uh this graphic kim well i mean it looks like we won the waccamo game didn't we so we started out with with the baseline here the r740xd and we measured the new order transactions per minute on that we then set up the r 750 in the very first rs 750 and we have the very all the details are laid out in the paper that you just referenced there um but we started out with a single raid controller with eight drives and we measured that we got a 7x increase and then in the second test we actually added another rig controller and another eight drives and then we we kind of upgraded the the processor a little bit we were able to even double that over the initial one so and you know how do we get there that's really the more important thing and you know the the critical part of this understanding and characterizing the workload so we have to know what kind of components to balance you know where are your bottlenecks at so typically an oltp online transaction processing is a mix of transactions that are generally two reads to every one and they're very random and the way this benchmark works is it randomly accesses different warehouses it executes these queries when it executes a read query it pulls that data into memory well once the data is into memory any kind of transactions are acted on it in memory so the actual database engine does in memory transactions then you have something called a transaction log that has to record all those modifications down to non-volatile media and that's based on something um you know just to make sure that you have um all the data in case somebody pulls the plug or something you know catastrophic happens you want to make sure that those are recorded um and then every once in a while um all those in-memory changes are written down to the disk in something called a checkpoint and then we can go ahead and clear that transaction log so there's a bunch of sequence of of different kinds of i o um that happen during the course of an oltp kind of transaction so your bottlenecks are found in the processor and the memory and the amount of memory you know the latency of your disks i mean it really the whole gamut everything could be a bottleneck within there so the trick is to figure out where your bottlenecks are and trying to release those so you can get the the best performance that you possibly can yeah the sequence of events that has to take place to do a right we often we take it for granted okay the the next uh set of data we're going to look at is like you said you're doing reads you're doing right we're going to we're going to bring up now the the data around log rights and and log reads so explain what we're looking at here so as i mentioned earlier the even though the transactions happen in memory um those recorded transactions get committed down to down to the disk but eventually they get committed onto disk what we do first is we do something called a log right it's a transaction log right and that way it allows the it allows the transaction to go ahead and process so the trick here is to have the lowest latency fast disks for that log and it's very critical for your consistency and also for rollbacks and something called asset transactions and operations the log reads are really important also for the recovery efforts so we try to manage our log performance um we want low latency we want very high iops for both reads and for rights but it's not just the logs there's also the database disks and what we see is initially during these benchmarks there's a bunch of reads that are going into the database data um and then ultimately after some period of time we see something called a checkpoint and we'll see this big flurry of rights come down so you have to be able to handle all those flurry of rights as they come down and they're committed down to the disk so some of our important design considerations here are is can our processor handle this workload do we have enough memory and then finally we have three storage considerations we have a database disk we have log disk and then of course there's a temp db as well so because we have the industry leading raid 5 performance we were able to use a raid 5 for the database and that's something that you know just years ago was like whoa oh don't ever use raid 5 on your database that is no longer true our raid 5 is is fast enough and has low enough latency to handle database and it also helps save money um and then for the raid 10 we use that for a log that's pretty standardized so the faster your processor the more cores you know when you double the disk um and we get more performance so yeah you know we just figured out where the bottlenecks were we cleared them out we were able to double that that's interesting go back in history a little bit when raid 5 was all the rage uh emc at the time now of course dell when they announced symmetrics they announced it with with raid 1 which was mirroring and they did that because it was heavily into mainframe and transaction processing and while there was you know additional overhead of you do you need two disk drives to do that the performance really outweighed that and so now we're seeing with the advent of new technologies that you you're solving that problem um i i guess the other thing of course is is rebuild times and we've kind of rethought that so the next set of data that we're going to look at is is is how long it takes to rebuild uh around the raid time so we'll bring that up now and you can kind of give us the the insights here well yeah so you can see that we've been able to reduce the rebuild times and you know how do we do that well i can tell you me and my fellow architects we have been spending the last uh probably the last two years focusing on trying to improve the rebuild so we you know it's not just rebuilding faster it's also how to eloquently handle all the host operations you can't just tell those sorry i'm busy doing rebuilds you've got to be able to handle that because business continuity is a very critical component of that so um so we do that through mirroring and preparity data layouts and so the rebuild times if you can if you can do a really good balance of making sure that you are supplying a sufficient host io that we actually very quickly in the background as soon as as we have a moment we start implementing those rebuilds um you know during those law periods and so making sure that we do aggressive rebuilds by while allowing those business operations to continue have always been a real critical part but we've been working on that a lot over the last couple of generations that said we always tell our customers always have a backup that's that's a critical part to uh to business continuity plans great i wonder if we can come back to the components inside the system how does what broadcom is supplying to dell in these servers contribute to these performance results specifically kim okay so specifically um we we provide the perk storage controller and so the dell r740xd actually has their series 10 h740p controller whereas the h the r750 has the generation 11 perc 11 h755n um so we own those um you know in terms of of trying to make sure that they are integrated properly into the system provided the highest possible performance um but not just the storage controller i want to make sure that everybody knows that we also have our broadcom net extreme e series these are gen 4 pcie 25 gig do ported ethernet controllers so in you know in a critical true deployment it is a really important part of the e-commerce uh business solution so we do own the storage um for these as well as the networking excellent okay so we kind of we went deep inside into the system but let's up level why does this matter to an organization what's the business impact of all this tech coming to fruition we you know as everybody always references there's a massive growth of data and data is required for success it doesn't matter if you're a fortune 500 company or you're just a small to medium business you know it that critical data needs protected and needs protected without the complexity or the overhead or the cost of such hyper-converged infrastructures or sand deployments so we're able to do this on bare metal um and it really helps with the tco so you know and the other thing is nvme right now is the fastest growing storage nvme is so fast um as well from a performance perspective as well so that that dell r 750 with the two perc 11 controllers in it it had over 51 terabytes of storage in a single server you know and that's pretty impressive but there's um so many different performance advantages that the rs 750 provides for sql servers as well so they've got you know the gen 3 intel xeon scalable processors we've got ddr4 3200 memory you know the faster memory is very critical for those in memory transactions as well we have gen 4 pcie it really does justify an upgrade and i can tell you dave that a little over a year ago i had you know i had one of these delos 750 servers sitting in my own house and i was testing it and i was just amazed at the performance i was doing different tpcc and tpch and tpce tests on it and i was telling dell wow this is really this is amazing this server is doing so so well so i was so excited could not wait to see it in print so thank you to the prowess team um for actually showing the world what these servers can do combined with the broadcom storage now speaking of the prowess team when you read the white papers um it really is focused on this small and medium-sized business market so people might be wondering well wait a minute why wouldn't folks just spin up this compute in the cloud why am i buying servers well that's a really good question you know that still you know the studies have shown that the majority of workloads are still on-prem um and also you know there's a challenge here with the skill sets there's a lack of developers for cloud and you know cloud architects so keeping these in prem where you actually own it it really does help keep costs down um and just the management of these r750s are fantastic and the support that dell provides as well great kim i love having you on and we'd like to have you back we're going to leave it there for now but thanks so much i really appreciate your time thanks dave so look this is really helpful in understanding that at the end of the day you still need microprocessors and memories and storage devices controllers and interconnects that we you know we just saw pat gelsinger at the state of the union address nudging the federal government to support semiconductor manufacturing and you know intel is going to potentially match tsm's 100 billion dollar capex commitment and that's going to be a tailwind for the surrounding components you know including semiconductor you know component core infrastructure designers like broadcom now this is a topic that we care about and and like i said kim we're going to have you back and we plan to continue our coverage under the hood in the future so thank you for watching this cube conversation this is dave vellante and we'll see you next time [Music] you

>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>> Hello everyone. And thanks for joining us today. My name is Brent Biddulph, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like 3Soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most retailers. And really just a quick level set before handing this over to my good friend, Kamil at 3Soft. IDC, Gartner, many other analysts kind of summed up an average here that I thought would be important to share just to level set the importance of demand forecasting in retail, and what's at stake, meaning the combined business value for retailers leveraging AI and IOT. So this is above and beyond what demand forecasting has been in the past, is a $371 billion opportunity. And what's critically important to understand about demand forecasting is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line? Retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that it's no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that, on its face is worth millions of dollars of improvement for any individual retailer. On top of that is balancing your inventory, getting the right product in the right place, and having productive inventory. And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analysts have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers, not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks, labor planning, and customer engagement. For purposes of today's conversation, we're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Kamil Volker to share with you what his team has been up to, and some of the amazing things are driving at top retailers today. So over to you, Kamil. >> I'm happy to be here and I'm happy to speak to you about what we deliver to our customers, but let me first introduce 3Soft. We are a 100 person company based in Europe, in Southern Poland, and we, with 18 years of experience specialized in providing what we call a data driven business approach to our customers. Our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and data management and how it can be translated into business profits. Adding artificial intelligence on top of that makes our solutions portfolio holistic, which enables us to realize very complex projects, which leverage all of those three pillars of our business. However, in the recent time, we also understood the services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail demand forecasting is in the product solutions. So that's why we created Occubee, our AI platform for data driven retail that also covers this area that we talked about today. I'm personally proud to be responsible for our technology partnerships with Cloudera and Microsoft. It's a great pleasure to work with such great companies and to be able to deliver the solutions to our customers together based on a common trust and understanding of the business, which cumulates at customer success at the end. So why should we analyze data at retail? Why is it so important? It's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to derive the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observed in retail, our online data analysis, that's the fastest growing sector, let's say for those data analytics services, which is of course based on the econ and online channels, availability to the customer. Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, let's say brick and mortar shops. They still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business related questions that need to be answered from the headquarter perspective. So is it actually good idea to open a store in a certain place? Is it a good idea to optimize a stock in a certain producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions, they need to be answered in retail every day. And with that massive amount of factors coming into the equation, it's really not that easy to base only on the integration and expert knowledge. Of course, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. The project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. That's like the holy grail in retail, of course, how to do it without flooding the stores with the goods. And in the same time, how to avoid empty shelves. From the perspective of the customer, it was obvious that we need to provide a very well, a very high quality of sales forecast to be able to ask for what will be the actual sales of the individual product in each store every day, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, our holistic approach implementing AI with data management background and these automation solutions all together created a platform that was able to significantly increase the sales for our customer just by minimizing out of stocks. In the same time, we managed to not overflood the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. Having said that, these results, of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works? Well in its principle, it's quite simple. We just collect the data. We do it online, we put that in our data, like based on the cloud, through other technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way. The huge and most important aspect of that is the use of the good tools to do the right job. Having said that, you can be sure that there is too many information in this data. And there is actually two-minute forecast created every night than any expert could ever check. This means our solution needs to be very robust. It needs to provide information with high quality and high veracity. There is plenty of different business process, which is based on our forecast, which need to be delivered on time for every product in each individual shop. Observing the success of this project and having the huge market potential in mind, we decided to create our Occubee, which can be used by many retailers who don't want to create a dedicated software that will be solving this kind of problem. Occubee is our software service offering, which is enabling retailers to go data driven path management. We create Occubee with retailers for retailers, implementing artificial intelligence on top of data science models created by our experts. Having data analysis in place based on data management tools that we use, we've written first attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why Occubee is open box solution, which means that you basically can implement that in your organization, without changing the process internally. It's all about mapping your process into the system, not the other way around. The fast trends and products collection possibilities allow the retailers to react to any changes, which occur in the sales every day. Also, it's worth to mention that really it's not only FMCG and we believe that different use cases, which we observe in fashion, health and beauty, home and garden, pharmacies, and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of Occubee. And we made everything we can to implement a solution, which covers all the needs. When you think about the factors that affect sales, there is actually huge variety of data that we can analyze. Of course, the transactional data that every dealer possesses, like sales data from sale from stores, from e-commerce channel, also averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. It's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, changes in weather or information about some seasonal stores, which can be very important during the summer and other holidays, for example. But on the other hand, having this information in one place makes the actual benefit and environment for the customer. Demand forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also the whole process of replenishment that can cover with different sets of machine learning models, and data management tools. We believe that analyzing data from different parts of the retail replenishment process can be achieved with implementing a data management solution based on Cloudera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line. When it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically first it's of course, out of stock minimization, which can be provided by simply better size focus, but also reducing overstocks by better inventory management can be achieved by us in the same time. Having said that, we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry, in almost every regular customer. >> Hey, thanks, Kamil. Having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly, the results of course. I mean, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers, you're working with... you're doubling average numbers that the industry overall is having, and most notably how the use of AI or Occubee has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera, and also how quickly it felt like, and this is my core question. Your team can cover or provide the solution to not only core center store, for example, in grocery, but you're covering fresh products. And frankly, there are solutions out on the market today that only focus on center store non-perishable departments. I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about and how you were approaching this in leveraging AI, that you're streamlining processes of legacy, demand, forecasting solutions that required more manual intervention, how quickly can you get people set up? And what is the overall process of like to get started with this software? >> Yeah, usually, it takes three to six months to onboard a new customer to that kind of solution. And frankly, it depends on the data that the customer has. Usually it's different for smaller, bigger companies, of course, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set is all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity. It's all really easy to start with, to work with them because everyone in the organization understands this data. For the bigger companies it might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may influence the length of the process. But we usually start with the customers with workshops. That's very important to understand the reasons because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our customers. >> Totally understand. And POS data, every retailer has right in, in one way shape or form. And it is the fundamental data point, whether it's e-comm or the brick and mortar data, every retailer has that data. So, that totally makes sense. But what you just described was months, there are legacy and other solutions out there, that this could be a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud. from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's no IT intervention, if you will, or hurdles in preparation to get the database set up and all of the work. I would imagine that part of the time savings to getting started, would that be an accurate description? >> Yeah, absolutely. In the same time, this actually lowering the business risks because we see the same data and put that into the data lake, which is in the cloud. We did not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said. >> Right. And you're meeting customers where they are, right? So as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data, or, one incorporated course online data with offline data, you have a roadmap and the ability to do that. So it is a building block process. So getting started with core data as with POS online or offline is the foundational component, which obviously you're very good at. And then having that ability to then incorporate other data sets is critically important because that just improves demand forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Kamil, I just have one final question for you. There are plenty of... not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're promoting an open box solution. And that is a key challenge for a lot of retailers that have seen black box solutions come and go. And especially in this space where you really need direct input from the customer to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >> Yeah, of course. So, we've seen in the past the failures of the projects based on the black box approach, and we believe that this is not the way to go, especially with this kind of, let's say specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in Occubee is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their focus for many years. We don't want to change that. We are not able to optimize it properly for sure as IT combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way, we create truly explainable experience for our customers, then can easily go for the whole process and see how the forecast was calculated. And what is the reason for a specific number to be there at the end of the day? >> I think that is invaluable. (indistinct) I really think that is a differentiator and what 3Soft is bringing to market. With that, thanks everyone for joining us today. Let's stay in touch. I want to make sure to leave Kamil's information here. So reach out to him directly, or feel free at any point in time obviously to reach out to me. Again, so glad everyone was able to join today, look forward to talking to you soon.

>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

>> Announcer: From around the globe it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, host of theCUBE. And it's theCUBE Virtual and Uli Homann who's here, Corporate Vice President of Cloud & AI at Microsoft. Thanks for comin' on. I love this session. Obviously, Microsoft one of the big clouds. Awesome. You guys partnering with IBM here, at IBM Think. I remember during the client-server days in the '80s, late '80s to early '90s the open systems interconnect was a big part of opening up the computer industry. That was networking, intra-networking and really created more LANs and more connections for PCs et cetera, and the world just went on from there. Similar now with hybrid cloud, you're seeing that same kind of vibe, right? You're seeing that same kind of alignment with distributed computing architectures for businesses. Where now you have, it's not just networking and plumbing, and connecting, you know, LANs and PCs, and printers, it's connecting everything. It's kind of almost a whole 'nother world, but similar movie, if you will. So this is really going to be good for people who understand that market. IBM does, you guys do. Talk about the alignment between IBM and Microsoft in this new hybrid cloud space. It's really kind of now standardized, but yet it's just now coming. >> Yeah, so again, fantastic question. So the way I think about this is first of all, Microsoft and IBM are philosophically very much aligned. We're both investing in key open source initiatives like the Cloud Native Compute Foundation, CNCF, something that we both believe in. We're both partnering with the Red Hat organization so Red Hat forms a common bond, if you so want to, between Microsoft and IBM. And again, part of this is how can we establish a system of capabilities that every client has access to, and then build on top of that stack. And again, IBM does this very well with their Cloud Paks which are coming out now with data and AI, and others. So open source, open standards are key elements and then you mentioned something critical which I believe is not under, misunderstood, but certainly not appreciated enough is this is about connectivity between businesses and so part of the power of the IBM perspective together with Microsoft is bringing together key business applications for health care, for retail, for manufacturing and really make them work together so that our clients that are-- critical scenarios get the support they need from both IBM as well as Microsoft on top of this common foundation of the CNCF and other open standards. >> You know, it's interesting, I love that point. I'm going to double-down and amplify that and continue to bring it up. Connecting between businesses is one thread but now, people, because you have an edge that's also industrial, business, but also people. People are also participating in open source, people have wearables, people are connected so they can, and also they're connecting with collaboration. So this kind of brings a whole 'nother architecture which I want to get into the solutions with you on on how you see that playing out. But first, I know, you know, you're a veteran with Microsoft for many, many years, for decades. Microsoft's core competency has been ecosystems, developer ecosystems, customer ecosystems. Today, that the services motion is build around ecosystems. You guys have that playbook, IBM's well versed in it, as well. How does that impact your partnerships, your solutions, and how you deal with down this open marketplace? >> Well, let's start with the obvious. Obviously, Microsoft and IBM will work together in common ecosystems. Again, I'm going to reference the CNCF again as the foundation for a lot of these initiatives. But then we are also working together in the Red Hat ecosystem because Red Hat has built an ecosystem that Microsoft and IBM are players in that ecosystem. However, we also are looking higher level 'cause a lot of times when people think ecosystems, it's fairly low-level technology. But Microsoft and IBM are talking about partnerships that are focused on industry scenarios. Again, retail for example, or health care and others where we're building on top of these lower-level ecosystem capabilities and then bringing together the solution scenarios where the strength of IBM capabilities is coupled with Microsoft capabilities to drive this very famous one plus one equals three. And then the other piece that I think we both agree on is the open source ecosystem for software development and software development collaboration. And GitHub is a common anchor that we both believe can feed the world's economy with respect to the software solutions that are needed to really, yeah, bring the capabilities forward, help improve the world's economy and so forth by effectively bringing together brilliant minds across the ecosystem and again, just Microsoft and IBM bringing some people, but the rest of the world obviously participating in that, as well. So thinking again, open source, open standards, and then industry-specific collaboration and capabilities being a key part. You mentioned people. We certainly believe that people play a key role, software developers and the GitHub notion being a key one. But there are others where again, Microsoft with Microsoft 365 has a lot of capabilities in connecting people within the organization and across organizations. And while we're using Zoom, here, a lot of people are utilizing Teams 'cause Teams is on the one side of collaboration platform, but on the other side is also an application host. And so bringing together people collaboration supported and powered by applications from IBM, from Microsoft and others, is going to be, I think, a huge differentiation in terms of how people interact with software in the future. >> Yeah, and I think that whole joint development is a big part of this new people equation where it's not just partnering in market, it's also at the tech, and you've got open source, and it's a just phenomenal innovation formula, there. So let's ask what solutions, here. I want to get into some of the top solutions you're doing that Microsoft that maybe with IBM. But your title as the Corporate Vice President Cloud & AI, c'mon, could you get a better department? I mean, more relevant than that? I mean, it's exciting. You know, cloud scale is driving tons of innovation, AI is eating software or changing the software paradigm. We're going to see that playing out. I've done dozens of interviews just in this past month on how AI's a more, certainly with machine learning, and having a control plane with data, changing the game. So tell us, what are the hot solutions for hybrid cloud and why is this a different ballgame than say, public cloud? >> Well, so first of all, let's talk a little bit about the AI capabilities and data because I think they're two categories. You are seeing an evolution of AI capabilities that are coming out. And again, I just read IBM's announcement about integrating the Cloud Pak with IBM Satellite. I think that's a key capability that IBM is putting out there and we are partnering with IBM in two directions, there. IBM has done a fantastic job to build AI capabilities that are relevant for industries, health care being a very good example, again, retail being another one. And I believe Microsoft and IBM will work on both partnership on the technology side as well as the AI usage in specific verticals. Microsoft is doing similar things. Within our Dynamics product line, we're using AI for business applications, for planning, scheduling, optimizations, risk assessments, those kind of scenarios. And of course, we're using those in the Microsoft 365 environment, as well. I always joke that despite my 30 years at Microsoft, I still don't know how to really use PowerPoint and I can't do a PowerPoint slide for the life of me, but with a new designer, I can actually get help from the system to make beautiful PowerPoint happen. So bringing AI into real life usage I think is the key part. The hybrid scenario is critical here, as well, especially when you start to think about real life scenarios like safety, worker safety in a critical environment, freshness of product. We're seeing retailers deploying cameras and AI inside the retail stores to effectively make sure that the shelves are stocked, that the quality of the vegetables, for example, continues to be high and monitored. And previously, people would do this on an occasional basis running around in the store. Now the store is monitored 24/7 and people get notified when things need fixing. Another really cool scenario set is quality. We are working with a Finnish steel producer that effectively is looking at the stainless steel as it's being produced and they have cameras on this steel that look at specific marks. And if these marks show up then they know that the stainless steel will be bad. And I don't know if you have looked at a manufacturing process, but the earlier they can get failures detected, the better it is because they can most likely, or more often than not, return the product back into the beginning of the funnel and start over. And that's what they're using. So you can see molten steel, logically speaking, with a camera and AI. And previously, humans did this which is obviously A, less reliable and B, dangerous because this is very, very hot, this is very glowing steel. And so increasing safety while at the same time improving the quality is something that we see in hybrid scenarios. Again, autonomous driving, another great scenario where perception AI is going to be utilized. So there's a bunch of capabilities out there that really are hybrid in nature and will help us move forward with key scenarios, safety, quality, and autonomous behaviors like driving and so forth. >> Uli, great, great insight. Great product vision. Great alignment with IBM's hybrid cloud space what all customers are lookin' for, now. And certainly multicloud around the horizon. So great to have you on. Great agility, and congratulations for your continued success. You've got a great area, cloud and AI, and we'll be keeping in touch. I'd love to do a deep dive, sometime. Thanks for coming on. >> John, thank you very much for the invitation and great questions, great interview. Love it, appreciate it. >> Thank you very much. Okay, theCUBE coverage here, at IBM Think 2021 Virtual. I'm John Furrier, your host. Thanks for watching. (soft electronic music) ♪ Dah-De-Da ♪ ♪ Dah-De ♪

(upbeat music) >> Narrator: From around the globe. It's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back to theCUBE coverage of IBM. Think 2021 virtual. I'm John Furrier, host of theCUBE. And this is theCUBE virtual and Uli Homann who's here Corporate Vice President, of cloud and AI at Microsoft. Thanks for coming on. I love this session, obviously, Microsoft one of the big clouds. Awesome. You guys partnering with IBM here at IBM Think. First of all, congratulations on all the success with Azure and just the transformation of IBM. I mean, Microsoft's Cloud has been phenomenal and hybrid is spinning perfectly into the vision of what enterprises want. And this has certainly been a great tailwind for everybody. So congratulations. So for first question, thanks for coming on and tell us the vision for hybrid cloud for Microsoft. It's almost like a perfect storm. >> Yeah. Thank you, John. I really appreciate you hosting me here and asking some great questions. We certainly appreciate it being part of IBM Think 2021 virtual. Although I do wish to see some people again, at some point. From our perspective, hybrid computing has always been part of the strategy that Microsoft as policed. We didn't think that public cloud was the answer to all questions. We always believed that there is multiple scenarios where either safety latency or other key capabilities impeded the usage of public cloud. Although we will see more public cloud scenarios with 5G and other capabilities coming along. Hybrid computing will still be something that is important. And Microsoft has been building capabilities on our own as a first party solution like Azure Stack and other capabilities. But we also partnering with VMware and others to effectively enable investment usage of capabilities that our clients have invested in to bring them forward into a cloud native application and compute model. So Microsoft is continuing investing in hybrid computing and we're taking more and more Azure capabilities and making them available in a hybrid scenario. For example, we took our entire database Stack SQL Server PostgreSQL and recently our Azure machine learning capabilities and make them available on a platform so that clients can run them where they need them in a factory in on-premise environment or in another cloud for example, because they trust the Microsoft investments in relational technology or machine learning. And we're also extending our management capabilities that Azure provides and make them available for Kubernetes virtual machine and other environments wherever they might run. So we believe that bringing Azure capabilities into our clients is important and taking also the capabilities that our clients are using into Azure and make it available so that they can manage them end to end is a key element of our strategy. >> Yeah. Thanks Uli for sharing that, I really appreciate that. You and I have been in this industry for a while. And you guys have a good view on this how Microsoft's got perspective riding the wave from the original computer industry. I remember during the client server days in the 80s, late 80s to early 90s the open systems interconnect was a big part of opening up the computer industry that was networking, internetworking and really created more lans and more connections for PCs, et cetera. And the world just went on from there. Similar now with hybrid cloud you're seeing that same kind of vibe. You seeing the same kind of alignment with distributed computing architectures for businesses where now you have, it's not just networking and plumbing and connecting lans and PCs and printers. It's connecting everything. It's almost kind of a whole another world but similar movie, if you will. So this is really going to be good for people who understand that market. IBM does, you guys do. Talk about the alignment between IBM and Microsoft in this new hybrid cloud space? It's really kind of now standardized but yet it's just now coming. >> Yeah. So again, fantastic question. So the way I think about this is first of all, Microsoft and IBM are philosophically very much aligned. We're both investing in key open source initiatives like the Cloud Native Computing Foundation, CNCF something that we both believe in. We are both partnering with the Red Hat organizations. So Red Hat forms a common bond if you still want to between Microsoft and IBM. And again, part of this is how can we establish a system of capabilities that every client has access to and then build on top of that stack. And again, IBM does this very well with their cloud packs which are coming out now with data and AI and others. And again, as I mentioned before we're investing in similar capabilities to make sure that core Azure functions are available on that CNCF cloud environment. So open source, open standards are key elements. And then you mentioned something critical which I believe is misunderstood but certainly not appreciated enough is, this is about connectivity between businesses. And so part of the power of the IBM perspective together with Microsoft is bringing together key business applications for healthcare, for retail, for manufacturing and really make them work together so that our clients that are critical scenarios get the support they need from both IBM as well as Microsoft on top of this common foundation of the CNCF and other open standards. >> It's interesting. I love that point. I'm going to double down and amplify that late and continue to bring it up. Connecting between businesses is one thread. But now people, because you have an edge, that's also industrial business but also people. People are participating in open source. People have wearables, people are connected. And also they're connecting with collaboration. So this kind of brings a whole 'nother architecture which I want to get into the solutions with you on on how you see that playing out. But first I know, you're a veteran with Microsoft for many, many years of decades. Microsoft's core competency has been ecosystems developer ecosystems, customer ecosystems. Today, that the services motion is built around ecosystems. You guys have that playbook IBM's well versed in it as well. How does that impact your partnerships, your solutions and how you deal with down this open marketplace? >> Well, let's start with the obvious. Obviously Microsoft and IBM will work together in common ecosystem. Again, I'm going to reference the CNCF again as the foundation for a lot of these initiatives. But then we're also working together in the ed hat ecosystem because Red Hat has built an ecosystem and Microsoft and IBM are players in that ecosystem. However, we also are looking a higher level there's a lot of times when people think ecosystems it's fairly low level technology. But Microsoft and IBM are talking about partnerships that are focused on industry scenarios. Again retail, for example, or healthcare and others where we're building on top of these lower level ecosystem capabilities and then bringing together the solution scenarios where the strength of IBM capabilities is coupled with Microsoft capabilities to drive this very famous one plus one equals three. And then the other piece that I think we both agree on is the open source ecosystem for software development and software development collaboration and GitHub is a common anchor that we both believe can feed the world's economy with respect to the software solutions that are needed to really bring the capabilities forward, help improve the wealth economy and so forth by effectively bringing together brilliant minds across the ecosystem. And again, just Microsoft and IBM bringing some people but the rest of the world obviously participating in that as well. So thinking again, open source, open standards and then industry specific collaboration and capabilities being a key part. You mentioned people. We certainly believe that people play a key role in software developers and the get hub notion being a key one. But there are others where, again, Microsoft with Microsoft 365 has a lot of capabilities in connecting people within the organization and across organizations. And while we're using zoom here, a lot of people are utilizing teams because teams is on the one side of collaboration platform. But on the other side is also an application host. And so bringing together people collaboration supported and powered by applications from IBM from Microsoft and others is going to be, I think a huge differentiation in terms of how people interact with software in the future. >> Yeah, and I think that whole joint development is a big part of this new people equation where it's not just partnering in market, it's also at the tech and you got open source and just phenomenal innovation, a formula there. So let's ask what solutions here. I want to get into some of the top solutions you're doing with Microsoft and maybe with IBM, but your title is corporate vice president of cloud and AI come on, cause you get a better department. I mean, more relevant than that. I mean, it's exciting. Your cloud-scale is driving tons of innovation. AI is eating software, changing the software paradigm. We can see that playing out. I've done dozens of interviews just in this past month on how AI is more certainly with machine learning and having a control plane with data, changing the game. So tell us what are the hot solutions for hybrid cloud? And why is this a different ball game than say public cloud? >> Well, so first of all let's talk a little bit about the AI capabilities and data because I think there are two categories. You're seeing an evolution of AI capabilities that are coming out. And again, I just read IBM's announcement about integrating the cloud pack with IBM Satellite. I think that's a key capability that IBM is putting out there and we're partnering with IBM in two directions there. Making it run very well on Azure with our Red Hat partners. But on the other side, also thinking through how we can optimize the experience for clients that choose Azure as their platform and IBM cloud Pak for data and AI as their technology, but that's a technology play. And then the next layer up is again, IBM has done a fantastic job to build AI capabilities that are relevant for industries. Healthcare being a very good example. Again, retail being another one. And I believe Microsoft and IBM will work on both partnerships on the technology side as well as the AI usage in specific verticals. Microsoft is doing similar things within our dynamics product line. We're using AI for business applications for planning, scheduling, optimizations, risk assessments those kinds of scenarios. And of course we're using those in the Microsoft 365 environment as well. I always joke that despite my 30 years at Microsoft, I still don't know how to read or use PowerPoint. And I can't do a PowerPoint slide for the life of me but with a new designer, I can actually get help from the system to make beautiful PowerPoint happen. So bringing AI into real life usage I think is the key part. The hybrid scenario is critical here as well. And especially when you start to think about real life scenarios, like safety, worker safety in a critical environment, freshness of product we're seeing retailers deploying cameras and AI inside the retail stores to effectively make sure that the shelves are stocked. That the quality of the vegetables for example, continues to be high and monitored. And previously people would do this on a occasional basis running around in the store. Now the store is monitored 24/7 and people get notified when things need fixing. Another really cool scenario set, is quality. We're working with a finished steel producer that effectively is looking at the stainless steel as it's being produced. And they have cameras on this steel that look at specific marks. And if these marks show up, then they know that the stainless steel will be bad. And I don't know if you've looked at a manufacturing process, but the earlier they can get a failure detected the better it is because they can most likely or more often than not return the product back into the beginning of the funnel and start over. And that's what they're using. So you can see molten steel, logically speaking with a camera and AI. And previously humans did this which is obviously a less reliable and be dangerous because this is very, very hot. This is very blowing steel. And so increasing safety while at the same time, improving the quality is something that we see hybrid scenarios. Again, autonomous driving, another great scenario where perception AI is going to be utilized. So there's a bunch of capabilities out there that really are hybrid in nature and will help us move forward with key scenarios, safety, quality and autonomous behaviors like driving and so forth. >> Uli, great insight, great product vision great alignment with IBM's hybrid cloud space with all customers are looking for now and certainly multi-cloud around the horizon. So great to have you on, great agility and congratulations for your continued success. You got great area cloud and AI and we'll be keeping in touch. I'd love to do a deep dive sometime. Thanks for coming on. >> John, thank you very much for the invitation and great questions. Great interview. Love it. Appreciate it. >> Okay, CUBE coverage here at IBM Think 2021 virtual. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>from around the globe. It's the queue with digital coverage of AWS public sector online brought to you by Amazon Web services. >>Everyone welcome back to the Cube's virtual coverage of AWS Public sector summit online. That's the virtual conference. Public Sector Summit is the big get together for Teresa Carlson and her team and Amazon Web services from the public sector, which includes all the government agencies as well as education state governments here in United States and also abroad for other governments and countries. So we're gonna do an analysis of Teresa's keynote and also summarize the event as well. I'm John Furrow, your host of the Cube. I'm joined with my co host of the Cube, Dave Volante Stew Minimum. We're gonna wrap this up and analyze the keynote summit a really awkward, weird situation going on with the Summit because of the virtual nature of it. This event really prides itself. Stew and Dave. We've all done this event. It's one of our favorites. It's a really good face to face environment, but this time is virtual. And so with the covert 19 that's the backdrop to all this. >>Yeah, so I mean, a couple of things, John. I think first of all, A Z, you've pointed out many times. The future has just been pulled forward. I think the second thing is with this whole work from home in this remote thing obviously was talking about how the cloud is a tailwind. But let's face it. I mean, everybody's business was affected in some way. I think the cloud ultimately gets a tail wind out of this, but but But I think the third thing is security. Public sector is always heavily focused on security, and the security model has really changed overnight to what we've been talking about for years that the moat that we've built the perimeter is no longer where organizations need to be spending money. It's really to secure remote locations. And that literally happened overnight. So things like a security cloud become much, much more important. And obviously endpoint security and other other things that we've talked about in the Cube now for last 100 days. >>Well, Steve, I want to get your thoughts cause you know, we all love space. Do we always want to go the best space events that they're gonna be virtual this year as well? Um, But the big news out of the keynote, which was really surprising to me, is Amazon's continued double down on their efforts around space, cyber security, public and within the public sector. And they're announcing here, and the big news is a new space business segment. So they announced an aerospace group to serve those customers because space to becoming a very important observation component to a lot of the stuff we've seen with ground station we've seen at reinvent public sector. These new kinds of services are coming out. It's the best, the cloud. It's the best of data, and it's the best of these new use cases. What's your thoughts? >>Yeah, interesting. John, of course. You know, the federal government has put together Space Forces, the newest arm of the military. It's really even though something it is a punchline. There's even a Netflix show that I believe got the trademark board because they registered for it first. But we've seen Amazon pushing into space. Not only there technology being used. I had the pleasure of attending the Amazon re Marcia last year, which brought together Jeff Bezos's blue origin as well as Amazon AWS in that ecosystem. So AWS has had a number of services, like ground Station that that that are being used to help the cloud technology extend to what's happening base. So it makes a lot of sense for for the govcloud to extend to that type of environment aside you mentioned at this show. One of the things we love always is. You know, there's some great practitioner stories, and I think so many over the years that we've been doing this show and we still got some of them. Theresa had some really good guests in her keynote, talking about transformation and actually, one of the ones that she mentioned but didn't have in the keynote was one that I got to interview. I was the CTO for the state of West Virginia. If you talk about one of those government services that is getting, you know, heavy usage, it's unemployment. So they had to go from Oh my gosh, we normally had people in, you know, physical answering. The phone call centers to wait. I need to have a cloud based contact center. And they literally did that, you know, over the weekend, spun it up and pulled people from other organizations to just say, Hey, you're working from home You know you can't do your normal job Well, we can train your own, we can get it to you securely And that's the kind of thing that the cloud was really built for >>and this new aerospace division day this really highlights a lot of not just the the coolness of space, but on Earth. The benefits of there and one of Amazon's ethos is to do the heavy lifting, Andy Jassy told us on the Cube. You know, it could be more cost effective to use satellites and leverage more of that space perimeter to push down and look at observation. Cal Poly is doing some really interesting work around space. Amazon's worked with NASA Jet Propulsion Labs. They have a lot of partnerships in aerospace and space, and as it all comes together because this is now an augmentation and the cost benefits are there, this is going to create more agility because you don't have to do all that provisioning to get this going spawned. All kinds of new creativity, both an academic and commercial, your thoughts >>Well, you know, I remember the first cloud first came out people talked a lot about while I can do things that I was never able to do before, you know, The New York Times pdf example comes to mind, but but I think what a lot of people forget is you know the point to a while. A lot of these mission critical applications Oracle databases aren't moving to the cloud. But this example that you're giving and aerospace and ground station. It's all about being able to do new things that you weren't able to do before and deliver them as a service. And so, to me, it shows a great example of tam expansion, and it also shows things that you never could do before. It's not just taking traditional enterprise APs and sticking them in the cloud. Yeah, that happens. But is re imagining what you can do with computing with this massive distributed network. And you know, I O. T. Is clearly coming into into play here. I would consider this a kind of I o t like, you know, application. And so I think there are many, many more to come. But this is a great example of something that you could really never even conceive in enterprise Tech before >>you, Dave the line on that you talked about i o t talk a lot about edge computing. Well, if you talk about going into space, that's a new frontier of the edge that we need to talk about >>the world. Glad it's round. So technically no edge if you're in space so again not to get nuance here and nerdy. But okay, let's get into the event. I want to hold on the analysis of the keynote because I think this really society impact public service, public sector, things to talk about. But let's do a quick review of kind of what's happened. We'll get to the event. But let's just review the guests that we interviewed on the Cube because we have the cube virtual. We're here in our studios. You guys were in yours. We get the quarantine cruise. We're still doing our job to get the stories out there. We talked to Teresa Carlson, Shannon Kellogg, Ken Eisner, Sandy Carter, Dr Papa Casey Coleman from Salesforce, Dr Shell Gentleman from the Paragon Institute, which is doing the fairground islands of researcher on space and weather data. Um, Joshua Spence math you can use with the Alliance for Digital Innovation Around some of this new innovation, we leave the Children's National Research Institute. So a lot of great guests on the cube dot net Check it out, guys. I had trouble getting into the event that using this in Toronto platform and it was just so hard to navigate. They've been doing it before. Um, there's some key notes on there. I thought that was a disappointment for me. I couldn't get to some of the sessions I wanted to, um, but overall, I thought the content was strong. Um, the online platform just kind of wasn't there for me. What's your reaction? >>Well, I mean, it's like a Z. That's the state of the art today. And so it's essentially a webinar like platforms, and that's what everybody's saying. A lot of people are frustrated with it. I know I as a user. Activity clicks to find stuff, but it is what it is. But I think the industry is can do better. >>Yeah, and just to comment. I'll make on it, John. One of things I always love about the Amazon show. It's not just what AWS is doing, But, you know, you walk the hallways and you walk the actual So in the virtual world, I walk the expo floor and its okay, Here's a couple of presentations links in an email address if you want to follow up, I felt even the A previous AWS online at a little bit more there. And I'm sure Amazon's listening, talking to all their partners and building out more there cause that's definitely a huge opportunity to enable both networking as well. As you know, having the ecosystem be able to participate more fully in the event >>and full disclosure. We're building our own platform. We have the platforms. We care about this guys. I think that on these virtual events that the discovery is critical having the available to find the sessions, find the people so it feels more like an event. I think you know, we hope that these solutions can get better. We're gonna try and do our best. Um, so, um well, keep plugging away, guys. I want to get your thoughts. They have you been doing a lot of breaking analysis on this do and your interviews as well in the technology side around the impact of Covert 19 with Teresa Carlson and her keynote. Her number one message that I heard was Covad 19 Crisis has caused a imperative for all agencies to move faster, and Amazon is kind of I won't say put things to the side because they got their business at scale. Have really been honing in on having deliverables for crisis solutions. Solving the problems and getting out to Steve mentioned the call centers is one of the key interviews. This is that they're job. They have to do this cove. It impacts the public services of the public sector that she's that they service. So what's your reaction? Because we've been covering on the commercial side. What's your thoughts of Teresa and Amazon's story today? >>Yeah, well, she said, You know, the agencies started making cloud migrations that they're at record pace that they'd never seen before. Having said that, you know it's hard, but Amazon doesn't break out its its revenue in public sector. But in the data, I look at the breaking analysis CTR data. I mean, it definitely suggests a couple of things. Things one is I mean, everybody in the enterprise was affected in some way by Kobe is they said before, it wouldn't surprise me if there wasn't a little bit of a pause and aws public sector business and then it's picking up again now, as we sort of exit this isolation economy. I think the second thing I would say is that AWS Public sector, based on the data that I see, is significantly outpacing the growth of AWS. Overall number one number two. It's also keeping pace with the growth of Microsoft Azure. Now we know that AWS, on balance is much bigger than Microsoft Azure and Infrastructures of Service. But we also know that Microsoft Azure is growing faster. That doesn't seem to be the case in public sector. It seems like the public sector business is is really right there from in terms of growth. So it really is a shining star inside of AWS. >>Still, speed is a startup game, and agility has been a dev ops ethos. You couldn't see more obvious example in public sector where speed is critical. What's your reaction to your interviews and your conversations and your observations? A keynote? >>Yeah, I mean something We've all been saying in the technology industry is Just imagine if this had happened under 15 years ago, where we would be So where in a couple of the interviews you mentioned, I've talked to some of the non profits and researchers working on covert 19. So the cloud really has been in the spotlight. Can I react? Bask scale. Can I share information fast while still maintaining the proper regulations that are needed in the security so that, you know, the cloud has been reacting fast when you talk about the financial resource is, it's really nice to see Amazon in some of these instances has been donating compute occasional resource is and the like, so that you know, critical universities that are looking at this when researchers get what they need and not have to worry about budgets, other agencies, if you talk about contact centers, are often they will get emergency funding where they have a way to be able to get that to scale, since they weren't necessarily planning for these expenses. So you know what we've been seeing is that Cloud really has had the stress test with everything that's been going on here, and it's reacting, so it's good to see that you know, the promise of cloud is meeting that scale for the most part, Amazon doing a really good job here and you know, their customers just, you know, feel The partnership with Amazon is what I've heard loud and clear. >>Well, Dave, one of these I want to get your reaction on because Amazon you can almost see what's going on with them. They don't want to do their own horn because they're the winners on the pandemic. They are doing financially well, their services. All the things that they do scale their their their position, too. Take advantage. Business wise of of the remote workers and the customers and agencies. They don't have the problems at scale that the customers have. So a lot of things going on here. These applications that have been in the i t world of public sector are old, outdated, antiquated, certainly summer modernize more than others. But clearly 80% of them need to be modernized. So when a pandemic hits like this, it becomes critical infrastructure. Because look at the look of the things unemployment checks, massive amount of filings going on. You got critical service from education remote workforces. >>these are >>all exposed. It's not just critical. Infrastructure is plumbing. It's The applications are critical. Legit problems need to be solved now. This is forcing an institutional mindset that's been there for years of, like, slow two. Gotta move fast. I mean, this is really your thoughts. >>Yeah. And well, well, with liquidity that the Fed put into the into the market, people had, You know, it's interesting when you look at, say, for instance, take a traditional infrastructure provider like an HP era Dell. Very clearly, their on Prem business deteriorated in the last 100 days. But you know HP Q and, well, HBO, you had some some supply chain problem. But Dell big uptick in this laptop business like Amazon doesn't have that problem. In fact, CEOs have told me I couldn't get a server into my data center was too much of a hassle to get too much time. It didn't have the people. So I just spun up instances on AWS at the same time. You know, Amazon's VD I business who has workspaces business, you know, no doubt, you know, saw an uptick from this. So it's got that broad portfolio, and I think you know, people ask. Okay, what remains permanent? Uh, and I just don't see this This productivity boom that we're now finally getting from work from home pivoting back Teoh, go into the office and it calls into question Stu, when If nobody is in the corporate office, you know the VP ends, you know, the Internet becomes the new private network. >>It's to start ups moving fast. The change has been in the past two months has been, like, two years. Huge challenges. >>Yeah, John, it's an interesting point. So, you know, when cloud first started, it was about developers. It was about smaller companies that the ones that were born in the cloud on The real opportunity we've been seeing in the last few months is, you know, large organizations. You talk about public sector, there's non profits. There's government agencies. They're not the ones that you necessarily think of as moving fast. A David just pointing out Also, many of these changes that we're putting into place are going to be with us for a while. So not only remote work, but you talk about telehealth and telemedicine. These type of things, you know, have been on our doorstep for many years, but this has been a forcing function toe. Have it be there. And while we will likely go back to kind of a hybrid world, I think we have accelerated what's going on. So you know, there is the silver lining in what's going on because, you know, Number one, we're not through this pandemic. And number two, you know, there's nothing saying that we might have another pandemic in the future. So if the technology can enable us to be more flexible, more distributed a xai I've heard online. People talk a lot. It's no longer work from home but really work from anywhere. So that's a promise we've had for a long time. And in every technology and vertical. There's a little bit of a reimagining on cloud, absolutely an enabler for thinking differently. >>John, I wonder if I could comment on that and maybe ask you a question. That's okay. I know your host. You don't mind. So, first of all, I think if you think about a framework for coming back, it's too said, You know, we're still not out of this thing yet, but if you look at three things how digital is an organization. How what's the feasibility of them actually doing physical distancing? And how essential is that business from a digital standpoint you have cloud. How digital are you? The government obviously, is a critical business. And so I think, you know, AWS, public Sector and other firms like that are in pretty good shape. And then there's just a lot of businesses that aren't essential that aren't digital, and those are gonna really, you know, see a deterioration. But you've been you've been interviewing a lot of people, John, in this event you've been watching for years. What's your take on AWS Public sector? >>Well, I'll give an answer that also wants to do away because he and I both talk to some of the guests and interview them. Had some conversations in the community is prep. But my take away looking at Amazon over the past, say, five or six years, um, a massive acceleration we saw coming in that match the commercial market on the enterprise side. So this almost blending of it's not just public sector anymore. It looks a lot like commercial cause, the the needs and the services and the APS have to be more agile. So you saw the same kind of questions in the same kind of crazy. It wasn't just a separate division or a separate industry sector. It has the same patterns as commercial. But I think to me my big takeaways, that Theresa Carlson hit this early on with Amazon, and that is they can do a lot of the heavy lifting things like fed ramp, which can cost a $1,000,000 for a company to go through. You going with Amazon? You onboard them? You're instantly. There's a fast track for you. It's less expensive, significantly less expensive. And next thing you know, you're selling to the government. If you're a start up or commercial business, that's a gold mine. I'm going with Amazon every time. Um, and the >>other >>thing is, is that the government has shifted. So now you have Covad 19 impact. That puts a huge premium on people who are already been setting up for digital transformation and or have been doing it. So those agencies and those stakeholders will be doing very, very well. And you know that Congress has got trillions of dollars day. We've covered this on the Cube. How much of that coverage is actually going for modernization of I T systems? Nothing. And, you know, one of things. Amazon saying. And rightfully so. Shannon Kellogg was pointing out. Congress needs to put some money aside for their own agencies because the citizens us, the taxpayers, we got to get the services. You got veterans, you've got unemployment. You've got these critical services that need to be turned on quicker. There's no money for that. So huge blind spot on the whole recovery bill. And then finally, I think that there's a huge entrepreneurial thinking that's going to be a public private partnership. Cal Poly, Other NASA JPL You're starting to see new applications, and this came out of my interviews on some of the ones I talked to. They're thinking differently, the doing things that have never been done before. And they're doing it in a clever, innovative way, and they're reinventing and delivering new things that are better. So everything's about okay. Modernize the old and make it better, and then think about something new and completely different and make it game changing. So to me, those were dynamics that are going on than seeing emerge, and it's coming out of the interviews. Loud and clear. Oh, my God, I never would have thought about that. You can only do that with Cloud Computing. A super computer in the Cloud Analytics at scale, Ocean Data from sale Drone using satellite over the top observation data. Oh, my God. Brilliant. Never possible before. So these are the new things that put the old guard in the Beltway bandits that check because they can't make up the old excuses. So I think Amazon and Microsoft, more than anyone else, can drive change fast. So whoever gets there first, well, we'll take most of the shares. So it's a huge shift and it's happening very fast more than ever before this year with Covert 19 and again, that's the the analysis. And Amazon is just trying to like, Okay, don't talk about us is we don't want to like we're over overtaking the world because outside and then look opportunistic. But the reality is we have the best solution. So >>what? They complain they don't want to be perceived as ambulance station. But to your point, the new work loads and new applications and the traditional enterprise folks they want to pay the cow path is really what they want to dio. And we're just now seeing a whole new set of applications and workloads emerging. What about the team you guys have been interviewing? A lot of people we've interviewed tons of people at AWS reinvent over the years. We know about Andy Jassy at all. You know, his his lieutenants, about the team in public sector. How do they compare, you know, relative to what we know about AWS and maybe even some of the competition. Where do you Where do you grade them? >>I give Amazon and, um, much stronger grade than Microsoft. Microsoft still has an old DNA. Um, you got something to tell them is bring some fresh brand there. I see the Jedi competition a lot of mud slinging there, and I think Microsoft clearly got in fear solution. So the whole stall tactic has worked, and we pointed out two years ago the number one goal of Jet I was for Amazon not to win. And Microsoft looks like they're gonna catch up, and we'll probably get that contract. And I don't think you're probably gonna win that out, right? I don't think Amazon is gonna win that back. We'll see. But still doesn't matter. Is gonna go multi cloud anyway. Um, Teresa Carlson has always had the right vision. The team is exceptional. Um, they're superb experience and their ecosystem partners Air second and NASA GPL Cal Poly. The list goes on and on, and they're attracting new talent. So you look at the benchmark new talent and unlimited capability again, they're providing the kinds of services. So if we wanted to sell the Cube virtual platform Dave, say the government to do do events, we did get fed ramp. We get all this approval process because Amazon customer, you can just skate right in and move up faster versus the slog of these certifications that everyone knows in every venture capitalists are. Investor knows it takes a lot of time. So to me, the team is awesome. I think that the best in the industry and they've got to balance the policy. I think that's gonna be a real big challenge. And it's complex with Amazon, you know, they own the post. You got the political climate and they're winning, right? They're doing well. And so they have an incentive to to be in there and shape policy. And I think the digital natives we are here. And I think it's a silent revolution going on where the young generation is like, Look at government served me better. And how can I get involved? So I think you're going to see new APS coming. We're gonna see a really, you know, integration of new blood coming into the public sector, young talent and new applications that might take >>you mentioned the political climate, of course. Pre Cove. It'll you heard this? All that we call it the Tech lash, right, The backlash into big tech. You wonder if that is going to now subside somewhat, but still is the point You're making it. Where would we be without without technology generally and big tech stepping up? Of course, now that you know who knows, right, Biden looks like he's, you know, in the catbird seat. But there's a lot of time left talking about Liz more on being the Treasury secretary. You know what she'll do? The big tech, but But nonetheless I think I think really it is time to look at big tech and look at the Tech for good, and you give them some points for that. Still, what do you think? >>Yeah, first of all, Dave, you know, in general, it felt like that tech lash has gone down a little bit when I look online. Facebook, of course, is still front and center about what they're doing and how they're reacting to the current state of what's happening around the country. Amazon, on the other hand, you know, a done mentioned, you know, they're absolutely winning in this, but there hasn't been, you know, too much push back if you talk culturally. There's a big difference between Amazon and AWS. There are some concerns around what Amazon is doing in their distribution facilities and the like. And, you know, there's been lots of spotlights set on that, um, but overall, there are questions. Should AWS and Amazon that they split. There's an interesting debate on that, Dave, you and I have had many conversations about that over the past couple of years, and it feels like it is coming more to a head on. And if it happens from a regulation standpoint, or would Amazon do it for business reason because, you know, one of Microsoft and Google's biggest attacks are, well, you don't want to put your infrastructure on AWS because Amazon, the parent company, is going to go after your business. I do want to pull in just one thread that John you and Dave were both talking about while today you know, Amazon's doing a good job of not trying todo ambulance case. What is different today than it was 10 or 20 years ago. It used to be that I t would do something and they didn't want to talk to their peers because that was their differentiation. But Amazon has done a good job of explaining that you don't want to have that undifferentiated heavy lifting. So now when an agency or a company find something that they really like from Amazon talking all their peers about it because they're like, Oh, you're using this Have you tried plugging in this other service or use this other piece of the ecosystem? So there is that flywheel effect from the cloud from customers. And of course, we've talked a lot about the flywheel of data, and one of the big takeaways from this show has been the ability for cloud to help unlock and get beyond those information silos for things like over 19 and beyond. >>Hey, John, if the government makes a ws spin out or Amazon spin out AWS, does that mean Microsoft and Google have to spin out their cloud businesses to? And, uh, you think that you think the Chinese government make Alibaba spin out its cloud business? >>Well, you know the thing about the Chinese and Facebook, I compare them together because this is where the tech lash problem comes in. The Chinese stolen local property, United States. That's well documented use as competitive advantage. Facebook stole all the notional property out of the humans in the world and broke democracy, Right? So the difference between those bad tech actors, um, is an Amazon and others is 11 enabling technology and one isn't Facebook really doesn't really enable anything. If you think about it, enables hate. It enables some friends to talk some emotional reactions, but the real societal benefit of historically if you look at society, things that we're enabling do well in free free societies. Closed systems don't work. So you got the country of China who's orchestrating all their actors to be state driven, have a competitive advantage that's subsidised. United States will never do that. I think it's a shame to break up any of the tech companies. So I'm against the tech lash breakup. I think we should get behind our American companies and do it in an open, transparent way. Think Amazon's clearly doing that? I think that's why Amazon's quiet is because they're not taking advantage of the system that do things faster and cheaper gets that's there. Ethos thinks benefits the consumer with If you think about it that way, and some will debate that, but in general Amazon's and enabling technology with cloud. So the benefits of the cloud for them to enable our far greater than the people taking advantage of it. So if I'm on agency trying to deliver unemployment checks, I'm benefiting the citizens at scale. Amazon takes a small portion of that fee, so when you have enabling technologies, that's how to me, The right capitalism model works Silicon Valley In the tech companies, they don't think this way. They think for profit, go big or go home and this has been an institutional thing with tech companies. They would have a policy team, and that's all they did. They didn't really do anything t impact society because it wasn't that big. Now, with networked economies, you're looking at something completely different to connected system. You can't handle dissidents differently is it's complex? The point is, the diverse team Facebook and Amazon is one's an enabling technology. AWS Facebook is just a walled garden portal. So you know, I mean, some tech is good, some text bad, and a lot of people just don't know the difference what we do. I would say that Amazon is not evil Amazon Web services particular because they enable people to do things. And I think the benefits far outweigh the criticisms. So >>anybody use AWS. Anybody can go in there and swipe the credit card and spin up compute storage AI database so they could sell the problems. >>The problems, whether it's covert problems on solving the unemployment checks going out, are serving veterans or getting people getting delivering services. Some entrepreneurs develop an app for that, right? So you know there's benefits, right? So this you know, there's not not Amazon saying Do it this way. They're saying, Here's this resource, do something creative and build something solve a problem. And that was the key message of the keynote. >>People get concerned about absolute power, you know, it's understandable. But if you know you start abusing absolute power, really, I've always believed the government should come in, >>but >>you know, the evidence of that is is pretty few and far between, so we'll see how this thing plays out. I mean, it's a very interesting dynamic. I point about why should. I don't understand why AWS, you know, gets all the microscopic discussion. But I've never heard anybody say that Microsoft should spend on Azure. I've never heard that. >>Well, the big secret is Azure is actually one of Amazon's biggest customers. That's another breaking analysis look into that we'll keep on making noted that Dave's do Thanks for coming to do great interviews. Love your conversations. Final words to I'll give you What's the big thing you took away from your conversations with your guests for this cube? Virtual coverage of public sector virtual summit >>so biggest take away from the users is being able to react to, you know, just ridiculously fast. You know it. Talk about something where you know I get a quote on Thursday on Friday and make a decision, and on Monday, on up and running this unparalleled that I wouldn't be able to do before. And if you talk about the response things like over nine, I mean enabling technology to be able to cut across organizations across countries and across domains. John, as you pointed out, that public private dynamic helping to make sure that you can react and get things done >>Awesome. We'll leave it there. Stew. Dave. Thanks for spending time to analyze the keynote. Also summarize the event. This is a does public sector virtual summit online Couldn't be face to face. Of course. We bring the Cube virtual coverage as well as content and our platform for people to consume. Go the cube dot net check it out and keep engaging. Hit us up on Twitter if any questions hit us up. Thanks for watching. >>Yeah, yeah, yeah, yeah, yeah, yeah

>>LA from Las Vegas. It's the cube covering AWS. Reinvent 20 nineteens brought to you by Amazon web services and they don't care along with its ecosystem partners. >>Hey, welcome back. Everyone is the cubes live covers in Las Vegas for AWS. Reinvent 2019 it's our seventh year covering Amazon reinvent. They've only had the conference for eight years. We've been documenting history. I'm John Farrow, stupid man. Dave Alante, John Walls, Jeff, Rick, they're all on the other step two sets sponsored by Intel. Want to thank their support without their generous support to our mission. We wouldn't be able to bring this great content. Our next guest to talk about the IOT edge jerk DDoSs column. Perfect. Welcome back. VP of IOT. Well the Greek names. Yeah, I'm half Greek, half German so I can expect, okay. Is smart. Good. So Derek, I gotta ask you, so IOT is hot. Explain quickly your role at AWS because you're not an I-Team specifically define your scope. So my scope is owning all or my team's sculpt is owning old software services and tools that deal with non it equipment. >>So when you go to AWS and look for IOT, all the service that you'll find, that's the scope of my teams and this it group which have all the it stuff and just feels like cars, manufacturing sensors, all of the axioms for the NFL, all that good stuff. So women, you're going to see Edelweiss so I go AWS, amazon.com and then you're fine either. means all of our compute, all of our databases, all of our storage and there's also all of our and Melanie and I and then there's an IOT section and there you find all of the goodness that we do for IOT. You know, it's exciting. Stu and I talking about all week here, the whole cloud native, you take the T out of cloud native, it's cloud naive. You've got the general commercial business and public sector barely getting their act together. They're transforming, they're doing it now. >>He's $1 trillion on a vouch. Trillions of dollars of of change coming. Good up business opportunity. But if they're having trouble transforming, you get this whole new world of industrial edge which requires computing cars manufactured. This is a hot area. So a lot of change happening. What is the most important story people should pay attention to in your area that that's notable for this collision of all this transformation? I think maybe the most notable story that we currently have is a corporation that they do with the VW, which is the largest a car manufacturer. And you were just lucky that via their CIO mountain Huffman being part of Verona for good's keynote, our CTO. So if you haven't seen that, just go and review the keynote of Verner and then as the larger part then he was talking about all of that, what he calls industrial 4.0, this digitization fourth revolution. And Martin did an awesome job explaining what are we doing together with them to build their industrial cloud. Yeah. >>Uh, well, one of the things we've been really watching is the, the extent that Amazon services are starting to push out. Uh, I've been super excited, really looking at some of the growth of there. Your team did a bunch of announcements ahead of the show including the one that caught my eye the most was the IOT green grass sport for Lambda and Docker. Maybe start there and walk us through some of the new pieces that in your org. Okay. >>Maybe for us to understand the offer three type of offerings for our customers. One is device software, which might sound strange that a cloud company actually gives you a software that it's not running on the cloud, but then you're talking about IOT. You need software running on your devices in order to be able to be controlled and communicate with the cloud and we have an offering in that area which is called IOT Greenglass, which is a software runtime that you can install on edge devices like gateways for example, and via announced junior additions to our IOT Greenglass. One is Docker supports, which was very important because up till now green were supporting machine learning at the edge and Lambda, which is our service offering, but many companies now more established enterprises said, you know what, I have legacy applications which I can package. Can I deploy them as well? >>Now you can deploy Docker containers, Lambda functions, and a melody edge all with one goal with green glass at the edge. So that was one of the announcements we did for our device >> software. They're, I want to get your thoughts on an area that we're reporting on and doing a lot of investigation, collecting a lot of data, talking to a lot of people and that's around the industrial IOT or IOT, industrial IOT. And one of our big concerns, I want to get your reaction to this and thoughts is security is of paramount importance because it's not just a DDoS attack or some malware which is causing credit card data or these kinds of theft. You could actually take over machines. People could die this and serious issues around the guarantee. This is the number one conversation. What is the state of the art security posture in your area around software and the edge? >>So at AWS, whether it's IOT or any other workloads, we always say if you have two primary zeros, one is security and one is operations. Because if any company puts their faith in us, if we are down, their business is down and if there would be any security issues, of course all the trust would be broken and we do the exact same approach. Now with IOT, so we built our services with security in mind. For example, when you connect to AWS IOT core, every single individual device needs to have certificates to be identified. If you require that you can encrypt your data, it doesn't even lo you to connect to the cloud without encryption. We have software, as I said, at the edge with Amazon free artists and Greengrass where we support all of the hardware TM modules that you have security postures there. If you have secrets managers, they even have an award winning clout. >>If you're like security tool, which is called IOT device management, but at any given point in time audits but the you configured correctly and does something like detection. If something's going wrong, like when you get your credit card and said, Hey, by the way, have you been in this country? Candy making any purchase? If you figure out if something's going wrong with your device >> and you feel good that it's built in from zero, I mean you've got DNS tax going on. What? I mean you feel comfortable that it's, I mean we believe whatever we build, you can never be 100% sure and security is always evolving. But we believe that we are at the forefront of being, you're always the latest and greatest technology at the hands of our customers. >>Jerks. That's really powerful. Cause I saw one of the other announcements was really taking the Alexa voice service integration, but if I understand it rightly, it pulls that core along. So you know part of me was like, it's like okay Alexa enabled everywhere. That's great. I don't need 700 devices in my house that all have that. But the security piece is going to be needed everywhere. So help us tease that out. >>Maybe, maybe don't understand what we did you ask about the other launches. We also launched something called AVS integration for IUT and AVS stands for Alexa voice services. So if you know Alexa, that's our digital assistant that runs for example an equity devices, but if you want to build a device as a third party, which you can directly talk to media, there's microphones and speakers that is called AVS or Alexa built in devices and if you wanted to build one today you needed to put quite some resources onto this device because it needs to understand you. It needs to have a lot of audio processing. That means there's a lot of memory involved and quite some processing. Now I'm using some technical terms. You need something like a cortex, a CPU which makes this device expensive. So the bill of material is quite elevated and we were working with our Alexa team saying is how can we make this really, really affordable? >>If you found a trick where we said let's offload all of this audio processing to the cloud that you an eSense can build very dumb devices. The only thing that these devices don't need to do is have microphones, have our speaker and what we call a week work detection. They need to wake up and you say, Alexa, echo computer, everything else gets streamed to the cloud. Ptosis sits there and comes back so that you can reduce cost for those devices by at least a factor of half. And we had a great customer on stage as well because if you can make so cheap Alexa built in devices, you can put this into a light switch and iDevices now believe it or not, non-sales light switch. Yup. Which you can now directly talk to, reach, talks back and place your music. They're talking about your role. Again, I want to understand that you are not technical side, your development teams. What are you, what do you do on a daily basis? What's your job? So officially I'm a VP of engineering, so I'm a tech guy, so I love the hoodie. By the way. This is tech. That's because I'm on video. Okay. >>It looks great. So I'm an engineer by Heights and at Amazon we don't have a separation between businesses and product management and engineering. They call it a single thread of leaders that we believe the teams have to own it all. So that means my teams on everything from the conception of their services, the development operations that what be called dev ops and also the business behind. So that means all of the services, whether it's free outro, screen grabs at the edge, but it's IOT core device management and defender or our data services like IOT analytics or your talked about industrial site wise, their health or being conceived by my teams. They have all been developed and they are all operated today so that all customers can use that as it make. What should people >>totally does. Thanks for clarifying. That's awesome. Uh, what should people pay attention to? What should we be reporting on in your area? What are some of the key things that people watching this should pay attention to in this, in your IOT area? What are the most important items and products and services that you're doing? I think >>one of the most important things to understand is be talk just before the interview about this, that a lot of the technical hurdles actually solve that because we have the software on devices, we have the connectivity controlled services, and we have all the analytic services to make sense of the data that you can take actions. You don't need to be an expert in machine learning anymore to do machine learning at AWS. You don't have to be an embedded software developer to get connected devices. You don't have to be a data scientist to understand what your data does. The most interesting part though is there is a cultural aspect of this because in the past you had to ideally most likely in your old company join said, Oh, I would like to connect something, so do I have a purchase acquisition? Can I go to my finance team? Does it install this today? You don't need that anymore. With AWS IOT, the same thing that happens with the cloud and it happens with IOT. So understanding that via very powerful tools for engineers in the company that you can build at any given point in time. I think that's maybe the most, >>and I think the it, I think that whole process of the time it takes, they go to the airport on Thanksgiving, go through TSA and knows all that pre ocracy. And then the other thing too is that the other IOT used to be kind of a closed system self, um, form dot devices. Now you've got with Clough, you've got a lot more range and compatibility. Can you talk about that address, address that issue? Because there might be still legacy out there and no problem. It's data's data, but those are the days come in the cloud. But there's now a new shift happening where it's not just, you know, fully monolithic OT devices if it, so the pasta >>monolithic what's called machine to machine, close systems, IOT is the opposite there. It's where you say now all the devices and connections can be done in between the devices and the cloud. So it's system of systems. And in order to make that happen. For example, when you call it the legacy systems, we also announced on Monday and our IOT day additional features for IOT core that you can migrate legacy systems much easier to the cloud without that you need to update your devices. >>Yeah. Dirk, one of the things I find most interesting about your space as you span between the consumer and the enterprise piece, so I remember a few years ago there was like a hackathon on building skills for Alexa and it got lots of people involved. There was a giveaway of lots of the devices there. You know, we used to talk about the consumerization of it. How is what's happening in the tumor world? You know, how is the enterprise going to take care of take that and transform business as we see IOT permeating everywhere. >>So the capabilities that you need, whether you're going in industrial or in consumer or in the medical or pick your favorite other vertical is in essence the same. You need to connect the devices. You need to ensure that they're secure. We talked about security. You need to make sense of the data, whether you do this in the home with your television or your light switch or your robot, or you do the exact same thing with the most sophisticated robot in the industry. It's the same thing. The good thing about us handling all of those sites is that the scale that we gain with literally hundreds of millions of devices now managed by our service in the backend of course means we will handle all of that scale also in the industry and the security and postures and complexity that we need to handle an industrial also benefits computer, so our consumer side, so you benefit from both sides, very cheap and scale on the one industrial benefit. Very complex. How do you solve that consumable benefit, so it's very fruitful synergies if you like, >>Oh, you guys love to solve problems at Amazon that's going to eat those. Yeah. Derek, thank you so much for coming on and sharing the insights and what you're working on and what's important. Congratulations on all your success. Thank you so much. The threaded leader here. Final question for you. Eighth year of reinvent. It gets bigger every year. Louder. Crazier for parties, more business development more. Exactly. I mean just, it's crazy. Yeah. It's just say work hard, play hard. What is your favorite thing going on here? What's the coolest thing that you've seen? >>I think the coolest thing, and it might sound a little cheeky, is, is the excitement from all of our customers and partners coming here every year. >>PR tells you to say, I'm not about fraud. I mean, you're talking about products. I love my products. I'm still so happy about that. I mean, I can talk to a light switch now. Well, you see the comma car and the other quad had the area that we have yet. It's a very different experience that you can do. Don't talk to your lights, which when you get home your wife will think you're going crazy. I love that. Thank you for coming on. Really appreciate it. Thanks for having cube coverage here. All I'm, we're going to wrap up here. Keep coverage with Derek runs all the IOT for with an AWS exciting new area. It's going to change the game on architecture and solutions are being baked out in real time. We're here breaking out the cube in real time. I'm John. Thanks for watching.

>> Live, from Miami Beach, Florida it's theCUBE covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to Miami everybody, sunny Miami. Dave Vellante here with Peter Burris. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise, and there's a lot of noise here, and there's a lot of signal here. VeeamON 2019, this is theCUBE's third year doing Veeam's big customer show. We started doing NOLA, last year was Chicago, a very hip location here at the Fountainebleau Hotel. Carey Stanton is here. He is the Vice President of business development and corporate dev, corp-dev at Veeam and Jeff Kalat, a CUBE alum, >> Yep, you bet. >> long-time friend of theCUBE, senior director of strategic alliances at Hewlett-Packard Enterprise. Gentlemen, welcome to theCUBE, good to see you. >> Thanks for having us. >> You're very welcome. Carey let me start with you. Uh, I really want to talk about sports with you, but anyway, we won't. We'll hold that off. (laughter) >> (Carey) One day. >> Momentum. You're relatively new to Veeam. But you've been here now a couple years. Where's this momentum coming from, from your perspective, as a recent Veeam entrant. >> Yeah, no, the momentum's coming from across the board, but I think a big momentum is coming from new product innovation that we're doing with Office 365, and we're just driving up subscription business momentum that we have for the pent-up demand that we had for Euphor. But a big part is coming from our relationships like we have with HPE. We invested heavily a few years ago when we announced that joint reseller agreement. What we've done is not just continued to sell but add a plethora of new solutions to it Jeff's going to talk about what we're doing with GreenLake adding SimpliVity, adding the overall solutions that we have. But that's a team that started two years ago with two people that we now have over 20 people just working, dedicated with HPE on co-selling. And I'm happy to say that our business in first half, or I should say year-to-date is up 50% year over year on a global reseller business. >> Well Jeff, theCUBE as you know, has been documenting the ebbs and flows of HP and HPE over the last, better part of a decade. And when HP split in two, to HPE and HP Inc. One of the things that-- And then sold the software business, or a large portion of it. One of the things that went was data protection. >> (Jeff) You got it. >> (Dave) And that just opened up a whole new set of opportunities and Veeam was obviously one of those. And it's starting to pay dividends. >> You got it, yeah, to that point, that evolution through HPE.nex, we were able to focus on our core. And the benefit, the inherit benefit is that we can partner with the best of class in the marketplace. And Veeam is considered best of class. So when it comes to data availability, data protection, we're all in. And we're actually, as a company, we're actually doubling down now in our partnership with Veeam. We've actually taken them from, maybe a traditional storage alliance, and taken them to be one of our top global strategic alliances in the line of the Microsoft's, the Veeam, or as the SAP's. Because we see great momentum, we see great customer adoption and interest and we see great innovation at the product level, but also in the whole global market chain. >> Well talk a little more about that because it was, the move allowed you to form new partnerships that dramatically expanded your TAM but, I'm interested in the nature of the partnership. Is it, just go to market, is there engineering integration? Talk about that a little bit. >> Our first step when we came together and said okay let's take this to the next level, we realized we need to narrow our focus to the core customer values and we really settled on three core areas of this relationship. One is first, data protection for, around our intelligence storage, as you know, our storage portfolio 3 Par, Nimble, we've had a great relationship there, we continue to drive co-innovation at the road map level, but also drive go-to-market activities and marketing and we have feet on the street actively selling. So the first one's really expanding our work with storage. Now we're taking it, we're extending, if you will, through consumption based data management, using, well HPE has GreenLake, Greenlake we see 40% of customers by 2020 are going to be consuming their data center IT more in a consumption model. There are inherent benefits of that. What we now have offered and launched just recently Backup,is a service through our flex capacity coming out of GreenLake, providing customers the choice, if you will, to move from a, not from a capital expenditure, but, by the drink, if you will, consumption base. So that's the second core area. And the third core area is new for us, and that's around our HCI portfolio. As you know, we purchased SimpliVity. Well, SimpliVity has a lot of inherent backup, dedupe compression in line, but there actually are some Zivik use cases that we're deploying out there that show how Simplivity in a Veeam environment can actually, customers can see actually incremental values. So, those are the three key areas we're focused on as we up-level this whole relationship and partnership. >> (Dave) So Carey, please. >> I was just going to say if you think of, we talk a lot about we go after the technical decision maker in all these, hundreds of people here at the conference. And then going towards the executive, the enterprise. And it's through relationships with HPE on this, the flex capacity, being able to go to a customer and offer a true enterprise solution that they're looking for, everyone wants as a service. And so we've closed multiple deals this year thanks to having the Greenlake. So, our relationship with HPE continues to elevate and the enterprise is a result of the solutions that we're doing. Not just selling storage, but selling a complete solution. >> Rathmeyer was kind of tongue-in-cheek this morning at the analyst and media event. He was talking about how in 2013 he predicted that Veeam would be a billion dollar company by 2018. And he said he missed it by six months. One of the reasons was because you know, you got the subscription model. So that's, you know GreenLake obviously is part of that, maybe not the predominant part yet but I think you said you have 40% you're saying will consume, as a service by 2020. >> 2020, actually soon. >> (Dave) Okay so pretty substantial. >> Yeah. >> What's driving that? Is it just CFO's want to go to opex? Or is it-- >> I think it's a, there are many, the value you get without locking yourself into every three years needing to do a total forklift upgrade of your infrastructure, that's one thing. The second thing is moving it from a capital expenditure to an opex expediture. It can be planned, it can be budgeted as well. The third thing is the customer doesn't have to mess with all the technology, updating the firmware, the drivers and all that. We will do it on their behalf, right? We give them the economics of cloud on prem and that's the beauty of that. So we believe, and lock-step in alignment with Veeam, the world is hybrid in the future. So on prem is here to live forever, but increasingly we need to leverage the assets in the cloud and this is providing the ability of doing it in a consumption model. >> And it's not just the economics it's the experience as well. >> Oh totally, if you want to, if you live in a house and you're a home owner, and you want a new bathroom, you put in a bathroom. If you're a renter you end up in a long, laborious negotiation that you're going to lose. And the same kind of notion is here as people realize there's greater strategic opportunities and options from how to use their data differently. They want access to those options. And that's the basis of agility. The opex to capex is good but you've got to put it in business context. It's how you create additional options in your data oriented investments. So, as you guys are moving forward are you starting to have that conversation with customers? And relating data, data value, asset management, Backup, Restore, to this broader picture, this broader strategic union you're putting together? >> Yeah and that is a key imperative of how we get even stronger in traction is telling the bigger picture. And you look at the world of yesterday, where it's just backup and recovery, look at the advent of edge devices and the amount of data that's being put at the edge. Now look at AI and machine learning where, the data is inherently needed to project the changes and the needs that are in the future. So, I think these all tie in to the play and I believe at GreenLake our consumption model can provide great benefits, above and beyond the traditional backup and recovery. >> And I was just going to add to it, is that it also brings in our ecosystems, so the relationship, that tier one relationship we both have within Microsoft. So when you start looking at a solution that the business owner wants, they want to be able to say I need cloud, I need on prem, I need backup recovery, and so by going through GreenLake they can encompass, we have a broader ecosystem that we're able to bring in versus just single thread in these discussions where you're going in and selling a data protection story and leaving but you didn't solve that broader customer problem, and with GreenLake, they are solving that overall problem. >> Yeah, I'd just like to say nothing really happens until you make a sale. You talked about some of the growth earlier. But why Veeam? Obviously you're getting some traction in the market but there's a lot of players out there that you could partner with. And you do partner with others. But why Veeam? What makes Veeam so special? >> I think one, inherently we are lock-step in agreement of the over-arching strategy, we talked about hybrid, we talked about portfolio. Two is we've got the engagement at all levels of our organization, which all stems truly from having a unified roadmap. Innovation has to happen at the roadmap level and you need to be lock-step aligned through the value chain in the way you take it to market, the way you align your sellers, the way you deliver a value proposition that truly is valuable to our customers. It's proven from our IDC research that customers who are deploying and purchasing HPE and Veeam solutions are seeing a 250 plus percent ROI on that investment. So there's this huge customer benefit, and why not go bigger and go bigger and go bigger with them. >> (Dave) Same question to you Carey. So why HPEE, why is HPEE so special as a partner? >> I think HPE first and foremost, being that first partner that came to us to want to go all in, as Jeff was talking about, from day one, and top down. So we're not just working with a department of HPE we have it from Antonio, from Jim Jackson down the stack in the organization. We were aligned from day one. They lead with data protection, it's no longer, it's a a nice to have, it's a requirement in every one of their sales processes. We're their lead partner that they have in data protection. And what we'd been able to do and have that enterprise visibility by them assisting us on our journey. So, from across the board, whether it's through management, through technology, or just in true go-to-market, they're by far our number one partner that we have on our sell-with motion. >> So Jeff, I want to talk to the group about GreenLake. And Carey, I'd love to hear your thoughts on it as well. What are the challenges that go into a consumption-based model for a company that's traditionally sold products. As part of this overall move in all industries, all sectors, from a product to a services orientation. How do introduce metrics that are associated with the service? Because it used to be you just sold a product. And the metrics for storage are different from the metrics from backup, different from the metrics from compute. So as you've gone to GreenLake, because I love GreenLake, what kind of specialized, or specific types of things, how are you selling it to try to tie that service into the business outcomes that your customers are trying to see? >> Well clearly, I believe, some of our first wins, early wins we were able to monitor and metric the value the customers were getting, the service levels they've received and so we have a number of different methods of capturing the data, the empirical data, on the service levels and being able to use that to then, use that in the selling motion to be able to articulate the experience and the expectations that come with that. >> What are some of the harder problems that your customers are asking you to solve? And how are you approaching it together? >> Well I think that what we're talking about with GreenLake here is a real hard problem to solve, right? Consumpton based across geographic regions, across different technologies, on prem, off prem, hybrid. And we don't have another partner that we can go to market with when we hear this from the customer. So when we hear it, we know that we can lean in. And we truly are, to follow up from your question, is the fact is that HPEE is solving all this and then bringing us in as their number one partner, is the differentiator that we love. So solving those problems at an enterprise level, and at a commercial level and doing it with one partner is easy, right? We're shortening the sales cycle, increasing the value to the customer. >> Yeah, one thing I have to say and it's always, complexity is always a problem and an issue, right? So it will always be a problem and an issue and we will always be striving to improve and improve the complexity. But you know, Veeam, we're super simple, right? And we, especially when you look in our HCI portfolio and that's all about driving simplicity, if you will, in a way you can deploy IT, you can scale it. So I think complexity is, and will always be a problem. But it's a given too and it will always be there. And we will always be striving to make it even easier and easier for our joint customers. >> Well one of the challenges that you face, especially as you go to a sevices-only model, is how do you put a price on the outcomes that you're delivering as opposed to the price on the assets that the person is taking? So I think one of the biggest challenges, and it sounds like you guys are pretty close to getting this together, but it's part of a broader portfolio, is where does this, let's put it slightly differently. We've talked about this before in some of the other interviews. backup is moved from a have to have it, for maybe compliance or it just makes good sense to have it, to a strategic business capability for a company that's increasingly differentiating itself on it's data assets. That moves this conversation about, as a service, into a different group and a different, different level. And that's what I'm wondering. Those metrics have got to be a big part of the conversation. Because the entire organization is now recognizing backup is more than just a bolt-on. >> Yeah. One example, one of our close partners, we're here with them, Island. So, disaster recovery as a service, right? They standardize on Nimble and Veeam and together, that combination to them was good enough to build their business on. So there's inherent value and we expect to continue to grow and be able to expose that value. 'Cause we believe more and more customers, not just your pure enterprises but, from your mid-market all the way up, can be able to utilize and see that value and experience it. >> Just a point of clarification if I could on the HCI piece. Specifically around SimpliVity. So SimpliVity was known for it's backup use cases. >> (Jeff) Sure. Still is. >> So where does Veeam and SimpliVity fit, versus Simplivity solo. >> Yeah, yeah. Well first and foremost yes, Simplivity has inherent, great data availability features, inherent in it. That's core to it. But in reality, for customers, let's say a mixed environment, whether it be virtualized, non-vitrualized, there are inherent benefits to having Veeam in addition to SimpliVity. Another example would be customers who want to really have the access to be able to do specific file restores. So we see capabilities in running Veeam in parallel with SimpliVity. Actually I see a lot of customers that are deploying SimpliVity are also deploying Veeam and there, it's an additive value that they're seeing. And they're able to parse out features and functionality and be able to increase their level of value that couldn't be done, just purely from a Simplivity standpoint alone. >> All right Carey, we'll give you the final word. >> The final word is-- >> Bumper sticker on VeeamON. >> (laughing) >> Bumper stickers. >> I would say that, what we're doing here with HPEE, we would say we're in the first inning. What we're seeing on the innovations that we have coming out later this year with HPEE, coming into next year, and we're just thrilled to be having them a platinum sponsor of VeeamON and look forward to another successful year. >> Awesome. Guys thanks so much for coming on. I got to ask you, Boston-based person, Bruins fan? >> (Carey) Bruins, yes. >> You worried about Tuulka, at all, a 12 day layoff? >> (Carey) Nope. >> No problem. >> (Carey) Nope, Chara's going to be nice and rested and-- >> (Dave) Chara, more Chara or less Chara? >> I'm going to, yes well. I got to take more thanks. >> Okay, all right, good. We'll see, we'll see. Go Bruins. All right guys thanks so much for coming out and thank you for watching. Keep it right there we'll be back with our next guest shortly right after this break. You're watching theCUBE from VeeamON, 2019 from Miami. Be right back.

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hi, I'm Peter Burress, and welcome to another CUBE conversation from our beautiful studios in wonderful Palo Alto, California. Enterprises have always struggled with how they're going to add more end points into their networks. More users, more devices, more machines, they need better speeds, lower latencies, greater security. How are they going to do it? Well, we've got a new set of standards coming along within the wifi world as well within the cellular world, to provide those greater densities, lower latencies, higher performance. Wifi Six is what we talk about within kind of the extension of the 802.11 family of protocols, but Wifi Six, like every other significant transformation has required that enterprises think differently about certain attributes of networking. So to have that conversation, we got Sachin Gupta who's a senior vice president of Cisco here, Sachin, welcome to theCUBE. >> Thanks Peter, very excited to be here. >> Alright, look, so I'm a CIO, and I am working with my team to incorporate these new technologies that are going to improve the quality of my endpoint services, and I'm looking at Wifi Six. What am I mainly worried about as I think about adopting these new technologies? >> So just before we just get into adopting of the new technologies, why are you going after Wi-Fi 6, like what's the reason for the CIO? And quite simply it's, all of the new use cases that are coming on, like everything, all the IoT endpoints have to connect securely, all the bandwidth hungry end users, and the immersive experiences I'm looking to enable, it could be augmented reality, it could be virtual reality, all those are driving a need for me to rethink access, and rethink the network overall. And Wi-fi 6 is one critical component of that. Wi-fi 6 promises four times the capacity, lower latency, a greater range, so the things you talked about in your set-up. So it's a wonderful technology to start addressing some of those problems, but in of it's own it's not sufficient. You got to go well beyond the standard in order to address the CIO problem. >> Okay, so specifically, so think about some of the adoption problems. I got the use cases nailed down, how am I thinking about where things are going to go? Am I going to have to lay out the network differently? What kinds of practical things do I have to start thinking about? >> Well first of all, you have to think about why are you moving, where are you moving with Wi-fi 6. So again, capacity, lower latency, better battery life, the new use cases it enables. After that you need to make sure that whatever you're going to connect, will interoperate. Right? So look, sometimes a standard comes out and it can take a few years before the endpoints and the infrastructure actually get the maximum capability from the new standard. And so we worked proactively with the likes of Samsung, with the likes of Intel, to make sure those endpoints, which any of the new Samsung Galaxy S10, already supports Wi-fi 6. Interaccess points work together to give you the best experience possible. So that's sort of step one. But there's many other things we need to think through. We're also thinking about the problem of just onboarding onto Wi-fi. You know the experience to onboard onto cellular, right? >> Oh, sure. >> You get off airplane mode >> And it works. >> It just works, you're on. What's the experience like on Wifi? >> Well it's certainly not just getting a message from my local carrier that I'm now roaming. You got to get on, yeah it's a lot more involved, you got to authenticate, exactly. >> Give me your phone number, give me your room number, I'll text you something, get on to the It's cumbersome, okay? And we want to make Wifi onboarding to something we call open roaming. Open roaming is a Cisco project, it's a consortium we've set up. That takes all the venue providers and the identity providers, brings them together. So that when you go round, and you roam with Wifi, you onboard the network just like you onboard with cellular. >> So get essentially the same experience you get in the cellular world. >> Same experience. It makes it easy for you to get connected. So those are some of the basic things, but you got to go beyond that then. Now you have to worry about, okay, what do those endpoints require, alright? Well, first of all you need to recognize what the endpoint is. Is this a light bulb, or is it a heart-rate monitor, is it a tablet of some sort, what is actually connecting? So for device recognition, and to understand the experience you're getting, I need virtual analytics. And that's something the infrastructure now needs to provide. So we for the first time now, we've embedded our own ACIG, our own silicon inside the access point. So that we can get visibility from layer one to seven. And now we can pinpoint, what is the device, is it behaving in a compliant way, and how do I deliver the right experience for it. So these are some of things to think about as you move, it's a yes I want Wi-fi 6, but again tying in back to the problem you're looking to solve, how does the entire solution address your problem. >> Alright so we've identified some of the issues that have to be addressed here, and Wifi Six is here. You said the Galaxy S10 already supports it. >> Our access points are shipping, yes. >> So talk to me about the role out of some of these new technologies, these new devices from Cisco, and how customers are going to have to think a little bit differently as they start to plan out their new network structure. >> That's a great question. So I think it's not about hey, I'm just going to roll out new AP's. You should really rethink networking. What am I trying to provide here? And that's why we came out with an architectural approach across the board which is intent based networking. And what we're really talking about there is how do you automate all of the things that IT needs to do, to deliver the security and experience for all of those users and things. How do you get the data, the power of data, the analytics out? And how do you deliver security and policy. >> But it's in the context of the application and the work that's being performed. >> Yes, it's the users and devices and the applications and data. What are you trying to achieve? That's what intent based networking is all about. And so, I love how your asking the question because if you think about the wireless AP's, we only talk about the top already with the endpoint, right? But then I think about the switching architecture, are you segmenting all of that traffic? Is it fully automated? Do you have an identity and policy engine? Can I take the location data that's coming out? Cause remember these APs now are multilingual. They speak BLE, they speak Zigbee and Thread, they're also Wi-fi 6. So how do I take the location data and deliver new business outcomes? How can I tell you that the wheelchair has left the premises? How can I tell you how many people walked in your store, verus walked outside it? How do I get you better asset utilization? Those outcomes are provided at the software step at the top. So you should really be thinking about what am I trying to do for my business, and what architectural approach allows me to deliver those outcomes that I'm looking for. And yes, Wi-fi 6 APs are one critical component there, but you should think about the entire solution though. >> So we got new access points that are Wifi Six enabled ready to go, how far back does this change go into the network? >> So the Wi-fi 6 APs, the beauty of these Wi-fi standards is they're backward compatible. So you can take all kinds of older endpoints, multiple generations, and get them to work in a Wi-fi 6 new environment. So that's nice because it's not a rip and replace of all your clients, when you put the new APs in, they're backward compatible, that's always the case. And a lot of the new software stack and the technology that I talked about with intent based networking, works with at least the two previous generations as well. So if you want some of that telemetry and analytics and security, you can start getting that with some of the APs you may already have, and then when you bring in Wi-fi 6, it's sort of purpose built for that architecture. >> Alright, so we've talked a lot about the use cases of the business side, let's spend a little bit of time describing the fact that you've got the sidecar co processor for analytics inside the APs. How is that going to change the work of IT, the work of network management and administration and security? >> That's a great one So, I'll give you one example of what that does. Today, if you want to go troubleshoot a wireless issue, you're literally walking around with a sensor acting like a client to go figure out what the behavior is, what's going on, how do I figure out what the interference is, why is the experience bad, right? Can take you hours, weeks, days, it's very costly. These new APs, and with our solution with W-fi 6, first of all, I get data with my relationship with Apple from the endpoint. So I get the view from a real client. Then on the access point itself, with that co processor, I can get layer one to seven data and packet captures to see did you fail during authentication, was there some sort of RF issue that's happening, what exactly is happening that's interfering with what's going on? Or, maybe the problem is not even there, it's somewhere else in the network. And the beauty of our Cisco DNA Center solution, which is our controller in intent based networking, is we see end to end. We see the entire network and we can help you pinpoint where that issue is and save a whole bunch of money you'd spend troubleshooting, to deliver the right experience. >> But it sounds as though some of the, historically, some of the analytics associated with network administration was very focused on the device. Intent based network is intended to focus on the application and service that's being provided, but the analytics didn't follow. So know what you're saying is we're going to follow the analytics so that the applications, the services become primary citizens within the network. >> That's exactly right So you have to be able to look at the client holistic view, the application performance holistic view, and the performance of each network element, and that's what the co processor that we talked about helps. Now another thing we did is, that portfolio now, on the enterprise side, we now run the same operating system that also helps simplify for IT. The entire access network with the Catalyst 9000 series, the new access points are called the Catalyst 9100, and we're making it part of one brand and one family, because it's one OS, one programmable architecture, one operational environment if you will, that simplifies the job of IT significantly as well, and then we're also introducing obviously with Wifi Six, our cloud managed Meraki access points to support those deployments as well. >> Alright so one more question on this and then I want to talk about something else in a second. But the beauty, or the essential feature of networking has to be a degree of openness. So new access points can talk to each other, new devices can talk to each other, et cetera. These are new technologies as you said they're going to roll out and diffuse, hopefully very very rapidly, but there will be both enterprise, but also some other network supplier issues. How is Cisco ensuring that your leadership and your thought leadership but also your engineering leadership gets into those other organizations at an appropriate rate so this entire industry can adopt and change and introduce these new kinds of capabilities. >> So I talked about that new family of the Catalyst 9000 series. Let me start there. So all the protocols we support are open interoperable so you can have my switch somebody else's AP, somebody else's AP my switch, all those combinations work. It supports net config open API's programmable models. We expose those through a Cisco dev net. So we have the largest developer community on top of sort of a networking infrastructure where you can write applications that can automate or can get data >> Or services? >> Or deploy services in a very open way. And then we do the same thing at our controller layer. On Cisco DNA Center, fully open so you can have partners ecosystem delivering services and applications on top of the network, on top of that controller. So we think about openness from every angle, and that's how you have to be in a networking world, right? I mean you need to be able to connect to anything. >> Right. Every significant change in networking, someone always presumed it was going to lead to various behavior by the leader to try to somehow close it down. You're saying that's not what's happening here. We're trying to dramatically extend the benefits and capabilities of networking because those enterprises need new use cases. >> But we are saying though, that if you buy that campus architecture, access architecture through Cisco, you're going to get a degree of consistency and automation and analytics and security that's unmatched. So you might as well go, but if you want to compose that with different components, that's absolutely doable. >> Alright, so one last question. The historical norm has been I get a cell service and I get Wifi. Cell had certain positive benefits, and Wifi had other positive benefits. We're talking about Wifi Six, but also we got to talk about 5G. How are the two of them going to work together in your estimation? >> Look, from a wireless standpoint, the problems that you're trying to solve are the same, right? I need more capacity, I need lower latency, more deterministic, better battery life, they're the same. So you need to solve those when you're in an SP outdoor ubiquitous environment, or whether your sort of indoor, where you have predominantly Wifi and that's where most of your traffic flows. So Wifi Six and 5G, it's a beautiful thing that they're both trying to allow you to be in this wireless first, cloud driven world, where most of your apps and data sit in the cloud, and where your experience is really optimized by the data and telemetry that's coming out of the infrastructure. So for me, it's not an or question, it's Wifi Six and 5G that allow you to start solving that problem. >> So everything just as we have today just more, better, faster, lower power. >> Yes, can I add one more thing? >> Of course! >> I just kind of need to do this, okay? So look, when you think about the wireless infrastructure and chaining that out, I talked about how it effects the rest of the network, right? So you do need to think about upgrading your switching infrastructure, we call it being wired for wireless, okay? So with that, we also introduced a new product called the Catalyst 9600. That's a modular core switch, so you're like why are you bringing this up Sasha? >> No, I know why you're bringing it up. >> After 20 years, we are providing the next generation of the Cat 6k, Cat 6k is iconic, it's the foundation of tens of thousands of mission critical networks in the world. This is next-gen, it's more than 10x the capacity, if you have all these endpoints and access points that have more capacity, you need to think about a switch that's bigger factor. >> Scales! >> But fits into intent based networking fully programmable the same way. Just want to do a shout-out for, look we've talked about every aspect of this. APs, switches, identity, everything. >> We're offering, Cisco is offering and the enterprises are going to adopt new classes of network technology at the endpoints, faster, better, but that's going to lead to new use cases, new services, and it's just going to drive that much more complexity and routing and switching and patching thorough the network, you got to be able to scale. >> Right, you have to think about all the components. >> Absolutely. Sachin Gupta is the senior vice president of Cisco, we've been talking about how to think through Wifi Six upgrades. Thank you very much for being on the CUBE. >> Thank you, Peter. >> And once again I'm Peter Burress, and this has been a CUBE conversation. Until next time. (funky music)

>> Announcer: Live, from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018, brought to you by ServiceNow. >> Welcome back to theCUBE's live coverage of ServiceNow Knowledge 18, live from Las Vegas. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. We're joined by Michael Hubbard, who is the VP Inspire program at ServiceNow. Thanks so much for coming on theCUBE. >> Happy to be back here, and for another year of this session. >> Always a pleasure to have you on. So, I want you to just refresh for our viewers, what the Inspire program is, who are you, what do you do? >> Perfect. So, as the name connotates, our job is to inspire the future of work. So as you are all learning about ServiceNow's new vision and purpose, to really make the world of work work better for people. We're finding that subset of 1% of folks that have a bold idea, a vision and a passion, for massive digital transformation, and a leader with both the power and the vision and span of control to say: if you'll partner with me, let's go get something done, in a 90-day sort of sprint, that has measurable business outcomes, mapped to a tactical approach, mapped to an inspirational sort of experience where digitization, digital transformation, it becomes real, because by the end of this process, you've got an example of it on your phone, in your environment, exciting your stakeholders and your employees. >> I wonder if we could talk about the past of work. There was a major, y'know, swing in the last 10-15 years of remote workers, the world flattening, and the emphasis was on giving people the tools, whether it was video, or good conferencing calling, etc, so that they could collaborate. And then you kind of saw the pendulum swing, there were a couple of companies, very high profile, certainly Yahoo, IBM, where they try to create the bee-hive effect, to really foster more collaboration. What are your thoughts on that pendulum swing, y'know, centralization, de-centralization, and what does the future of work look like, to your customers? >> So the pleasure of my job is that I live in this conversation, all week, every week, with some of the most transformative business and IT leaders in the global 2000. Your examples, Dave, they hit upon sort of tools that tried to catalyze a different way of working. We gave somebody chat, or we gave somebody the ability to work from home because they had internet connected to their house, and they had a phone line, and what else do you need, maybe a webcam. But these tools didn't fundamentally change the flow of work through the enterprise, right, and so I think the future of work, in terms of comparing it to the attempts of the past, it's a more fundamental shift that says, people process technology, governance, culture, purpose, all have to evolve, and I think there's finally enough hunger to do the hard work, not of throwing a new tool at an employee, or throwing a new policy at a user group, but changing all those other elements, those systemic elements, because overall productivity per employee has not changed. Overall satisfaction with your experience, and the pleasure of being at work, is not getting better fast enough, and you compare it to what we've enjoyed as consumers, in our personal life, and the contrast has gotten so stark that there's finally that passion among business leaders to say: enough's enough, it's time to stop buying point solutions, and start looking at the holistic change that's going to improve revenue per employee, improve my retention rates of my top talent, attract millennial and post-millennial talent, and are looking for partners that will take that holistic view of a platform, that'll work with those tools, but will knit it all together for a big outcome. >> So it sounds great, can you tell us, give us some examples of some success stories? >> Absolutely, so we work, we're very selective, we're an investment in some of our most ambitious customers, we work with about 1% of those. So, for example, Accenture has been a great partner for us, and go to market-serving customers, but I'm speaking about their CIO organization, folks like Tom Breezy and Andrew Wilson, who lead experience transformation, lead employee centricity, and lead the IT work, working with them on making leave of absence easier, because women in the workforce, and getting them back into the workforce after a pregnancy or a troubled pregnancy, that immediately yields benefits to their most tangible source of revenue, which is billable credible resources to serve their clients. So if we can help them with the generational women issues, that will really help their customers, their investors, and their top-line. So that's the type of work we do with Accenture, Virgin Trains is another great example. Virgin Trains were doing work, of course in good old ITSM, good old make IT better, but outside of IT, how can we make your experience on the platform better, in terms of empowering the people for Virgin Trains working the platform, working the train car, to have the right answer for you when you have a problem, to empower them with better knowledge, better workflow, so that they're able to ask the enterprise for help, and then action the answer for you as the employee. Allianz Life is another example, huge insurance company, and they're facing what many financial services firms are facing, which is that balance between agile business and the need for governance and compliance. So we worked with Steve, their chief compliance officer, to change the way that they manage the underwriting and approval of new policies, so it both allows them to make the business move faster and reduce the costs to underwrite and manage and comply to federal regulations. Doesn't have that much to do with IT, but the foundation of a platform that changes how work flows through enterprise across different stakeholders, and across many tools, and Dave, as you said, "mediums," that's what it's all about. >> So many companies that we talk to really dance around the automation issue, and you heard John Donahoe this morning saying look, we're all about automating workflows, so we have to take this head on. What are the conversations like amongst the Inspire customers, with regards to automation, machines replacing humans, etc, could we explore that a little bit? >> Yes, so as you'll hear more and more from ServiceNow, and as we're seeing within our Inspire customer base, there's two sort of threads that we tend to pull on. One thread is we try to find those opportunities for technology and automation to be in service of people, versus the inverse of suddenly now we're all just supporting the tech, and we're trying to just eke out a little piece of value to still add as people inside of a tech revolution, we're turning that around, and we think we can get the noise out the way of the people, by having the technology to serve them, workflow's a great example, alert's a great example, machine learning to solve the easy, repeatable problems is a great example, and that will free up the humans to do the things that make us human, that are more evolved, that are more advanced, that require empathy, etc. So that's one thread we pull on a lot within Inspire, is finding those human moments, cause moments really matter, and then empowering and transforming the ability for that person to serve their fellow employees or their customers. The second thread we pull on is we really push back on the idea, whether it's automation or any other sort technology buzz word trend, push back on the idea of incremental improvement. So if you have a process that's five days, we're not going to talk about how we can get it to four and a half, we're going to talk about why we can't get it to zero, And for regulatory reasons, that human element of needing empathy and interaction and building rapport, there might be reasons it creeps back up to a day, but let's start with that zero-based budgeting approach that says "five days, start with what if we "tried to get it to zero?" And that changes the frame of the conversation on automation from being about maybe attacking a certain percentage of people or time and trying to take a little cost out, to resetting the purpose of how that process supports an outcome in an enterprise. >> I want to ask you about that tension between the human-centered, the empathetic approach, versus the business, the business processes, the business that needs to get done. What are some of the challenges that your customers have faced, that you sort of see as the biggest pain points to implementing some of the changes that you want to see changed? >> So the hardest the thing to create for us, as an advisory team with the customer, is urgency. So what we have to find first is urgency, that today is not good enough. Change is a mandate, it's a requirement, there's no if, there's just a how, right, and that's why we focus on just 1%, because not everyone's ready for that type of a commitment to change. Once you have the urgency, you have to have vision, so we work with a lot of great customers, but we will never know your business the way you do, we'll never know your customers the way you do, so you have to bring your half of that vision. We'll spark ideas about what other people are doing and what's possible, and you've got to bring that back to a relevant outcome for your business. And different companies have different cultures, with different purpose statements, and some will resonate with taking out costs, some will resonate with empowering their employees, some will be all about, let's say in the healthcare space, we've done work with VITAS hospice care. If you think about hospice, of course it's not about just the nurse, of course it's not about just the patient, it's actually about coordinating the family, because it's the family that often needs the most support and interaction in that process, and so you really have to understand, you can push through the tension if you get to a meaningful purpose statement around what makes that company's existence necessary, and why people choose to work there, and that's really the start of every Inspire engagement, is getting that alignment. >> Michael, one of the drivers of digital transformation is fear, fear of missing out, "FOMA", but also fear of getting disrupted. Ginni Rometty at a conference, at the Think conference recently, used the term "incumbent disruptors." I would think that resonates with a lot of your customers, we want to be the disruptors, not get disrupted, some defense, yes, but we also want to go on offense. What are your thoughts on your customers' ability to be incumbent disruptors, and what role does ServiceNow play in that? >> Great question, and two thoughts to the answer. One is: ServiceNow lives in that intersection too, because we're getting big enough now that we start to worry about the upstarts, perhaps, in our own market space, as we look at customers who have been with us for years, have rolled us out broadly, suddenly we're the incumbent. So we are, in our own world, are thinking about making sure we are a disruptive incumbent, and continue to drive that value for our customers, but to take it back to our customers instead of ourselves. The key there is that tension, to use the word you used earlier, of those- let's take FinTech in financial services. FinTech startups, they're all trying to race to create a market disruption, create a wedge in a marketplace, of a consistent use case with a group of consistent business problems they're solving, while all the incumbents have all the capital, access to markets, access to cultures, brand credibility in the world, and they just don't know if they're going to have enough time to move their giant battleship before this little swift boat sweeps around them and takes a flanking position. So it's a very real challenge, and where we tend to focus is with those big companies, as a catalyst, bringing our whatever's in the water of Silicon Valley out to New York, or to London, or wherever, and helping them get a little of that swift boat style into what is really a big aircraft carrier group that they're trying to turn. >> Financial services is a really interesting case study, because it really, that industry has not yet been disrupted in a big way, even though like you said, there's a lot of FinTech swift boats trying to go after 'em. Do you think traditional incumbent financial services firms will lose control of payment systems, or do you think they will respond? >> Well we have an interesting member of our company, our CEO who, of course, has some history with PayPal, so that'd be great question for Mr Donahoe. I think it's too early to tell, but I also don't think it'll be a binary answer. What we're seeing when we work with some of these large companies is a very different fear or challenge around disruption in emerging markets versus established markets. So in established markets, they probably are going to get the time to reinvent themselves, because of the amount of momentum they have with customers, the amount of stickiness they have with customers. I mean the simplest truth that I've found in whether you win or lose a disruption battle with a customer is how hard it is for that customer to give up their relationship with you. It's the same in divorce, it's the same in changing airlines it's the same in changing credit cards. You've got all your points in one place. So in these established markets I think they're going to have the time to really succeed, but in emerging markets, that's where the battleground is really sitting. >> Yeah and financial service firms have always done a pretty good job of getting on to that next wave. >> We'll have to ask John Donahoe. >> We will, we will, and he's coming up soon, so... But thank you so much for coming on theCUBE again, it's always a pleasure to talk to you Michael. >> Yeah, fantastic to see you both, and it's just exciting to see this show continue to grow, and to have new customers, not just CIOs, but chief people officers, heads of talent, joining the conversation around the future of work. >> Dave: Awesome, thanks Michael! >> Thank you. >> Well thanks to you for joining our conversation. >> Michael: You bet. >> I'm Rebecca Knight, for Dave Vellante, we will have more from ServiceNow Knowledge 18, coming up just after this. (light techno music)

(quiet jazz) >> Announcer: Live from Los Angeles, it's theCUBE covering Open Source Summit North America 2017. Brought to you by the Linux Foundation and Red Hat. (upbeat techno music) >> Okay, welcome back, everyone, live in Los Angeles to theCUBE's exclusive coverage of the Open Source Summit in North America, I'm John Furrier. My cohost, Steve Miniman. Our next guest is Jono Bacon, who is the founder of Jono Bacon Consulting in the community. A great talk here-- >> Jono: Thank you. >> at Open Source Summit. Great to see you. >> Yeah, thank you for having me on. >> Congratulation on all your recent success, on the personal and business side. Congratulations, great to see you. So, bottom line, Open Source Summit is kind of powered by the Linux Foundation, but pretty significant accomplishment and State of the Union, if you will, calling an Open Source Summit, big tent event. What's your view on this? How do you explain to folks watching? Is this a new event, is it a combination of multiple events, certainly a great, great big tent, >> Jono: Yeah. >> cross pollination. Whatever you want to call it. But what is this event about? Share your opinion. >> I think it's interesting, and I don't work for the Linux Foundation, but I've worked very closely with them for a number of years. And I think what we've been seeing is that in the earlier days of open source, there was, you know, the Linux foundation have played a fairly key role in certain specific areas. And in recent years, they've become a real center of gravity around open source in a variety of different areas, from automotive to cloud and beyond. And obviously there's a ton of events that are happening all over the world. And the open source thing I think is interesting because it's really an umbrella event that's got four other events that are part of it. So the event that I was running, which we launched this time around, was the Open Community Conference, which is kind of like one thread of this broader event. So one of the things I like about it is is different events from my experience draw different types of audiences. The Linux Foundation events have traditionally brought a lot of professionals who work in the industry. In a similar way, that happens at OSCON as well. But I like that the events kind of become a little bit more organized and diversified into those four areas. And I think what happens then is you get a greater bandwidth of content and discussions that go with that. >> I think it's an interesting point of these other streams, if you will, kind of going into the big tent event. It's got an ecosystem vibe to it, cause you don't want to lose the specialty of the topics and interest at the events that matter for the audiences on a content basis and face-to-face communications. But it's interesting that they're taking this approach because, when you look at it, the scale that's coming, in open source generally, categorically, if you put all of the code together, it's exponentially growing. >> Jono: Oh, yeah. >> So, there's a flood coming, there's a big open source flood of code coming. So, I think it's time to think architecturally about the dams and the rivers and the flows. To your point, this is a super important point in history. >> Oh, it's without question. And one of the things that's interesting to me is in my work as a consultant, when I help companies to build communities, it's broken into a few different layers. For example, so one is a technology layer, like which of the lego bricks that you're going to choose to put together, and how do you click them together in different ways? And that's where I think the LF has become a real center of gravity around what those projects are and how to integrate. But the other thing that we're starting to see more and more of is the formalization of the software development lifecycle, which is, it's not nearly just writing code anymore. It's about automated testing and continuous delivery and deployment, and all these different pieces. So I think we're seeing a formalization of the Lego bricks, but also the instructions for how you click them together. And that's really important if we're going to broaden out this bubble. Because this is a bubble that we're in right now. This is full of invariably tech companies talking about technology. But when we get into the bigger enterprises, when we get into non-tech into the-- >> John: Blocking and tackling, the realities are there. >> And there is so much nuance wrapped up in open source that it's alien to the people outside of this world, that we need to build that better interface for that. >> And that's just putting some hardening around either software or process that there's some comfort and reliability to the users. >> I'll give you one example. Like one company that I was working with, who were a large hardware company, fairly unfamiliar with open source. And one of the first questions they asked me was, "What does success look like? We know what all these options are, we see all the things that people are talking about, but we don't know how to determine what success is." And I think even just that, it seems like an obvious thing to the people in this room, but it's not obvious to a lot of people who are new to consumer technology this way. >> They want to see a finish line or some KPI that's says, we're done! >> Jono: Exactly! >> Shipped! >> And also because this is technology that's built by a broad diverse community of people, you then, a lot of these organizations then say, "So, what is my expected social responsibility here?" So, like how do I participate in this world that I'm broadly unfamiliar with? To me it's like a hip hop guy who's trying to join a metal band. You know? (John laughs) It works differently. >> It's completely different genres of developers and also environments. So, what's your advice to customers? Because they have to navigate because the mainstream adoption of Linux, obviously, and now new projects as they graduate or come to fruition will be deployed. So there is an ops, the DevOps certainly is a movement we're seeing, we can agree on. But now I got to put it into production. I'm a bank or I'm an enterprise. Hey, I got some guys that are monitoring. We're not that active, but we're happy to use it, be a user. How do you talk to that customer? >> Jono: Right. >> The way which I try to approach it is is to break it into a few different areas. The first thing is to first of all make sure that everybody's got the same sense of what the problem is that you want to solve. One of the things that was most transformative to me when I started consulting was it's amazing how many people think they're solving the same problem, but they're actually on a completely different grade of the same problem. So to me, what I like to do, is I like to define what I call a set of key themes which are these are the big rocks that we want to target in a time frame, six months or a year, or whatever it might be. Particularly with, when you're either doing community strategy or development, or you're doing a level of open source, it's fundamentally cross-functional. It involves marketing, engineering, product, there are executive stakeholder requirements, and then there's the people on the ground who are delivering those, so getting those themes in place I think is critical. But then to me what's important next, is to break a broader strategy down into smaller, consumable pieces. I think one of the things where a lot of companies get stuck is they're aware of these different Lego bricks that are available to them. They're aware of some optimizations in terms of workflow, but it's such a huge thing to bring into an organization that invariable is already got a very, very, stodgy or very specific culture that they've got to somewhat unseat. So to me, you need that combination of permissive, top-down approach, which is invariably your exec saying we see value in this, but then you need to break the strategy and the execution down into smaller manageable pieces that a team can wrap their head around. >> We talked to the Cisco guy, Ed, and he was, we were talking about DevNet, a huge developer community for Cisco. DevNet Create was kind of their cloud-native group that they've put together, great little skunk works, worked out great. But those are two languages. It's two worlds. The semantics of what they're saying is the same thing, but the translation is needed. This seems to be a common thread within the DevOps community now that the rubber hits the road, and people see the obvious benefits of what is true private cloud or cloud native. So, how do you go ahead? You provide like a dictionary, and say, "Hey, here's the translation. Okay, he really means that." I mean, are you being more herding the cats, being a translator, or is the client further along than that in your mind? >> It varies, it does vary from company to company. And a chunk of this, at least from my experience, is there is a significant translation layer. One of the things I talked about in my keynote on Monday was I see collaboration ... When I do community strategy, but fundamentally, it really is organizational design. It's just outside of a company in some cases, and sometimes inside of a company. In an organization, you'll have a set of stakeholders making decisions, and then the people who've got to execute on those decisions. And there is often a massive translation layer between them. I run a conference called the Community Leadership Summit each year at OSCON, and every year a couple hundred community managers come along, and I hear the same story from a lot of them, which is, I joined this company, I started building out, I started doing my work and my manager wasn't happy. And to me it's because the execs are defining value that they want to see, but it's not getting translated into tatics, and invariably a lot of the folks who are coming into it-- >> John: Where their ROI calculations are-- >> Yeah, a lot of that's-- >> They're not seeing a real answer. They don't know what success looks like. >> And they come in, and they don't necessarily have the strategic background to internalize that requirement into a place that they can move it forward. So, you get this kind of, this impedance mismatch. So, a big chunk of what I tend to do is to really try to understand what those requirements are and to work across the organization to try and-- >> John: You're doing architecture? Like what would be organizational behavior architecture in the wild, but also an arbiter to the managers. It's looking good, it's like you're trying to the score of the game. You're keeping-- >> Jono: And some days as well, as I'm sure anyone who's watching this, will have seen this with the companies they work with, this isn't rocket science. You know, what someone says they want, this is going to sound incredibly patronizing, it's not meant to, but when someone says what they want, invariably what they actually want is not that thing. So for example, I was working with a company a couple of months ago and they were saying, "We just want growth. We absolutely want to grow as quickly as we can." And when I dug into it with their CEO, what they really wanted was brand recognition and acceptance. And those are two very different challenges that you got to approach there. >> John: Stu, get a word in, I'm sorry if I've taken all of it. >> Yeah, John's passionate about community if you can't tell. The question I have for you is, building a community takes time, and things are changing faster than ever. How do you help people manage that pace of change versus I want results? It seems strategy is something that is for today, and we're changing often. So, how do you manage that give and take of growing yet breaking? >> It's a great question. And again, I think it varies. To me, there's some fundamental pieces that are involved in the way that I, and I take one approach and other people will take different approaches, I'm certainly not the only person who's doing this. The approach that I like to take is is we first of all need to treat communities as a journey. I think a lot of people think we have a product or a service, let's get people interested, and it's seen as a series of individual interactions with individual people. Whereas the way I like to look at it is when that person discovers your product, your service, your framework, whatever it may be, there's a journey from how they learn about it, how they go up an on-ramp to get something done, how you get people making their first contribution or how they derive their first piece of value, and then how you incentivize and reward them to keep them moving along the journey. So to me I look at it as this zoomed-out birds-eye view of this journey that I want to craft. And then I like to break that down into small bite-sized pieces that form the strategy. But the other thing is, and this varies depending on the company, is to what level of transparency and openness you need to communicate with different people. So, for example, one of the first things I do with inner source when people bring in open source principles inside a company is to make sure we have weekly reports going out and we're updating the stakeholders, more specifically, on a regular cadence. Because in that kind of environment where there's an existing enterprise, we all see these like digital transformation consultants come in-- >> Oh god, it's a total gravy train. They make the bookings and the billings. Reminds me of the old ERP deployments. Write a big fat check, and it'd be like, all these consultants come in and make all the cash. >> I think a lot of people look around thinking, alright, Lunchbox, you'll be here for a year. You'll be gone then, all right, and we'll go on to the next thing now our CEO cares about. So to me it's like-- >> John: Well, the consulting is being disrupted. It's interesting, you're a contrarian in your world because you have a consulting firm, but the old model things used to be the next gig is get that next consulting gig, so you worked not to actually put yourself out of a job, which is where the client wants to get. And that's where Agile and cloud has come in. It's interesting is, this is where the work product is. You know what success is in that model. You can come in and say, look, we did our work, everything. You've got a community that's vibrant. You got operational, they operationalized your value. >> Jono: Yep. >> You don't need me anymore, unless you want me. So, it's one of those kinds of conversations. Your thoughts? >> I agree. And it's interesting you mentioned Agile. One of the things that I've noticed as well, and I'm sure lots of not just consultants but people notice this as well is there are, I think there are broadly two types of people in the world. I think there's people who take a very kind of organic and somewhat animated approach to how they do things. And then there's some people who really need a roadmap. They need to follow a plan. I think a lot of people who are building organizational design or building communities default to we need to create a process and a workflow so people can follow that and we can have a sense of order. I don't think most people naturally want to work like that. I think there's a reason why people don't stick with to-do lists. It's because people like to have a more organic way of working. And a good example of this, in my mind, is Agile. Some people will take Agile to the nth degree with story points and epics and a lot of that kind of stuff-- >> You serve the process, the process doesn't serve the objective. I mean, it's the classic effectiveness model. But, I mean, that's the whole point. I mean, you could foreclose opportunities if you're too structured. But yet you got to have some boundaries, let the ball bounce around. So, you kind of want both. What is the ideal in your mind? >> In my mind, the approach that I'm a big fan is an approach called munsing, which was a story of, I forget his name, there's a story of a guy back in like the 50s. And he basically owned a TV factory. And what he'd do is he'd go up to like an engineer who's building one of these big, bulky old TVs, and he'd basically pull out components until it stopped working. And then he'd put that last component in so it would be the minimum level of components for it to work. Ended up saving the company a ton of money. I like to take the same approach process. What's the minimum level that you need that gives people the creativity to be successful in a predictable way? So, like with Agile, these epics and stories and things like that, I think a lot of that stuff is just there to deal with crappy product managers, like people who aren't very good at manning your project. No process is going to deal with someone who's not good at organizing. >> You need to bring to me the right level of the human ingredient and the process is what keeps people ticking over-- >> The other thing too that I find in that area is people kind of redefine, or they maybe mischaracterize what outcome is. Everyone's outcome driven. Love that word. (Jono laughs) It's all about the outcome. In this case, the TV's got to work with a less amount of moving parts. >> Jono: Right. >> That's the outcome. And so, outcomes can be bastardized if you will, could be really mangled in its definition. How do you work with clients on trying to really temper and set the expectations on what the outcome is? Cause the manager still wants to know what the outcome is going to be. So, do you reverse engineer from there? How do you tackle that? >> Jono: It's interesting. A big chunk of it for me is just being realistic. There is no minimum amount of work that needs to be put in to achieve any kind of community. I think you can build a tiny community with one person. However, depending on the requirements and the goals, there's just certain things you have to do. And there's certain time and resources that are required. And also just expectations. Like one of the expectations that some people wrestle with I think is, if you're building a community they're either inside your organization or outside, it's only going to succeed if a broader set of people participate. You know, we see this trend where you hire a community manager and that person lives in a forum or a slack channel to build out the community. Doesn't work. >> John: Yeah. >> Because the people in that community want access to other people. >> This value creation mindset in communities. Value has to be a group dynamic. This individual contributions, I get that. But the group dynamic is critical. Not just a message board moderator. I mean, that's basically what you're saying. >> Jono: Exactly. >> That's a message board. >> Nobody wants to deal with >> John: That's a tool. >> the interface of the thing you care about. And that's the community manager. So, a chunk of this then is a different mindset in how people operate. One of my clients is a company called HackerOne. I wrapped up work with them a little while ago, and their CEO is this guy called Mårten Mickos who-- >> John: Yeah, Mårten's great CUBE alumni. >> Phenomenal. For me, he's one of the people I most respect in our industry. >> John: He's a great strategic thinker, understands community, knows tech. Great guy >> Jono: Amazing. >> One of the things that he said when he joined HackerOne was I want everybody in this company to know a hacker. Everybody's got to know our audience. Everybody's got to understand the needs, the desires, the insecurities, the worries, the dynamics, otherwise we can't build a community. It's not just hiring a person to interface to that. That's one of the trickiest things because, again, it takes time. >> John: It's alignment to the audience. >> Right >> John: This is classic. >> Ingratiating in and actually being cool. Aligning with them >> Right. And if it's done well it's really rewarding because I think people who ordinarily wouldn't see the fruits of their labor. >> Well, Jono, I want to get your thoughts as we wrap up the segment here on what's exciting you about potential new things that are coming around the corner. Obviously, we see the promise of blockchain which could have a great big application for communities. We're doing some things with it now that we're testing in our community around trying to create these new value networks. Certainly, there's new tooling coming out. Things like theCUBE and content and communities. New things are coming. The growth is going to be here which is going to create great new opportunities. >> Jono: Yeah. >> What are you excited about as you want to navigate the community landscape? Because the thesis is more people are coming in, more rivers of distinct audiences are going to want specialty but yet the broad market ... What are you excited about the community opportunity? From compensation to interaction to culture. What's your thoughts? >> There's a few things I'll subdivide it into things that relate to my bread and butter which is communities and things just more broadly in technology. The one thing I'm really excited about communities is I feel like the value proposition has become well understood, is not just in open source but outside with Proctor & Gamble, H&R Block, Harley Davidson, all these examples. Where people see the value in doing this work and doing it well. And that's great because I think we're improving the state-of-the-art of how we do this. One of the reasons why I got into this was I want my career to leave a fingerprint on structured, predictable ways in which we can do this as opposed to seeming magic science that a lot of people seem to think community is. >> John: Or a series of one-offs that are not understood or can't be operationalized or leveraged in any way. >> Jono: Yeah, exactly. From a technology perspective, there's a bunch of things. I'm really excited about crowdsource security, things like HackerOne, Bugcrowd, Synack, things like that. I think there's a lot of excitement in my mind around bringing open source into financial services. I think that's an industry that's ripe to be disrupted which is a sentence I never thought I'd ever say. Ripe to be disrupted. (John laughs) And then I'm also really excited about the work that's going on obviously in A.I., but the intersection of A.I. with kind of like voice control. Obviously, things such as Google Home and Alexa, but also things like Mycroft. I think blockchain is interesting. It's kind of less interesting to me. It's not really something I've really been following very closely, but I think it is. I think it's pretty neat. But then also just the formalization of the end-to-end software development lifecycle and how we're seeing, you know, GitHub was transformative in technology for a lot of companies. And now we're seeing GitHub as one piece, and you've got continuous delivery and continuous deployment. And also, we manage ideas, the project manager, all that kind of stuff. >> I think there's a lot of transformative ideas coming. And I think it's super exciting. Congratulations on all the great work you're doing. >> Jono: Thank you. Appreciate it. >> I just think that the self-governing community model that's now becoming mainstream people are starting to figure out how to balance that with the command and control top down and hierarchy job definition specifics, and balancing that. I think the self-governing open source model certainly prove that. And communities as a working example of what you can operationalize. >> It's exciting. >> And crowdsourcing just takes it to the consumer level. >> Right. >> Okay, it's working there too. Okay, great job. Thanks for coming on. >> Thank you. >> John: Jono Bacon, >> John: Bacon Consulting. This is theCUBE. I'm John Furrier, Stu Miniman. More live coverage after this short break. (upbeat techno music)

>> Announcer: Live, from Las Vegas, it's The Cube, covering VMworld 2017, brought to you by VMware, and its ecosystem partners. >> I'm Stu Miniman, here with Justin Warren, and you're watching The Cube, the worldwide leader in tech covered. Happy to welcome back to the program, Satyam Vaghani, who is now the Vice President of Technology with Nutanix. Satyam, great to see you, you've got a long history with the VMware community, always good to catch up with you, how have you been? >> Likewise too, great to see you guys again. I've been good. >> All right, so I like, it's always the progression as to what discussed at the show. I was kind of poking at VMware a little bit, that in 2015 it was any device in any application one cloud, and the keynote this morning it was any device, any application, any cloud, and we're highlighting one of the things VMware's highlighting is NSX, they want to be kind of the interconnected fabric between the cloud and the edge. And of course I bring that up because, besides the virtualization stuff that you've done for years, you've got a little bit of a different role now, you joined Nutanix through the PernixData acquisition, so why don't you tell us what's been keeping you busy and excited these days. >> In a word, or in two words, edge computing for sure. You know, this has been striking to me too because I walked in to pick up my badge this morning, and the first thing I see at VMworld is IoT, you know there's this big area right next to where you pick up the badge. And so it's quite telling as to how computing is evolving. As you just said, you know NSX is one way to quantify that evolution from, you know, how do we stretch the cloud all the way into the edge. There's some other ways. And so yeah, I've been working on what defines that operating system that can converge the edge and the cloud into one seamless piece. >> There's definitely a lot of excitement around edge, but as with any early technology, if you're talking to a telco it means one thing, if you're talking to somebody involved in centers and IoT, it kind of means a different thing. We're not talking about kind of the robo-use cases that some people looked at, kind of HCIS, so what is that, what is edge computing in your parlance, and that OS, how does that fit into it? >> Great question, in fact I'll come back to the talking to different verticals, and how we can make some sense out of all those disparate conversations, maybe, you know as second part of my answer. But you know, the edge in my definition, is a way to collect, digitize the real world, that's one thing, but then take real-time decisions on all this data that is coming in from the real world. And so in my sense, the edge is all about taking real-time decisions on data, and then the cloud long-term is going to be about taking some long-term decisions about the same data or some subset of that data. You know, that Tesla is a great post-agile example right, as you know, in a self-driving car, you want to figure out whether you're going to get into an accident or not right at the car, which is the edge. But the longer term notion of how Teslas can be made to drive better can be all done in the cloud, deep learning et cetera. And so there is this very fluid movement of data and code between the two systems, that we as operating system vendors need to make possible. Otherwise, every agile application, which is really a hybrid application, some part of the logic runs here on the edge, some part of the logic runs on the cloud. It's going to be tremendously difficult to create these applications. And you mentioned, and very rightfully so, is you know, the interpretation of what edge computing or IoT means is very different by vertical. And so in fact right now, my number one mission is to look at all these different use cases and figure out what's common, right? I think back 30, 40 years ago, when the guys sat down to write POSIX, right, you know people figured out what is it that an operating system can provide that makes sense to 200,000 different applications. I think it's time to answer that exact same question for the world of machines, right? What is it that an operating system can provide which makes sense in the world of machines, that sells transportation, that sells defense, that sells health care, and blah blah blah, et cetera. >> So what are some of the things you've discovered in the research you've done so far? Have you got some early hints of what those pieces of commonality are? >> For sure, great question. And so you know, one common thread that unifies all these different use cases is your, most of the use cases are around getting insights from the data that's being collected, whether it's image data, whether it's raw numbers, whatever it is, and so you know, machine learning and analytics seem to form the core of any interesting application subset that you're looking at. And so then the question is, can you make it from an operating system point of view, can you make it very easy to build applications around machine learning, can you make it very easy to build applications around analytics? And the third thing is, you know, it's very, this is not about running virtual machines anymore, yeah, sure, inside at the very crux of the system you might be running virtual machines, but it's about a developer-centric world. So, the question is, can you make it such that the developers can just deploy code without worrying about whether the code runs in a virtual machine, in a container, in a combination thereof? Can you make it very easy for data to move across without the developer having to explicitly code develop? And so that's the common parts. >> I wonder, you know, what your thoughts are contextually when you go in there? So we actually had, at Wikibon we have a weekly research call, we were talking about serverless last week, and we actually saw, you know, for edge computing, serverless, really good, Amazon has their Greengrass, leverages Lambda to go in there because, sure VMs seem a little heavy for some of these applications. What should we look at kind of from that same point? >> I agree, I agree. It seems to be more serverless and maybe containers, because sometimes when you're trying to do serverless applications you need a bunch of infrastructure that then has to be packaged into containers. And so, between the two probably is where the compute part of edge computing or IT is going to be edge. >> Can you talk to us, you know, Nutanix, I think there's, obviously Nutanix and VMware, strong partnership for some things, which is, you know about three quarters of Nutanix customers are running VMware, but differences of opinion as to some other things that, you know, VSAN versus Nutanix, and Nutanix has AHV. I've heard you saying operating system a bunch of time, is that an extension of AHV, is it something else? VMware has plans as to how they're going to go from IoT, can you maybe compare, contrast what your vision, how that matches with your skillsets inside Nutanix versus, you know VMware who you know real well. >> Oh yeah, for sure. And so AHV, the distributed storage fabric, which Nutanix has, I think that is a great kind of, you know, substrate on top of which to now build a much more application-oriented edge computing kind of you know, stack, right. And so the edge computing stack is more about what other data services that you can provide, which are not data stores anymore, you know, it's things like structured data as a service, or unstructured data as a service, or streaming data as a service, and function as a service to your point of serverless computing or containers. And so I think this becomes, the Nutanix asset becomes a great substrate on top of which to build this much more application-oriented architecture. You know the VMware story I would imagine is kind of sort of the same, although VMware is I think remarkably leading with NSX, because you know, there's network, the extended network between the cloud and the edge is obviously a great problem to solve. I have my own biases, so I tend to lead with the application side of things, right, which is, you know, what does it, as I said, what does it take to make the next POSIX layer for machine learning or for analytics, and then the infrastructure base is obviously important, bit it is worry less, you know, I'm sure we are going to solve it. And so, in some sense the two stories are evolving in a very congruent manner. We'll see. I think the market is so big, and the use cases are so diverse, that for a change, we can potentially, if we kind of do it in a cooperative manner, we can probably evolve an interesting standard, interesting stack that is kind of sort of you know, stable. And so I think this could potentially be a story of cooperation as opposed to throwing stones at each other, but you know, time will tell. >> One of the things that VMware talked about in the keynote was security, and the nature of security being baked into things, and you mentioned that your focus is on applications, and certainly developers of applications, they don't really care anything about infrastructure at all. So this service consumption is something that they think is quite interesting. But you also mentioned how fast things are changing. Those three things at once, on the edge, how do you see security services developing so that they can be easily consumed by people, instead of it being this really difficult infrastructure problem? >> I wish I had just an easy one line answer, but I think I'll explain it, just the abstract of how I see this evolving through an example, right. Because if you think about iOS and Android, they have a very structured way of doing notification, and no app can do notifications by itself, it just has to get in. So I think, you know, probably it's time to solve security that way, right. You know, it's time to put some structure to how you can program the movement of data, the collection of data, the deployment of compute, of program onto a substrate, and if you tightly control that movement of data and the deployment of compute and so on, then there is a higher chance that you are going to be able to, from an operating system provider point of view, probably you are going to be able to deliver a much more secure system, because you've taken the bulk load of security programming away from the application developer, and made it an operating system core service, right? And of course that requires you to impose a framework on how you develop applications, which is, you know, the notification examples, right. I've imposed a framework on how you deliver notifications, and by doing so, I've kind of, you know, made a very seamless or very good experience. I think >> So it's taking a very opinionated approach to doing things? >> Satyam: I think so, yeah. >> Which, that does restrain choice a little bit, and one thing that various vendors say is, well we're all about customer choice. But sometimes there is too much choice. However on the flipside, IT so far has been making choices about how you should do security so far that haven't really worked out so well. So what do you think needs to change for this opinionated design to feel like the correct one that people will want to use, and not then feel constrained and, but I want to be able to choose to write my own encryption algorithms. >> I think if we can democratize the availability of data, so you know, the edge computing stacks are all about collecting as much data as possible, and then writing great applications on it. So if there is a security framework which doesn't impede the ability of different tenants, whether it's analysts, whether it's application developers, whether it's regular operators like the guy running the airport, if it doesn't impede their ability to interface with data, and if it doesn't impede their ability to deploy interesting compute on top of that data, then probably we have a way to go. So I think having a great security framework, but in way that still democratizes the availability of data, the movement of data, and the deployment of compute, if we can achieve that, I know I'm asking for a bit too much, (Justin and Stu laugh) but probably the source of my next five patents. I think it's a doable thing, it's a doable thing. >> Satyam, why does Nutanix have a right to be a player in IoT? You know, I look at it, you know these are going to be some pretty gnarly ecosystems. I look at, you know, we did some early work with GE when they were coming up with the idea for the industrial internet, you know there's some really big challenges, you know, you're not going to create those centers and all those devices, you know, some of the really big companies in the world are working on some, you know, the messaging protocols and things like that. Where realistically does Nutanix play and how do you fit into that really big and very immature ecosystem today? >> Great question. So various different takes on it. One is, I think it's a natural evolution of the company strategy. We started by saying, look, here's hyperconvergence 1.0 the ability to converge, compute, storage and networking into this box. Hyperconvergence 2.0, which is what we are executing right now as we speak, is this orchestration layer between public and private cloud. So we have converging public and private into one. So things like Calm, et cetera. And so we naturally think the next step of our strategy, hyperconvergence 3.0, if you will, is a convergence of the edge and the cloud. And so just from the point of view of how we evolve the enterprise cloud operating system, we think this is the natural place for it to grow into as a piece of technology. The other way to look at it is, it's time to build the next hypervisor, and the next hypervisor is the hypervisor for data as it moves around in all these clouds between ridiculously disparate places, right, from an oil rig all the way to some mainland data center or from an airport into the cloud and so on and so forth. And so the process of creating hypervisor for data is a distributed data systems problem, which we have been really good at, historically. And so we think we deserve a good cut at it. And the last thing I'll say is, if you think about how ideal world, you used the example of G, great respect for companies like that, but think about it, you know, those companies had to evolve all of that by themselves because they happened to be the sensor vendor, and so the G's of the world, the Honeywells of the world, they had to not only make the sensors, but now they also had to make some compute capability to make some sense out of all that data. But now that sensors are here, they are here to stay, they are all open to your point, you know, the protocols to get data out of it, MQTT, CoAP, this that, everything is open. So now it's genuinely the time to focus on the data and code aspect of the data that's coming in. You know, 40 times more data is going to hit the edge than the cloud will ever see in three years. And you know, that deserves a, kind of a swing from traditional data operating systems guys like Nutanix and some of the other guys you see on the show floor. >> You mentioned it's a distributed computing problem, which is obviously very hard to do. But there's a networking aspect, and that's one thing, particularly in cloud and other things, it tends to get overlooked quite a bit, I mean the poor network people feel left out. What implications do you think there's going to be for this level of data from all of these sensors and the amount of computing and decision-making that will happen at the edge, as distinct from in the cloud, talking between different devices in the edge, but then also providing the data back to cloud. What are some of the network impacts that that's going to have? >> Great question, there's so many of them but, something that is front and foremost in my mind, because I literally got out of a customer conversation on this, is you know, for example, there's this customer who does smart oil rigs, and they now need to move that data, some of it at least, to the cloud, but the network is really flaky. So there is obviously the problem of securing that network, and so on, but now there's also the problem of what does that mean to the application itself? Can you express the flakiness of the network as a policy that a programmer can program towards? You know, when flakiness of the network happens, literally from a programming and API point of view, the programmer can make a choice as to, you know, for all the streaming data that's coming in, the programmer can choose either to drop all of it, you know, when the network's flaky, or to you know, have a data moving system which can catch up, when the network becomes stable again. So all those things now become a, these are all infrastructure problems that now manifest themselves into how you program the system, and so we are having some conversations around that. And so yeah, you know, there is so many interesting possibilities here, I don't even know where to start. >> Whole new programming abstractions that we may not even be using today. >> Exactly. And there's obviously the hardware part of the problem. People are creating LoRa networks, they are creating NBD, this new type of LT network just for IoT data, and so there's a whole bunch happening in hardware as well. >> Satyam, pull this all together for us. What, you know, you said you're meeting with customers. What can they do today and what are some of the major kind of milestones that we should be looking for to say, oh, I'm hearing from Nutanix customers they're doing this, we've reached that point, you know, give us a little bit of a today and maybe a year or two out if you can. >> Great question. So I think the here and now thing customers can do as well, apart from the obvious thing of figuring out what other interesting ways that their business wants to monetize data coming out of their own operational infrastructure. Assuming they've figured that out, you know the important thing they can do now, is to start creating their dispersed cloud, right. Cloud of assets out in the field, you know, in factories and airports and oil rigs and aircraft carriers, that they can manage centrally, right, and that they can deploy applications into centrally. And so now they don't need a specialist on an oil rig to figure out how to operate infrastructure. So that's a here and now problem. They can set it up right now, centrally managed clouds, with central application orchestration on those clouds. The second part of the thing, is to create a new generation infrastructure, which is one level higher than virtual machines and data stores. It's infrastructure that gives their developers the ability to use data as a service, both on the edge and on the cloud, and to deploy compute as literally code, functional service, either on the edge or on the cloud, and so that gives them a homogeneous system, a much more developer-friendly system to deploy applications that can, you know, that can consume data at a volume, at a pace that we have never seen before. And the only way to make that consumption possible is if we can make the movement of data, the availability of data across this dispersed cloud very, very easy. So that's step two, is a data and code hypervisor, something that we haven't seen yet in the industry. >> Satyam Vaghani, pleasure to catch up with you as always. We look forward to watching the progress as all of this progresses. >> Thanks guys, I'm jazzed. >> From Justin Warren and myself, we'll be back with lots more coverage here, from VMworld 2017, you're watching The Cube. (electronic music)

(energetic electronic music) >> Hello and welcome to a special CUBE presentation of the future of networking with Riverbed. I'm John Furrier, host of the CUBE. We're here with Paul O'Farrell, Senior Vice President and General Manager of SteelHead, Steelhead Connect. SD-WAN in action. Well, good to have you on The CUBE. Thanks for joining us. >> Great to be here. >> So, future of networking. This is something that we talk a lot about in our conversations, because the cloud's exploding, cloud business model. On-premise, true, private cloud. Hybrid, connecting to public clouds, is changing the game for app developers and large enterprises and how they do business. But it always comes back down to networking, 'cause everyone wants to know what's going on with networking. What is the future of networking? What's your perspective? >> Yeah, well John, as you said, everything's going to the cloud. But if you're a large multinational organization, you can't just click your finger and move your entire infrastructure to the cloud. But for the workloads that you do manage to move to AWS or Google Cloud or Azure, the good thing is that your IT organization is able to get out of the low-value-added activity of managing boxes and get into more strategic higher-impact activities and projects. So, if you think about moving a workload to the cloud, all of a sudden your organization is out of the business of managing boxes, managing servers, storage, and backup. But the challenge is that networking and the infrastructure required to connect all of that is still stuck in the past. And much of the way you manage a network really hasn't changed that much since the, certainly in enterprise networks, since the mid-90's when routers first really became popular. >> Give an example of why it's so hard, because I mean everyone wants networking to be faster. You have still move packets around the network. I mean boxes are changing. We know that the surveys are all pointing to non-differentiated labor being automated away. And that's clearly from the research. It's not a question of when, it's a question of when will, I mean not a question of how, when it's going to happen. So that puts pressure on the companies. When do they move from the manual networking to more automation? So give an example of some of the use cases. >> Yeah, so for a long time, as I said, the way you manage a network hasn't really changed. And in the last couple of years, we've seen the growth of a new market segment, or a new market, called software-defined WAN. So, taking some of the concepts of software-defined networking that had been trapped in the data center and then bringing those out onto the wide area network. And one of the big drivers was around the idea of, since there's so much more traffic going to the Internet, going to the cloud, I need a simpler way of managing that traffic. And I'd like to do it at a software level. I'd like to manage it based on policies and simple configurations that I could apply centrally as opposed to going down to the level of IP addresses and port numbers, as you have to in the sort of more traditional approach. So I think a lot of the initial impetus for people to look at new ways and new approaches to networking has been around this concept of direct-to-net, the desire to use more Internet transport, lower-cost Internet transport in the network. And that's sort of where it starts. And after that, you get to, what we at Riverbed believe is a bigger transformation of networking, which sort of begins with SD-WAN, but probably ultimately is more about really cloud networking. >> Some will say, and I'll get your reaction to this, that networking is outdated. Your thoughts? Is it outdated? Is it just moving too slow? Is it advanced? What are some of the, where's the progress bar on this conversation that's been kicking around the industry around networking needs to get updated and modernized? And is it outdated? What's your thoughts? >> Yeah, so as you said, at some level, you're always going to need networking, right? You've got to move packets around the network. You've got to connect applications with people and resources across the network. And it's particularly true in enterprises. But where I think the network has become stuck somewhat in terms of its evolution is that the traditional approach to configuring and managing devices, pre-staging routers and then shipping to a location where you have to do some more configuration on them, that piece of it I think has not evolved enough. But we're at a point now where a lot of the simplicity, the policy-based approach that you see in other parts of cloud infrastructure can now be applied to networking, that you can abstract away some of the complexity of the underlying network and then present that to an admin in a very simple fashion that looks very similar in terms of the experience to what happens when you deploy an application in the cloud, in AWS or Amazon. If you think about it, you can spin up an application and get it up and running in a matter of hours, if not minutes. You can deploy applications all over the world. Now, if you had asked somebody to do that 10 years ago, they would have looked at you like you're crazy. I want an application running in Frankfurt. And I want an application running in Seattle. And I want you to have it done by this afternoon, and by the way. Where it all falls down though is when I ask you to connect every root in my organization to those applications and have it done in a matter of hours or minutes. That's where it gets really hard using the traditional approaches. >> And, by the way, just to put in a point of clarification. I remember back when I was living in the 90's, 'cause what you described sounds like the 90's, that's a six-week project. Not like hours. That's like weeks. I got to make sure that the routers, we've got to configure the tables. All these manual efforts. But you're hitting on one of the things that is the future that people talk about, is really balancing the agility of doing something really fast, that's what the cloud is bringing to the table, with managing complexity. So that's one thread. So I want to talk about that. But also can I talk about the elephant in the room, which is, is my job going to go away? 'Cause, you know, a lot of those guys that are doing this command line interface stuff have built a job around their knowledge around configuring, which is not an agile. So they've got to be agile. So they're potentially at risk. So, future career. But the mandate of managing the complexity with agility. >> Yeah, so the industry obviously evolves over time. And, as you look at, again, go back to different parts of the infrastructure stack or the IT environment, you could have said just exactly the same, made exactly the same argument to me about servers and storage and backup administrators. Now, to my knowledge, those people haven't gone away. The total number of people working in the IT industry has not shrunk. If anything, it's grown significantly. So I think it's much more about freeing people from some of these laborious tasks that really don't add a lot of value and then redirecting those people to delivering on higher-impact initiatives. You know, a lot of talk in the industry these days, no matter actually what vertical you're in, about digital strategies, about transforming your business, and really what you want is to take your IT resources and your IT personnel and have them work on those projects and not have them-- >> John: The high-yield projects. >> Exactly. And to the extent possible, to automate a lot of the workflows and the way you manage day-to-day administration of the network, whether it's in the design phase, the deployment phase, or the management phase, of your network infrastructure, make that simpler and more intuitive and ultimately more like a consumer application, the types of workflows we're used to when we use web-based applications. Or perhaps, more reasonably, make it more like how you manage an application in AWS or Google Cloud or Azure. >> So your point about the server guys, the storage guys, their jobs never went away. First of all, there's more data coming than ever before, so they're always going to have a good job. So you're saying that is also applied to networking. >> Paul: Mm-hmm. >> It's still super important. >> Paul: Absolutely. >> And there's going to be more network, certainly with IOT on the horizon. You're going to have more connection points than ever before. So you're saying that tasks may go away, but the job will shift to other things, whether it's up the stack or other function that's related to adding value. >> Absolutely. So, the individual components that are deployed in the network that make the traffic, that allow the traffic to flow, that allow you to get the packets around the network, allow you to connect different parts of your enterprise, none of that goes away. But it just maybe takes a different form. And you mentioned IOT, for example. I mean that's a big question and a big challenge for a lot of organizations. How do you manage a network environment where you have more and more devices coming on the network? And instead of having, 10's, 100's of clients on a wireless network, for example, you could have 100's or 1,000's in a facility. And that's the type of new networking challenges that would be interesting to address as opposed to doing things that are, by their nature, manual and arguably can be done with a lot more automation. >> So I'm going to make a statement. And I want you to either agree or disagree or add some color to it. The future of networking is about automation, embracing automation to add value. And just as a point of validation, IOT, whatever trend that's happening right now that people get excited about, are all probably about machine learning. And everyone's saying that AI is going to solve the problem, which is simply just saying, technology's going to help with the automation. That's kind of my take on it. Your thoughts on that? Because that essentially is the validation. So the future of networking is, get used to automation. It's coming down the road pretty fast. >> So I think the first step towards taking some of that machine learning know-how and AI and applying it to networking is to automate networking. Make it easier. Make it policy-based. Don't make it about CLI commands. Make it about more manual configuration about scripting. The next step will be to apply machine learning and be able to have self-healing networks, being able to have networks which are aware of the types of-- >> Self-healing networks? Self-healing networks for having self-healing cars. Self-driving everything. I mean this is essentially the automation of what we're seeing. >> Sure, but let's start, let's not run before we can walk. Let's start with application-aware networks. How about that as an idea? Where at least the network doesn't think it's just passing packets, but actually knows what application it's using and is applying policies in an automatic fashion, whether it's to choose the optimal path for traffic or whether it's to apply security policies based on who the user is and what they're trying to do. So you should be able to do all that. And that is something that we built in our new product. >> Okay, so I would say that in hearing you, complexity is addressed by automation and software. >> Paul: Mm-hmm. >> The agility is really the application awareness of that. >> Yeah, I think that's a reasonable characterization of how to think about the future of networking, sure. >> Okay, so I want to get your thoughts on SD-WAN. We're hearing about that. With the cloud, and whether you're running true private cloud and hybrid and public, it's all an operating model. It's all a new way to think about provisioning networks and managing it. Isn't everything a WAN now? I mean, if you almost conceptually as a mind exercise say, the notion of local area networks and wide area networks are kind of, with the whole cloud thing, with the perimeter being decimated, and APIs flying around and microservices. I mean isn't everything a WAN now? >> Sure, I mean the whole concept of the WAN feels a little dated right now. I mean, if you think about it, if your kids are on the web or using their favorite social networking, and for some reason they can't get on the Internet, they rarely come down to you and say, "Hey Dad, the WAN's broken." So I mean clearly, people who live in the enterprise world still think in terms of wide area networks. But more and more, you're right. If you think about it, all of the different users who are coming on your network, whether employees or whether they're customers or partners, they're coming on using WiFi. There's a blurring of the line between the Internet, between the private enterprise network, traditionally referred to as the WAN, and the LAN. All of that is merging. And a lot of the technologies that Riverbed has been developing are really around this concept of SD-WAN, not just SD-WAN, but SD-LAN as well, and the ability to provide a single connectivity fabric across LAN, WAN, Cloud, and to the extent you still have data centers, most large enterprises will have those, data center as well. >> Great. And so competition. Let's talk about competition. You mentioned the CLI. Cisco's a market leader in all this. Your position vis-a-vis Cisco and how you look at the competition? >> Yeah, so Riverbed as a company has competed in various ways with large networking companies like Cisco for many, many years, since we started as a company. It is interesting that Cisco is trying to reposition itself, sees a need to change the way it delivers solutions for enterprise networking. It started by developing some of those capabilities within the organization and then more recently has made an acquisition of a startup, which we think is interesting, because it really validates the market now for SD-WAN. And we welcome it many ways, 'cause we think it's really the beginning of a shakeout and a maturing of the whole space. We think we're going to see that. >> You can't talk about the future of networking without talking about WiFi, because everyone who goes to a sporting event or concert, they lose their LTE, they go to the WiFi. And connectivity is like the lifeblood. You guys recently had an acquisition. What's the future like with you guys and WiFi? >> Yeah, we recently acquired a company called Xirrus, which was a company that had set out to build the fastest, most scalable, most, and this is really the key point, densest WiFi in the world in a very secure manner as well. And it was started by some of the pioneers of the WiFi industry, people who were in WiFI before it was even called WiFi. And so we thought they had an extraordinarily interesting technology. And what was particularly exciting about it is that they had also developed a cloud management approach to managing WiFi. As you said, WiFi is, on one hand you could think that WiFi is kind of a solved problem. It's been around for quite a while. But it's also become incredibly critical to not just enterprise networks, but to everyday life. We sometimes say that WiFi has become the inalienable right of every global citizen, good WiFi. If you think about the last time you checked in a hotel, what's probably the first thing you'd do is see if you can get on the WiFi. And if you have a bad WiFi experience, it doesn't matter how soft the Egyptian cotton on the sheets, on the bed is, >> John: It's plumbing. >> or how delicious the chocolate is on the pillow. >> People complain most about WiFi. >> Exactly. So if you think about it, some of our biggest customers now that we've entered the WiFi, and particularly the cloud-managed WiFi, business, our education, K through 12 and universities, on many university campuses, the users can have six, eight, 10 devices per person. Now, in the typical enterprise, maybe you don't have quite that many. But we're certainly all heading in that direction. And then you combine that with IOT and how people would like to put a lot of sensors and other devices on the network, then you're getting to a point where you really need incredibly dense WiFi. >> I mean IOT is about power and connectivity. WiFi gives great connectivity. Future of networking. Just summarize as we wrap this segment up for the folks watching who are practitioners in their jobs every day, trying to figure out the future, what's the bottom line of the future of networking? If you can give that statement and an example of how you guys are working with other practitioners. >> Sure. Well, first of all, I think the transformation that's occurring in the broader IT industry with the rise of the cloud and cloud networking and cloud computing is really extending now to the networking industry. A lot of the simplicity, the workflows, the automation and policy-based approaches is now extending to the space that network administrators have traditionally lived in. And I think that's really an opportunity for practitioners, as you call them, to really start using a set of more interesting and more capable tools that then will allow them to free themselves up from some of the lower-value-added activities to doing some really interesting things in the organization and to be an enabler of some of these new digital strategies and cloud strategies that their organizations are trying to execute. >> An example of companies you've worked with that might be a case study that you can share real quick? >> Well, what we're seeing is an awful lot of retailers, for example. So it's interesting. You see all the pressure that traditional retailers are experiencing from online e-commerce retailers. And what we're seeing is that more and more, they are using in-store WiFi. They're looking to put a lot more band-width into the stores to give customers in the store an incredibly compelling online experience while they're shopping. For two purposes. One is because they want to engage that customer while they're in the store. And also because they may want to do analytics and understand their behavior while they're in a store. But they want to do that at the same time as ensuring that some of their business-critical applications are up and running. So if you think about SD-WAN or cloud networking, it really provides the ability for us to do that, augment the WAN, deliver more band-width, lower cost band-width, into the store, but also give an incredibly compelling experience and have it all managed centrally with a simple policy-based approach. >> All right, the future of networking here at the CUBE Studio. Paul O'Farrell, Senior Vice President, General Manager at Riverbed. I'm John Furrier with the Cube. Thanks for watching.

(energetic electronic music) >> Hello and welcome to a special CUBE presentation of the future of networking with Riverbed. I'm John Furrier, host of the CUBE. We're here with Paul O'Farrell, Senior Vice President and General Manager of SteelHead, Steelhead Connect. SD-WAN in action. Well, good to have you on The CUBE. Thanks for joining us. >> Great to be here. >> So, future of networking. This is something that we talk a lot about in our conversations, because the cloud's exploding, cloud business model. On-premise, true, private cloud. Hybrid, connecting to public clouds, is changing the game for app developers and large enterprises and how they do business. But it always comes back down to networking, 'cause everyone wants to know what's going on with networking. What is the future of networking? What's your perspective? >> Yeah, well John, as you said, everything's going to the cloud. But if you're a large multinational organization, you can't just click your finger and move your entire infrastructure to the cloud. But for the workloads that you do manage to move to AWS or Google Cloud or Azure, the good thing is that your IT organization is able to get out of the low-value-added activity of managing boxes and get into more strategic higher-impact activities and projects. So, if you think about moving a workload to the cloud, all of a sudden your organization is out of the business of managing boxes, managing servers, storage, and backup. But the challenge is that networking and the infrastructure required to connect all of that is still stuck in the past. And much of the way you manage a network really hasn't changed that much since the, certainly in enterprise networks, since the mid-90's when routers first really became popular. >> Give an example of why it's so hard, because I mean everyone wants networking to be faster. You have still move packets around the network. I mean boxes are changing. We know that the surveys are all pointing to non-differentiated labor being automated away. And that's clearly from the research. It's not a question of when, it's a question of when will, I mean not a question of how, when it's going to happen. So that puts pressure on the companies. When do they move from the manual networking to more automation? So give an example of some of the use cases. >> Yeah, so for a long time, as I said, the way you manage a network hasn't really changed. And in the last couple of years, we've seen the growth of a new market segment, or a new market, called software-defined WAN. So, taking some of the concepts of software-defined networking that had been trapped in the data center and then bringing those out onto the wide area network. And one of the big drivers was around the idea of, since there's so much more traffic going to the Internet, going to the cloud, I need a simpler way of managing that traffic. And I'd like to do it at a software level. I'd like to manage it based on policies and simple configurations that I could apply centrally as opposed to going down to the level of IP addresses and port numbers, as you have to in the sort of more traditional approach. So I think a lot of the initial impetus for people to look at new ways and new approaches to networking has been around this concept of direct-to-net, the desire to use more Internet transport, lower-cost Internet transport in the network. And that's sort of where it starts. And after that, you get to, what we at Riverbed believe is a bigger transformation of networking, which sort of begins with SD-WAN, but probably ultimately is more about really cloud networking. >> Some will say, and I'll get your reaction to this, that networking is outdated. Your thoughts? Is it outdated? Is it just moving too slow? Is it advanced? What are some of the, where's the progress bar on this conversation that's been kicking around the industry around networking needs to get updated and modernized? And is it outdated? What's your thoughts? >> Yeah, so as you said, at some level, you're always going to need networking, right? You've got to move packets around the network. You've got to connect applications with people and resources across the network. And it's particularly true in enterprises. But where I think the network has become stuck somewhat in terms of its evolution is that the traditional approach to configuring and managing devices, pre-staging routers and then shipping to a location where you have to do some more configuration on them, that piece of it I think has not evolved enough. But we're at a point now where a lot of the simplicity, the policy-based approach that you see in other parts of cloud infrastructure can now be applied to networking, that you can abstract away some of the complexity of the underlying network and then present that to an admin in a very simple fashion that looks very similar in terms of the experience to what happens when you deploy an application in the cloud, in AWS or Amazon. If you think about it, you can spin up an application and get it up and running in a matter of hours, if not minutes. You can deploy applications all over the world. Now, if you had asked somebody to do that 10 years ago, they would have looked at you like you're crazy. I want an application running in Frankfurt. And I want an application running in Seattle. And I want you to have it done by this afternoon, and by the way. Where it all falls down though is when I ask you to connect every root in my organization to those applications and have it done in a matter of hours or minutes. That's where it gets really hard using the traditional approaches. And, by the way, just to put in a point of clarification. I remember back when I was living in the 90's, 'cause what you described sounds like the 90's, that's a six-week project. Not like hours. That's like weeks. I got to make sure that the routers, we've got to configure the tables. All these manual efforts. But you're hitting on one of the things that is the future that people talk about, is really balancing the agility of doing something really fast, that's what the cloud is bringing to the table, with managing complexity. So that's one thread. So I want to talk about that. But also can I talk about the elephant in the room, which is, is my job going to go away? 'Cause, you know, a lot of those guys that are doing this command line interface stuff have built a job around their knowledge around configuring, which is not an agile. So they've got to be agile. So they're potentially at risk. So, future career. But the mandate of managing the complexity with agility. >> Yeah, so the industry obviously evolves over time. And, as you look at, again, go back to different parts of the infrastructure stack or the IT environment, you could have said just exactly the same, made exactly the same argument to me about servers and storage and backup administrators. Now, to my knowledge, those people haven't gone away. The total number of people working in the IT industry has not shrunk. If anything, it's grown significantly. So I think it's much more about freeing people from some of these laborious tasks that really don't add a lot of value and then redirecting those people to delivering on higher-impact initiatives. You know, a lot of talk in the industry these days, no matter actually what vertical you're in, about digital strategies, about transforming your business, and really what you want is to take your IT resources and your IT personnel and have them work on those projects and not have them-- >> John: The high-yield projects. >> Exactly. And to the extent possible, to automate a lot of the workflows and the way you manage day-to-day administration of the network, whether it's in the design phase, the deployment phase, or the management phase, of your network infrastructure, make that simpler and more intuitive and ultimately more like a consumer application, the types of workflows we're used to when we use web-based applications. Or perhaps, more reasonably, make it more like how you manage an application in AWS or Google Cloud or Azure. >> So your point about the server guys, the storage guys, their jobs never went away. First of all, there's more data coming than ever before, so they're always going to have a good job. So you're saying that is also applied to networking. >> Paul: Mm-hmm. >> It's still super important. >> Paul: Absolutely. >> And there's going to be more network, certainly with IOT on the horizon. You're going to have more connection points than ever before. So you're saying that tasks may go away, but the job will shift to other things, whether it's up the stack or other function that's related to adding value. >> Absolutely. So, the individual components that are deployed in the network that make the traffic, that allow the traffic to flow, that allow you to get the packets around the network, allow you to connect different parts of your enterprise, none of that goes away. But it just maybe takes a different form. And you mentioned IOT, for example. I mean that's a big question and a big challenge for a lot of organizations. How do you manage a network environment where you have more and more devices coming on the network? And instead of having, 10's, 100's of clients on a wireless network, for example, you could have 100's or 1,000's in a facility. And that's the type of new networking challenges that would be interesting to address as opposed to doing things that are, by their nature, manual and arguably can be done with a lot more automation. >> So I'm going to make a statement. And I want you to either agree or disagree or add some color to it. The future of networking is about automation, embracing automation to add value. And just as a point of validation, IOT, whatever trend that's happening right now that people get excited about, are all probably about machine learning. And everyone's saying that AI is going to solve the problem, which is simply just saying, technology's going to help with the automation. That's kind of my take on it. Your thoughts on that? Because that essentially is the validation. So the future of networking is, get used to automation. It's coming down the road pretty fast. >> So I think the first step towards taking some of that machine learning know-how and AI and applying it to networking is to automate networking. Make it easier. Make it policy-based. Don't make it about CLI commands. Make it about more manual configuration about scripting. The next step will be to apply machine learning and be able to have self-healing networks, being able to have networks which are aware of the types of-- >> Self-healing networks? Self-healing networks for having self-healing cars. Self-driving everything. I mean this is essentially the automation of what we're seeing. >> Sure, but let's start, let's not run before we can walk. Let's start with application-aware networks. How about that as an idea? Where at least the network doesn't think it's just passing packets, but actually knows what application it's using and is applying policies in an automatic fashion, whether it's to choose the optimal path for traffic or whether it's to apply security policies based on who the user is and what they're trying to do. So you should be able to do all that. And that is something that we built in our new product. >> Okay, so I would say that in hearing you, complexity is addressed by automation and software. >> Paul: Mm-hmm. >> The agility is really the application awareness of that. >> Yeah, I think that's a reasonable characterization of how to think about the future of networking, sure. >> Okay, so I want to get your thoughts on SD-WAN. We're hearing about that. With the cloud, and whether you're running true private cloud and hybrid and public, it's all an operating model. It's all a new way to think about provisioning networks and managing it. Isn't everything a WAN now? I mean, if you almost conceptually as a mind exercise say, the notion of local area networks and wide area networks are kind of, with the whole cloud thing, with the perimeter being decimated, and APIs flying around and microservices. I mean isn't everything a WAN now? >> Sure, I mean the whole concept of the WAN feels a little dated right now. I mean, if you think about it, if your kids are on the web or using their favorite social networking, and for some reason they can't get on the Internet, they rarely come down to you and say, "Hey Dad, the WAN's broken." So I mean clearly, people who live in the enterprise world still think in terms of wide area networks. But more and more, you're right. If you think about it, all of the different users who are coming on your network, whether employees or whether they're customers or partners, they're coming on using WiFi. There's a blurring of the line between the Internet, between the private enterprise network, traditionally referred to as the WAN, and the LAN. All of that is merging. And a lot of the technologies that Riverbed has been developing are really around this concept of SD-WAN, not just SD-WAN, but SD-LAN as well, and the ability to provide a single connectivity fabric across LAN, WAN, Cloud, and to the extent you still have data centers, most large enterprises will have those, data center as well. >> Great. And so competition. Let's talk about competition. You mentioned the CLI. Cisco's a market leader in all this. Your position vis-a-vis Cisco and how you look at the competition? >> Yeah, so Riverbed as a company has competed in various ways with large networking companies like Cisco for many, many years, since we started as a company. It is interesting that Cisco is trying to reposition itself, sees a need to change the way it delivers solutions for enterprise networking. It started by developing some of those capabilities within the organization and then more recently has made an acquisition of a startup, which we think is interesting, because it really validates the market now for SD-WAN. And we welcome it many ways, 'cause we think it's really the beginning of a shakeout and a maturing of the whole space. We think we're going to see that. >> You can't talk about the future of networking without talking about WiFi, because everyone who goes to a sporting event or concert, they lose their LTE, they go to the WiFi. And connectivity is like the lifeblood. You guys recently had an acquisition. What's the future like with you guys and WiFi? >> Yeah, we recently acquired a company called Xirrus, which was a company that had set out to build the fastest, most scalable, most, and this is really the key point, densest WiFi in the world in a very secure manner as well. And it was started by some of the pioneers of the WiFi industry, people who were in WiFI before it was even called WiFi. And so we thought they had an extraordinarily interesting technology. And what was particularly exciting about it is that they had also developed a cloud management approach to managing WiFi. As you said, WiFi is, on one hand you could think that WiFi is kind of a solved problem. It's been around for quite a while. But it's also become incredibly critical to not just enterprise networks, but to everyday life. We sometimes say that WiFi has become the inalienable right of every global citizen, good WiFi. If you think about the last time you checked in a hotel, what's probably the first thing you'd do is see if you can get on the WiFi. And if you have a bad WiFi experience, it doesn't matter how soft the Egyptian cotton on the sheets, on the bed is, >> John: It's plumbing. >> or how delicious the chocolate is on the pillow. >> People complain most about WiFi. >> Exactly. So if you think about it, some of our biggest customers now that we've entered the WiFi, and particularly the cloud-managed WiFi, business, our education, K through 12 and universities, on many university campuses, the users can have six, eight, 10 devices per person. Now, in the typical enterprise, maybe you don't have quite that many. But we're certainly all heading in that direction. And then you combine that with IOT and how people would like to put a lot of sensors and other devices on the network, then you're getting to a point where you really need incredibly dense WiFi. >> I mean IOT is about power and connectivity. WiFi gives great connectivity. Future of networking. Just summarize as we wrap this segment up for the folks watching who are practitioners in their jobs every day, trying to figure out the future, what's the bottom line of the future of networking? If you can give that statement and an example of how you guys are working with other practitioners. >> Sure. Well, first of all, I think the transformation that's occurring in the broader IT industry with the rise of the cloud and cloud networking and cloud computing is really extending now to the networking industry. A lot of the simplicity, the workflows, the automation and policy-based approaches is now extending to the space that network administrators have traditionally lived in. And I think that's really an opportunity for practitioners, as you call them, to really start using a set of more interesting and more capable tools that then will allow them to free themselves up from some of the lower-value-added activities to doing some really interesting things in the organization and to be an enabler of some of these new digital strategies and cloud strategies that their organizations are trying to execute. >> An example of companies you've worked with that might be a case study that you can share real quick? >> Well, what we're seeing is an awful lot of retailers, for example. So it's interesting. You see all the pressure that traditional retailers are experiencing from online e-commerce retailers. And what we're seeing is that more and more, they are using in-store WiFi. They're looking to put a lot more band-width into the stores to give customers in the store an incredibly compelling online experience while they're shopping. For two purposes. One is because they want to engage that customer while they're in the store. And also because they may want to do analytics and understand their behavior while they're in a store. But they want to do that at the same time as ensuring that some of their business-critical applications are up and running. So if you think about SD-WAN or cloud networking, it really provides the ability for us to do that, augment the WAN, deliver more band-width, lower cost band-width, into the store, but also give an incredibly compelling experience and have it all managed centrally with a simple policy-based approach. >> All right, the future of networking here at the CUBE Studio. Paul O'Farrell, Senior Vice President, General Manager at Riverbed. I'm John Furrier with the Cube. Thanks for watching.

>> Announcer: Live from Boston, Massachusetts it's the Cube, covering Red Hat Summit 2017. Brought to you by Red Hat. >> Welcome back to Boston, everybody. This is Red Hat Summit and this is the Cube, the leader in live tech coverage. I'm Dave Vellante, with my co-host, Stu Miniman and Matt Hicks is here. Is the Vice President of the software engineering for OpenShift and management, at Red Hat. Matt, welcome to the Cube. >> Thank you very much, good to be here. >> So this is where all the action is, is management and management of Clouds and inter Clouds and intra clouds, and it's the sort of next big battleground and you guys seem to be, doin really well there. Have a lot of momentum. >> It's been a good year. I think it's going to be a great year going forward, cause it, it adds a lot of customer value you know, they're seeing the drive to get applications across all these environments, and I think we've hit a good balance of what we can provide in OpenShift, or middle work portfolio management and you hear a lot of customers talking about it all through summits. >> Well we saw some pretty sick demos this morning. I got to ask ya, it was basically the reference model, was okay, got some web logic, and web sphere apps. You know, wink, wink. And you want to modernize them, and so you guys just showed like a five click modernization process. Is it really that simple? Are people really, really doing that? >> Yeah. We have customers that have moved thousands of applications like that, and they're all different sorts of applications. But going from, a proprietary EE stack to getting on something closer to EAP. To deploying it on OpenShift, that is our bread and butter. And it's great because EAP can take advantage of OpenShift, lets customers re-platform the apps that they have. And like we said on Key Net, it sort of frees up your time then to start building the fun stuff. Building the next apps, and you know we've had a ton of success with that. >> Matt so we had the opportunity to talk to some of the innovation award winners. What we haven't actually gotten to cover too much yet, is all the news. So there were a number of announcements in your space, wonder if you could help us, kind of unpack for our audience. >> Sure thing. So we, You will hear a lot about the, just the enterprise production adoption, of the new technologies. Because one of the things for us, it's easy to come up and talk about new technologies. We like actually bringing customers up that have taken that new technology to production. So that's one of the big themes you'll see here at Summit. We launched OpenShift IO. Which for us actually had great success of OpenShift as Hybrid Net platform, Prod. But as you heard from United Health Group, Optum this morning. They have 10,000 plus developers to roll that out to. And we knew we needed to close the gap on how to get empowered developers. So OpenShift IO was the new Cloud based services for that. We will also announce and talk about our container health index. So when you start really making the bed on containers, how do you know what's inside of em, how do you get a simple grading system to understand like A through F. How well maintained is this. As well as being able to look under the covers and understand what goes into that A or what goes into that F. >> And maybe explain that a little bit more, because I think about like, you know, okay, I remember like in the virtualization world, I understood that. So many of containers live a lot shorter life, so, is there, is this just a dashboard that rolls that up, because I want to know probably the general health of what's going on, because there's no way humans going to be able to keep track of it. And I mean, we're not all Google with two billion containers, being brought up and killed every week. But it tends to be, at least from what I've seen, tell me if you see otherwise, that most containers are still much shorter lived than OS's. Or you know, VM4B4. >> You know I think that's, it's one of the advantages. Is that they can be pretty volatile, like that effect. You know, we have capabilities, like in OpenShift, like Image Streams driven to say, "How do you respond and incorporate this?" At the end of the day, if you can grab a container that in our world has an A rating, no security vulnerabilities today, and in a week, you could have multiple critical CVE's, that have been open that now affect that container. And so the benefit of containers is, you can re-roll em, and you can consume that update, but if you don't know about it, and you stay on that old version, you carry the same risk as if you had an out of date OS, that was very static. >> Yeah, I think that answers back to, you know, Ben Gustav, that golden image. And they would pardon that, and they'd leave it that way for two to five years. Right. And we all laughed because my friends in the security space is like, that's the biggest problem we have, is you're not ready for that. So this is, understanding what you've got out there, being able to address that, remediate, you know, push out changes, or know like hey, if you haven't, this is what you're at risk of. >> Absolutely. And that creates for us, it creates this foundation of, both trust between our customers and Red Hat, with their consuming. But then also between Red Hat and our ISV's. Because most of out ISV's, they're not in the Linux business or they're building specialized middle work capabilities on our products. So it's equally important for them to understand that if they're on an out of date version of RHEL, and they've embedded that into their container, that can cause as many problems, and they need to apply the updates in their stack as our customers. >> But that kind of gets to the business model a little bit. And you're engineering, but so I have an engineering question. But, I think most people in our audience understands that you know, Red Hat is a company built on, open source. And you know people say, "Why buy the cows, the milk is free." Well you've perfected that model, you know, 2.4 billion dollars in revenue. Three billion dollars in bookings. So you're obviously doing something right, although, not many have been able to, actually nobody's been able to create a business model like this. My question is from an engineering stand point. When, you're built on open source, and you're not, driven toward a proprietary mindset of okay, let's lock them in to the next REV. How does that change, sort of the engineering mindset, the culture and the protocol going forward. >> I love it. I have been in Red Hat 11 plus years, and everyday you're not tied into, dropping a new feature and pushing customers to that new version for revenue. And so it changes our mindset of, how do we provide value across the entire range of supported offerings that we have. In the case of RHEL, you could stay on some versions of RHEL for quite a while, and we provide value there in keeping that thing working. But at the same point, we're constantly moving this along, adding new innovation. We're able to provide value there. And it, as an engineer, it is refreshing. Sorry. >> I'll chat for a minute. So you, you know, a lot of companies that are 20 plus years old, are criticized. Oh, they don't, innovate. You hear that all the time. They do incremental R and D. And it's true. They may spend a lot on R and D, but R and D is like a feature here, or another feature there. Design, to just keep putting the crumbs out. And what you're saying is, incremental is not, really fundamental part of your plan. >> Absolutely. We can, you know, we want to provide the same value for our customer if they're on RHEL six, or they're looking towards the next major version of RHEL. And they can move anywhere on that life cycle, and that's what they get as part of their subscription. Same thing with OpenShift. And that choice of customers, of being able to take a product, consume anywhere on the life cycle of it, it's good for customers and it's nice for us, because they're just different ways that you innovate. Of driving like, the next new great feature. Then you have other customers, that you are going to provide value through stability. >> So, when you, we go to a lot of these events, as you can imagine. And when you talk to the traditional, you know, software players, you get this massive dose, of well we do that too. We do containers, and, you know, we do Cloud, and we do Hybrid, and. So help us understand, the difference between how they do it and how you do Cloud. >> I think for us, if we picked containers, you know, I was talking to a group of customers this morning of every upstream technology we pick, that we're going to pull together into our products, We don't just pick em up and re-package em and give em to a customer, because we're a support business. So if it breaks at 3 a.m and I have to re-roll a kernel to be able to fix it, I have to understand every piece in the stack. So we start with, we're going to drive a contributor position in the technologies. We pick our bets and we go all in on those areas. So Cooper Netties will carry you know with Google as you know a great technical partner, we run the majority of the SIGs with them. We have a top contributor position, and that we invest really heavily in understanding that technology inside and out. And I think that's what shows in the customer value of we could certainly take stuff, repackage it and ship it. It doesn't carry the same value as being able to work with a customer, drive new features into the product and keep them running in PROD. >> Matt so you mentioned Cooper Netties. And I was actually a little surprised this morning in the key note, I didn't hear Cooper Netties. And I think the reason was, because I heard a lot about OpenShift, and that's just your mechanism for rolling that out there. I'm assuming your customers kind of understand that maybe you could help, you know, explain that a little bit more. >> Absolutely. And so, OpenShift is our enterprise, distribution Cooper Netties is, and that's sort of the business we're in. We have Linux and RHEL is our enterprise distribution of that. We now have Cooper Netties, this really popular community. OpenShift is our distribution of that, and for our customers. >> I was just saying, I guess you couldn't call it RECK. Which, Red Hat Enterprise, Cooper Nettie, probably wouldn't be a good idea. >> The world changes too fast. You pick names a long time ago. But it's a nice motto, because we know it. It's what we've done for a long time, and it builds on everything we've done with RHEL and it connects our middleware portfolio as well. So I've been on the op side, and I've been on the development side, and I love seeing us address stuff right in the gap there for customers. And I think that's why we're seeing so much customer traction. It's a sweet spot for where they've had pain, and it adds a lot of value for em. >> Could you speak a little bit of your customers. Where are they with containers, Cooper Netties, that whole adoption. >> A lot of them in production. Which is nice. It's nice from a support business, because if you have excitement, or if you have early traction, we're a subscription business, so we want to make sure you know, the more customers use it, the more you know, they're going to grow and actually utilize it. And when you hear customers like UHG saying, the 4000 projects built on OpenShift there. Those are, they have built up significant deployments on that, and Barkways, and I know we have a whole list of em that are here today. And so I like that fact of, it's not just a cool technology. Customers have taken all the way into production. And they're being really successful with it. which as an engineer you love. You want to see people using your products and solving problems with them. >> Absolutely. Matt you talked about the ethos of commitment and committers, to open source projects. One of the challenges for a company like yours, is you got to support a lot of different projects. So though, you saying, you make your bets. We've talked a lot about okay, will there ever be another Red Hat that emerges in the big data space. You see Cloud air, and Hortonworks, and they're always sort of lookin at those guys, as possibility. But they always sight the challenge of having to support so many projects. How do you manage that and did you, you've been with Red Hat for a while, did you hit a tipping point, at some point? Cause I mean certainly you have software margin, 80, 90% you know margins. You got a great operating you know margins. So you've crossed that chasm so to speak to pick a bromide, but, others have had such a challenge. Is it because they have to support those projects and it just takes a long time? And you guys baked over 20 years. I wonder if you can give us some insight there. >> You know, I think it's as much art as it is science, I would love to say. Like this is a you know, cold formula that we apply but, we have a good gut feeling for, if you're going to back a technology, or an upstream project, you want to make sure that it's going to expand beyond your own investment, and we've certainly made a lot of wrong bets that the technology doesn't evolve. But you've got to be able to change, and when we see some of the early indicators like in Cooper Netties. Those are the ones where, we like how it's governed, we like how it's structured, we like the other players that are in there, and that's just been one of the unique aspect of Red Hat, is we pick pretty well. >> So Matt, I'm wondering if you're willing to comment, we were at Dockercon a couple of weeks ago, they've done a shift to, how they're managing kind of, but the Moby project to do the open source stuff, what's your take on that? What's Red Hat's positioning there? It's been an interesting dynamic between Docker and Red Hat to watch the last couple of years. >> Yeah you know, I think Moby for us, it's one of, it's about 16 hundred different upstream projects that we pull in across our portfolios. And so, we're certainly watching it, and we're seeing them evolve. We've been involved for the technology for a while now, but we don't necessarily know where that's going to go right now. But we certainly look a it like we do, you know the whole, breath of open source projects we pull in. >> What else is on your horizon? What's exciting you these days? >> You know, I think just seeing the reality of Hybrid Cloud becoming, it's becoming real for our customers. Where they're able, you know, you probably saw some of the Amazon announcements today where, you're able to take services, that might be in the public Cloud and now pull them on Premise. You heard customers talk about taking OpenShift and running that all the way out to the public Cloud. And we love that aspect, because you know, being able to use infrastructure to power applications, I think it's going to change IT and, then all the pieces that emanate around that, it's exciting for ISV's, it's exciting you know, around our management products from Ansible to Cloud forms. It's just a lot that we can do there. >> On the management products, you know, what Dave said, one of the Bromides out there, when I became an analyst seven years ago, it's like we can say, well it's security and management are the biggest problems we have. I feel like I can go to that well anytime I need to do. How are we doing in industry and management. Obviously you've got your position, but you know, as the surface area of the landscape is just expanding exponentially, every. You talked about how many customers are multi Cloud today. So you know, we know there's not a single thing that can do everything but, how are we doing as an industry, in Red Hot specifically? >> I think form Red hat's position, we've had a lot of success with Ansible. Just becoming a core automation technology, cause I think the one common thread is, you have so many choices, you have so many pieces, you have to start automating them. How we did IT 15 years ago, just will not. It won't scale anymore. I think building up from that stack. How you move to policy based management, that's earlier in the space. But there is a ton of capabilities and we've seen customers using, you know from our perspective, it's combining Cloud forms on orchestration, and satellite for content, Ansible for automation. Because I describe it, so I have the operation teams that run our OpenShift online environments. That's a, a relatively small group of people that manages millions of applications. And they change faster than a human could push a button. And so, as customers get into that world, you know we're certainly not in the Google world yet, but when you get that 4A it changes how you have to manage it. It has to become automated, it has to become policy driven, and then it's fun. I like it. Like doing ops in the 90s versus how you do it today. It is refreshing as an operator to just have these tools are your fingertips. >> High frequency application development. Matt thanks very >> It really is! >> Much for coming on the Cube. It's great to see you, and congratulations and good luck going forward. >> Fantastic, thanks S. >> You're welcome. Alright keep it right there everybody. Stu and I will be right back with our next guest. This is Cube, we're live from Red Hat Summit in Boston. We'll be right back. (upbeat music)