>> Announcer: From Seattle, Washington it's The Cube covering AWS Imagine. Brought to you by Amazon Web Services. >> Welcome back everybody, Jeff Frick here with The Cube. We're here in downtown Seattle at the AWS Imagine Education Conference. It's the second year of the conference. It's really successful so much now they have another education conference, excuse me, Imagine Conference coming up for nonprofits, but this is the education one. About 800 people and we're excited to have, I think they had representatives from like 40 countries here. It's amazing, such a small conference with such great global representation. We've got our first guest, all the way from Valencia, Spain. He is Inaki Bilbao Estrada and the Vice Chancellor for Internationalization and Innovation at the CEU Universidad Cardenal Herrera. It's a mouthful, welcome. >> Thank you very much. >> So first off, impressions from the show, from the keynotes this morning. >> It was very impressive, the keynote by Andrew Co Intersession by Amazon. We were impressed, we were included in the keynote and we are very proud of having been included in the keynote for our Alexa skill. >> Great, so before we get into kind of what they talked about, let's back up a few steps in terms of what you are trying to accomplish as an institution. So give us a little bit of background on the college, how big it was, and kind of what was going on and what you wanted to really do differently. >> We are a Spanish University. We belong with CEU San Pablo Foundation which owns three universities in Spain, Barcelona, Madrid, and Valencia. We are a not for profit universities and in Valencia, in our case, we are very proud that we used to be a local university with only 300 international students eight years ago and right now we have reached 2500 international students which represents around 30-33% of the population of the university. We are right now 8000 undergraduate students and 3000 graduate students. >> So that's pretty amazing. So as you said, you were really kind of a regional university and you decided you wanted more international students. Why did you want more international students and then once you made that goal, what were some of the major objectives at the beginning of this process or problems that you had to overcome? >> It was a trend in higher education institutions but for us it was very important for two reasons, one the sustainability of the university, but also and I think the main reason is that we wanted to have our students to have a global experience. We wanted to become a global university based in Valencia, but we have right now more than 80 countries represented on campus. >> Wow, so what were some of the big hurdles that you saw that were going to get in the way of attracting more of these international students? >> So it was very important for us to adapt all our processes to our students. For this we have a very helpful firm partnered on campus. It was the IT department with Jose Roch in charge of this department and through technology we have been able to escalate and automate, get the automation of all of this process in order to reach bigger number of international students. So we have adapted all the processes to the needed of our international students, our new population of international students. >> Right, so you were highlighted today for a very specific thing, for a very specific device, which is Alexa, and voice as an interface and we saw some of the Alexa stuff last year, in terms of the kids asking it, you know, when is my test, is my homework due, these types of things, but you guys are actually taking it to the next level. Explain to the folks what you guys have done with Alexa. >> So we have used Alexa to introduce a virtual assistant for all our students national and international students and one other things that have been highlighted in the keynote is that is not only in English, but also in Spanish. Like this we are covering the two most speaked language on campus, English and Spanish. >> So it's bi, so you've got a bilingual Alexa in the room. >> Yeah, yeah, yeah. So for us it was very important as explained before that technologies had been asked to cover all the population of students, not only part of them. >> Right and using English is kind of universal language, regardless of what their native tongue is. >> Yeah, yeah. >> So did you have to build all this from scratch? How much was Amazon helping you to do the English to Spanish translation, was it written in Spanish, how did some of those logistics work out? >> So we began six months ago with the project with the help of Amazon, they were very, very, very helpful for us. With Ana Cabez and Juan Manuel Gomez from the UK team of Amazon and they guided us how to develop the Alexa skills for the goals that we set with them, what we wanted to achieve with the virtual assistant for our students. >> And yeah, so the skills are the things that you actually write, so how many different skills did you write especially for your students? >> So we, what we are doing is to build only one, but we are integrating all the services in one only skill. So we are integrating services related with what my next assignment on Blackboard, which are my grades, how can I book a room in the library or another space of the university, locations of the different services or professors of the university. We are integrating a lot of services, but in one skill because we don't want the students to have to switch between skills. >> Jeff: Right, right. >> So we're aiming to have one virtual assistant for the students in only one skill. >> So that's interesting, I didn't even think about all the integration points that you have. But you've got integration points in all these other systems. The room booking services, the library services, Blackboard and the other educational services. >> The learning management system. >> So how many points of integration are there? >> A lot we are working right now, we are focusing around five, seven integration points, because also we are integrating it with our CRM in order to have personalized message to different segments of our students, depending of if they are due to get some documentation to the registrar office. We think that integration with CRM allows us to give personalized message and notification to our students depending on the situations. >> Jeff: Right. >> So it's not a general notification for all of the students on campus. >> Right, that's awesome. Again, highlighted in the keynote really I think is the first kind of bilingual implementation of Alexa. So that's terrific. I want to shift gears a little bit about innovation and transformation. We go to a lot of tech shows, talk to a lot of big companies, everybody wants to digitally transform and innovate. Traditionally education hasn't been known as the most progressive industry in terms of transforming. You said right off the bat, that's your job is about transformation and innovation. Where's that coming from? Is that from the competitive world in which you live? Is that a top-down leadership directive? What's kind of pushing basically the investment in this innovation around your guys' school? >> So I believe that education can be disrupt in the next five, ten years. So what we think at the university is that we have to be closer to this disruption and in this sense we are working a lot to improve the students' experience of our students on campus because if not we think that it makes no sense to study on campus when you can go online. >> Jeff: Right. >> So that's why we're using technology to improve the students' experience on campus. So we are trying to avoid those things that have no value added for the students through technology and through the digital transformation. In order that we have more time for these value added interaction between the staff, academic and nonacademic staff, with the students. >> Right, and then how has the reception been with the staff, both the academic staff and the nonacademic staff because clearly the students are your customers, your primary customer, but they're a customer as well. So how have they embraced this and got behind it? >> So I seen all the institutions and you have a part of the institution that is not so in favor of these innovations, but the big number of professors and staff have seen the benefits of not to have to answer email Saturday night because the virtual assistant is 24 hours seven days a week. So they've seen the benefits of how technology can give them more time for these value added interaction with their students. For this in order to avoid only top-down decisions we have created digital ambassador programs which this program what we do is to share with our professors and with our nonacademic staff what we are planning and how they see the project. >> Jeff: Right, right. >> And we are integrating their opinions and their suggestions in the program. >> So you're six months into it you said since you launched it. >> Yeah. >> Okay, I'm just curious if you could share any stories, biggest surprises, things that you just didn't expect. I always like long and unintended consequences, you know, as you go through this process. >> So one of things is in Spain, Alexa was launched in November, last November so it's very new. >> Jeff: Very new. >> Very new in Spain. There's no voice assistant in the last nine months, it have exploded, but we didn't have before. So the students have been very impressed that the university were working at this level with the technology so new because it was even new for them, even if they are younger and they knew a lot about this technology. They were impressed that the university so quickly reacted to the introduction of the technology. The other point is through innovation, we are also using Alexa for the digital transformation of learning and teaching. We have launched an innovation program for quizzes for the students. And we have the huge amount of volunteers that they want to see how it works. >> Right, right, just curious too, to get your take on voice as an interface. You made an interesting comment before we turned the cameras on that email just doesn't work very well for today's kids. They don't use it. They're not used to using it. But voice still seems to really be lagging. I get an email from Google every couple of days saying, here ask your Google Assistant this or ask your Alexa this, you know, we still haven't learned it. From where you're sitting and seeing kind of this new way to interact and as you said get away from these emails in the middle of the night that ask, when's my paper due and I could ask the assistant. How do you see that evolving? Are you excited about it? Do you see voice as really the centerpieces of a lot of these new innovations or is it just one of many things that you're working on? >> So I think the difference is that usually higher education institutions would have use of email for communication with students with so massive amount of emails. I think what they feel with the voice assistants is that they have the freedom to choose what they want to know or not to know. So if they can ask voice, virtual assistant, as in one case, they have the freedom when they want the information. >> Jeff: Right. >> So I think its a big difference between emails, in an email you decide when you send the information to the students, with voice technologies, the student, it's the student who is asking when they want the information. >> Jeff: Right. >> So I think it's important for them. >> It's huge because they never ask for the email. >> No, they, and after they tell us that it wasn't important information that they didn't check the email. >> Right. >> They complain that they don't have the right information. >> Right, well Inaki, thank you for sharing your story and congratulations on this project. Sounds like you're just getting started, you've got a long ways to go. >> Thank you so much. >> All right, thank you. He's Inaki, I'm Jeff. You're watching the Cube, we're in downtown Seattle at AWS Imagine Education Conference. Thanks for watching. See you next time. 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>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards Brought to you by Amazon Web Services. >> All right, welcome back to "the Cube's" coverage here from Palo Alto, California in our studio with remote interviews during this time of COVID-19 with our quarantine crew. I'm John Furrier, your host of "the Cube" and we have here the award winners for the best EDU solution from NorthBay Solutions, Jim Keller, the president and from Harvard Business Publishing and the University of Pittsburgh, Chad Burton, PhD and Data Privacy Officer of University of Pittsburgh IT. Thanks for coming on gentlemen, appreciate it. >> Thank you. >> So, Jim, we'll start with you. What is the solution that you guys had got the award for? And talk about how it all came about. >> Yeah, thank you for asking and it's been a pleasure working with Chad and the entire UPitt team. So as we entered this whole COVID situation, our team really got together and started to think about how we could help AWS customers continue their journey with AWS, but also appreciate the fact that everyone was virtual, that budgets were very tight, but nonetheless, the priorities remained the same. So we devised a solution which we called jam sessions, AWS jam sessions, and the whole principle behind the notion is that many customers go through AWS training and AWS has a number of other offerings, immersion days and boot camps and other things, but we felt it was really important that we brought forth a solution that enables customers to focus on a use case, but do it rapidly in a very concentrated way with our expert team. So we formulated what we call jam sessions, which are essentially very focused two week engagements, rapid prototyping engagements. So in the context of Chad and UPitt team, it was around a data lake and they had been, and Chad will certainly speak to this in much more detail, but the whole notion here was how does a customer get started? How does, a customer prove the efficacy of AWS, prove that they can get data out of their on premises systems, get it into AWS, make it accessible in the form, in this case, a data lake solution and have the data be consumable. So we have an entire construct that we use which includes structured education, virtual simultaneous rooms where development occurs with our joint rep prototyping teams. We come back again and do learnings, and we do all of this in the construct of the agile framework, and ideally by the time we're done with the two weeks, the customer achieves some success around achieving the goal of the jam session. But more importantly, their team members have learned a lot about AWS with hands on work, real work, learn by doing, if you will, and really marry those two concepts of education and doing, and come out of that with an opportunity then to think about the next step in that journey, which in this case would be the implementation of a data lake in a full scale project kind of initiative. >> Chad, talk about the relationship with NorthBay Solutions. Obviously you're a customer, you guys are partnering on this, so it's kind of you're partnering, but also they're helping you. Talk about the relationship and how the interactions went. >> Yeah, so I would say the challenge that I think a lot of people in my role are faced with where the demand for data is increasing and demand for more variety of data. And I'm faced with a lot of aging on premise hardware that I really don't want to invest any further in. So I know the cloud's in the future, but we are so new with the cloud that we don't even know what we don't know. So we had zeroed in on AWS and I was talking with them and I made it very clear. I said "Because of our inexperience, we have talented data engineers, but they don't have this type of experience, but I'm confident they can learn." So what I'm looking for is a partner who can help us not only prove this out that it can work, which I had high confidence that it could, but help us identify where we need to be putting our skilling up. You know, what gaps do we have? And AWS has just so many different components that we also needed help just zeroing in on for our need, what are the pieces we should really be paying attention to and developing those skills. So we got introduced to NorthBay and they introduced us to the idea of the jam session, which was perfect. It was really exactly what I was looking for. We made it very clear in the early conversations that this would be side by side development, that my priority was of course, to meet our deliverables, but also for my team to learn how to use some of this and learn what they need to dive deeper in at the end of the engagement. I think that's how it got started and then I think it was very successful engagement after that. >> Talk about the jam sessions, because I love this. First of all, this is in line with what we're seeing in the marketplace with rapid innovation, now more than ever with virtual workforces at home, given the situation. You know, rapid agile, rapid innovation, rapid development is a key kind of thing. What is a jam session? What was the approach? Jim you laid a little bit about it out, but Chad, what's your take on the jam sessions? How does it all work? >> I mean, it was great, because of large teams that NorthBay brought and the variety of skills they brought, and then they just had a playbook that worked. They broke us up into different groups, from the people who'd be making the data pipeline, to the people who then would be consuming it to develop analytics projects. So that part worked really well, and yes, this rapid iterative development. Like right now with our current kind of process and our current tool, I have a hard time telling anybody how long it will take to get that new data source online and available to our data analysts, to our data scientists, because it takes months sometimes and nobody wants that answer and I don't want to be giving that answer, so what we're really focused on is how do we tighten up our process? How do we select the right tools so that we can say, "We'll be two weeks from start to finish" and you'll be able to make those data available. So the engagement with NorthBay, the jam session scheduled like that really helped us prove that once you have the skills and you have the right people, you can do this rapid development and bring more value to our business more quickly, which is really what it's all about for us. >> Jim, I'll get your thoughts because, you know, we see time and time again with the use cases with the cloud, when you got smart people, certainly people who play with data and work with data, They're pretty savvy, right? They know limitations, but when you get the cloud, it's like if a car versus a horse, right? Got to go from point A to point B, but again, the faster is the key. How did you put this all together and what were the key learnings? >> Yeah, so John, a couple of things that are really important. One is, as Chad mentioned, really smart people on the U-PIT side that wanted to really learn and had a thirst for learning. And then couple that with the thing that they're trying to learn in an actual use case that we're trying to jointly implement. A couple of things that we've learned that are really important. One is although we have structure and we have a syllabi and we have sort of a pattern of execution, we can never lose sight of the fact that every customer is different. Every team member is different. And in fact, Chad, in this case had team members, some had more skills on AWS than others. So we had to be sensitive to that. So what we did was we sort of used our general formula for the two weeks. Week one is very structured, focused on getting folks up to speed and normalize in terms of where they are in their education of AWS, the solution we're building and then week two is really meant to sort of mold the clay together and really take this solution that we're trying to execute around and tailor it to the customer so that we're addressing the specific needs, both from their team member perspective and the institution's perspective in total. We've learned that starting the day together and ending the day with a recap of that day is really important in terms of ensuring that everyone's on the same page, that they have commonality of knowledge and then when we're addressing any concerns. You know, this stuff we move fast, right? Two weeks is not a long time to get a lot of rapid prototyping done, so if there is anxiety, or folks feel like they're falling behind, we want to make sure we knew that, we wanted to address that quickly, either that evening, or the next morning, recalibrate and then continue. The other thing that we've learned is that, and Chad and entire U-Pit team did a phenomenal job with this, was really preparation. So we have a set of preliminary set of activities that we work with our customers to sort of lay the foundation for, so that on day one of the jam session, we're ready to go. And since we're doing this virtually, we don't have the luxury of being in a physical room and having time to sort of get acclimated to the physical construct of organizing rooms and chairs and tables and all that. We're doing all that virtually. So Chad and the team were tremendous in getting all the preparatory work done Thinking about what's involved in a data lake, it's the data and security and access and things our team needed to work with their team and the prescription and the formula that we use is really three critical things. One is our team members have to be adept at educating on a virtual whiteboard, in this case. Secondly, we want to do side by side development. That's the whole goal and we want team members to build trust and relationships side by side. And then thirdly, and importantly, we want to be able to do over the shoulder mentoring, so that as Chad's team members were executing, we could guide them as we go. And really those three ingredients were really key. >> Chad, talk about the data lake and the outcome as you guys went through this. What was the results of the data Lake? How did it all turn out? >> Yeah, the result was great. It was exactly what we were looking for. The way I had structured the engagement and working with Jim to do this is I wanted to accomplish two things. I wanted to one, prove that we can do what we do today with a star schema mart model that creates a lot of reports that are important to the business, but doesn't really help us grow in our use of data. So there was a second component of it that I said, I want to show how we do something new and different that we can't do with our existing tools, so that I can go back to our executive leadership and say "Hey, by investing in this, here's all the possibilities we can do and we've got proof that we can do it." So some natural language processing was one of those and leveraging AWS comprehend was key. And the idea here was there are, unfortunately, it's not as relevant today with COVID, but there are events happening all around campus and how do students find the right events for them? You know, they're all in the calendar. Well, with a price of natural language processing using AWS comprehend and link them to a student's major, so that we can then bubble these up to a student "Hey, do you know of all these thousands of events here are the 10 you might be most interested in." We can't do that right now, but using these tools, using the skills that that NorthBay helped us develop by working side by side will help us get there. >> A beautiful thing is with these jam sessions, once you get some success, you go for the next one. This sounds like another jam session opportunity to go in there and do the virtual version. As the fall comes up, you have the new reality. And this is really kind of what I like about the story is you guys did the jam session, first of all, great project, but right in the middle of this new shift of virtual, so it's very interesting. So I want to get your thoughts, Chad, as you guys looked at this, I mean on any given Sunday, this is a great project, right? You can get people together, you go to the cloud, get more agile, get the proof points, show it, double down on it, playbook, check. But now you've got the virtual workforce. How did that all play out? Anything surprise you? Any expectations that were met, or things that were new that came out of this? 'Cause this is something that is everyone is going through right now. How do I come out of this, or deal with current COVID as it evolves? And then when I come out of it, I want to have a growth strategy, I want to have a team that's deploying and building. What's your take on that? >> Yeah, it's a good question and I was a little concerned about it at first, because when we had first begun conversations with NorthBay, we were planning on a little bit on site and a little bit virtual. Then of course COVID happened. Our campus is closed, nobody's permitted to be there and so we had to just pivot to a hundred percent virtual. I have to say, I didn't notice any problems with it. It didn't impede our progress. It didn't impede our communication. I think the playbook that NorthBay had really just worked for that. Now they may have had to adjust it and Jim can certainly talk to that, But those morning stand-ups for each group that's working, the end of day report outs, right? Those were the things I was joining in on I wasn't involved in it throughout the day, but I wanted to check in at the end of the day to make sure things are kind of moving along and the communication, the transparency that was provided was key, and because of that transparency and that kind of schedule they already had set up at North Bay, We didn't have any problems having it a fully virtual engagement. In fact, I would probably prefer to do virtual engagements moving forward because we can cut down on travel costs for everybody. >> You know, Jim, I want to get your thoughts on this, 'cause I think this is a huge point that's not just represented here and illustrated with the example of the success of the EDU solution you guys got the award for, but in a way COVID exposes all the people that have been relying on waterfall based processes. You've got to be in a room and argue things out, or have meetings set up. It takes a lot of time and when you have a virtual space and an agile process, yeah you make some adjustments, but if you're already agile, it doesn't really impact too much. Can you share your thoughts because you deployed this very successfully virtually. >> Yeah, it's certainly, you know, the key is always preparation and our team did a phenomenal job at making sure that we could deliver equal to, or better than, virtual experience than we could an on-site experience, but John you're absolutely right. What it forces you to really do is think about all the things that come natural when you're in a physical room together, but you can't take for granted virtually. Even interpersonal relationships and how those are built and the trust that's built. As much as this is a technical solution and as much as the teams did really phenomenal AWS work, foundationally it all comes down to trust and as Chad said, transparency. And it's often hard to build that into a virtual experience. So part of that preparatory work that I mentioned, we actually spend time doing that and we spent time with Chad and other team members, understanding each of their team members and understanding their strengths, understanding where they were in the education journey and the experiential journey, a little bit about them personally. So I think the reality in the in the short and near term is that everything's going to be virtual. NorthBay delivers much of their large scale projects virtually now. We have a whole methodology around that and it's proven actually it's made us better at what we do quite frankly. >> Yeah it definitely puts the pressure on getting the job done and focusing on the creativity in the building out. I want to ask you guys both the same question on this next round, because I think it's super important as people see the reality of cloud and this certainly has been around, the benefits of there, but still you have the mentality of "we have to do it ourselves", "not invented here", "It's a managed service", "It's security". There's plenty of objections. If you really want to avoid cloud, you can come up with something if you really looked for it. But the reality is is that there are benefits. For the folks out there that are now being accelerated into the cloud for the reasons with COVID and other reasons, What's your advice to them? Why cloud? What's the bet? What comes out of making a good choice with the cloud? Chad, as people sitting there going "okay, I got to get my cloud mojo going" What's your advice to those folks sitting out there watching this? >> So I would say, and Jim knows this, we at Pitt have a big vision for data, a whole universe of data where just everything is made available and I can't estimate the demand for all of that yet, right? That's going to evolve over time, so if I'm trying to scale some physical hardware solution, I'm either going to under scale it and not be able to deliver, or I'm going to invest too much money for the value I'm getting. By moving to the cloud, what that enables me to do is just grow organically and make sure that our spend and the value we're getting from the use are always aligned. And then, of course, all the questions about, scalability and extensibility, right? We can just keep growing and if we're not seeing value in one area, we can just stop and we're no longer spending on that particular area and we can direct that money to a different component of the cloud. So just not being locked in to a huge expensive product is really key, I think. >> Jim, your thoughts on why cloud and why now? Obviously it's pretty obvious reasons, but benefits for the naysayer sitting on the fence? >> Yeah, it's a really important question, John and I think Chad had a lot of important points. I think there's two others that become important. One is agility. Whether that's agility with respect to if you're in a competitive market place, Agility in terms of just retaining team members and staff in a highly competitive environment we all know we're in, particularly in the IT world. Agility from a cost perspective. So agility is a theme that comes through and through over and over and over again, and as Chad rightfully said, most companies and most organizations they don't know the entirety of what it is they're facing, or what the demands are going to be on their services, so agility is really, is really key. And the second one is, the notion has often been that you have to have it all figured out before you can start and really our mantra in the jam session was sort of born this way. It's really start by doing. Pick a use case, pick a pain point, pick an area of frustration, whatever it might be and just start the process. You'll learn as you go and not everything is the right fit for cloud. There were some things for the right reasons where alternatives might be be appropriate, but by and large, if you start by doing and in fact, through jam session, learn by doing, you'll start to better understand, enterprise will start to better understand what's most applicable to them, where they can leverage the best bang for the buck, if you will. And ultimately deliver on the value that IT is meant to deliver to the line of business, whatever that might be. And those two themes come through and through. And thirdly, I'll just add speed now. Speed of transformation, speed of cost reduction, speed of future rollout. You know, Chad has users begging for information and access to data, right? He and the team are sitting there trying to figure how to give it to them quickly. So speed of execution with quality is really paramount as well these days. >> Yeah and Chad also mentioned scale too, cause he's trying to scale up as key and again, getting the cloud muscles going for the teams and culture is critical because matching that incentives, I think the alignment is critical point. So congratulations gentlemen on a great award, best EDU solution. Chad, while I have you here, I want to just get your personal thoughts, but your industry expert PhD hat on, because one of the things we've been reporting on is in the EDU space, higher ed and other areas, with people having different education policies, the new reality is with virtualized students and faculty, alumni and community, the expectations and the data flows are different, right? So you had stuff that people used, systems, legacy systems, kind of as a good opportunity to look at cloud to build a new abstraction layer and again, create that alignment of what can we do development wise, because I'm sure you're seeing new data flows coming in. I'm sure this kind of thinking going on around "Okay, as we go forward, how do we find out what classes to attend if they're not onsite?" This is another jam session. So I see more and more things happening, pretty innovative in your world. What's your take on all this? >> My take, so when we did the pivot, we did a pivot right after spring break to be virtual for our students, like a lot of universities did. And you learn a lot when you go through a crisis kind of like that and you find all the weaknesses. And we had finished the engagement, I think, with NorthBay by that point, or were in it and seeing how if we were at our future state, you know, might end up the way I envisioned the future state, I can now point to these specific things and give specific examples about how we would have been able to more effectively respond when these new demands on data came up, when new data flows were being created very quickly and able to point out to the weaknesses of our current ecosystem and how that would be better. So that was really key and this whole thing is an opportunity. It's really accelerated a lot of things that were kind of already in the works and that's why it's exciting. It's obviously very challenging and at Pitt we're really right now trying to focus on how do we have a safe campus environment and going with a maximum flexibility and all the technology that's involved in that. And, you know, I've already got, I've had more unique data requests come to my desk since COVID than in the previous five years, you know? >> New patterns, new opportunities to write software and it's great to see you guys focused on that hierarchy of needs. I really appreciate it. I want to just share with you a funny story, not funny, but interesting story, because this highlights the creativity that's coming. I was riffing on Zoom with someone in a higher ed university out here in California and it wasn't official business, was just more riffing on the future and I said "Hey, wouldn't it be cool if you had like an abstraction layer that had leveraged Canvas, Zoom and Discord?" All the kids are on Discord if they're gamers. So you go "Okay, why discord? It's a hang space." People, it's connective tissue. "Well, how do you build notifications through the different silos?" You know, Canvas doesn't support certain things and Canvas is the software that most universities use, but that's a use case that we were just riffing on, but that's the kind of ideation that's going to come out of these kinds of jam sessions. Are you guys having that kind of feeling too? I mean, how do you see this new ideation, rapid prototype? I only think it's going to get faster and accelerated. >> As Chad said, his requests are we're multiplying, I'm sure and people aren't, you know, folks are not willing to wait. We're in a hurry up, 'hurry up, I want it now' mentality these days with both college attendees as well as those of us who are trying to deliver on that promise. And I think John, I think you're absolutely right and I think that whether it be the fail fast mantra, or whether it be can we make even make this work, right? Does it have legs? Is it is even viable? And is it even cost-effective? I can tell you that we do a lot of work in Ed tech, we do a lot of work in other industries as well And what the the courseware delivery companies and the infrastructure companies are all trying to deal with as a result of COVID, is they've all had to try to innovate. So we're being asked to challenge ourselves in ways we never been asked to challenge ourselves in terms of speed of execution, speed of deployment, because these folks need answers, you know, tomorrow, today, yesterday, not six months from now. So I'll use the word legacy way of thinking is really not one that can be sustained, or tolerated any longer and I want Chad and others to be able to call us and say, "Hey, we need help. We need help quickly. How can we go work together side by side and go prove something. It may not be the most elegant, it may not be the most robust, but we need it tomorrow." And that's really the spirit of the whole notion of jam session. >> And new expectations means new solutions. Chad, we'll give you the final word. Going forward, you're on this wave right now, you got new things coming at you you're getting that foundation set. What's your mindset as you ride this wave? >> I'm optimistic. It really is, it's an exciting time to be in this role, the progress we've made in the calendar year 2020, despite the challenges we've been faced with, with COVID and budget issues, I'm optimistic. I love what I saw in the jam session. It just kind of confirmed my belief that this is really the future for the University of Pittsburgh in order to fully realize our vision of maximizing the value of data. >> Awesome! Best EDU solution award for AWS public sector. Congratulations to NorthBay Solutions. Jim Keller, president, and University of Pittsburgh, Chad Burton. Thank you for coming on and sharing your story. Great insights and again, the wave is here, new expectations, new solutions, clouds there, and you guys got a good approach. Congratulations on the jam session, thanks. >> Thank you, John. Chad, pleasure, thank you. >> Thank you. >> See you soon. >> This is "the Cube" coverage of AWS public sector partner awards. I'm John Furrier, host of "the Cube". Thanks for watching. (bright music)

(stirring music) >> Announcer: From Seattle, Washington, it's theCUBE. Covering AWS IMAGINE Nonprofit. Brought to you by Amazon Web Services. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown Seattle, Washington, actually right on the waterfront. It has been a spectacular visit here for the last couple of days. And we're back in Seattle for AWS IMAGINE. We were here a couple weeks ago for AWS IMAGINE Education. This is a different version of the conference, really focused around government and nonprofits, and we're really excited to kick off our day with the guy coming right off the keynote who's running this, he's Dave Levy. He's the vice president for U.S. Government and Nonprofit for AWS. Dave, great to see you, and congrats on the keynote. >> Thank you, thanks for having me, too. We're really excited. >> Absolutely. So as you're talking about mission and purpose, and as I'm doing my homework for some of the topics we're going to cover today, these are big problems. I couldn't help but think of a famous quote from Jeff Hammerbacher from years ago, who said, "The greatest minds of my generation "are thinking about how to make us click ads." And I'm so happy and refreshed to be here with you and your team to be working on much bigger problems. >> Yeah, well thank you. We're very excited, we're thrilled with all the customers here, all the nonprofits, all the nongovernmental organizations, all of our partners. It's just very exciting, and there are a lot of big challenges out there, and we're happy to be a part of it. >> So it's our first time here, but you guys have been doing this show, I believe this is the fourth year. >> Its fourth year, yeah. >> Give us a little background on the nonprofit sector at AWS. How did you get involved, you know, what's your mission, and some of the numbers behind. >> Well, it's one of the most exciting part of our businesses in the worldwide public sector. And we have tens of thousands of customers in the nonprofit sector, and they are doing all sorts of wonderful things in terms of their mission. And we're trying to help them deliver on their mission with our technology. So you see everything from hosting websites, to doing back office functions in the cloud, running research and donor platforms, and so it's just a very exciting time, I think. And nonprofit missions are accelerating, and we're helping them do that. >> Yeah, it's quite a different mission than selling books, or selling services, or selling infrastructure, when you have this real focus. The impact of some of these organizations is huge. We're going to talk to someone involved in human trafficking. 25,000,000 people involved in this problem. So these are really big problems that you guys are helping out with. >> They're huge problems, and at Amazon, we really identify with missionaries. We want our partners and our customers to be able to be empowered to deliver on their mission. We feel like we're missionaries and we're builders at Amazon, so this is a really good fit for us, to work with nonprofits all over the world. >> And how did you get involved? We were here a couple weeks ago, talked to Andrew Ko. He runs EDU, he'd grown up in tech, and then one of his kids had an issue that drove him into the education. What's your mission story? >> Well, on a personal level, I'm just passionate about this space. There's so much opportunity. It's everything from solving challenges around heart disease, to research for cancer, patient care, to human trafficking. So all of those things resonate. It touches all of our lives, and I'm thrilled to be able to contribute, and I've got a fantastic team, and we've got amazing customers. >> Right. It's great. Did a little homework on you, you're a pretty good, interesting guy too. But you referenced something that I thought was really powerful, and somebody interviewing you. You talked about practice. Practice, practice, practice, as a person. And you invoked Amara's Law, which I had never heard for a person, which is we tend to overestimate what we can do in the short term, but we underestimate what we can do in the long term. And as these people are focused on these giant missions, the long term impacts can be gargantuan. >> Yeah, I think so. Like you said, we're tackling some huge problems out there. Huge, difficult problems. Migrations, diseases. And, you know, it takes a while to get these things done. And when you look back on a ten year horizon, you can really accomplish a lot. So we like to set big, bold, audacious goals at Amazon. We like to think big. And we want to encourage our customers to think big along with us. And we'll support them to go on this journey. And it may take some time, but I'm confident we can solve a lot of the big problems out there. >> But it's funny, there's a lot of stuff in social now where a lot of people don't think big enough. And you were very specific in your keynote. You had three really significant challenges. Go from big ideas to impact. Learn and be curious, and dive deep. Because like you said, these are not simple problems. These aren't just going to go away. But you really need to spend the time to get into it. And I think what's cool about Amazon, and your fanatical customer focus, to apply that type of a framework, that type of way of go to market into the nonprofit area, really gives you a unique point of view. >> I hope so. And we're doing a lot of really cool things here at the conference. We've got a Working Backwards session. One of the things about working backwards that's really interesting is the customer's at the center of that. And it all starts with the customer. I can't tell you how many times I've been in a meeting at Amazon where somebody has said, wait a second. This is what we heard these customers say, this is what we heard about their mission. And it's all about what customers want. So we're really excited that our customers here and our nonprofits here are going to be going through some of those sessions, and hopefully we can provide a little innovation engine for them by applying Amazon processes to it. >> For the people that aren't familiar, the working backwards, if I'm hearing you right, is the Amazon practice where you actually write the press release for when you're finished, and then work backwards. So you stay focused on those really core objectives. >> Yeah, that's right. It's start with your end state in mind and work backwards from there. And it starts with a press release. And certainly those are fun to write, because you want to know what you're going to be delivering and how you're going to be delivering it, and frankly how your customers and how your stakeholders will be responding. So it's a really great exercise, helps you focus on the mission, and sets up the stage for delivery in the future. >> It's funny, I think one of the greatest and easy simple examples of that is the Amazon Go store. And I've heard lots of stores, I've been it now a couple times up here, in San Francisco, and the story that I've heard, maybe you know if it's true or not, is that when they tried to implement it at first, they had a lot of more departments. And unfortunately it introduced lines not necessarily at checkout, but other places in the store. And with that single focus mission of no lines, cut back the SKUs, cut back the selection, and so when I went in it in San Francisco the other day, and it gave me my little time in the store, the Google search results? It was, I think, a minute and 19 to go in, grab a quick lunch, and then get back on my way. So really laser-focused on a specific objective. >> Yeah, and that's the point of the working backwards process. It's all about what customers want, and you can refine that and continue to refine that, and you get feedback, and you're able to answer those questions and solve those difficult problems. >> That's great. Well, Dave, thanks for inviting us here for the first time again. Congrats on the keynote, and we look forward to a bunch of really important work that your customers and your team are working on, and learning more about those stories. >> Thanks, we're thrilled. Very thrilled. >> All right. He's Dave, I'm Jeff. You're watching theCUBE. We're in Seattle at the AWS IMAGINE Nonprofit. Thanks for watching, we'll see you next time. (light electronic music)

>> from Seattle Washington It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back there, buddy Geoffrey here with the Cube. We're in downtown Seattle Day Ws Imagine Edie, you event. It's their education event and every education Everything from K through 12. The higher education community College Retraining after service is a really great show. It's a second year. We're happy to be here. We've got somebody has come all the way from Spain to talk about his very special company. It's Ignasi. Nuclear is he is >> the CEO of click dot edu. Yeah, nice. You see? Welcome. >> Thank you are way really pleased to be with you. >> Great. So tell us, kind of what is clicky? Do you What? What is kind of your core value? >> It's ah, platform that makes all the things that the school needs seeing atleast in Spain. So it's a miss system also on elements also the communication with the family that Petra is Ah Wei Tau financial the school and also a lot of things that they are related on >> right? And you've been around for a while. So when did the company started? How was kind of some basic numbers on how many customers do you have? Could you operate in a lot of countries? A lot of schools? >> The as we have schools working with us already in all of Spain, Also in Chile, Colombia, Arneson, UK. On also in a little country in Europe that is called Andorra. So we're really happy because you have more than 1,000,000 off users working with us. >> 1,000,000. Congratulations. And is it mainly do you specialize between, say, K through 12 or higher education? Or we're kind of all over the place? >> Yes, we're focusing K 12 schools. So the one off the important parts are the communication with parents on dhe to follow all the things that the student. That's >> right. So you guys have a very special thing that you're announcing here at the show is really focusing on Alexa for K through 12 which nobody else is doing. That's really something unique that you guys, How did you get in that? What did you see in voice communication and Alexa that you couldn't do in the platform before that? You really saw the opportunity? >> Yes. All the people say is that >> the future or the present Now is the voice on all we will communicate by boys in the future over Internet. You see a lot off young guys doing all the things my boys know, right? Texting, etcetera. So we thought that it could be a nice idea that the communication between parents and also for a students to the school and be on in the other way, could be could be by boys. So we imagine how to do >> it on. We did it. It's really knew. >> When did you start it? When did you start that project? >> This project we began three months ago, >> three months ago. So, >> yeah, it's really, really knew the boy's idea, right? It was in >> a show that I have seen. Ah ah, law. A lot of people were talking about that, but there were, at least in Spain, in the Spanish. Nothing about so with it, we can be the first. So >> we leave. That's >> great. So before we turn the >> cameras on, we're talking about some of the issues that you have in one of the ones is integration to all these systems because, you know, I have kids. I might have multiple kids in a couple different grades. You have kids and a fine looking for access on their homework or their test scores. You know he's got integrate with all those different back ends to keep things private. But you're kind of in a good spot because your system is the one that's on the back end, right? Yeah, so that worked pretty well. And then the other piece, he talked about his two way voice. I don't think a lot of people think in voice communication, yet it's still more of an ask and get a reply asking and get a reply. But you guys are actually pushing notice vacations from the school, out to the families using voice. How's that working out? You know what are some of the use cases? Yeah, >> it's like it's like the parent can ask Toe elixir, for example, What's a home or for tomorrow for one of your son or daughter on DA on The Echo tell you about that. So it's really impressive, because in that moment the system goes to the school system to get that information on our system. Yeah, on Alexa translating voice So it's It's It's funny >> I just think it's funny that I get e mails from all my digital assistants telling me, suggesting things that I should ask them because it's really not native yet as as an interface to work with these machines. But, well, he's mentioned that the young people voices much more natural. So I wonder if there's been some surprises or some things you didn't expect in terms of people comfort level with voice as a way to communicate with me. >> Say, I think it's, ah most natural way also for us that we are not not if but off course. So we communicate better by boys and writing or texting. So, so off course. It's the future because it's another away. So the use off that systems goes up because off that. So I think it's the most the most thing that for for causes more surprising, >> right? And so will you guys supply the Alexa? It's for people's homes. Or is it something they can tap into their existing Alexa Yeah, >> uh, usually, ah, the case for using that is in your home or else on your phone so you can install licks on your phone and you can ask them. I'll see if the UK fun ankle, >> but handle it. But how do I look? How do I hook my existing echo? Yeah, yes, I bought into the school system. >> Yes, because sometimes some universities are They pulled their A coin. I don't know in the university, or but you can use your echo that you are using it for other things. Listen, music me Listen, missing music or whatever >> and you >> can use the >> same. Yeah, you can. You >> only have to, like, download an >> app for >> your phone. There >> is more less is the same us Alexa to >> install, click in the Web or a skill that it's cow. It's called right, and then you >> have it. So what's next? What's on the road? Map on the voice specifically, Where do you see this kind of evolving over the next little while? >> Yes, our our next goal in the parties that they can use the teachers in the school. The boy systems also so for doing what they do every day in ah Maur writing or whatever, we can do it by voice. For example, interview with the parents, a transcript or, for example, to say that somebody hasn't come to the school or toe tell to the Transportacion that something is company. These kind of things is what we are. Imagine it's in our next things that we will do it with voice. >> It'll be Lexa in the classroom, hoping, thinking, Yeah, right. What about privacy? I would imagine knows funny. In the early days of Cloud, security was a was was not good of the show stopper. People were concerned about 10 years later. Now security is a strength of cloud, right? It's probably more secure than most people's data centers or disgruntled employees. I would imagine privacy and security. This is probably pretty top of mind in the school district as well as a lot of personal information. Are they comfortable? Do they kind of get the security of cloud and cloud infrastructure, or is that still sticking point? >> You know that in Europe there are really strict low of our protection off that right, so we are really concerned about that. So we are talking with the school's what kindof systems. They will be comfortable because you want to use it, so we'll have to find >> the clue to do that. But It's really >> important, I think, all over the world, but in the stage or in Europe who are really concerned about that. So we'll see how to find it. But we can create a private skill, right? Yes, because there are birds shown off, Alexa, that is for business. So you can create your provide things on. You don't have to be for that. Somebody's listening. You >> right? All right. So the last last question here at the conference and you come last year? >> No. So what do >> you know? Just your impressions of the conference Has it nice to be with a bunch of like minded, you know, kind of forward thinking educators because because education doesn't always get the best reputation being kind of forward looking. But here you're surrounded. So I just wonder you could share some of your thoughts of the of the event so far. Yeah, >> I think this guy no five ins give you more motivation on you. Increase your you're way t to see that there are a lot of people that is pushing to innovate and do the things different. So really, really interesting to goto some machine learning. Ah, suppose is shown about California. What? They are doing that right? So I'm really interested. >> Good. Get all right. Look Nazi. Thanks for taking a few minutes. And, uh, congratulations on that project. That's really crazy. Thank >> you for your interest in. >> All right, >> Jeff, you're watching the Cube. Where it aws Imagine in downtown Seattle. Thanks for watching. We'll see you next time.

>> From Seattle, Washington, it's the Cube! Covering AWS Imagine, brought to you by Amazon web services. >> Hey welcome back everybody, Jeff Rick here with the Cube. We're in downtown Seattle at the AWS Imagine EDU Conference, it's the second year of the conference, we came up last year, I think it was like 400 people, this year's like 800 people, like all the Amazons, it grows and grows and grows. Really again, specifically a carve out from the public sector group, all about education, that's K-12, that's higher education, it's community college education, it's retraining vets, it's a huge thing. We're really excited to have the ring leader this whole event, he's just coming off the keynote, he's Andrew Ko, he's a global education director for AWS, working for Teresa. Andrew, great to see you. >> Thank you very much for having us here. >> What an event! >> Yes! >> And good job on the keynote, you guys covered a lot of different segments. This education opportunity challenge-- >> Yah. >> Is so multifaceted. >> Yes. >> Now how do you kind of organize again, what are the ways that you kind of look at this opportunity? >> Well, that's a great point, we could go on for days and for so many of the important topics, but we've really broken it down into three themes that we've carried on from last year. Really wanted to help and assist when it comes to employability. As we talk about the growth of AWS Cloud, what we're finding is there's a tremendous amount of lack of skilled talent to really fulfill those demands. So workforce is one of those particular areas. Secondly, we're seeing a tremendous growth on machine learning. The way to really predict things, whether it's student success or research. Finally, we also have a third theme that is come around innovation and transformation. Not so much always about the IT, but how are people moving along quickly on their Cloud journey? And really enabling a lot of their stakeholders, like researchers, medical centers, as well as students, to really adopt and learn technology but also embrace it in very very new innovative ways. >> Right. It's it's funny, there was a video showed in the keynote with Andy and I just want to pull the quote where you said it's not about protecting today, the infrastructure-- >> Yup. >> And we've joked many times on air about if when the time machine and you pulled somebody from 1760 and they came here-- >> Andrew: Yah. >> The only thing they'd recognize is the schoolhouse, right? >> Andrew: Right. >> But you guys are really working to change that. Everything from really, Cloud as an infrastructure efficiency play-- >> Andrew: Right. >> All the way through Cloud as an enabler for innovation, doing some really crazy things with Alexa and some of the other projects that are underway. >> Absolutely. And and we always start with our customers first. They're really the ones that have that vision and want to ensure that it's improved, and so we're excited to be a part of that journey. And as just a couple examples on how that is starting to change, is through this adaptive way of looking at information and data, and as an example as I mentioned that we're going to have an incredible panel sessions of many of our speakers, and one of which I like to call out is with the California Community College. They have over 2.1 million students at any given year, and now with the technology, they can start to try to look at patterns of success for students, patterns of challenges, and really start to make education more interactive, which is a one-way like what you were mentioning maybe it was a hundred years ago. >> Right, with the chalkboard. (chuckles) >> So it's so funny with, we talk about ML and AI-- >> Yup. >> You know, everyone's talks in the paper about, you know, the machines are going to take all of our jobs, but if you go to the back pages of the paper, I don't know if they have that anymore-- >> Yah. (chuckles) >> There's a whole lot of open recs, right? >> Yup. >> People can't hire fast enough for these jobs-- >> Right. >> So it's actually that's a much bigger problem than them taking jobs away right now, so this re-skilling is really really significant. >> Absolutely. And we always say that there's not necessarily always a jobs gap, but it's really a skills gap that are going unfulfilled. So there is a change in a lot of the talents that are required, but that's why it's so important for us representing education. That's not just about the infrastructure but how do we better prepare not just the learners of today that need some re-skilling, but also the learners for tomorrow, and provide them a pathway in a way to be interested in it, but also more importantly, getting jobs. >> Jeff: Right. >> The end of day, it's not just about a learning thing, it's about an economic thing. And so we're finding all those announcements as you heard earlier, such as Brazil. With SENAI, they're going to now announce that this curriculum is going to be available for 2.5 million education learners across the entire country, working with 740 universities so we're really excited to be behind that, and we would love to take the credit but really it's our customers, it's our leaders, it's those individuals that are really cutting edge and making those things happen. >> Jeff: Right. So again, last year was a lot about the community college and the certification of those programs, the accreditation. This year you're introducing bachelor programs, and-- >> Yes. >> Really amazing statement in the keynote about the governor of the state of Louisiana-- >> Yes. >> Basically dictating the importance of having a four-year degree based on Cloud skills. That's pretty significant. >> It's exciting. I mean, and I would say, as living in Virginia we're excited to see Northern Virginia alongside with Santa Monica Community College and Columbus Day Community College jointly together created, it wasn't us that created it, it was actually the faculty members and we got together created it, and the governor of Louisiana just took it to the next level. He really, alongside with his leadership team, of the individual leaders of the state community colleges as well as the universities said not only are we going to adopt the two-year across the state but we're going to have it articulate, allowing for students to get credit at the four-year. >> Jeff: Right. >> And why that's important, Jeff, is that we want to make sure that the pathway has on-ramps of how and where you can intersect and to get re-skilled, but also off-ramps. Some of them may get jobs right away at community college, some of them want to go to a four-year and go have more deeper learning and a different experience so-- >> Jeff: Right. >> All those options are now open. >> Right. >> And having that governor just indicates that it's important at a massive massive scale. >> Jeff: Yah. So another thing, we we have to talk about Alexa right? I forget how many millions of units you said are sold-- >> Hundred million devices last time I checked, yah. 70,000 skills. >> Lots and lots of skills, right, the skills. So it's pretty interesting in terms of really kind of helping the universities, beside just be more efficient with the Cloud infrastructure but actually appeal to their customers' students-- [Andrew] Yah. >> In a very very different way. And a pretty creative way to use Alexa and what's what's fascinating to me is I don't think we've barely scratched the surface-- >> Andrew: That's correct. >> Of voice, as a UI. >> Andrew: Yah. >> We won't. We're old, we have thumbs. (chuckles) >> But the kids coming up, right? Eventually that's going to flip-- >> Andrew: Right. >> And it's going to be more voice than keyboards so you guys took an interesting tack from the beginning, opening up the API to let people program it, versus just learning-- >> Absolutely. >> Another method. So some exciting skills, what are some of the ones that that surprise you as you go around-- >> Well-- >> To visit these customers? >> There's so many of them, it's hard to announce and discuss all of them but I would definitely say yes, this next generation, not the old fuddy-duddies like me, learn very differently now. And they're expecting to learn very differently and I think voice and natural user interface is going to be the big thing that people are going to be comfortable to talk to things and have responses back, and some of the things that we announced with our partners, well actually a few weeks ago that we mentioned in the keynote, like Kahoot!, one of the larger interactive ways of young students learning from gamification. Now they can actually speak to it, and engage in much different ways rather than just typing on a keyboard or or coding or typing things in phones, so that's exciting. Or ACT. As you just mentioned earlier, you have a young rising sophomore in a university. They probably had to, she or he had to probably study in order to get into college. Well, what if there was a voice-enabled advisor of how to take the test and the examination and that's what ACT launched. >> Jeff: Right. >> Just some small examples, and now we want to extend that excitement by encouraging other education technology companies to enroll their application by South by Southwest that we're going to announce the winners there-- >> Jeff: Right. >> Next year. So to have a lot of energy, have the educators, and just build on that incredible momentum. >> Alright Andrew, so before I let you go, I know that you got a couple thousand people here waiting to talk to you. (chuckles) The other thing is you guys have gone outside the classroom, right? >> Mm-hmm. >> Really interesting conversation about helping active-duty marines learn how to use data. Really interesting conversations about bringing the big data revolution more heavily into research and more heavily into medical and more heavily into those types of activities that happen at top-tier universities. >> Andrew: Yah. >> Really different way to again apply this revolution that's been happening on the commercial side, the enterprise side into which we play, and and helping people adapt and and evolve and really embrace big data as a tool in solving these other problems. >> Absolutely. And I think you mentioned some very important points there. Number one for us, we always think of learners as individuals that are just growing up through the educational system. But we also have learners that are lifelong learners, that have changing careers or alternating changing, so we're excited to be a part of the announcement with Northern Virginia Community College where they created a special program for Marine Corps, so they can come out and learn data intelligence, that would be applied for all, but also focused with the Marine Corps individuals there to really learn another skill set and apply it to a new occupation. >> Jeff: In their active duty. This is not for when they come out-- >> Absolutely. >> For for re-train. This is in while they're in their >> Very important. >> In their existing job. >> Absolutely. And that so that when they come out they have now applied skills in addition to the skills that they've learned being in the Marine Corps, so that they can also become really productive right after their enlistment there. >> Jeff: Right. >> And then you mentioned about research, I mean that is also an exciting thing that people so often also forget, that education also extends out there, and so like UCLA, they've created a new department blending medicine as well as engineering to tackle very important research like cancer and genomics, and so those complicated facets are now no longer is IT a separate conversation, but it's an infused way where much more high-performance computing can handle some interesting research to accelerate the outcomes. >> Right. Well Andrew, well thanks for inviting us to be here for the ride. We've we've been along the AWS ride (chuckles) >> For a while, from summits in 2012 and reinvents so we know it's going to grow, we're excited to watch it, and we'll see you next year. >> Jeff, thank you very much, and the ride is just beginning. >> Alright. He's Andrew, I'm Jeff, you're watching the Cube, we're in downtown Seattle at the AWS Imagine EDU Conference. Thanks for watching. (upbeat music)

(upbeat tropical music) >> Announcer: Live from San Juan, Puerto Rico. It's The Cube, covering Blockchain Unbound, brought to you be Blockchain Industries. (upbeat tropical music) >> Hello, everyone. Welcome to this special Cube conversation. We are on the ground in Puerto Rico for Blockchain Unbound and Restart Week, a variety of blockchain, cryptocurrency, industry events, a lot of action here. All the thought leaders, the pioneers, a lot of the people making it happen, from entrepreneurs to the investors, and entrepreneurs who made it in bitcoin blockchain, as well as participants in the local community. Our next guest is Michael Angelo, the co-founder of EDU Block, Edublock.io, EDUblock.io. Interesting story here, he's got a school chain, I call it, going around. Michael, welcome to the conversation. >> It's great to be here. >> So talk about what you guys do. And I think it's super fascinating, you guys are creating a value chain with the university system. Obviously, you know, the first thing that jumps to mind is, hey, it's like the internet. Connect the internet with TCP/IP and next thing you know, the internet's born, the web is born. You're doing something really fascinating with your project, connecting the universities here in Puerto Rico. Take a minute to explain what you're working on. >> Okay, so, Edublock is an educational platform based in Puerto Rico. So what we're doing is, we're connecting every single university in the island to work on open-source projects, to make solutions for the private sector. >> And so you're enabling this actual connectedness, so you got the blockchain which can enable that, cryptocurrency in Puerto Rico is certainly hot. A lot of the ecosystem blending in, coming into the country, into the area; people are excited. What's going on in the front lines? As the young kids are looking at this revolution, this is a massive wave, they've got to be inspired. They've got to look at this as an opportunity. What's some of the things that you're seeing on the front-lines, there? >> Okay, well let me tell you. So, people are scared here. So, Edublock wants to create transparency in blockchain and make people trust us and trust the movement. So we see a bunch of people coming here, and we see a tremendous potential for the island. We could become an emerging market through blockchain technology. But people are scared. Most people come here and they talk about the how and the what, so Edublock wants to talk about the why. So, why is... We want to educate, we want to make this transparent. We want to change the lives of a bunch of people, teach them, so they can become the next world leaders. >> And really, enabling them with tools. So Brock Pierce gave the keynote here, to the kickoff of Blockchain Unbound, as part of a kind of a pitch competition with d10e. Great message, power of we, not me, is really what makes it happen. Paying it forward, cultural ethos. It's global, so this whole global economy's shaping. This is an opportunity for a digital nation to emerge. How do you guys talk about that? The young guys going in there, the developers. The trust needs to be there. What are some of the things people are working on? What kind of things do you imagine happening with Edublock? What are some of the things on your mind, there? >> Okay, so basically what we're doing is, anyone who's coming here in the island, we're just asking them, if you want outreach, you have an ICO, you have a big project, so we have this ecosystem that's running. We have software developers, and you want to teach people. So if you have your ICOs, you have a project, you give it to us, we just lay it down in the ecosystem and see how it works, trial and error. And it's a win-win, 'cause it's free. So you win, you win the expansion here in the island, and we win knowledge. >> So basically, you guys are opening up your arms, saying, hey, throw us what you got, we'll kick the tires, we'll give it a dry run, we'll give you feedback, there's some learnings that are shared. Is that kind of the thing you guys are thinking about? Is that what you're referring to? >> Yeah, that's exactly what we're doing as of now. So we have few projects, we're working with ListCoin, and we have a few ICOs of ourselves, that I cannot go into details right now. But some big projects, that I think some software developers in the island that have talent, could work on, and just develop. >> Michael, talk about who's working with you guys. Who's helping you out? Give some shout-outs, who's involved in the project, what kind of momentum do you have? And what are you guys looking for, for continued support? >> So, we're looking for people that come to the island and have big ICOs. We're looking to just speak with them, see if they could give us some feedback on what we have to do to move along this project. So we're working with Link Puerto Rico, it's a software development company here in the island. So they're helping us with the curriculum. So we're working hands-on with ICOs, but we also want to teach. So we have to make a curriculum. So we teach people that have no idea. The other day, we had an event where we taught 50 people how to create a smart contract from scratch. Those are 50 people who are not the same anymore. So we're working with Brock Pierce, he's going to be one of the main speakers at our event. We're going to have an event the 17th. You can register at Edublock.eo, it's totally free. Why did this event come to be? So, we have Blockchain Unbound, right? So it's about $1,000. So most people want to be part of this event that can't be. Most humans, that's too of a hefty pay. >> John: Yeah, it's a lot of cash. >> It's a lot of cash. You know, $1,000 is food; $1,000 is gas, a whole semester is $1,000. So what we did was, we grabbed 14 main speakers from Blockchain Unbound, Enrique Martinez, Brock Pierce, ListCoin, ArtCoin, they're going to be talking about microgrids, about housing. So we got a university, we have the people. It's free, so anyone can come. All you have to do is register at Edublock.io. >> Great stuff, Michael, this is fantastic. I love what you're doing, and I'm really thankful you're doing it. And because, when you get people together, magic happens. And I think what's really exciting is that the market is accepting that now. And Brock talked about that on stage today, here at Blockchain Unbound, announcing his restart venture fund. 100% dedicated to entrepreneurs. And he's structuring it in a way, where... I mean, not a lot of preference here. So he gets a little bit carved out for the managers of the fund, and they got some lot of cash they're managing. But it's all about feeding the entrepreneurial ecosystem for venture development. >> And that's great, that's why Edublock has to be a thing. 'Cause we are the educational system in the island. And so, if this is a movement that's happening here, and this is going to become the epicenter of this multi-billion dollar market, we need to have people prepared for this. We have to create the transparency. So that's why Edublock is such an important thing, here in the island. >> I love what you're doing, the young people. I see it in Silicon Valley, all around the United States and around the world. Trust matters, reputation matters, who you work with matters. And I love your project. It reminds me of when I interviewed Vint Cerf many years ago, father of the internet. TCP/IP connected three universities, four universities, five universities, and then multiple universities. That became the backbone for the internet. I see what you're doing as something as game-changing. You can connect the universities and then the curriculum, and keep it decentralized, no central authority, you have the trust and you have the voices of the people and software and applications. That's super fantastic. >> By the way, I just want to say something right now. You don't have to be a software developer to be in Edublock. So, most people are scared that if they aren't a programmer, they don't have experience, the don't know solidity, they can't be part of Edublock. The thing is, we're teaching from scratch, as well. We're working with software, we're working with hardware, we're working with a team of daily traders. Miners, we're going to teach how to make GPU, how to make an A6 from scratch. So you're going to learn a lot of things, and it's free. >> Great point. That brings up the community question. Because the point is, you don't have to be a coder. You're in the community. So, I want to ask you, what is the community like right now? What's it look like? It sounds like it's robust, it's active. What do you and the guys hope to do with the development of the community? >> Okay so the community, I would say it's divided, as of now. So most people are scared, they don't know what's going on. Most people that come here start off with the what, the how, and people are scared. But the young people, are like, yo, this is happening. This is not a moment, this is a movement. This is a movement and they're just so happy to be part of it. >> Well, I got to tell ya, as an old guy like me, I've seen many waves. When the waves come, you jump on it. And I'm so excited that you're doing what you're doing. Appreciate what you're doing. Michael Angelo, co-founder of EDU block.io, Edublock.io. They have a big event on the 17th, if you want to check it out. We're going to try to do a swing by with The Cube, but congratulations. Bringing the content to the masses, that's our job at The Cube, that's what we do, that's our mission. And thanks for taking this time, appreciate it. >> Of course. >> I'm John Furrier, with The Cube. Thanks for watching. (upbeat electronic music)

>> Announcer: Live from New York City, it's The Cube, covering IBM data science for all. Brought to you by IBM. >> Welcome back to data science for all. This is IBM's event here on the west side of Manhattan, here on The Cube. We're live, we'll be here all day, along with Dave Vallente, I'm John Walls Poor Dave had to put up with all that howling music at this hotel last night, kept him up 'til, all hours. >> Lots of fun here in the city. >> Yeah, yeah. >> All the crazies out last night. >> Yeah, but the headphones, they worked for ya. Glad to hear that. >> People are already dressed for Halloween, you know what I mean? >> John: Yes. >> In New York, you know what I mean? >> John: All year. >> All the time. >> John: All year. >> 365. >> Yeah. We have with us now the head of data science, and the VP at Galvanize, Nir Kaldero, and Nir, good to see you, sir. Thanks for being with us. We appreciate the time. >> Well of course, my pleasure. >> Tell us about Galvanize. I know you're heavily involved in education in terms of the tech community, but you've got corporate clients, you've got academic clients. You cover the waterfront, and I know data science is your baby. >> Nir: Right. >> But tell us a little bit about Galvanize and your mission there. >> Sure, so Galvanize is the learning community for technology. We provide the training in data science, data engineering, and also modern software engineering. We recently built a very large, fast growing enterprise corporate training department, where we basically help companies become digital, become nimble, and also very data driven, so they can actually go through this digital transformation, and survive in this fourth industrial revolution. We do it across all layers of the business, from the executives, to managers, to data scientists, and data analysts, and kind of transform and upscale all current skills to be modern, to be digital, so companies can actually go through this transformation. >> Hit on one of those items you talked about, data driven. >> Nir: Right. >> It seems like a no-brainer, right? That the more information you give me, the more analysis I can apply to it, the more I can put it in my business practice, the more money I make, the more my customers are happy. It's a lay up, right? >> Nir: It is. >> What is a data driven organization, then? Do you have to convince people that this is where they need to be today? >> Sometimes I need to convince them, but (laughs) anyway, so let's back up a little bit. We are in the midst of the fourth industrial revolution, and in order to survive in this fourth industrial revolution, companies need to become nimble, as I said, become agile, but most importantly become data driven, so the organization can actually best respond to all the predictions that are coming from this very sophisticated machine intelligence models. If the organization immediately can best respond to all of that, companies will be able to enhance the user experience, get insight about their customers, enhance performances, and et cetera, and we know that the winners in this revolution, in this era, will be companies who are very digital, that master the skills of becoming a data driven organization, and you know, we can talk more about the transformation, and what it consisted of. Do you want me to? >> John: Sure. >> Can I just ask you a question? This fourth wave, this is what, the cognitive machine wave? Or how would you describe it? >> Some people call it artificial intelligence. I think artificial intelligence is like big data, kind of like a buzz word. I think more appropriately, we should call it machine intelligence industrial revolution. >> Okay. I've got a lot of questions, but carry on. >> So hitting on that, so you see that as being a major era. >> Nir: It's a game changer. >> If you will, not just a chapter, but a major game changer. >> Nir: Yup. >> Why so? >> So, okay, I'll jump in again. Machines have always replaced man, people. >> John: The automation, right. >> Nir: To some extent. >> But certain machines have replaced certain human tasks, let's say that. >> Nir: Correct. >> But for the first time in history, this fourth era, machine's are replacing humans with cognitive tasks, and that scares a lot of people, because you look at the United States, the median income of the U.S. worker has dropped since 1999, from $55,000 to $52,000, and a lot of people believe it's sort of the hollowing out of that factor that we just mentioned. Education many believe is the answer. You know, Galvanize is an organization that plays a critical role in helping deal with that problem, does it not? >> So, as Mark Zuckerberg says, there is a lot of hate love relationship with A.I. People love it on one side, because they're excited about all the opportunities that can come from this utilization of machine intelligence, but many people actually are afraid from it. I read a survey a few weeks ago that says that 36% of the population thinks that A.I. will destroy humanity, and will conquer the world. That's a fact that's what people think. If I think it's going to happen? I don't think so. I highly believe that education is one of the pillars that can address this fear for machine intelligence, and you spoke a lot about jobs I talk about it forever, but just my belief is that machines can actually replace some of our responsibilities, right? Not necessarily take and replace the entire job. Let's talk about lawyers, right? Lawyers currently spend between 40% to 60% of the time writing contracts, or looking at previous cases. The machine can write a contract in two minutes, or look up millions of data points of previous cases in zero time. Why a lawyer today needs to spend 40% to 60% of the time on that? >> Billable hours, that's why. >> It is, so I don't think the machine will replace the job of the lawyer. I think in the future, the machine replaces some of the responsibilities, like auditing, or writing contracts, or looking at previous cases. >> Menial labor, if you will. >> Yes, but you know, for example, the machine is not that great right now with negotiations skills. So maybe in the future, the job of the lawyer will be mostly around negotiation skills, rather than writing contracts, et cetera, but yeah, you're absolutely right. There is a big fear in the market right now among executives, among people in the public. I think we should educate people about what is the true implications of machine intelligence in this fourth industrial revolution and era, and education is definitely one of those. >> Well, one of my favorite stories, when people bring up this topic, is when Gary Kasparov lost to the IBM super computer, Blue Jean, or whatever it's called. >> Nir: Yup. >> Instead of giving up, what he said is he started a competition, where he proved that humans and machines could beat the IBM super computer. So to this day has a competition where the best chess player in the world is a combination between humans and machines, and so it's that creativity. >> Nir: Imagination. >> Imagination, right, combinatorial effects of different technologies that education, hopefully, can help keep those either way. >> Look, I'm a big fan of neuroscience. I wish I did my PhD in neuroscience, but we are very, very far away from understanding how our brain works. Now to try to imitate the brain when we don't know how the brain works? We are very far away from being in a place where a machine can actually replicate, and really best respond like a human. We don't know how our brain works yet. So we need to do a lot of research on that before we actually really write a very strong, powerful machine intelligence model that can actually replace us as humans, and outbid us. We can speak about Jeopardy, and what's on, and we can speak about AlphaGo, it's a Google company that kind of outperformed the world champion. These are very specific tasks, right? Again, like the lawyer, the machines can write beautiful contracts with NLP, machines can look at millions and trillions of data and figure out what's the conclusion there, right? Or summarize text very fast, but not necessarily good in negotiation yet. >> So when you think about a digital business, to us a digital business is a business that uses data to differentiate, and serve customers, and maintain customers. So when you talk about data driven, it strikes me that when everybody's saying digital business, digital transformation, it's about a data transformation, how well they utilize data, and if you look at the bell curve of organizations, most are not. Everybody wants to be data driven, many say they are data driven. >> Right. >> Dave: Would you agree most are not? >> I will agree that most companies say that they are data driven, but actually they're not. I work with a lot of Fortune 500 companies on a daily basis. I meet their executives and functional leaders, and actually see their data, and business problems that they have. Most of them do tend to say that they are data driven, but truly just ask them if they put data and decisions in the same place, every time they have to make a decision, they don't do it. It's a habit that they don't yet have. Companies need to start investing in building what we say healthy data culture in order to enable and become data driven. Part of it is democratization of data, right? Currently what I see if lots of organizations actually open the data just for the analyst, or the marketers, people who kind of make decisions, that need to make decisions with data, but not throughout the entire organization. I know I always say that everyone in the organization makes decisions on a daily basis, from the barista, to the CEO, right? And the entirety of becoming data driven is that data can actually help us make better decisions on a daily basis, so how about democratizing the data to everyone? So everyone, from the barista, to the CEO, can actually make better decisions on a daily basis, and companies don't excel yet in doing it. Not every company is as digital as Amazon. Amazon, I think, is actually one of the most digital companies in the world, if you look at the digital index. Not everyone is Google or Facebook. Most companies want to be there, most companies understand that they will not be able to survive in this era if they will not become data driven, so it's a big problem. We try at Galvanize to address this problem from executive type of education, where we actually meet with the C-level executives in companies, and actually guide them through how to write their data strategy, how to think about prioritizing data investment, to actual implementation of that, and so far we are highly successful. We were able to make a big transformation in very large, important organizations. So I'm actually very proud of it. >> How long are these eras? Is it a century, or more? >> This fourth industrial? >> Yeah. >> Well it's hard to predict that, and I'm not a machine, or what's on it. (laughs) >> But certainly more than 50 years, would you say? Or maybe not, I don't know. >> I actually don't think so. I think it's going to be fast, and we're going to move to the next one pretty soon that will be even more, with more intelligence, with more data. >> So the reason I ask, is there was an article I saw and linked, and I haven't had time to read it, but it talked about the Four Horsemen, Amazon, Google, Facebook, and Apple, and it said they will all be out of business in 50 years. Now, I don't know, I think Apple probably has 50 years of cash flow in the bank, but then they said, the one, the author said, if I had to predict one that would survive, it would be Amazon, to your point, because they are so data driven. The premise, again I didn't read the whole thing, was that some new data driven, digital upstart will disrupt them. >> Yeah, and you know, companies like Amazon, and Alibaba lately, that try kind of like in a competition with Amazon about who is becoming more data driven, utilizing more machine intelligence, are the ones that invested in these capabilities many, many years ago. It's no that they started investing in it last year, or five years ago. We speak about 15 and 20 years ago. So companies who were really a pioneer, and invested very early on, will predict actually to survive in the future, and you know, very much align. >> Yeah, I'm going to touch on something. It might be a bridge too far, I don't know, but you talk about, Dave brought it up, about replacing human capital, right? Because of artificial intelligence. >> Nir: Yup. >> Is there a reluctance, perhaps, on behalf of executives to embrace that, because they are concerned about their own price? >> Nir: You should be in the room with me. (laughing) >> You provide data, but you also provide that capability to analyze, and make the best informed decision, and therefore, eliminate the human element of a C-suite executive that maybe they're not as necessary today, or tomorrow, as they were two years ago. >> So it is absolutely true, and there is a lot of fear in the room, especially when I show them robots, they freak out typically, (John and Dave laugh) but the fact is well known. Leaders who will not embrace these skills, and understanding, and will help the organization to become agile, nimble, and data driven, will not survive. They will be replaced. So on the one hand, they're afraid from it. On the other side, they see that if they will not actually do something, and take an action today, they might be replaced in the future. >> Where should organizations start? Hey, I want to be data driven. Where do I start? >> That's a good question. So data science, machine learning, is a top down initiative. It requires a lot of funding. It requires a change in culture and habits. So it has to start from the top. The journey has to start from executive, from educating and executive about what is data science, what is machine learning, how to prioritize investments in this field, how to build data driven culture, right? When we spoke about data driven, we mainly speaks about the culture aspect here, not specifically about the technical side of it. So it has to come from the top, leaders have to incorporate it in the organization, the have to give authority and power for people, they have to put the funding at first, and then, this is how it's beautiful, that you actually see it trickles down to the organization when they have a very powerful CEO that makes a decision, and moves the organization quickly to become data driven, make executives look at data every time they make a decision, get them into the habit. When people look up to executives, they try to do the same, and if my boss is an example for me, someone who is looking at data every time he is making a decision, ask the right questions, know how to prioritize, set the right goals for me, this helps me, and helps the organization better perform. >> Follow the leader, right? >> Yup. >> Follow the leader. >> Yup, follow the leader. >> Thanks for being with us. >> Nir: Of course, it's my pleasure. >> Pinned this interesting love hate thing that we have going on. >> We should address that. >> Right, right. That's the next segment, how about that? >> Nir Kaldero from Galvanize joining us here live on The Cube. Back with more from New York in just a bit.

>> Narrator: Live from Las Vegas, it's the Cube covering Interconnect 2017, brought to you by IBM. >> Welcome back to Interconnect 2017 from Las Vegas everybody, this is the Cube the leader in live tech coverage. Bina Halmann is here, she's a Cube alumn and the vice president of offering management for storage and software defined at IBM and she's joined by Tahir Ali, who's the director of Enterprise Architecture at the City of Hope Medical Center. Folks, welcome to the Cube- >> Tahir: Thank you very much. >> Thanks so much for coming on. >> Bina: Thanks for having us. >> So Bina we'll start with you been on the cube a number of times. >> Yes. >> Give us the update on what's happening with IBM and Interconnect. >> Yeah, no it's a great show. Lots of exciting announcements and such. From an IBM perspective storage we've been very busy. Filling out our whole flash portfolio. Adding a complete set of hybrid cloud capabilities to our software defined storage. It's been a great 2016 and we're off to a great start in 2017 as well. >> Yeah [Inaudible] going to be here tomorrow >> That's right. so everbody's looking forward to that. So Tahir, let's get into City of Hope. Tell us about the organization and your role. >> Sure, so City of Hope if one of the forty seven comprehensive cancer centers in the nation. We deal with cancer of course, HIV, diabetes and other life threatening diseases. We are maybe 15 to 17 miles east of Los Angeles. My role in particular, I'm a Director of Enterprise Architecture so all new technologies, all new applications that land on City of Hope, we go through all the background. See how the security is going to be, how it's going to implement in our environment, if it's even possible to implement it. Making sure we talk to our business owners, figure out if there's a disaster recovery requirement if they have a HA requirement, if it's a clinical versus a non-clinical application. So we look at a whole stack and see how a new application fits into the infrastructure of City of Hope. >> So you guys to a lot of research there as well or? >> Absolutely. >> Yeah. >> So we are research, we are the small EDU and we are the medical center so- >> So a lot of data. >> A whole lot of data. Data just keeps coming and keeps coming and it's almost like never ending stream of data. Now with the data it's not only just data- Individual data is also growing. So a lot of imaging that happens for cancer research, or cancer medical center, gets bigger and bigger per patient as the three dimensional imaging is here. We look at resolution that is so much more today than it used to be five years. So every single image itself is so much bigger today than it used to be five years ago. Just a sheer difference in the resolution and the dimensions of the data. >> So what are the big drivers in your industry, and how is it affecting the architecture that you put forward? >> Right, so I think that a couple of huge things that are maybe two or three huge conversion points, or the pivot points that we see today. One of them is just the data stream as I mentioned earlier. The second is because a lot of the PHI and hipaa data that we have today- Security is a huge concern in a lot of the healthcare environment. So those two things, and it's almost like a catch 22. More data is coming in you have to figure out where you're going to put that data. But at the same time you got to make sure every single bit is secured enough. So there's a catch 22 where its going, where you have to make sure that data keeps coming and you keep securing the same data. Right so, those two things that we see pivoting the way we strategize around our infrastructure. >> It's hard, they're in conflict in way, >> Tahir: Absolutely. >> Because you've got to lock the data up but then you want to provide accessibilty... >> Tahir: Absolutely. >> as well. So paint a picture of your infrastructure and the applications that it's supporting. >> Right, so our infrastructure is mainly in-house, and our EMR is currently off-prem. A lot of clinical and non-clinical also stay in-house with us in our data center on-prem. Now we are kind of starting to migrate to cloud technologies more and more, as just things are ballooning. So we are in that middle piece where some of our infrastructure in in-house, slowly we are migrating to cloud. So we are at like at a hybrid currently. And as things progress I think more and more is going to go to the cloud. But for a medical center security is everything. So we have to be very careful where our data sits. >> So Bina when you hear that from a client >> Bina: Mm-hmm (affirmative) >> how do you respond? And you know, what do you propose? >> Bina: Yeah. >> How does it all... >> Yeah well- >> come about. >> You know as we see clients like Tahir, and some of the requirements in these spaces. Security is definitely a key factor. So as we develop our products, as we develop capabilities we ensure that security is a number one focus area for us. Whether it's for the on-prem storage, whether it's for the data that's in motion from moving from the on-prem into the cloud, and secure completely all the way through where the client has the control on the security, the keys et cetera. So a lot goes into making sure as we architect these solutions for our clients, that we focus on security. And of course some of the other requirements, industry specific requirements, are all also very important and we focus in on those as well. Whether it's regulatory or compliance requirements, right. >> So from a sort of portfolio standpoint what do you guys do when there's all kinds of innovations over that last four or five years coming in with flash, we heard about object stores this morning, we got cloud, you got block, you've got file, what are you guys doing? >> So we do a lot of different things, so from having filers in-house to doing block storage from- And the worst thing now these days with big data is, as the data is growing the security needs are growing but the end result with the researchers and our physicians the data availability needs to be fast. So now comes a bigger catch 22, where the data is so huge but at the same time they want that all of that very quickly on their fingertips. So now what do you do? That's where we bring in a lot of the flash to upfront it. 10 to 12 percent of our infrastructure has flash in the front, this way all the rendering, or all the rights that happen or- First land on the flash. So everybody who writes, feels like it's a very quick write. But there's a petabytes and petabytes behind the scene that could be on-prem, it could be on the cloud, but they don't need to know that. Its, everything lands so fast that it looks like it's just local and fast. So there's a lot of crisscross that is happening, and started maybe four five years ago with the speed of data is not going to be slow. The size of data increasing like crazy and then security is becoming a bigger and bigger concern as you know. Maybe every month or month and a half there's a breach somewhere that people have to deal with. So we have to handle all of that in one shot. So you know, it's more than just infrastructure itself. There's policies, there's procedures, there's a lot that goes around. >> So when you think about architecting, obviously you think about workloads and- >> Tahir: Of Course. >> what the workload requirement is, it's no a one size fits all. >> Tahir: Right right. >> So where do you start, do you start with- >> Tahir: Sure. >> Sort of, you know a conversation with the business? >> Sure, sure. >> How much money do you got? >> So we don't really deal with the money at all. We provide the best possible solution for that business requirement. So the conversation happens, "tell us what you're looking for." "We're looking for a very fast XYZ." "Okay tell us what exactly you need." "Here's the application, we want it available all the time, "and this is how it's going to look like, "it can't be down because our patients are depending on it". So on and so forth. We take that, we talk to our vendors. We look at exactly how it's architected. If it's- Let's just say it's three-tiered. There's a web, there's an app and then there's a database. You already know by default that if it's a database it's going to go on a high transactional IO where either it's a flash or a very fast spinning disc with a lot of spindles. From there you get the application. Could be a virtual machine, could not be a virtual machine. From there you get to a web tier. Web tiers are usually always on a virtual infrastructure. Then you realize if you want to put it on a DMZ so people from outside can get to it, or it's only for internal use. Then you draw the entire architecture diagram out. Then you price it out, you said "Okay if you want this to be "always on, maybe you need a database that is always on." Right, or you need a database that replicates 24/7. That has a cost associated to that. If you have an application- If wanted two application maybe it's a costier application it could be HA it could not be HA, so there's a cost to that. Web servers are kind of, you know cheaper tier of virtual machines. And then there's a architecture diagram, all the requirements are met in there. And there's a cost associated to that, saying business unit here is how much it's going to cost and this is what you will have. >> Okay so that's where the economics, >> Exactly >> comes into play. Okay this is what your requirements are >> Yep. >> This is, based on that what we would advise. >> Exactly, yeah. >> And then essentially it's can you afford it. >> Right right. (laughs) If you want to buy a house that is a three bedrooms and three bathrooms in Palo Alto, versus a six bedrooms and then seven bathrooms in Palo Alto it's going to be a financial impact that you might not like. (laughs) So it's one of those, right. So what you want has a financial impact on your end solution and that's what we provide. We don't force somebody to get something. We just give them- Hey how many kids do you have? Four kids, then maybe you need a five bedroom house. Right so we kind of do that. >> Is it common discussion? >> Yeah it is, it is. And that's, as you know, some of the things we do focus on. Right, as we- In addition to the security aspect of it of course, is around the automation, around driving in the efficiencies. Because at the end of the day, you know, whether as capital expands or operational expands you want to optimize for both of those. And that's where as we architect the solutions, develop the offerings, we ensure that we build-in capabilities, whether it's storage efficiency capabilities like virtualization, or de-dupe or compression. But as well as this automated tiering. Tiering off from flash to lower tier, whether it's on-prem lower, slower- >> Tahir: Could be a disc. >> speed disc or tape or even off to the cloud, right. And being able to do that, provide that I think addresses many of our clients' needs. That's a common requirement that we do hear. >> And as mentioned 10 to 12 percent of it if flash. >> Tahir: Right. >> The rest, you know ninety percent or so is something else. That's economics, correct? >> Right so- >> And how do you see that changing? >> So I think the percentage won't really change. I think the data size will change. So you have to just think about things, just in generality. Just what you do today. You know when you take a picture, maybe you look at it the first three days, even if you have a phone. After three days, maybe you look at it maybe once every two months. After three months, guess what? You will always never look at them. They're kind of moved away from even your memory banks in your head. Then you say, "Oh I was looking through it". And then maybe once in awhile you look at it. So you have to look at the behavior. A lot of the applications have the same behavior, where the new data is required right away. The older the data gets, the more archival state it gets. It gets warmer and then it gets colder. Now, as a healthcare institute we have to devise something that is great financially, also has the security, and put away in a way where we can pull it without having pain to put it back. So that's where the tiering comes to play. Doesn't matter how we do it. >> And your planning assumption is that the cost disparity between flash and other forms of storage will remain. That other- >> So- >> forms will remain cheaper. >> Right, so we are hoping, but I think the hybrid model of flash- So once you do a hybrid with flash and disc, then it becomes a little more economically suitable for a lot of the people. They do the same thing, they do tiering, but they make it look like a bigger platform. So it's like, "We can give you a petabyte "but it's going to look like flash." It doesn't work like that. They might have 300 terabyte of flash, 700- but it's so integrated quickly, that they can pull it and push it. Then there's a read-aheads write-aheads that takes that advantage to make it look like it. That will drop your pricing. The special sauce that transfer the data between slower and flash discs. >> Two questions for you. >> Sure. >> What do you look for in a supplier? And what drives you nuts about a supplier, that you don't want a supplier to do? >> Sure. So personally speaking, this is just my personal opinion. A stable environment a tried and true vendor is important. Somebody who has a core competency of doing this for a longer term is what I personally look at. There's a lot of new players who come in, they stay for a couple of years, they explode, somebody takes them over or they just kind of vanish. Or certain people outside of their core competency. So if Toyota started to make- Because they wanted to save money they said, "Hey Toyota from now on will make "the tires that are called Toyota." But Toyota is not a tire company. Other companies, Bridgestone and Michelin's have been making tires for a very long time. So the core competency of Toyota is building the cars and not the tires. So when I see these people, or the vendors saying, "Okay I can give you this this this this and this and that and the security and that. Maybe three out of those five things are not their core competency. So I start to wonder if the whole stack is worth it because there's going to be some weakness because they don't have the core competency. That's what I look at. What drives me crazy is, every single time somebody comes to meet with me they want to sell me everything and the kitchen sink under one umbrella. And the answer is one single pane of glass to manage everything. Life is not that easy, I wish it was but it really is not. (laughs) So those two things are- >> Selling the fantasy right. Now Bina we'll give you the last word. Interconnect, give us your final thoughts. What should we know about what's going on in software-defined and IBM storage. >> Yeah you know lots of announcements at Interconnect. You heard, as you talked about, cloud optic storage we've got great new pricing models and capabilities and overall software-defined storage. We're continuing to innovate, continue add capabilities like analytics and you'll see us doing more and more on cognitive. Cognitive storage management to get more out of the data, help clients get more and more information and value out of their data. >> What's the gist of the new pricing models, just um- >> Flexible pricing model depending on how the both hybrid as well as the three tiered on-prem and in between. But really cold as well as a flexible pricing model where depending on how you use the data you know you get consistent pricing so between on-prem and in the cloud. >> So more cloud-like pricing >> Yes, exactly. >> Great. >> Yep. >> Easier consumption, excellent. Well Bina Tahir thanks very much for coming to the cube. >> Yes yes thank you. >> Dave: Pleasure having you. >> Thank you. >> Thank you for having us. >> Dave: You're welcome. Alright keep it right there everybody we'll be back with our next guest and a wrap, right after this short break. Right back. (upbeat music)

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)