>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental 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)