>> From the SiliconANGLE Media office in Boston, Massachusetts, it's "theCUBE." (intro music) Now, here's your host, Dave Vellante. >> Hello, everyone and welcome to this week's episode of Wikibon's CUBE Insights, Powered by ETR. In this Breaking Analysis, we're going to take a break from our traditional spending assessment and share with you our advice on how to deal with this crisis, specifically shifting your physical to digital in the age of Coronavirus. So, we're not going to be digging into the spending data. I talked to ETR this week, and they are obviously surveying on the impact of COVID-19, but those results won't be ready for a little bit. So, theCUBE team has been in discussions with over 20 companies that have events planned in the near term and the inbound call volume has been increasing very rapidly. Now, we've been doing digital for a decade, and we have a lot of experience, and are really excited to share our learnings, tools, and best practices with you as you try to plan through this crisis. So look, this is uncharted territory. We haven't ever seen a country quarantine 35 million people before, so of course everyone is panicked by this uncertainty but our message, like others, is don't panic but don't be complacent. You have to act and you have to make decisions. This will reduce uncertainty for your stakeholders, your employees, and of course, your community. Now as you well know, major physical events are dropping very fast as a risk mitigation measure. Mobile World Congress, HIMSS canceled, Kube-Con was postponed, IMB Think has gone digital, and so it goes. Look, if you have an event in the next three weeks, you have little choice but to cancel the physical attendee portion of that event. You really have three choices here. One is to cancel the event completely and wait until next year. Now the problem with that is, that type of capitulation doesn't really preserve any of the value related to why you were originally holding the physical event in the first place. Now you can do what Kube-Con did and postpone til the summer or kind of indefinitely. Okay, that's a near-term recision on the event, but now you're in limbo. But if you can sort out a venue down the road, that might work. The third option is to pivot to digital. It requires more thought but what it does is allow you to create an ongoing content ark that has benefits. The number-one complaint brands tell us about physical events is that after the event, they don't create a post-event halo effect. A digital strategy that expands time will enable that. This is important because when the market calms down, you're going to be able to better-leverage digital for your physical events. The key question you want to ask is, what are the most important aspects of that physical event that you want to preserve? And then start thinking about building a digital twin of those areas. But it's much more than that. And I'll address this opportunity that we think is unfolding for you a little later. Your challenge right now is to act decisively and turn lemons into lemonade with digital. Experiences are built around content, community, and the interaction of people. This is our philosophy. It's a virtuous cycle where data and machine intelligence are going to drive insights, discovery by users is going to bring navigation which leads to engagement and ultimately outcomes. Now, very importantly, this is not about which event software package to use. Do not start there. Start with the outcome that you want to achieve and work backwards. Identify the parts of that outcome that are achievable and then work from there. The technology decision will be easy and fall out of it if you take that path. So out of a high-level, you have two paths. One, which is the preferred path is to pivot to digital, on the right-hand side, especially if your event is in March or early April. Two is hold your physical event, but your general counsel is going to be all over you about the risks and precautions that you need to take. There are others better than I to advise you on those precautions. I've listed some here on the left-hand side and I'm going to publish this on Wikibon, but you know what to do there. But we are suggesting advising for the near-term events that you optimize for digital. That's the right side. Send out a crisp and clear communications, Adobe has a good example, that asks your loyal community to opt-in for updates and start the planning process. You want to identify the key objectives of your event and build a digital program that maximizes the value for your attendees and the maps to those objectives. We're going to share some examples that theCUBE participated in this week on what might look like the digital event, and we'll share that with you. Event software should come last. Don't even worry about that until you've envisioned your outcome. And I'll talk about software tools a little bit later. So new thinking is required, we believe. The old way was a big venue, big bang event, you get thousands of people. You're spending tons of money on a band. There's exhibitor halls. You're not going to preserve that, obviously. Rather, think about resetting the physical and optimizing for digital which really is about serving a community. Now let's talk about, again, what that might look like in the near-term and then we're going to close on how we see this evolving to a new era. The pattern emerging with our sponsors and our clients is, they want to preserve five key content areas from physical. Not necessarily all of them but in some combination. First is the keynotes. You bring together a captive audience, and you have your customers there, they want to hear from executives. Your customers have made a bet on you, and they want to feel good about it. So one is keynotes. Two is the breakout sessions, the deeper dives from subject matter experts. Third are technical sessions. A big reason customers attend these events is to get technical training. Four is to actually share news in a press conference-like format. And the fifth area that we've seen is, of course, theCUBE. Many of our customers have said, "We not only want you to turn to turnkey the digital event, we want to plug theCUBE into our digital production that we are running." Now these are not in stone, they're just examples of what some of the customers are doing, and they're blending keynotes into their press conference, and there's a lot of different news cases. I want to stress that, initially, everyone's mindset is to simply replicate physical to digital. It's fine to start there, but there's more to this story that we'll address later on. So let's have a look at what something like this might look like in the near-term. Here's an example of a digital event we did this week with a company called "Aviatrix." Small company but very nice look for their brand which is a priority for them. You can see the live audience vibe. This was live but it can be pre-recorded. All the speakers were together in one place. You can see the very high production value. Now, some of our clients have said, "Look, soon we want to do this completely remote with 100 percent of the speakers distributed." And our feeling is that's much more challenging for high-value events. Our strong recommendation is plan to get the speakers into a physical venue. And ideally, get a small VIP/influencer audience to be there. Make the audience feel important with a vibe of a VIP event. Yeah, you can wait a few weeks to see how this thing shakes out, and if travel loosens up, then you can pull this off. But for your Brand value, you really want to look as professional as possible. Same thing for keynotes. You can see how good this looks. Nice stage, lighting, the blue lights, and a live audience. This is a higher-end production with a venue, and food, and music for the intros and outros, very professional audio and visual. And this requires budget. You got to think about at least 200 to 300 thousand dollars and up for a full-blown event that you bring in influencers and the like. But you have options. You can scale it down. You can host the event at your facility. Host it off at our facility in Palo Alto. I'll talk about that a little later. Use your own people for the studio audience. Use your own production people and dial back the glam, which will lower the cost. Just depends on the brand that you want to convey, and of course, your budget. Now as well, you can run the event as a live or as a semi-live. You can pre-record some of all of the segments. You can have a portion, like the press conference and/or the keynotes, run live and then insert the breakouts into the stream as a semi-live, or as on-demand assets. You have options. Now before I talk about technical sessions, I want to share another best practice. theCUBE this week participated in a digital event at Stanford with the Women in Data Science organization, WiDS, and we plugged into their digital platform. WiDS is amazing. They created a hybrid physical/digital event, and again, had a small group of VIPs and speakers onsite at Stanford with keynotes and panels and breakouts, and then theCUBE interviews all were streaming. What was really cool is they connected to dozens and dozens of outposts around the globe, and these outposts hosted intimate meet-ups and participated in the live event. And, of course, all the content is hosted on-demand for a post-event halo effect. I want to talk a little bit about technical sessions. Where as with press conferences and keynotes, we're strongly recommending a higher scale and stronger brand production. With technical sessions, we see a different approach working. Technical people are fine with you earbuds and laptop speakers. Here's an example of a technical talk that Dan Hushon, who is the Senior VP and CTO at DXC, has run for years using the CrowdChat platform. He used the free community edition, along with Google Handouts, and has run dozens and dozens of these tech talks designed for learning and collaboration. Look, you can run these weekly as part of the pre-game, up to your digital event. You can run them day of the event, at the crescendo, and you can continue the cadence post-event for that halo effect that I've been talking about. Now let's spend the moment talking about software tooling. There are a lot of tools out there. Some, super functional. Some are monolithic and bloated. Some are just emerging. And you might have some of these, either licensed or you might be wed to one. Webinar software, like ON24 and Brightcove, and there's other platforms, that's great, awesome. From our standpoint, we plug right into any platform and are really agnostic to that. But the key is not to allow your software to dictate the outcome of your digital event. Technology should serve the outcome, not the reverse. Let me share with you theCUBE's approach to software. Now first thing I want to tell you is our software is free. We have a community editions that are very robust, they're not neutered. And we're making these available to our community. We've taken a CloudNative horizontally scalable angle bringing to bear the right tools for the right job. We don't think of software just to hold content. Rather, we think about members of the community and our goal is to allow teams to form and be successful. We see digital events creating new or evolving roles in organizations where the event may end, but the social organization and community aspect lives on. Think of theCUBE as providing a membrane to the conference team and a template for organizing and executing on digital events. Whether it's engaging in CrowdChats, curating video, telling stories post-event, hosting content, amplifying content, visualize your community as a whole and serve them. That's really the goal. Presence here is critical in a digital event, "Oh hey, I see you're here. "Great, let's talk." There are a number of news cases, and I encourage you to call us, contact us, and we'll focus on how to keep it simple. We have a really simple MVP use case that we're happy to share with you. All right, I got to wrap. The key point here is we see a permanent change. This is not a prediction about Coronavirus. Rather, we see a transformation created with new dynamics. Digital is about groups which are essentially a proxy for communities. Successful online communities require new thinking and we see new roles emerging. Think about the protocol stack for an event today and how that's going to change. Today is very structured. You have a captive audience, you got a big physical venue. In the future, it may evolve to multiple venues and many runs of shows. Remote pods rules around who is speaking. Self-forming schedules is not going to be the same as today. We think digital moves to a persistent commitment by the community where the group collectively catalyzes collaboration. Hosting an online event is cool, but a longterm digital strategy doesn't just move physical to digital. Rather, it reimagines events as an organic entity, not a mechanism or a piece of software. This is not about hosting content. Digital communities have an emotional impact that must be reflected through your brand. Now our mission at theCUBE has always been to serve communities with great content. And it's evolving to provide the tools, infrastructure, and data for communities, to both self-govern and succeed. Even though these times are uncertain and very difficult, we are really excited to serve you. We'll make the time to consult with you and are really thrilled to share what we've learned in the last 10 years and collaborate with you to create great outcomes for audiences. Okay, that's a wrap. As always, we really appreciate the comments that we get on our LinkedIn posts, and on Twitter, I'm @DVellante, so thanks for that. And thank you for watching, everyone. This is Dave Vellante for theCUBE Insights, Powered by ETR. And we'll see you next time. (outro music)

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

>> Announcer: Live from San Fransisco It's theCUBE Covering PURE Accelerate 2017. Brought to you by PURESTORAGE. >> Welcome back to PURE Accelerate. This is theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host Stu Miniman. This is PURE Accelerate. We're here at Pier 70. Brian McDaniel is here he's an infrastructure architect at the Baylor College of Medicine, not to be confused with Baylor University in Waco Texas, anymore. Brian Welcome to theCUBE. >> Thanks for having me appreciate it. >> You're very welcome. Tell us about the Baylor College of Medicine. >> So, Baylor College of Medicine is a, first and foremost, a teaching facility but also the leader in research and development for healthcare in the Texas Medical Center in Houston Texas. We currently employ roughly 1,500 physicians and so they occupy a multitude of institutions, not only at Baylor but other facilities and hospitals in and around the Texas Medical Center. >> So, it's kind of' healthcare morning here Stu. We've been talking about electronic medical records, meaningful use, the Affordable Care Act, potential changes there, HIPAA, saving lives. These are big issues. >> We're not at the HIMSS Conference Dave? >> We should be at HIMMS. So these are big issues for any organization in healthcare. It's just exacerbates the challenges on IT. So, I wonder if you can talk about some of the drivers in your business, compliance, and in new tech and maybe share with us some of the things that you're seeing. >> Absolutely so first and foremost, we are an Epic system shop. That's our EMR. So, from a enterprise and clinical operation, that is our number one mission critical application. It provides your electronic medical records to our staff, regardless of where they're physically located at. So that alone is a demanding type of solution if you will, the mobility aspect of it. Delivering that in a fast manner and a repeatable manner is upmost important to our physicians because they're actually seeing patients and getting to your records and being able to add notes and collaborate with other institutions if necessary. So, time to market is very important and accessibility is also up there. >> Right so, you mentioned that collaboration and part of that collaboration is so much data now, being able to harness that data and share it. Data explodes everywhere but in healthcare, there's so much data to the extent we start instrumenting things. What are you guys doing with all that data? >> Right now, it lives within the clinical application, right in Epic, but as you pointed out that is where the value is. that is where your crown jewels so to speak are at. That data is now being looked at as a possible access point outside of the clinical operation. So, it's environment is going to be even more important going forward, when you look to branch out into some of the basic sides in more of a research, to gain access to that clinical data. That historically has been problematic for the research to be done accessing that information. >> So, in the corporate we like to think of, from an IT perspective, you got to run the business, you got to grow the business, you got to transform the business. It's a little different in healthcare. You kind of got to comply. A lot of your time is spent on compliance and regulation changes and keeping up with that. And then there's got to be a fair amount that's at least attempting to do transformation and in kind of keeping up with the innovations. Maybe you could talk about that a little bit. >> Absolutely, particularly on the innovation side, we work closely with out partners at Epic and we work to decide roadmaps and how that fits into the Baylor world. Case in point, a year ago we were set to go to the new version of Epic, which was 2015. And Epic is nice enough to lay out requirements for you and say, here's what your system needs to meet in order to comply with Epic standards. So, they give you a seal of approval, so to speak. And there's monetary implications for not meeting those requirements. So it's actually dollars and cents. It's not just , we want you to meet this. If you do then there's advantages to meeting it. So, they provided that to us and went though the normal testing phases and evaluations of our current platform, both from compute and storage. And honestly we struggled to meet their requirements with our legacy systems. So the team was challenged to say well, what can we do to meet this? We have our historical infrastructures, so if we're going to deviate from that, let's really deviate and look at what's available to the market. So, Flash comes to mind immediately. So, there's a multitude of vendors that make Flash storage products. So we started meeting with all of 'em, doing our fact finding and our data gathering, meeting with all of 'em. First and foremost, they have to be Epic certified. That eliminated a couple of contenders right off the bat. Right? You're not certified. >> I would expect some of the startups especially. >> It did. Some of the smaller, Flash vendors, for example, one of 'em came in and we said, well, what do you do with Epic? And they said what's Epic. And you kind of scratch your head and say thank you. >> Thank you for playing. >> Here's the door. So, it eliminates people but then when we meet with PURE, and we talked to them and we meet 'em and you get to really know that the family and the culture that they bring with the technology. Yes it's got to be fast but Flash is going to be fast. What else can you do? And that's where you start learning about how it was born on Flash, how it was native to Flash and so you get added benefits to the infrastructure by looking at that type of technology, which ultimately led us there, where we're at running Epic on our Flash arrays. >> And Brian, you're using the Flash stack configuration of converge infrastructure. It sounds like it was PURE that lead you that way as opposed to Cisco? Could you maybe walk us through that? >> That's very interesting, so we're a UCS shop. We were before PURE. So when PURE came in, the fact that they had a validated design with the Flash stack infrastructure, made it all that more easier to implement the PURE solution because it just is modular enough to fit in with our current infrastructure. That made it very appealing that we didn't have to change or alter much. We just looked at the validated design that says, here's your reference architecture, how it applies to the Flash stack. You already have UCS. We love it, we're a big fan. And here's how to implement it. And it made the time to market, to get production work loads on it, very quick. >> And the CVD that you got from Cisco, that's Cisco plus PURE but was it healthcare Epic specific or was that the PURE had some knowledge for that that they pulled in? >> So, that was one of the value adds that we feel PURE brought was the Epic experience. And whether that's scripting, the backups, and if you're familiar with Epic, the environmental refreshes that they have to do. There's seven Epic environments. And they all have to refresh off of each other and play off of each other so, >> So you have a window that you have to hit right. >> And you do right? And historically that window's been quite large. And now, not so much which makes everybody happy. >> Hey, that's what weekends are for. >> Absolutely, yeah, our DBAs attest to that right? So, we would like to think we've made their world and life a little bit more enjoyable 'cause those weekends now, they're not having to babysit the Epic refreshes. Back to the point of Epic experience, that was instrumental in the decision makings from a support with the PURESTORAGE help desk, awareness of what it takes to run Epic on PURE, and then going forward knowing that there's a partnership behind Epic and PURE and certainly Baylor College of Medicine as we continue to look at the next versions of Epic, whether that's 2018 and on to 2020, whatever that decision is, we know that we have a solid foundation now to grow. >> And Brian I'm curious, you've been a Cisco shop for a while, Cisco has lots of partnerships as well as, they've got a hyper-converged offering that they sell themselves. What was your experience working with Cisco and do they just let you choose and you said, I want PURE and they're like, great? Do you know? What was that like? >> To your point, there's validated designs for many customers and Cisco is kind of at the hub of that, that core with the compute and memory of the blade systems, the UCS. They liked the fact that we went with PURE 'cause it does me a validated design. And they have others with other vendors. The challenge there is how do they really integrate with each other from tools to possibly automation down the road, and how do they truly integrate with each other. 'Cause we did bring in some of the other validated design architecture organizations and I think we did our due diligence and looked at 'em to see how they differentiate between each other. And ultimately, we wanted something that was new and different approach to storage. It wasn't just layering your legacy OS on a bunch of Flash drives and call it good. Something that was natively born to take advantage of that technology. And that's what ultimately led us to PURE. >> Well, PURE has a point of view on the so called hyper-converged space. You heard Scott Dietzen talking this morning. What's your perspective on hyper-convergence? >> Hyper-converge is one of those buzz words that I think gets thrown out of there kind of off the cuff if you will. But people hear it and get excited about it. But what type of workloads are you looking to take advantage of it? Is it truly hyper-converged or is it just something that you can say you're doing because it sounds cool? I think to some degree, people are led astray on the buzzwords of the technology where they get down to say, what's going to take advantage of it? What kind of application are you putting on it? If your application, in our case, can be written by a grad student 20 years ago that a lab is still using, it does it make sense to put it on hyper-converged? No, because it can't take advantage of the architecture or the design. So, in a lot of ways, we're waiting and seeing. And the reason we didn't go to a hyper-converged platform is a, Epic support and b, we were already changing enough to stay comfortable with the environment and knowing that come Monday morning, doctors will be seeing patients and we're already changing enough, that was another layer that we chose not to change. We went with a standard UCS configuration that everyone was already happy with. That made a significant difference from an operational perspective. >> Essentially, your processes are tightly tied to Epic and the workflow associated with that. So from an infrastructure perspective, it sounds like you just don't want it to be in the way. >> We don't. The last thing we want in infrastructure getting in the way. And quite frankly, it was in the way. Whether that was meeting latency requirements or IOPS requirements from the Cache database or the Clarity database within the Epic system, or if was just all of are just taking a little bit longer than they expect. We don't want to be that bottleneck, if you will, we want them to be able to see patients faster, run reports faster, gain access to that valuable data in a much faster way to enable them to go about their business and not have to worry about infrastructure. >> Brian, PURE said that they had, I believe it's like 25 new announcements made this morning, a lot of software features. Curious, is there anything that jumped out at you, that you've been waiting for and anything still on your to do list that you're hoping for PURE or PURE and it's extended ecosystem to deliver for you? >> Great question, so at the top of that list is the replication of the arrays, whether that's in an offsite data center or a colo and how that applies to an Epic environment that has to go through this flux of refreshes, and from a disaster or business continuity standpoint, we're actively pursuing that, and how that's going to fit with Baylor. So, we're very excited to see what our current investment, free of charge by the way, once you do the upgrade to 5.0, is to take advantage of those features, with replication being one of 'em. >> And then, I thought I heard today, Third Sight is a service. Right? So you don't have to install your own infrastructure. So, I'm not sure exactly what that's all about. I got to peel the onion on that one. >> To be determined right? When we look at things like that, particularly with Epic, we have to be careful because that is the HIPAA, PHI, that's your records, yours and mine, medical records right? You just don't want that, if I told you it's going to be hosted in a public cloud. Wait a minute. Where? No it's not. We don't want to be on the 10 o'clock news right? However, there's things like SAP HANA and other enterprise applications that we certainly could look at leveraging that technology. >> Excellent, we listen, thank you very much Brian for coming on theCUBE. We appreciate your perspectives and sort of educating us a little bit on your business and your industry anyway. And have a great rest of the show. >> Yeah, thank you very much. Appreciate it. >> You're welcome. Alright keep it right there everybody. This is theCUBE. We're back live right after this short break from PURE Accelerate 2017. Be right back.

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the RSA Conference in downtown San Francisco. We're live, it's 40,000 people all talking about security, and we're excited for a first-time attendee of RSA. We're joined by John Smith, a solutions architect from ExtraHop Networks. Welcome, John. >> Hey, thanks for having me. >> Absolutely. So you said it's your first time to the RSA Conference? I'm just curious, kind of first impressions of the show? >> Wow. Well, there's certainly a lot of people here. It's the biggest show I've ever been to. We've been to Synergy, HIMSS, a couple of them. I think HIMSS might have more people, but it certainly seems more crowded. People are more involved in the booths here, asking a lot of really good questions. A lot of ones and zeros people at the booth, so you really got to be on your toes (laughs) when you're talking to folks. (Jeff laughs) >> All right, for the people that aren't familiar with ExtraHop, give us kind of the overview, what you guys are all about. >> So we're a real-time IT analytics product that uses wire data to provide, at least in the security space, the biggest play we have is more around surveillance and invisibility. One of the first two controls that SANS recognizes as being, that you need to secure your environment, is asset inventory and the ability to see what applications are running on those assets. A lot of the tools in the security industry try to engineer down to that, to try to give you that. That's one of the, a lot of security people will kind of name that as one of the more difficult things to get. We start there. So we are a wire data analytics, that's kind of the core of what we do, so we don't require any IP addresses, we don't, or, I'm sorry, we don't require any agents, we don't require any SNMP, any ping sweeps or anything like that. If it has an IP address, it can't hide from us. So that means whether it's an IOT device or a medical device that's been compromised, if it's someone who wants to work in the dark and they've got a NACL that's blocking people, the minute they communicate with someone else, they're made and they can't hide from us. So what we've seen in our, with our customer base, is kind of a burgeoning security practice where people are actually using the appliance more in a security use case, and that's probably our fastest-growing use case right now. >> So what was the core of the business before? You said ExtraHop's been around for 10 years, but you're new here. What was kind of the core business before your security practice really grew? >> So the core of the business, and, you know, there's three kind of major areas. There's, we generally use the wire as a data source. So we position the customer to interact directly with the wire and the data that's coming across it. So that can be break, fix, and performance of your different web applications from layer two up to layer seven. A lot of that is business intelligence. We had an online retailer that wanted to know, you know, the average of income of people who filled out their credit app by ZIP code so that they could adjust pricing. That used to be a complicated OLAP job on the back end. We were able to give that to them in real time so that they could see, "Hey, people in this ZIP code make $300 a month more "than people in this ZIP code, we can raise prices here." So business intelligence and break, fix, and performance are big ones, and then of course in the security place, or the security space, where we're able to provide full accountability for every single IP address on the network, has been very powerful. >> Interesting. So you said you had some announcements that you guys are making here at the show? >> Yeah, so we have, are announcing our SaaS offering, which is another, it's basically a machine-learning, a cloud-based machine-learning platform that allows us to do some anomaly detection without the need to, you know, a lot of your cloud-based anomaly detection tools require you to forward terabytes of data so that then they can look at it, analyze it, and then maybe an hour later you get some information that you've been breached or that there's a problem-- >> That, or a day. >> Yeah, or, maybe, yeah. >> Months and months and months. >> Exactly. We're kind of unique in that we're able to, you know, what our Atlas program is able to essentially interrogate systems that are deployed around the world, currently around the U.S., it's a U.S. offering today, but basically we can interrogate those systems for any types of anomalies that happen. Actually, in the run up to the offering, we had a customer that was able to reroute some traffic because they were able to see the mirai botnet was starting to meddle with some of the performance of different parts of their infrastructure. So having the ability to be able to provide customers visibility into what's going on on their networks without the burden of making them FTP data up to you so that then you can evaluate it, one, you don't have the infrastructure burden of sending the data to you and the delay with that, but in addition to that, you're able to provide some real-time visibility. One of the things we've noticed is that the people who have the ability to interpret the data and to kind of parse and tell you when there is an anomaly, they're very overworked and they're spread really thin in a lot of their organizations. We augment that capability by doing some of that heavy lifting for them so that we can say, "Hey, did you know you have 1,000% increase in, you know, "DNS traffic from this particular host?" >> Right. >> That type of visibility that you can do in real time, so that if you have multiple branches around the country, we can provide that visibility from one centralized location. >> Yeah, it's all about the real time, right? Real time is in time, hopefully. >> Real time, and really, the money is in the mash-up, right? We've had a lot of really, one of the things I've noticed over the years is thread intelligence has really matured, and I think that's great, but if you can't marry that with some of your own intelligence that's going on on your own networks, you know, the value is really a lot tougher to realize. If you can ad hoc or if you can engage in some ad hoc thread intelligence by leveraging a platform like ExtraHop that can do the evaluation and thread things like anomalous behavior, that makes your agility to deal with today's threats really, really, a lot more effective. Most threats, as you're probably aware, happen, I think 93% of them happen within a minute. Dealing with that with humans, dealing with that with logs, is, it's really, really tough to do. I love logs and I love humans, but if you can position yourself to engage in programmatically dealing with that, we see orchestration is becoming, you know, kind of an emerging technology, and we're uniquely positioned to be able to interact with any sort of orchestration engines, something like a phantom, you know, things like that, where we can observe some actionable data, and then we have an open platform that can then integrate with the orchestration they're after. >> All right. Well, John, that was a great summary. We're going to leave it there, thanks for stopping by. The money's in the mash-up, did I get it right? >> John And Jeff: The money's in the mash-up. >> Baby. >> All right. >> All right. >> He's John Smith, I'm Jeff Frick. You're watching theCUBE from RSA. >> Thank you. >> Thanks for watching. (upbeat music)