(upbeat music) >> Hello and welcome to this CUBE conversation here presenting the "AWS Startup Showcase: "New Breakthroughs in DevOps, Data Analytics "and Cloud Management Tools" featuring Opsani for the cloud management and migration track here today, I'm your host John Furrier. Today, we're joined by Patrick Conte, Chief Commercial Officer, Opsani. Thanks for coming on. Appreciate you coming on. Future of AI operations. >> Thanks, John. Great to be here. Appreciate being with you. >> So congratulations on all your success being showcased here as part of the Startups Showcase, future of AI operations. You've got the cloud scale happening. A lot of new transitions in this quote digital transformation as cloud scales goes next generation. DevOps revolution as Emily Freeman pointed out in her keynote. What's the problem statement that you guys are focused on? Obviously, AI involves a lot of automation. I can imagine there's a data problem in there somewhere. What's the core problem that you guys are focused on? >> Yeah, it's interesting because there are a lot of companies that focus on trying to help other companies optimize what they're doing in the cloud, whether it's cost or whether it's performance or something else. We felt very strongly that AI was the way to do that. I've got a slide prepared, and maybe we can take a quick look at that, and that'll talk about the three elements or dimensions of the problem. So we think about cloud services and the challenge of delivering cloud services. You've really got three things that customers are trying to solve for. They're trying to solve for performance, they're trying to solve for the best performance, and, ultimately, scalability. I mean, applications are growing really quickly especially in this current timeframe with cloud services and whatnot. They're trying to keep costs under control because certainly, it can get way out of control in the cloud since you don't own the infrastructure, and more importantly than anything else which is why it's at the bottom sort of at the foundation of all this, is they want their applications to be a really a good experience for their customers. So our customer's customer is actually who we're trying to solve this problem for. So what we've done is we've built a platform that uses AI and machine learning to optimize, meaning tune, all of the key parameters of a cloud application. So those are things like the CPU usage, the memory usage, the number of replicas in a Kubernetes or container environment, those kinds of things. It seems like it would be simple just to grab some values and plug 'em in, but it's not. It's actually the combination of them has to be right. Otherwise, you get delays or faults or other problems with the application. >> Andrew, if you can bring that slide back up for a second. I want to just ask one quick question on the problem statement. You got expenditures, performance, customer experience kind of on the sides there. Do you see this tip a certain way depending upon use cases? I mean, is there one thing that jumps out at you, Patrick, from your customer's customer's standpoint? Obviously, customer experience is the outcome. That's the app, whatever. That's whatever we got going on there. >> Sure. >> But is there patterns 'cause you can have good performance, but then budget overruns. Or all of them could be failing. Talk about this dynamic with this triangle. >> Well, without AI, without machine learning, you can solve for one of these, only one, right? So if you want to solve for performance like you said, your costs may overrun, and you're probably not going to have control of the customer experience. If you want to solve for one of the others, you're going to have to sacrifice the other two. With machine learning though, we can actually balance that, and it isn't a perfect balance, and the question you asked is really a great one. Sometimes, you want to over-correct on something. Sometimes, scalability is more important than cost, but what we're going to do because of our machine learning capability, we're going to always make sure that you're never spending more than you should spend, so we're always going to make sure that you have the best cost for whatever the performance and reliability factors that you you want to have are. >> Yeah, I can imagine. Some people leave services on. Happened to us one time. An intern left one of the services on, and like where did that bill come from? So kind of looked back, we had to kind of fix that. There's a ton of action, but I got to ask you, what are customers looking for with you guys? I mean, as they look at Opsani, what you guys are offering, what's different than what other people might be proposing with optimization solutions? >> Sure. Well, why don't we bring up the second slide, and this'll illustrate some of the differences, and we can talk through some of this stuff as well. So really, the area that we play in is called AIOps, and that's sort of a new area, if you will, over the last few years, and really what it means is applying intelligence to your cloud operations, and those cloud operations could be development operations, or they could be production operations. And what this slide is really representing is in the upper slide, that's sort of the way customers experience their DevOps model today. Somebody says we need an application or we need a feature, the developers pull down something from get. They hack an early version of it. They run through some tests. They size it whatever way they know that it won't fail, and then they throw it over to the SREs to try to tune it before they shove it out into production, but nobody really sizes it properly. It's not optimized, and so it's not tuned either. When it goes into production, it's just the first combination of settings that work. So what happens is undoubtedly, there's some type of a problem, a fault or a delay, or you push new code, or there's a change in traffic. Something happens, and then, you've got to figure out what the heck. So what happens then is you use your tools. First thing you do is you over-provision everything. That's what everybody does, they over-provision and try to soak up the problem. But that doesn't solve it because now, your costs are going crazy. You've got to go back and find out and try as best you can to get root cause. You go back to the tests, and you're trying to find something in the test phase that might be an indicator. Eventually your developers have to hack a hot fix, and the conveyor belt sort of keeps on going. We've tested this model on every single customer that we've spoken to, and they've all said this is what they experience on a day-to-day basis. Now, if we can go back to the side, let's talk about the second part which is what we do and what makes us different. So on the bottom of this slide, you'll see it's really a shift-left model. What we do is we plug in in the production phase, and as I mentioned earlier, what we're doing is we're tuning all those cloud parameters. We're tuning the CPU, the memory, the Replicas, all those kinds of things. We're tuning them all in concert, and we're doing it at machine speed, so that's how the customer gets the best performance, the best reliability at the best cost. That's the way we're able to achieve that is because we're iterating this thing in machine speed, but there's one other place where we plug in and we help the whole concept of AIOps and DevOps, and that is we can plug in in the test phase as well. And so if you think about it, the DevOps guy can actually not have to over-provision before he throws it over to the SREs. He can actually optimize and find the right size of the application before he sends it through to the SREs, and what this does is collapses the timeframe because it means the SREs don't have to hunt for a working set of parameters. They get one from the DevOps guys when they send it over, and this is how the future of AIOps is being really affected by optimization and what we call autonomous optimization which means that it's happening without humans having to press a button on it. >> John: Andrew, bring that slide back up. I want to just ask another question. Tuning in concert thing is very interesting to me. So how does that work? Are you telegraphing information to the developer from the autonomous workload tuning engine piece? I mean, how does the developer know the right knobs or where does it get that provisioning information? I see the performance lag. I see where you're solving that problem. >> Sure. >> How does that work? >> Yeah, so actually, if we go to the next slide, I'll show you exactly how it works. Okay, so this slide represents the architecture of a typical application environment that we would find ourselves in, and inside the dotted line is the customer's application namespace. That's where the app is. And so, it's got a bunch of pods. It's got a horizontal pod. It's got something for replication, probably an HPA. And so, what we do is we install inside that namespace two small instances. One is a tuning pod which some people call a canary, and that tuning pod joins the rest of the pods, but it's not part of the application. It's actually separate, but it gets the same traffic. We also install somebody we call Servo which is basically an action engine. What Servo does is Servo takes the metrics from whatever the metric system is is collecting all those different settings and whatnot from the working application. It could be something like Prometheus. It could be an Envoy Sidecar, or more likely, it's something like AppDynamics, or we can even collect metrics off of Nginx which is at the front of the service. We can plug into anywhere where those metrics are. We can pull the metrics forward. Once we see the metrics, we send them to our backend. The Opsani SaaS service is our machine learning backend. That's where all the magic happens, and what happens then is that service sees the settings, sends a recommendation to Servo, Servo sends it to the tuning pod, and we tune until we find optimal. And so, that iteration typically takes about 20 steps. It depends on how big the application is and whatnot, how fast those steps take. It could be anywhere from seconds to minutes to 10 to 20 minutes per step, but typically within about 20 steps, we can find optimal, and then we'll come back and we'll say, "Here's optimal, and do you want to "promote this to production," and the customer says, "Yes, I want to promote it to production "because I'm saving a lot of money or because I've gotten "better performance or better reliability." Then, all he has to do is press a button, and all that stuff gets sent right to the production pods, and all of those settings get put into production, and now he's now he's actually saving the money. So that's basically how it works. >> It's kind of like when I want to go to the beach, I look at the weather.com, I check the forecast, and I decide whether I want to go or not. You're getting the data, so you're getting a good look at the information, and then putting that into a policy standpoint. I get that, makes total sense. Can I ask you, if you don't mind, expanding on the performance and reliability and the cost advantage? You mentioned cost. How is that impacting? Give us an example of some performance impact, reliability, and cost impacts. >> Well, let's talk about what those things mean because like a lot of people might have different ideas about what they think those mean. So from a cost standpoint, we're talking about cloud spend ultimately, but it's represented by the settings themselves, so I'm not talking about what deal you cut with AWS or Azure or Google. I'm talking about whatever deal you cut, we're going to save you 30, 50, 70% off of that. So it doesn't really matter what cost you negotiated. What we're talking about is right-sizing the settings for CPU and memory, Replica. Could be Java. It could be garbage collection, time ratios, or heap sizes or things like that. Those are all the kinds of things that we can tune. The thing is most of those settings have an unlimited number of values, and this is why machine learning is important because, if you think about it, even if they only had eight settings or eight values per setting, now you're talking about literally billions of combinations. So to find optimal, you've got to have machine speed to be able to do it, and you have to iterate very, very quickly to make it happen. So that's basically the thing, and that's really one of the things that makes us different from anybody else, and if you put that last slide back up, the architecture slide, for just a second, there's a couple of key words at the bottom of it that I want to want to focus on, continuous. So continuous really means that we're on all the time. We're not plug us in one time, make a change, and then walk away. We're actually always measuring and adjusting, and the reason why this is important is in the modern DevOps world, your traffic level is going to change. You're going to push new code. Things are going to happen that are going to change the basic nature of the software, and you have to be able to tune for those changes. So continuous is very important. Second thing is autonomous. This is designed to take pressure off of the SREs. It's not designed to replace them, but to take the pressure off of them having to check pager all the time and run in and make adjustments, or try to divine or find an adjustment that might be very, very difficult for them to do so. So we're doing it for them, and that scale means that we can solve this for, let's say, one big monolithic application, or we can solve it for literally hundreds of applications and thousands of microservices that make up those applications and tune them all at the same time. So the same platform can be used for all of those. You originally asked about the parameters and the settings. Did I answer the question there? >> You totally did. I mean, the tuning in concert. You mentioned early as a key point. I mean, you're basically tuning the engine. It's not so much negotiating a purchase SaaS discount. It's essentially cost overruns by the engine, either over burning or heating or whatever you want to call it. I mean, basically inefficiency. You're tuning the core engine. >> Exactly so. So the cost thing is I mentioned is due to right-sizing the settings and the number of Replicas. The performance is typically measured via latency, and the reliability is typically measured via error rates. And there's some other measures as well. We have a whole list of them that are in the application itself, but those are the kinds of things that we look for as results. When we do our tuning, we look for reducing error rates, or we look for holding error rates at zero, for example, even if we improve the performance or we improve the cost. So we're looking for the best result, the best combination result, and then a customer can decide if they want to do so to actually over-correct on something. We have the whole concept of guard rail, so if performance is the most important thing, or maybe some customers, cost is the most important thing, they can actually say, "Well, give us the best cost, "and give us the best performance and the best reliability, "but at this cost," and we can then use that as a service-level objective and tune around it. >> Yeah, it reminds me back in the old days when you had filtering white lists of black lists of addresses that can go through, say, a firewall or a device. You have billions of combinations now with machine learning. It's essentially scaling the same concept to unbelievable. These guardrails are now in place, and that's super cool and I think really relevant call-out point, Patrick, to kind of highlight that. At this kind of scale, you need machine learning, you need the AI to essentially identify quickly the patterns or combinations that are actually happening so a human doesn't have to waste their time that can be filled by basically a bot at that point. >> So John, there's just one other thing I want to mention around this, and that is one of the things that makes us different from other companies that do optimization. Basically, every other company in the optimization space creates a static recommendation, basically their recommendation engines, and what you get out of that is, let's say it's a manifest of changes, and you hand that to the SREs, and they put it into effect. Well, the fact of the matter is is that the traffic could have changed then. It could have spiked up, or it could have dropped below normal. You could have introduced a new feature or some other code change, and at that point in time, you've already instituted these changes. They may be completely out of date. That's why the continuous nature of what we do is important and different. >> It's funny, even the language that we're using here: network, garbage collection. I mean, you're talking about tuning an engine, am operating system. You're talking about stuff that's moving up the stack to the application layer, hence this new kind of eliminating of these kind of siloed waterfall, as you pointed out in your second slide, is kind of one integrated kind of operating environment. So when you have that or think about the data coming in, and you have to think about the automation just like self-correcting, error-correcting, tuning, garbage collection. These are words that we've kind of kicking around, but at the end of the day, it's an operating system. >> Well in the old days of automobiles, which I remember cause I'm I'm an old guy, if you wanted to tune your engine, you would probably rebuild your carburetor and turn some dials to get the air-oxygen-gas mix right. You'd re-gap your spark plugs. You'd probably make sure your points were right. There'd be four or five key things that you would do. You couldn't do them at the same time unless you had a magic wand. So we're the magic wand that basically, or in modern world, we're sort of that thing you plug in that tunes everything at once within that engine which is all now electronically controlled. So that's the big differences as you think about what we used to do manually, and now, can be done with automation. It can be done much, much faster without humans having to get their fingernails greasy, let's say. >> And I think the dynamic versus static is an interesting point. I want to bring up the SRE which has become a role that's becoming very prominent in the DevOps kind of plus world that's happening. You're seeing this new revolution. The role of the SRE is not just to be there to hold down and do the manual configuration. They had a scale. They're a developer, too. So I think this notion of offloading the SRE from doing manual tasks is another big, important point. Can you just react to that and share more about why the SRE role is so important and why automating that away through when you guys have is important? >> The SRE role is becoming more and more important, just as you said, and the reason is because somebody has to get that application ready for production. The DevOps guys don't do it. That's not their job. Their job is to get the code finished and send it through, and the SREs then have to make sure that that code will work, so they have to find a set of settings that will actually work in production. Once they find that set of settings, the first one they find that works, they'll push it through. It's not optimized at that point in time because they don't have time to try to find optimal, and if you think about it, the difference between a machine learning backend and an army of SREs that work 24-by-seven, we're talking about being able to do the work of many, many SREs that never get tired, that never need to go play video games, to unstress or whatever. We're working all the time. We're always measuring, adjusting. A lot of the companies we talked to do a once-a-month adjustment on their software. So they put an application out, and then they send in their SREs once a month to try to tune the application, and maybe they're using some of these other tools, or maybe they're using just their smarts, but they'll do that once a month. Well, gosh, they've pushed code probably four times during the month, and they probably had a bunch of different spikes and drops in traffic and other things that have happened. So we just want to help them spend their time on making sure that the application is ready for production. Want to make sure that all the other parts of the application are where they should be, and let us worry about tuning CPU, memory, Replica, job instances, and things like that so that they can work on making sure that application gets out and that it can scale, which is really important for them, for their companies to make money is for the apps to scale. >> Well, that's a great insight, Patrick. You mentioned you have a lot of great customers, and certainly if you have your customer base are early adopters, pioneers, and grow big companies because they have DevOps. They know that they're seeing a DevOps engineer and an SRE. Some of the other enterprises that are transforming think the DevOps engineer is the SRE person 'cause they're having to get transformed. So you guys are at the high end and getting now the new enterprises as they come on board to cloud scale. You have a huge uptake in Kubernetes, starting to see the standardization of microservices. People are getting it, so I got to ask you can you give us some examples of your customers, how they're organized, some case studies, who uses you guys, and why they love you? >> Sure. Well, let's bring up the next slide. We've got some customer examples here, and your viewers, our viewers, can probably figure out who these guys are. I can't tell them, but if they go on our website, they can sort of put two and two together, but the first one there is a major financial application SaaS provider, and in this particular case, they were having problems that they couldn't diagnose within the stack. Ultimately, they had to apply automation to it, and what we were able to do for them was give them a huge jump in reliability which was actually the biggest problem that they were having. We gave them 5,000 hours back a month in terms of the application. They were they're having pager duty alerts going off all the time. We actually gave them better performance. We gave them a 10% performance boost, and we dropped their cloud spend for that application by 72%. So in fact, it was an 80-plus % price performance or cost performance improvement that we gave them, and essentially, we helped them tune the entire stack. This was a hybrid environment, so this included VMs as well as more modern architecture. Today, I would say the overwhelming majority of our customers have moved off of the VMs and are in a containerized environment, and even more to the point, Kubernetes which we find just a very, very high percentage of our customers have moved to. So most of the work we're doing today with new customers is around that, and if we look at the second and third examples here, those are examples of that. In the second example, that's a company that develops websites. It's one of the big ones out in the marketplace that, let's say, if you were starting a new business and you wanted a website, they would develop that website for you. So their internal infrastructure is all brand new stuff. It's all Kubernetes, and what we were able to do for them is they were actually getting decent performance. We held their performance at their SLO. We achieved a 100% error-free scenario for them at runtime, and we dropped their cost by 80%. So for them, they needed us to hold-serve, if you will, on performance and reliability and get their costs under control because everything in that, that's a cloud native company. Everything there is cloud cost. So the interesting thing is it took us nine steps because nine of our iterations to actually get to optimal. So it was very, very quick, and there was no integration required. In the first case, we actually had to do a custom integration for an underlying platform that was used for CICD, but with the- >> John: Because of the hybrid, right? >> Patrick: Sorry? >> John: Because it was hybrid, right? >> Patrick: Yes, because it was hybrid, exactly. But within the second one, we just plugged right in, and we were able to tune the Kubernetes environment just as I showed in that architecture slide, and then the third one is one of the leading application performance monitoring companies on the market. They have a bunch of their own internal applications and those use a lot of cloud spend. They're actually running Kubernetes on top of VMs, but we don't have to worry about the VM layer. We just worry about the Kubernetes layer for them, and what we did for them was we gave them a 48% performance improvement in terms of latency and throughput. We dropped their error rates by 90% which is pretty substantial to say the least, and we gave them a 50% cost delta from where they had been. So this is the perfect example of actually being able to deliver on all three things which you can't always do. It has to be, sort of all applications are not created equal. This was one where we were able to actually deliver on all three of the key objectives. We were able to set them up in about 25 minutes from the time we got started, no extra integration, and needless to say, it was a big, happy moment for the developers to be able to go back to their bosses and say, "Hey, we have better performance, "better reliability. "Oh, by the way, we saved you half." >> So depending on the stack situation, you got VMs and Kubernetes on the one side, cloud-native, all Kubernetes, that's dream scenario obviously. Not many people like that. All the new stuff's going cloud-native, so that's ideal, and then the mixed ones, Kubernetes, but no VMs, right? >> Yeah, exactly. So Kubernetes with no VMs, no problem. Kubernetes on top of VMs, no problem, but we don't manage the VMs. We don't manage the underlay at all, in fact. And the other thing is we don't have to go back to the slide, but I think everybody will remember the slide that had the architecture, and on one side was our cloud instance. The only data that's going between the application and our cloud instance are the settings, so there's never any data. There's never any customer data, nothing for PCI, nothing for HIPPA, nothing for GDPR or any of those things. So no personal data, no health data. Nothing is passing back and forth. Just the settings of the containers. >> Patrick, while I got you here 'cause you're such a great, insightful guest, thank you for coming on and showcasing your company. Kubernetes real quick. How prevalent is this mainstream trend is because you're seeing such great examples of performance improvements. SLAs being met, SLOs being met. How real is Kubernetes for the mainstream enterprise as they're starting to use containers to tip their legacy and get into the cloud-native and certainly hybrid and soon to be multi-cloud environment? >> Yeah, I would not say it's dominant yet. Of container environments, I would say it's dominant now, but for all environments, it's not. I think the larger legacy companies are still going through that digital transformation, and so what we do is we catch them at that transformation point, and we can help them develop because as we remember from the AIOps slide, we can plug in at that test level and help them sort of pre-optimize as they're coming through. So we can actually help them be more efficient as they're transforming. The other side of it is the cloud-native companies. So you've got the legacy companies, brick and mortar, who are desperately trying to move to digitization. Then, you've got the ones that are born in the cloud. Most of them aren't on VMs at all. Most of them are on containers right from the get-go, but you do have some in the middle who have started to make a transition, and what they've done is they've taken their native VM environment and they've put Kubernetes on top of it so that way, they don't have to scuttle everything underneath it. >> Great. >> So I would say it's mixed at this point. >> Great business model, helping customers today, and being a bridge to the future. Real quick, what licensing models, how to buy, promotions you have for Amazon Web Services customers? How do people get involved? How do you guys charge? >> The product is licensed as a service, and the typical service is an annual. We license it by application, so let's just say you have an application, and it has 10 microservices. That would be a standard application. We'd have an annual cost for optimizing that application over the course of the year. We have a large application pack, if you will, for let's say applications of 20 services, something like that, and then we also have a platform, what we call Opsani platform, and that is for environments where the customer might have hundreds of applications and-or thousands of services, and we can plug into their deployment platform, something like a harness or Spinnaker or Jenkins or something like that, or we can plug into their their cloud Kubernetes orchestrator, and then we can actually discover the apps and optimize them. So we've got environments for both single apps and for many, many apps, and with the same platform. And yes, thanks for reminding me. We do have a promotion for for our AWS viewers. If you reference this presentation, and you look at the URL there which is opsani.com/awsstartupshowcase, can't forget that, you will, number one, get a free trial of our software. If you optimize one of your own applications, we're going to give you an Oculus set of goggles, the augmented reality goggles. And we have one other promotion for your viewers and for our joint customers here, and that is if you buy an annual license, you're going to get actually 15 months. So that's what we're putting on the table. It's actually a pretty good deal. The Oculus isn't contingent. That's a promotion. It's contingent on you actually optimizing one of your own services. So it's not a synthetic app. It's got to be one of your own apps, but that's what we've got on the table here, and I think it's a pretty good deal, and I hope your guys take us up on it. >> All right, great. Get Oculus Rift for optimizing one of your apps and 15 months for the price of 12. Patrick, thank you for coming on and sharing the future of AIOps with you guys. Great product, bridge to the future, solving a lot of problems. A lot of use cases there. Congratulations on your success. Thanks for coming on. >> Thank you so much. This has been excellent, and I really appreciate it. >> Hey, thanks for sharing. I'm John Furrier, your host with theCUBE. Thanks for watching. (upbeat music)

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john ferrier host of the cube. Got a great story here. Navigating Covid 19 with Watson advertising and weather channel conversations. Sherry back steen. Who's the gM of Watson advertising in the weather company. Sherry, thanks for coming on the cube. My favorite part of IBM think is to talk about the tech and also the weather company innovations. Thanks for coming on. >>Hi, happy to be here, john >>So COVID-19 obviously some impact for people that working at home. Um normally you guys have been doing a lot of innovation around weather weather data um certainly huge part of it. Right. And so lots been changing with AI and the weather company and IBM so let's first start before we jump in, just a little background about what your team has created because a lot of fascinating things here. Go ahead. >>Yeah. So when the pandemic started, you know we looked at the data that we were seeing and of course in weather accuracy and accurate data is really important trusted data. And so we created a COVID-19 hub on our weather channel app and on weather.com. And essentially what it was is an aggregated area where consumers could get the most up to date information on covid cases, deaths in their area, trends see heat maps uh information from the C. D. C. And what was unique about it. It was to a local level. Right so state level information is helpful but we know that consumers uh me included. I need information around what's happening around me. And so we were able to bring this down to a county level which we thought was really helpful for consumers >>share as watching sports on tv. And recently, a few months ago, the Masters was on and you saw people getting back into real life, It's almost like a weather forecast. Now. You want to know what's going on in the pandemic. People are sharing that. They're getting the vaccine. Um, really interesting. And so I want to understand how this all came together with you guys. Is was it something that has a weather data, a bunch of geeks saying, hey, we should do this for companies, but take us to the thought process with their team. Was it like you saw this as value? How did you get to this? Because this is an interesting user benefit. I want to know the weather, I want to know if it's safe. These are kind of a psychology of a user expectation. How did you guys connect the dots here for this project? >>Well, we certainly do have a very passionate team of people, um some weather geeks included, um and you're absolutely right watching the Masters a few months ago was amazing to see, you know, some sense of normality happening here. But you know, we looked at, you know, IBM, the weather company, like, how do we help during this pandemic? And when we thought about it, we looked at there's an amazing gap of information. And as the weather channel, you know, what we do is bring together data, give people insights and help them make decisions with that. And so it was really part of our mission. It's always been that way to give information to keep people safe. And so all we did is took a different data set and provided the same thing. And so in this case, the covid data set, which we actually had to, you know, aggregate from different sources whether it was the C. D. C. The World Health Organization uh State governments or county governments to provide this to consumers. But it was really really natural for us because we know what consumers want. You know we all want information around where we live, right? And then we want to see like where our friends live, where our relatives live to make sure that they're okay. And then that enables people to make the decisions that are right for their family. And so it was really really natural for us to do that. And then of course we have the technology to be able to scale to hundreds of millions of people. Which is really important. >>It's not obvious until you actually think about that. It's so obvious. Congratulations. What a great innovation. What were the biggest challenges you guys had to face and how did you overcome it? Because I'm curious. I see you've got a lot of, lot of large scale data dealing with diversity of data with weather. What was the challenges with Covid? And how did you overcome it? >>So again, without a doubt it was the data because you're looking at one, we wanted that county level data. So you're looking at multiple sources. So how do we aggregate this data? So first finding that trusted source that that we could use. But then how do you pull it in in an automated way? And the challenge was it with the State Department, the county departments that data came in all kinds of formats. Some counties used maps, some use charts, some use pds to get that information. So we had to pull all this unstructured data, uh, and then that data was updated at different times. So some counties did it twice a day, some did it once day, different time zones. So that really made it challenging. And so then, you know, so what we did is this is where the power of A I really helps because a I can take all of that data, bring in and organize it and then we could put it back out to the consumer in a very digestible way. And so we were able to do that. We built an automated pipeline around that so we can make sure that it was updated. It was fresh and timely, which was really important. But without a doubt looking at that structured data and unstructured data and really helping it to make sense to the consumer was the biggest challenge. And what's interesting about it. Normally it would take us months to do something like that. I challenged the team to say we don't have months, we have days. They turned that around in eight days, which was just an amazing herculean feat. But that's really just the power of, as you said, passionate people coming together to do something so meaningful. >>I love the COVID-19 success stories when people rally around their passion and also their expertise. What was the technology to the team used? Because the theme here at IBM think is transformation innovation, scale. How did you move so fast to make that happen? >>So we move fast by our Ai capabilities and then using IBM cloud and so really there's four key components are like four teams that worked on it. So first there was the weather company team um and because we are a consumer division of IBM, we know what consumers want. So we understand the user experience and the design, but we also know how to build an A. P. I. That can scale because you're talking about being able to scale not only in a weather platform. So in the midst of covid weather still happened, so we still had severe weather record breaking hurricane season. And so those A. P. S. Have to scale to that volume. Then the second team was the AI team. So that used the Watson AI team mixed with the weather Ai team to again bring in that data to organize that data. Um And we used Watson NLP so natural natural language processing in order to create that automated pipeline. Then we had the corralled infrastructure so that platform team that built that architecture and that data repository on IBM cloud. And then the last team was our data privacy office. So making sure that that data was trusted that we have permission to use it uh and just know really that data governance. So it's all of that technology and all of those teams coming together to build this hub for consumers. Um And it worked I mean we would have about four million consumers looking at that hub every single day. Um and even like a year later we still have a couple million people that access that information. So it's really kind of become more like the weather checking the weather's come that daily habit. >>That's awesome. And I gotta I gotta imagine that these discoveries and innovations that was part of this transformation at scale have helped other ways outside the pandemic and you share how this is connected to um other benefits outside the pandemic. >>Yeah so absolutely um you know ai for businesses part of IBM strategy and so really helping organizations to help predict um you know to help take workloads and automate them. So they're high valued employees can work on you know other work. And also you know to bring that personalization to customers. You know, it's really a i when I look at it for my own part of a IBM with the weather company, three things where I'm using this technology. So the first one is around advertising. So the advertising industry is at a really um you know, pivotal part right now, a lot of turmoil and challenges because of privacy legislation because big tech companies are um you know, getting rid of tracking pixels that we normally use to drive the business. So we've created a suite of AI solutions for publishers for you know, different players within the ad tech space, um which is really important because it protects the open web, so like getting covid information or weather information, all of that is free information to the public. We just ask that you underwrite it by seeing advertising so we can keep it free. So those products protect the open red. So really, really important. Then on the consumer side of my business, within the weather channel, we actually used Watson Ai um to connect health with weather. So we know that there's that connection, some health um you know, issues that people have can be impacted by weather, like allergies and flew. So we've actually used Watson Ai to build a um Risk of flu that goes 15 days out. So we can tell people in your local area this one actually goes down to the zip code level, um the risk of flu in your area or the risk of allergies. So help to manage your symptoms, take your prescription. So, um that's a really interesting way. We're using AI and of course weather dot com and our apps are on IBM cloud, so we have this strong infrastructure to support that. And then lastly, you know, our weather forecasting has always been rooted in a i you take 100 different weather models, you apply ai to that to get the best and most accurate forecasts that you deliver. Um and so we are using these technologies every day to, you know, move our business forward and to provide, you know, weather services for people. >>I just love the automation and as users have smartphones and more instrumentation on their bodies, whether it's wearables, people will plan their day around the weather, and retail shops will have a benefit knowing what the stock and or not have on hand and how to adjust that. This, the classic edge computing paradigm, fascinating impact. You wouldn't think about that, but that's a pretty big deal. People are planning >>around >>the weather data and making that available is critical. >>Oh, absolutely. You know, every business needs a weather strategy because whether it impacts your supply chain, um agriculture, should I be watering today or not even around, you know, um, if you think about energy and power lines, you know, the vegetation growth over power lines can bring power lines down and it's a disruption, you know, to customers and power. So there's just when you start thinking about it, you're like, wow, whether really impacts every business, um, not to say just consumers in general and their daily lives. >>And uh, and there's a lot of cloud scale to that can help companies whether it's um be part of a better planet or smarter planet as it's been called, and help with with global warming. I mean, you think about this is all kind of been contextually relevant now more than ever. Super exciting. Um Great stuff. I want to get your take on outside of um the IBM response to the pandemic more broadly outside of the weather. What are you guys doing um to help? Are you guys doing anything else with industry? How could you talk a little bit more about IBM s response more broadly to the pandemic? >>Yeah so IBM has been you know working with government academia, industry is really from the beginning uh in several different ways. Um you know the first one of the first things we did is it opened up our intellectual property. So R. I. P. And our technology our supercomputing To help researchers really try to understand COVID-19 some of the treatments and possible cures so that's been really beneficial as it relates to that. Um Some other things though, that we're doing as well is we created a chat bots that companies and clients could use and this chat but could either be used to help train teachers because they have to work remotely or help other workers as well. Um and also the chatbots was helping as companies started to re enter back to the workforce and getting back to the office. So the chatbots been really helpful there. Um and then, you know, one of the things that we've been doing on the advertising side is we actually have helped the ad council with their vaccine campaign. Um It's up to you is the name of the campaign and we delivered a ad unit that can dynamically assemble a creative in real time to make sure that the right message was getting out the right time to the right person. So it's really helped to maximize that campaign to reach people um and encourage them if it's the right thing for them, you know where the vaccines are available. Um and that you know, they could take those. So a lot of great work that's going on within IBM. Um and actually the most recent thing just actually in the past month is we release the Digital Health Pass in cooperation with the state of new york. Um and this is a fantastic tool because it is a way for individuals to keep their private information around their vaccines or you know, some of the Covid test they've been having on a mobile device that's secure and we think that this is going to be really important as cities start to reopen um to have that information easily accessible. >>Uh sure, great insight, um great innovation navigating Covid 19 a lot of innovation transformation at IBM and obviously Watson and the weather company using AI and also, you know, when we come out of Covid post, post Covid as real life comes back, we're still going to be impacted. We're gonna have new innovations, new expectations, tracking, understanding what's going on, not just the weather. So thanks >>for absolutely great >>work. Um, awesome. Thank you. >>Great. Thanks john good to see you. >>Okay. This is the cubes coverage of IBM. Think I'm john for a host of the cube. Thanks for watching. Yeah.

>> Announcer: From around the globe. It's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Oh, welcome back to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host of theCUBE. We've got a great story here. Navigating COVID-19 with Watson advertising and weather channel conversations, Sheri Bachstein, who's the GM of Watson Advertising in the weather company. Sheri, thanks for coming on theCUBE. My favorite part of IBM Think is to talk about the tech and also the weather company innovations. Thanks for coming on. >> Hi, happy to be here John. >> So COVID-19 obviously some impact for people that working at home. Normally you guys have been doing a lot of innovation around weather, weather data, certainly huge part of it. And so lots been changing with AI and the weather company and IBM, so let's first start before we jump in just a little background about what your team has created because a lot of fascinating things here. Go ahead. >> Yeah, so when the pandemic started, we looked at the data that we were seeing and of course in weather accuracy and accurate data is really important trusted data. And so we created a COVID-19 hub on our weather channel app and on weather.com and essentially what it was is an aggregated area where consumers could get the most up-to-date information on COVID cases, deaths in their area, trends see heat maps, information from the CDC. And what was unique about it, it was to a local level, right? So state level information is helpful, but we know that consumers me included. I need information around what's happening around me. And so we were able to bring this down to a County level which we thought was really helpful for consumers >> Sheri's watching sports on TV. And recently a few months ago, the masters was on and you saw people getting back into real life. It's almost like a weather forecast. Now you want to know what's going on in the pandemic. People are sharing that they're getting the vaccine, really interesting. And so I want to understand how this all came together with you guys. Was it something that as a weather data and a bunch of geeks saying, Hey, we should do this for companies but take us to thought process 113. Was it like you saw this as value? How did you get to this? Because this is an interesting user benefit. I want to know the weather. I want to know if it's safe. These are kind of a psychology of a user expectation. How did you guys connect the dots here for this project? >> Well, we certainly do have a very passionate team of people some weather geeks included and you're absolutely right. Watching the masters a few months ago was amazing to see some sense of normality happening here. But we looked at IBM and the weather company like how do we help during this pandemic? And when we thought about it we looked at there's an amazing gap of information. And as the weather channel, what we do is bring together data give people insights and help them make decisions with that. And so it was really part of our mission. It's always been that way to give information to keep people safe. And so all we did is took a different data set and provided the same thing. And so in this case, the COVID data set which we actually had to aggregate from different sources whether it was the CDC, the world health organization, a state governments, our County governments to provide this to consumers. But it was really, really natural for us because we know what consumers want. We all want information around where we live, right? And then we want to see like where our friends live, where our relatives live to make sure that they're okay. And then if that enables people to make the decisions that are right for their family. And so it was really, really natural for us to do that. And then of course we have the technology to be able to scale to hundreds of millions of people, which is really important. >> Yeah, it's not obvious until you actually think about it, then it's so obvious. Congratulations, what a great innovation what were the biggest challenges you guys had to face and how did you overcome it? Because I'm curious, I see you got a lot of large scale data dealing with diversity of data with weather. What was the challenges with COVID and how did you overcome it? >> So again, without a doubt it was the data, because you're looking at one, we wanted that County level data. So you're looking at multiple sources. So how do we aggregate this data? So first finding that trusted source that we could use but then how do you pull it in, in an automated way? And the challenge was it with the state departments, the County departments, that data came in, all kinds of formats. Some counties used maps, some use charts some use PDFs to get that information. So we had to pull all this unstructured data and then that data was updated at different times. So some counties did it twice a day some did it once a day, different time zones. So that really made it challenging. And so then, so what we did is this is where the power of AI really helps, because AI can take all of that data bring it in, organize it, and then we could put it back out to the consumer in a very digestible way. And so we were able to do that. We built an automated pipeline around that so we can make sure that it was updated. It was fresh and timely, which was really important but without a doubt, looking at that structured data and unstructured data and really helping it to make sense to the consumer was the biggest challenge. And I'll, what's interesting about it. Normally it would take us months to do something like that. I challenged the team to say, we don't have months. We have days. They turned that around in eight days which was just an amazing Herculean feat but that's really just the power of as you said, passionate people coming together to do something so meaningful. >> I love the COVID-19 success stories when people rally around their passion and also their expertise, what was the technology did the team use? Because the theme here at IBM Think is, transformation, innovation, scale. How did you move so fast to make that happen? >> So we moved fast by our AI capabilities and then using IBM cloud. And so really there's four key components or like four teams that worked on it. So first there was the weather company team. And because we are a consumer division of IBM we know what consumers want. So we understand the user experience and the design but we also know how the build an API that can scale because you're talking about being able to scale not only in a weather platform. So in the midst of COVID weather still happen. So we still had severe weather record breaking hurricane season. And so those APIs have to scale to that volume. Then the second team was the AI team. So that used the Watson AI team mixed with the weather AI team to again bring in that data to organize that data. And we use Watson NLP. So natural language processing in order to create that automated pipeline. Then we had the collateral infrastructure. So that platform team that built that architecture and that data repository on IBM cloud. And then the last team was our data privacy office. So making sure that that data was trusted that we have permission to use it and just really that data governance. So it was all of that technology and all of those teams coming together to build this hub for consumers. And it worked, I mean we would have about 4 million consumers looking at that hub every single day. And even like a year later, we still have a couple million people that access that information. So it's really kind of become more like the weather checking the weather, that daily habit. >> That's awesome. And I got to imagine that these discoveries and these innovations that was part of this transformation that scale I've helped other ways outside of the pandemic. Can you share how this is connected to other benefits outside the pandemic? >> Yeah, so absolutely, AI for business is part of IBM strategy. And so really helping organizations to help predict, to help take workloads and automate them. So they're high valued employees can work on other work and also to bring that personalization to customers is really AI. When I look at it for my own part of a IBM with the weather company, three things where I'm using this technology. So the first one is around advertising. So the advertising industry is at a really pivotal part right now, a lot of turmoil and challenges because of privacy legislation because big tech companies are getting rid of tracking pixels that we normally use to drive the business. So we've created a suite of AI solutions for publishers, for different players within the ad tech space which is really important because it protects the open web. So like getting COVID information or weather information all of that is free information to the public. We just ask that you underwrite it by saying advertising so we can keep it free. So those products protect the open read. So really, really important. Then on the consumer side of my business within the weather channel we actually use Watson AI to connect health with weather. So we know that there's that connection. Some health issues that people have can be impacted by weather like allergies and flu. So we've actually used Watson AI to build a risk of flu that goes 15 days out. So we can tell people in your local area this one actually goes down to the zip code level the risk of flu in your area or the risk of allergies. So it help to manage your symptoms, take your prescription. So that's a really interesting way we're using AI and of course, weather.com and our apps are an IBM cloud. So we have this strong infrastructure to support that. And then lastly our weather forecasting has always been rooted in AI. You take a hundred different weather models you apply AI to that to get the best and most accurate forecast that you deliver. And so we are using these technologies every day to move our business forward and to provide weather services for people. >> I just love the automation as users have smartphones and more instrumentation on their bodies, whether it's wearables, people will plan their day around the weather and retail shops will have a benefit knowing what to stock or not have on hand and how to adjust that this the classic edge computing paradigm, fascinating impact. You wouldn't think about that, but that's a pretty big deal. People are planning around the weather data and making that available as critical. >> Oh, absolutely. Every business needs a weather strategy because whether it impacts your supply chain, agriculture should I be watering today or not, even around if you think about energy and power lines, the vegetation growth of our power lines can bring power lines down and it's a disruption, to customers and power. So there's just, when you start thinking about it you're like, wow, weather really impacts every business not to say just consumers in general and their daily life. >> Yeah, and there's a lot of cloud scale too, that can help companies whether it's be part of better planet or smarter planet as it's been called and help with, with global warming. I mean, you think about this is all kind of been contextually relevant now more than ever super exciting, great stuff. I want to get your take on outside of the IBM response to the pandemic, more broadly outside of the weather. What are you guys doing to help? Are you guys doing anything else with industry? How could you, talk a little bit more about IBM's response more broadly to the pandemic? >> Yeah, so IBM has been working with government academia industries really from the beginning in several different ways. The first, one of the first things we did is it opened up our intellectual property. So our IP and our technology, our super computing to help researchers, really try to understand COVID-19, some of the treatments and possible cures. So that's been really beneficial as it relates to that. Some other things though that we're doing as well is we created a Chatbot that companies and clients could use. And this Chatbot could either be used to help train teachers because they have to work remotely or help other workers as well. And also the Chatbot was helping as companies started to reenter back to the workforce and getting back to the office. So the Chatbot has been really helpful there. And then one of the things that we've been doing on the advertising side is we actually have helped the ad council with their vaccine campaign. It's up to you as the name of the campaign. And we delivered a ad unit that can dynamically assemble a creative in real time to make sure that the right message was getting out the right time to the right person. So it's really helped to maximize that campaign to reach people. And they encourage them if it's the right thing for them, where the vaccines are available and that they could take those. So a lot of great work that's going on within IBM and actually the most recent thing just actually in the past month is we released the digital health pass in cooperation with the state of New York. And this is a fantastic tool because it is a way for individuals to keep their private information around their vaccines, or some of the COVID tests they've been having on a mobile device that's secure. And we think that this is going to be really important as cities start to reopen to have that information easily accessible. >> Awesome Sheri, great insight, great innovation navigating COVID-19, lots of innovation transformation at IBM and obviously Watson and the weather company using AI. And also, when we come out of COVID post COVID, as real life comes back, we're still going to be impacted. We're going to have new innovations, new expectations, tracking, understanding what's going on not just the weather. So thanks for doing that great work. Awesome, thank you. >> Great, thanks John. Good to see you. >> This is theCUBE's coverage of IBM Think, I'm John Furrier, the host of theCUBE. Thanks for watching. (upbeat music)

>> Announcer: From Miami Beach, Florida, it's The Cube, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone. We are here with The Cube coverage for two days. We're wrapping up, getting down on day one in the books for the Acronis Global Cyber Summit 2019. I'm John Furrier, your host of The Cube. We are in Miami Beach, the Fontainebleau Hotel. I'm personally excited for this next guest because I'm a huge Red Sox fan, even though I got moved out to California. Giants is in a different area. National League is different than American League, still my heart with the Red Sox. And we're here with an industry veteran, seasoned professional in IT and data, Brian Shield. Boston Red Sox Vice President of Technology and IT. Welcome to The Cube, thanks for joining us. >> Thank you. It's great to be here. >> John: So congratulations on the rings. Since I moved out of town, Red sox win their World Series, break the curse of the Bambino. >> Hey we appreciate that. Thank you. >> My family doesn't want me back. You got to show >> Yeah, maybe I'll put this one up for the, maybe someone can zoom in on this. Which camera is the good one? This one here? So, there ya go. So, World Series champs for at least for another week. (laughter) >> Bummer about this year. Pitching just couldn't get it done. But, good team. >> Happens. >> Again, things move on, but you know. New regime, new GM going to come on board. >> Yup. >> So, but in general, Red Sox, storied franchise. Love it there. Fenway Park, the cathedral of baseball parks. >> Brian: Defnitely. >> And you're seeing that just play out now, standard. So just a great place to go. We have tickets there. So, I got to ask you. Technology, sports, really is modernized faster than I think any category. And certainly cyber security forced to modernize because of the threats. But sports, you got a business to run, not just IT and making the planes run on time. >> Sure. >> Scouts, money, whatever. >> Fans. >> You got fan experience. >> Stadium opportunities. >> Club management, scouts are out there. So you got business, team, fans. And data's a big part of it. That's part of your career. Tell us what the cutting edge innovation is at the Red Sox these days. >> I think baseball in general, as you indicated, it's a very evolving kind of environment. I mean historically I think people really sort of relish the nostalgia of sports and Fenway Park being as historic as it is, was probably the pinnacle of that, in some respects. But Red Sox have always been leaders and baseball analytics, you know. And everyone's pretty familiar with "Moneyball" and Brad Pitt. >> John: Is that a true story, he turned down the GM job? >> I'm told it is. (laughter) I don't know if I fully vetted that question. But over the last six, seven years, you know we've really turned our attention to sort of leveraging sort of technology across the businesses, right? Not just baseball and analytics and how we do scouting, which continues to evolve at a very rapid pace. But also as you pointed out, running a better business, understanding our fans, understanding fan behavior, understanding stadiums. There's a lot of challenges around running an effective stadium. First and foremost to all of us is really ensuring it's a great fan experience. Whether it's artificial intelligence, or IoT technologies or 5G or the latest Wifi, all those things are coming up at Fenway Park. You and I talked earlier about we're about to break ground for a new theater, so a live theater on the outside, beyond the bleachers type of thing. So that'll be a 5,400-seat arena, 200 live performances a year, and with e-sports, you know, complementing it. It just gives you an example of just how fast baseball is sort of transitioning. >> And the theater, is that going to be blown out from where that parking garage is, structure and going towards >> So the corner of Landsdown and Ipswich, if you think of that sort of corner back there, for those that are familiar with the Fenway area. So it's going to be a very big change and you'll see the difference too from within the ballpark. I think we'll lose a couple of rows of the bleachers. That'll be replaced with another gathering area for fans and things like that, on the back end of that theater. So build a great experience and I think it really speaks to sort of our ability to think of Fenway as more of a destination, as a venue, as a complementary experience. We want people to come to the area to enjoy sports and to enjoy entertainment and things. >> You know Brian, the consumerization of IT has been kicked around. Last decade, that was a big buzzword. Now the blending of a physical event and digital has certainly consumed the world. >> Absolutely. >> And we're starting to see that dynamic. You speak to a theater. That's a physical space. But digital is also a big part of kind of that complementary. It's not mutually exclusive for each other. They're integrated business models. >> Absolutely. >> So therefore, the technology has to be seamless. The data has to be available. >> Yup. >> And it's got to be secure. >> Well the data's got to be ubiquitous, right? I mean you don't want to, if we're going to have fans attending theater and then you're going to go to Fenway Park or they leave a game and then go to some other event or they attend a tour of Fenway Park, and beyond maybe the traditional what people might think about, is certainly when you think about baseball and Fenway Park. You know we have ten to twelve concerts a year. We'll host Spartan games, you know. This Christmas, I'm sorry, Christmas 2020 we now have sort of the Fenway Bowl. So we'll be hosting the AAC ACC championship games there with ESPN. >> John: Hockey games? >> Hockey games. Obviously we have Liverpool soccer being held there so it's much more of a destination, a venue for us. How we leverage all the wonderful things about Fenway Park and how we modernize, how we get basically the best of what makes Fenway Park as great as it is, yet as modern as we can make it, where appropriate to create a great fan experience. >> It's a tough balance between balancing the brand and having things on brand as well. >> Sure. >> Does that come into your job a lot around IT? Saying being on brand, not kind of tearing down the old. >> Yeah absolutely. I think our CEOs and leadership team, I mean it's not success for us if you pan to the audience and everyone is looking at their phone, right? That's not what we aspire to. We aspire to leverage technology to simplify people's experience of how do you get to the ballpark, how do I park, how do I get if I want to buy concessions or merchandise, how do I do it easily and simply? How do we supplement that experience with maybe additional data that you may not have had before. Things like that, so we're doing a lot of different testing right now whether it's 4D technologies or how we can understand, watch a play from different dimensions or AI and be able to perhaps see sort of the skyline of Boston since 1912, when Fenway Park launched... And so we sort of see all these technologies as supplemental materials, really kind of making it a holistic experience for fans. >> In Las Vegas, they have a section of Las Vegas where they have all their test beds. 5G, they call it 5G, it's really, you know, evolution, fake 5G but it's a sandbox. One of the challenges that you guys have in Boston, I know from a constraint standpoint physically, you don't have a lot of space. How do you sandbox new technologies and what are some of the things that are cool that people might not know about that are being sandboxed? So, one, how do you do it? >> Yeah. >> Effectively. And then what are some of the cool things that you guys are looking at or things they might not know about that would be interesting. >> Sure. Yeah so Fenway Park, we struggle as you know, a little bit with our footprint. You know, honestly, I walk into some of these large stadiums and I get instant jealousy, relative to just the amount of space that people have to work with and things. But we have a great relationship with our partners so we really partner, I think, particularly well with key partners like Verizon and others. So we now have 5G partially implemented at Fenway Park. We expect to have it sort of fully live come opening day next year. So we're really excited about that. We hope to have a new version of Wifi, the latest version of Wifi available, for the second half of the year. After the All-Star Break, probably after the season's over. But before our bowl game hopefully. We're looking at some really interesting ways that we can tease that out. That bowl game, we're really trying to use that as an opportunity, the Fenway Bowl, as an opportunity to make it kind of a high-tech bowl. So we're looking at ways of maybe doing everything from hack-a-thons to a pre-egaming sort of event to some interesting fan experiential opportunities and things like that. >> Got a lot of nerds at MIT, Northeastern, BU, Bentley, Babson, all the schools in the area. >> Yeah, so we'll be reaching out to colleges and we'll be reaching out to our, the ACC and AACs as well, and see what we can do to kind of create sort of a really fun experience and capitalize on the evolving role of e-sports and the role that technology can play in the future. >> I want to get to the e-sports in a second but I want to just get the plug in for Acronis. We're here at their Global Cyber Summit. You flew down for it, giving some keynote speeches and talks around security. It's a security company, data protection, to cyber protection. It is a data problem, not a storage appliance problem. It's a data problem holistically. You get that. >> Sure. Sure. >> You've been in the business for a long time. What is the security kind of posture that you guys have? Obviously you want to protect the data, protect privacy. But you got to business. You have people that work with you, supply chain, complex but yet dynamic, always on environment. >> That's a great question. It's evolving as you indicated. Major League Baseball, first and foremost, does an outstanding job. So the last, probably last four plus years, Major League Baseball has had a cyber security program that all the clubs partake in. So all 30 clubs are active participants in the program. They basically help build out a suite of tools as well as the ability to kind of monitor, help participate in the monitoring, sort of a lot of our cyber security assets and logs And that's really elevated significantly our posture in terms of security. We supplement that quite a bit and a good example of that is like Acronis. Acronis, for us, represents the ability for us to be able to respond to certain potential threats like ransom-ware and other things. As well as frankly, what's wonderful about a tool like this is that it allows us to also solve other problems. Making our scouts more efficient. We've got these 125 scouts scattered around the globe. These guys are the lifeblood of our, you know, the success of our business. When they have a problem, if they're in Venezuela or the Dominican or someplace else, in southeast Asia, getting them up and running as quickly as we can, being able to consume their video assets and other things as they're scouting prospects. We use Acronis for those solutions. It's great to kind of have a partner who can both double down as a cyber partner as well as someone who helps drive a more efficient business. >> People bring their phone into the stadiums too so those are end points now connecting to your network. >> Definitely. And as you pointed out before, we've got great partnerships. We've got a great concession relationship with Aramark and they operate, in the future they'll be operating off our infrastructure. So we're in the point of rolling out all new point-of-sale terminals this off-season. We're excited about that 'cause we think for the first time it really allows us to build a very comprehensive, very secure environment for both ourselves and for all the touchpoints to fans. >> You have a very stellar career. I noticed you were at Scudder Investments back in the '80s, very cutting-edge firm. FTD that set the whole standard for connecting retailers. Again, huge scale play. Can see the data kind of coming out, they way you've been a CIO, CTO. The EVP CIO at The Weather Channel and the weather.com again, first mover, kind of pioneer. And then now the Red Sox, pioneering. So I got to ask you the modernization question. Red Sox certainly have been cutting-edge, certainly under the last few owners, and the previous Henry is a good one, doing more and more, Has the business model of baseball evolved, 'cause you guys a franchise. >> Sure. >> You operate under the franchisor, Major League Baseball, and you have jurisdictions. So has digital blurred the lines between what Advanced Media unit can do. You got communities developing outside. I watch the games in California. I'm not in there but I'm present digitally. >> Sure. Sure. >> So how has the business model flexed with the innovation of baseball? >> That's a great question. So I mean, first off, the relationship between clubs like ours and MLB continue to evolve. We have a new commissioner, relatively new commissioner, and I think the whole one-baseball model that he's been promoting I think has been great. The boundaries sometimes between digital assets and how we innovate and things like that continues to evolve. Major League Baseball and technology groups and product groups that support Major League Baseball have been a fantastic partner of ours. If you look at some of the innovations with Statcast and some of the other types of things that fans are now becoming more familiar with. And when they see how fast a runner goes or how far a home run goes and all those sort of things, these kinds of capabilities are on the surface, but even like mobile applications, to make it easy for fans to come into ballparks and things like that really. What we see is really are platforms for the future touchpoints to all of our customers. But you're right, it gets complicated. Streaming videos and people hadn't thought of before. >> Latin America, huge audience for the Red Sox. Got great players down there. That's outside the jurisdiction, I think, of the franchise agreement, isn't it? (laughs) >> Well, it's complicated. As this past summer, we played two games in England, right? So we enjoy two games in London, sadly we lost to the Yankees in both of those, but amazing experience and Major League Baseball really hats off to those guys, what they did to kind of pull that together. >> You mentioned Statcast. Every year when I meet with Andy Jassy at AWS, he's a sports fan. We love to talk sports. That's a huge, kind of shows the power of data and cloud computing. >> No doubt. >> How do you guys interface with Statcast? Is that an Amazon thing? Do they come to you? Are they leveraging dimensions, camera angles? How does that all work? Are you guys involved in that or? >> Brian: Oh yeah, yeah. >> Is that separate? >> So Statcast is just one of many data feeds as you can imagine. One of the things that Major League Baseball does is all that type of data is readily available to every club. So every club has access to the data. The real competitive differentiator, if you will, is how you use it internally. Like how your analysts can consume that data. We have a baseball system we call Beacon. We retired Carmine, if you're familiar with the old days of Carmine. So we retired Carmine a few years ago with Beacon. And Beacon for us represents sort of our opportunity to effectively collapse all this information into a decision-making environment that allows us to hopefully to kind of make the best decisions to win the most games. >> I love that you're answering all these questions. I really appreciate it. The one I really want to get into is obviously the fan experience. We talked about that. No talent on the field means no World Series so you got to always be constantly replenishing the talent pool, farm system, recruiting, scouting, all these things go on. They're instrumental. Data's a key driver. What new innovations that the casual fan or IT person might be interested in what's going on around scouting and understanding the asset of a human being? >> Right. Sure. I mean some of this gets highly confidential and things, but I think at a macro level, as you start to see both in the minor leagues and in some portions of the major leagues, wearable technologies. I think beyond just sort of player performance information that you would see traditionally with you might associate it with like Billy Beane, and things like that with "Moneyball" which is evolved obviously considerably since those days. I mean understanding sort of player wellness, understanding sort of how to get the most out of a player and understanding sort of, be able to kind of predict potential injuries and accelerate recoveries and being able to use all of this technology where appropriate to really kind of help sort of maximize the value of player performance. I mean, David Ortiz, you know, I don't know where we would have been in 2018 without, you know, David. >> John: Yeah. >> But like, you know >> Longevity of a player. >> Absolutely. >> To when they're in the zone. You wear a ring now to tell you if you're sleeping well. Will managers have a visual, in-the-zone, don't pull 'em out, he can go an extra inning? >> Well, I mean they have a lot of data. We currently don't provide all that data to the clubhouse. I mean, you know, and so If you're in the dugout, that information isn't always readily available type of thing. But players know all this information. We continue to evolve it. At the end of the day though, it's finding the balancing act between data and the aptitudes of our coaching staff and our managers to really make the wise decisions. >> Brian, final question for you. What's the coolest thing you're working on right now? Besides the fan having a great experience, 'cause that's you kind of touched on that. What's the coolest thing that you're excited about that you're working on from a tech perspective that you think is going to be game-changing or interesting? >> I think our cloud strategy coming up in the future. It's still a little bit early stage, but our hope would be to kind of have clarity about that in the next couple months. I think is going to be a game-changer for us. I think having, you know, we enjoy a great relationship with Dell EMC and yet we also do work in the cloud and so being able to leverage the best of both of those to be able to kind of create sort of a compelling experience for both fans, for both player, baseball operations as well as sort of running an efficient business, I think is really what we're all about. >> I mean you guys are the poster child for hybrid cloud because you got core, data center, IoT, and >> No doubt. So it's exciting times. And we're very fortunate that with our relationship organizations like Dell and EMC, we have leading-edge technologies. So we're excited about where that can go and kind of what that can mean. It'll be a big step. >> Okay two personal questions from me as a fan. One is there really a money-counting room like in the movie "The Town"? Where they count a big stack of dollar bills. >> Well, I'm sure there is. I personally haven't visited it. (laughs) I know it's not in the room that they would tell you it is on the movie. (laughter) >> And finally, can The Cube get press passes to cover the games, next to NESN? Talk tech. >> Yeah, we'll see what we can do. >> They can talk baseball. We can talk about bandwidth. Right now, it's the level five conductivity. We're looking good on the pipes. >> Yeah we'll give you a tech tour. And you guys can sort of help us articulate all that to the fans. >> Thank you so much. Brian Shield, Vice President of Technology of the Boston Red Sox. Here talking about security and also the complications and challenges but the mega-opportunities around what digital and fan experiences are with the physical product like baseball, encapsulates kind of the digital revolution that's happening. So keep covering it. Here in Miami, I'm John Furrier. We'll be right back after this short break. (techno music)

>> Voiceover: Live from San Francisco, it's The Cube. Covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Okay, welcome back, everyone. Live here in San Francisco, this is The Cube's exclusive coverage of Samsung Developer Conference, SDC 2017. I'm John Furrier, co-founder of SiliconANGLE Media, and co-host of The Cube. My next guest is Dominic Venuto, who is the General Manager of the consumer division of The Weather Channel, and Watson Advertising, which is part of The Weather Company. Welcome to The Cube. >> Thank you for having me. >> Finally, I got the consumer guy on. I've interviewed The Weather Company folks from the IBM side, two different brands. One's the data, big data science operation going on, the whole Weather Company. But Weather Channel, the consumer stuff, Weather Underground, that's your product. >> Yes, you saved the best for last. We touch the consumer. >> So, weather content is good. So obviously, the hurricanes have been in the news over the years. Out here in California, the fires. People are interested in whether the impact, it used to be a unique thing on cable, go to the Weather Channel, check the forecast, read the paper. Now with online apps, weather is constantly a utility for users. So it's not a long-tail editorial product. It's pretty fundamental. >> Yeah, we want to be where our consumers are. Fundamentally we want to help people make better decisions and propel the world. And since weather touches everything, we need to be where the consumers are. So now, with all the digital touchpoints, whether that's your phone, or its a watch, your television, desktop if you still have one and you're still using it, as some of us do. We want to be there, for that very reason. And in fact, what we're aiming for, is to move from a utility, because if we are going to help people make better decisions, a utility only goes so far, would be a platform to anticipate behavior and drive decisions. >> So tell me about the Weather Underground and the weather.com consumer product. They're all one in the same now? Obviously one was very successful, with user generated content. This is not going away. Explain the product side of The Weather Channel consumer division. >> Yeah, so we have two brands in our portfolio, Weather Underground, which is more of a challenger brand. It's very data rich, and visualizes data in a number of different ways, that a certain user group really loves. So if you're a weather geek, as we call them, an avid aficionado of weather, and you really want to really get in there and understand what's happening, and look at the data, then Weather Underground is a platform. >> So for users to tie into, to put up weather stations, and other things that might be relevant. >> Exactly so, we started out in 2001, originally the first IOT implementation at the consumer level, connected devices. Where you could connect a personal weather station, put one in your back yard, and connect it to our platform, and feed hyper-local data into our network. And then we feed that into our forecast, to improve that, and actually validate whether the forecast is right or not, based on what people have at home. And we've hit a recent milestone. We've got over 250,000 personal weather stations connected to the network, which we are super thrilled about. And now, what we are doing is, we are extending that network to other connected devices, and air quality is a big topic right now, in other parts of the world, especially in Asia, where air quality is not always where it should be, that's a big thing we think we can... >> That's a big innovation opportunity for you, I mean, you point out the underground product was part of maker-culture, people do-it-yourself weather stations, evolve now into really strong products. That same dynamic could be used for air control, not just micro-climates. >> Exactly, yeah. >> In California, we had a problem this week. >> Exactly, California is a good example, really topical, where cities may have had great air quality, and all of the sudden the environment changes, and you want to know, what is it like? What is the breathing quality like outside right now? And you can come to our network and see that. And we're growing the air quality sensors every month, it's only been up a few months right now, so that's expanding quite well. >> So for the folks that don't know, The Weather Channel back end, has a huge data-driven product. I don't want to get into that piece, because we've talked about it. Go to youtube.com/siliconangle, search Weather Company. You'll see all our great videos from the IBM events, that are out, if you want the detail. But I do want to ask you, what's really happening with you guys, there's two things. One is, it's an app and content for devices, like Samsung is using. And two, essentially you're an IOT network. Sensors are sensors, whether they're user-generated, or user-populated, you guys are deploying a serious IOT capability. >> Absolutely, it's one of the reasons that IBM acquired The Weather Company, which houses the brands of Weather Underground and The Weather Channel, is that we have this fantastic infrastructure, this IOT infrastructure, ingesting large amounts of data, processing it, and then serving it back out to consumers at scale globally. >> What are you guys doing there with Samsung? Anything just particular in the IOT side, or? >> We've got a couple of initiatives going on with Samsung, a few I can't mention right now, but stay tuned. Some really cool things in the connect-at-home, that we're excited about, that builds on some of the work... >> Nest competitor? >> Not exactly a Nest competitor. Think more kitchen. >> Kitchen, okay. >> Think more kitchen. >> We had the goods, cooking in the kitchen, from our previous guest. So the question is, IOT personal, I get that. What else is going on with IOT, with you guys, that you can share? Lifestyle, in the home is great, but... >> So again, going back to how do we help people make better decisions, now that we are collecting data from not just personal weather stations, but air quality monitors, we are collecting it from cars, we are collecting it from the cell phone. We are really able to ingest data at scale, and when you're doing that, we've got hundreds of thousands of data sets that we are feeding into our models, when you do that, we've solved the computing challenge, now we are applying machine-learning and artificial intelligence to process this and extract insights. To validate data sets, in our forecast, and then deliver that back to the end user. >> One of the tech geek themes we talk about all of the time is policy-based something. Programming, setting the policy. So, connecting the dots from what you're saying is, I'm driving my car, and I want to know if it's hot, or the road temperature. I might want to know if I'm running too fast, and my sensor device on me wants to impact the weather, for comfortable breathing for me, for instance. The lifestyle impacts, the content of data, is not just watching a video on The Weather Channel. >> No, it's not. >> So this is a new user experience. It's immersive, it's lifestyle-oriented, it's relevant. What are some of the products you're doing with Samsung, that can enable this new user expectation? >> One of the products that we have right now, we we're one of the initial partners for the Made for Samsung program, is, we've got calendar integration in our app. So now we know, if you've got a meeting coming up, and you need to travel to get there, maybe there's a car trip involved, we know, obviously, the forecast. We know what traffic might be, and we can give you heads up, an alert, that says, hey you might want to leave 15 minutes early for that meeting coming up. That's in the Samsung product right now, which is really, again, helping people make better decisions. So we've got a lot of examples like that. But again, the calendar integration in the Made for Samsung app is really exciting. We recently announced, in fact I think it was this morning, we announced integration with Trip Advisor. So similarly, if we see time on your calendar, and the weather is fine for the weekend, we might suggest outdoor activities for you to go and explore, using Trip Advisor's almost one-billion library of events that they have. >> What's the coolest thing you guys are working on right now? >> Oh, that's a very long list. I say that I'm probably the luckiest guy in IBM right now, because I get to work with millions of consumers, we reach 250 million consumers a month, and I'm also bringing Watson to consumers, and artificial intelligence, which is a unique challenge to solve. Introducing consumers to a new paradigm of user interaction and abilities. So, I think the most exciting thing is taking artificial intelligence and machine-learning, and bringing that to consumers at scale, and solving some of the challenges there. >> Well contratulations. I'm a big fan of IBM, what they're doing with weather data, The Weather Company, The Weather Channel. Bringing that data and immersing it into these new networks that are being created, new capabilities, really helps the consumer, so. Hope to see you at the Think conference coming up next year. >> Yes, we are excited about that, and stay tuned, we may have some more exciting stuff to unveil. >> Make sure our writers get ahold of it, break the stories. It's The Cube, bringing you the data. The weather's fine in San Francisco today. I'm John Farrier with The Cube. More live from San Francisco, from the SDC Samsung Developer Conference, after this short break. (electronic music)

>> Announcer: Live from Boston Massachusets it's The Cube covering Red Hat Summit 2017. Brought to you by Red Hat. >> Welcome back to The Cubes coverage of the Red Hat Summit, I'm your host Rebecca Knight, along with my cohost Stu Miniman. We are joined by Jim Wasko, he is the vice president of Open Systems at IBM. Thanks so much for joining us. >> Thanks for having me. >> So, before we get into the new ways in which IBM and Red Hat are working together, give us a little history on the IBM, Red Hat alliance and contextualize things for us. >> Oh sure, sure, so we started with Linux back in the very late '90's as a strategic initiative for IBM, and so Red Hat was one of the key players at that time. We worked with other Linux vendors who no longer exist. Linux Care was one of the companies we worked with, Mandrake, things along those lines. But Red Hat has been a constant through all of that. So we started in the very early days with Red Hat and we had an X86 line at the time, and then as well as Power NZ, and even in the very early days, we had ports of Red Hat running on IBM, all of IBM's hardware. >> And the alliance is going strong today? >> Yes it is, yes it is. So we have that long history and then as Red Hat transformed as a company into their enterprise software and REL in particular, that really matured, as far as our relationship was concerned, and I'm the engineering VP with Red Hat, and we just had a very strong collaborative relationship. We know how to work upstream, they obviously work very well upstream. We've worked in the Fedora Project, as a staging area for our platforms and so, yeah, we've known each other very well. I've been working on Linux at IBM since November of 2000. >> Jim, so IBM, long history with Open Source, I remember when it was the billion dollars invested in Linux. We covered on The Cube when Power became Open Power. Companies like Google endorsing Open Power. Bring us up to speed as to Open Power, how that fits with what you're doing with Red Hat and what you're talking about on the show here. >> Oh yeah, so Open Power was really about opening up hardware architecture as well as the operating system and firmware. And so, as that's progressed Red Hat has also joined in that Open Power initiative. If you look at when we started, just a small group of companies kicked it off, and today we're over 300 companies, including Red Hat as a part of Open Power foundation. They're also board members, so as a key partner in strategic partner of ours, they've recognized that it's an ecosystem that is worth participating in, because it's very disruptive, and they've been very quick to join us. >> That's good, we've talked to Jim Lighthurst about how they choose and they look for communities that are going to do good things for the industry, for the world, for the users, so, it's a nice endorsement to have Red Hat participate, I would think. >> Oh, it is, they don't enter into anything lightly. And so, their participation really is a signal, I think, in the marketplace, that this is a good strategic initiative for the industry. >> Where do you see as the biggest opportunities for growth, going forward. >> Opportunities for growth, there's quite a few. A lot of people don't realize that Linux is really the underlying engine for so many things that we do in the technology world. It's everything from embedded into the automotive industry, if you've got Onboard computer, which most new cars do, 80% of those are Linux. If you talked about web serving, websites, front ends, it's Linux, you know. I know with my mom, she's like "What do you work on?" and I say Linux you know, and she's like "Is that like Windows?" and I'm like "No." And then I tell her, you know Mom you've used it, probably a dozen times today, and then I give her examples. And so, all the new innovation tends to happen on Linux. If we look at HyperLedger, and Blockchain in particular, good example, that's one that takes a lot of collaboration, a lot of coordination if it's going to have a meaningful impact on the world. And so, it starts with Linux as foundation to it. So, any of those new technologies, if you look at what we're doing with quantum computing for example, it takes a traditional computer to feed it, and a tradition computer for the output, and we don't have time to go into details behind that but, Linux fed, as a part of it, because really that's where the innovation is taking place. >> Jim, could you expand a little bit more on the Hyperledger and Blockchain piece? A lot of people still, I think they understand BitCoin and digital currency there, but it's really some of the distributed and open source capabilities that these technologies deliver to the market, have some interest and use cases, what's the update on that? >> Oh that's a good question. So, a lot of people think of BitCoin and that says a very limited use case. As we look at Hyperledger, we notice that it could be applied in so many more ways than just a financial kind of way. Where we've done, it is logistics, and supply chain, we've implemented it at IBM for our supply chain and we've taken data from Weather.com, company that we've acquired, and we use that for our logistics for end of quarter for example. So that's something that was easier for us to implement, because it's all within our company. But then we are expanding that through partners. So that's an example where you could do supply chain logistics, you could do financials. But really, in order for that to work 'cause it's a distributed ledger, you need everybody in the ecosystem to participate. It can't be one company, can't be two companies. And so, that's why very early on we recognized we should jointly start up a project that the Linux Foundation, called Hyperledger, to look at what's the best and how could we all collaborate because we're all going to benefit from it, and it will be transformative. >> So what are you doing there, because as you said, these do present big challenges because there has to buy in from everyone? >> Yeah so if I look at the Hyperledger project specifically at the Linux Foundation, we've got customers of ours like JPMC for example, founding member and participant, we've got a distribution partners, we've got technology partners all there and so we contributed early code. Stuff we'd done in research, as kind of like a building block. And then we have members, both from research and product development side of the house, that are constantly working in that upstream community on the source code. >> And continually contributing, and okay... >> Yeah, well continually contributing, that's on the technology side. On the business side we're doing early proof of concepts, so we worked early with a company called Everledger that looks at the history of diamonds and tracks them beginning to end, and the ultimate goal of that is to eliminate blood diamonds from the marketplace and so if you know, it's also a very good market to begin because it's a limited set of players. So you can implement the technology, you can do the business processes behind it and then demonstrate the value. So that's an early project. Most of the financial institutions are doing stuff, whether it's stock trading or what have you. And so we're doing early proof of concept, so we're taking both technology and business, you marry 'em together as Jim Whitehurst said the other day you know, what's the minimal viable product, lets get that out there, lets try it out, lets learn. >> Release early release often. >> Yes, and then modify quickly, don't start with something you think is overly baked, and find that you have to shelf it in order to kind of back track and make corrections. >> And what is like to mesh those two cultures, the technology and the business? I mean, do you find that there is a clash? >> We have not. Now at IBM it was not a simple transition back in the late '90's. There were people that thought Open Source would be just a flash in the pan, and here we are so many years later, that's not true. And so early on, like I said, there were a lot of internal kind of debates, but that debate is long since settled, so we don't have that. And if you look across our different business divisions, even within our company, whether its Cloud, whether it's Cognitive, whether it's systems business, all use Open Source. Whether we contribute everything externally and we're using third party packaged, or we consume it ourselves. And we see that as happening across industry, even with out clients. Some that you might think are very traditional, they recognize that's where the innovation is taking place. And so, you always look at balancing is this viable, is that healthy? Or is still the commercially available stuff the better stuff? Just a quick story, I had a development team and we were doing Agile and we needed a tool to do to track our sprints and everything like that, and so, all of my developers were Open Source developers, and so that's their bias. If we're going to use software, it has to be Open Source, they went and evaluated a couple projects and they found Open Source software that had been abandoned, they were smart enough to recognize we also acquired a company called Rational, and Rational Team Concert does this, but it's proprietary. And so they initially resisted it, but then they looked at these Open Source project and saw, if we picked up that code, we maintain it forever, and we're alone. That is as worthless, as it can be, because there's no benefit. Doing Open Source, where you have multiple people contributing, you give an added benefit. So they went with our in house stuff, Rational Team Concert. Just showed the maturity of the team that even though they think Open Source is really the best thing in life, you've got to balance the business with it. >> Jim, so we look at the adoption of Open Source, it took many years to mature. Today, you talk about things like Cognitive, it's racing so fast, give us a little bit of look forward, you know, what's changing your space? What are you looking forward to? What would we expect to see from you by the time we come back next year? >> Sure, so a lot of what you've heard here at the conference so a lot of things that we're doing, are often offered in a Cloud platform, or as a hosted service, or as a service. So, for example, we do have Blockchain as a service available today. And it's running the back end is on mainframe cloud, for example, running Linux. Other examples of that, looking at new applications for quantum computing. Well that requires severengic freezing in order to keep those cubits alive. And so that's a hosted thing, and we actually have that available online, people can use that today. So I think that you're going to see a lot of early access, even for commercial applications. Early access so people can try it, and then based on their business model, like we've heard from clients this week, sometimes they'll need it on prem, and for various business reasons, and other times they can do it on the cloud and we'll be able to provide that. But we give them early access via cloud and as a service. And I think that's what you're going to see a lot in the industry. >> And it's this hybrid mix, as you said, some on prem, some off prem, okay. >> Jim: Yes. >> Well Jim, thanks so much for joining us, we really appreciate you sitting down with us. >> You're welcome, and thanks for your time. >> I'm Rebecca Knight, for Stu Miniman, we'll have more from the Red Hat Summit after this. (upbeat electronic music)