>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

>> Announcer: Live from Boston, Massachusetts this is the CUBE covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston everybody this is the CUBE. We're live here at Spark Summit East in the Hynes Convention Center. This is the CUBE, check out SiliconANGLE.com for all the news of the day. Check out Wikibon.com for all the research. I'm really excited about this session here. Al Essa is here, he's the vice president of analytics and R&D at McGraw-Hill Education. And I'm so excited because we always talk about digital transformations and transformations. We have an example of 150 year old company that has been, I'm sure, through many transformations. We're going to talk about a recent one. Al Essa, welcome to the CUBE, thanks for coming on. >> Thank you, pleasure to be here. >> So you heard my little narrative up front. You, obviously, have not been with the company for 150 years (laughs), you can't talk about all the transformations, but there's certainly one that's recent in the last couple of years, anyway which is digital. We know McGraw Hill is a print publisher, describe your business. >> Yeah, so McGraw Hill Education has been traditionally a print publisher, but beginning with our new CEO, David Levin, he joined the company about two years ago and now we call ourselves a learning science company. So it's no longer print publishing, it's smart digital and by smart digital we mean we're trying to transform education by applying principles of learning science. Basically what that means is we try to understand, how do people learn? And how they can learn better. So there are a number of domains, cognitive science, brain sciences, data science and we begin to try to understand what are the known knowns in these areas and then apply it to education. >> I think Marc Benioff said it first, at least the first I heard he said there were going to be way more Saas companies that come out of non-tech companies than tech companies. We're talking off camera, you're a software company. Describe that in some detail. >> Yeah, so being a software company is new for us, but we've moved pretty quickly. Our core competency has been really expert knowledge about education. We work with educators, subject matter experts, so for over a hundred years, we've created vetted content, assessments, and so on. So we have a great deal of domain expertise in education and now we're taking, sort of the new area of frontiers of knowledge, and cognitive science, brain sciences. How can learners learn better and applying that to software and models and algorithms. >> Okay, and there's a data component to this as well, right? >> So yeah, the way I think about it is we're a smart digital company, but smart digital is fueled by smart data. Data underlies everything that we do. Why? Because in order to strengthen learners, provide them with the optimal pathway, as well as instructors. We believe instructors are at the center of this new transformation. We need to provide immediate, real-time data to students and instructors on, how am I doing? How can I do better? This is the predictive component and then you're telling me, maybe I'm not on the best path. So what's my, "How can I do better?" the optimal path. So all of that is based on data. >> Okay, so that's, I mean, the major reason. Do you do any print anymore? Yes, we still do print, because there's still a huge need for print. So print's not going to go away. >> Right. Okay, I just wanted to clarify that. But what you described is largely a business model change, not largely, it is a business model change. But also the value proposition is changing. You're providing a new service, related, but new incremental value, right? >> Yeah, yeah. So the value proposition has changed, and here again, data is critical. Inquiring minds want to know. Our customers want to know, "All right, we're going to use your technology "and your products and solutions, "show us "rigorously, empirically, that it works." That's the bottom line question. Is it effective? Are the tools, products, solutions, not just ours, but are our products and solutions have a context. Is the instruction effective? Is it effective for everyone? So all that is reliant on data. >> So how much of a course, how much of the content in a course would you prepare? Is it now the entire courseware and you instrument the students interaction with it? And then, essentially you're selling the outcomes, the improved outcomes. >> Yeah, I think that's one way to think about it. Here's another model change, so this is not so much digital versus non-digital, but we've been a closed environment. You buy a textbook from us, all the material, the assessments is McGraw Hill Education. But now a fundamental part of our thinking as a software company is that we have to be an open company. Doesn't mean open as in free, but it's an open ecosystem, so one of the things that we believe in very much is standards. So there's a standard body in education called IMS Global. My boss, Stephen Laster, is on the board of IMS Global. So think of that as, this encompasses everything from different tools working together, interoperability tools, or interoperability standards, data standards for data exchange. So, we will always produce great content, great assessments, we have amazing platform and analytics capability, however, we don't believe all of our customers are going to want to use everything from McGraw Hill. So interoperability standards, data standards is vital to what we're doing. >> Can you explain in some detail this learning science company. Explain how we learn. We were talking off camera about sort of the three-- >> Yeah, so this is just one example. It's well known that memory decays exponentially, meaning when you see some item of knowledge for the first time, unless something happens, it goes into short-term memory and then it evaporates. One of the challenges in education is how can I acquire knowledge and retain knowledge? Now most of the techniques that we all use are not optimal. We cram right before an exam. We highlight things and that creates the illusion that we'll be able to recall it. But it's an illusion. Now, cognitive science and research in cognitive science tells us that there are optimal strategies for acquiring knowledge and recalling it. So three examples of that are effort for recall. If you have to actively recall some item of knowledge, that helps with the stickiness. Another is space practice. Practicing out your recall over multiple sessions. Another one is interleaving. So what we do is, we just recently came out with a product last week called, StudyWise. What we've done is taken those principles, written some algorithms, applies those algorithms into a mobile product. That's going to allow learners to optimize their acquisition and recall of knowledge. >> And you're using Spark to-- >> Yeah, we're using Spark and we're using Databricks. So I think what's important there is not just Spark as a technology, but it's an ecosystem, it's a set of technologies. And it has to be woven together into a workflow. Everything from building the model and algorithm, and those are always first approximations. We do the best we can, in terms of how we think the algorithm should work and then deploy that. So our data science team and learning science team builds the models, designs the models, but our IT team wants to make sure that it's part of a workflow. They don't want to have to deal with a new set of technologies, so essentially pressing the button goes into production and then it doesn't stop there, because as Studywise has gone on the market last week, now we're collecting data real-time as learners are interacting with our products. The results of their interactions is coming in to our research environment and we're analyzing that data, as a way of updating our models and tuning the models. >> So would it be fair to say that it was interesting when you talked about these new ways of learning. If I were to create an analogy to Legacy Enterprise apps, they standardize business transactions and the workflows that went with them. It's like you're picking out the best practices in learning, codifying them into an application. And you've opened it up so other platforms can take some or all and then you're taking live feedback from the models, but not just tuning the existing model, but actually adding learning to the model over time as you get a better sense for how effort of recall works or interleaving works. >> Yeah, I think that's exactly right. I do want to emphasize something, an aspect of what you just said is we believe, and it's not just we believe, the research in learning science shows that we can get the best, most significant learning gains when we place the instructor, the master teacher, at the center of learning. So, doing that, not just in isolation, but what we want to do is create a community of practitioners, master teachers. So think of the healthcare analogy. We have expert physicians, so when we have a new technique or even an old technique, What's working? What's not working? Let's look at the data. What we're also doing is instrumenting our tools so that we can surface these insights to the master practitioners or master teachers. George is trying this technique, that's working or not working, what adjustments do we need to make? So it's not just something has to happen with the learner. Maybe we need to adjust our curriculum. I have to change my teaching practices, my assessments. >> And the incentive for the master practitioners to collaborate is because that's just their nature? >> I think it is. So let's kind of stand back, I think the current paradigm of instruction is lecture mode. I want to impart knowledge, so I'm going to give a lecture. And then assessment is timed tests. In the educational, the jargon for that is summit of assessments, so lecture and tests. That's the dominant paradigm in education. All the research evidence says that doesn't work. (laughs) It doesn't work, but we still do it. >> For how many hundreds of years? >> Yeah. Well, it was okay if we needed to train and educate a handful of people. But now, everyone needs to be educated and it's lifelong learning rate, so that paradigm doesn't work. And the research evidence is overwhelming that it doesn't work. We have to change our paradigm where the new paradigm, and this is again based on research, is differentiated instruction. Different learners are at different stages in their learning and depending on what you need to know, I'm at a different stage. So, we need assessments. Assessments are not punitive, they're not tests. They help us determine what kind of knowledge, what kind of information each learner needs to know. And the instructor helps with the differentiated instruction. >> It's an alignment. >> It's an alignment, yeah. Really to take it to the next stage, the master practitioners, if they are armed with the right data, they can begin to compare. All right, practices this way of teaching for these types of students works well, these are the adjustments that we need to make. >> So, bringing it down to earth with Spark, these models of how to teach, or perhaps how to differentiate the instruction, how to do differentiated assessments, these are the Spark models. >> Yeah, these are the Spark models. So let's kind of stand back and see what's different about traditional analytics or business intelligence and the new analytics enabled by Spark, and so on. First, traditional analytics, the questions that you need to be able to answer are defined beforehand. And then they're implemented in schemas in a data warehouse. In the new order of things, I have questions that I need to ask and they just arise right now. I'm not going to anticipate all the questions that I might want to be able to ask. So, we have to be enable the ability to ask new questions and be able to receive answers immediately. Second, the feedback loop, traditional analytics is a batch mode. Overnight, data warehouse gets updated. Imagine you're flying an airplane, you're the pilot, a new weather system emerges. You can't wait a week or six months to get a report. I have to have corrective course. I have to re-navigate and find a new course. So, the same way, a student encounters difficulty, tell me what I need to do, what course correction do I need to apply? The data has to come in real-time. The models have to run real-time. And if it's at scale, then we have to have parallel processing and then the updates, the round trip, data back to the instructor or the student has to be essentially real-time or near real-time. Spark is one of the technologies that's enabling that. >> The way you got here is kind of interesting. You used to be CIO, got that big Yale brain (laughs) working for you. You're not a developer, I presume, is that right? >> No. >> How did you end up in this role? >> I think it's really a passion for education and I think this is at McGraw Hill. So I'm a first generation college student, I went to public school in Los Angeles. I had a lot of great breaks, I had great teachers who inspired me. So I think first, it's education, but I think we have a major, major problem that we need to solve. So if we look at... So I spent five years with the Minnesota state colleges and university system, most of the colleges, community colleges are open access institutions. So let me just give you a quick statistic. 70% of students who enter community colleges are not prepared in math and english. So seven out of 10 students need remediation. Of the seven out of 10 students who need remediation, only 15% not 5-0, one-five succeed to the next level. This is a national tragedy. >> And that's at the community college level? >> That's at the community college level. We're talking about millions of students who are not making it past the first gate. And they go away thinking they've failed, they incurred debt, their life is now stuck. So this is playing itself out, not to tens of thousands of students, but hundreds of thousands of students annually. So, we've got to solve this problem. I think it's not technology, but reshaping the paradigm of how we think about education. >> It is a national disaster, because often times that's the only affordable route for folks and they are taking on debt, thinking okay, this is a gateway. Al, we have to leave it there. Awesome segment, thanks very much for coming to the CUBE, really appreciate it. >> Thank you very much. >> All right, you're welcome. Keep it right there, my buddy, George and I will be back with our next guest. This is the CUBE, we're live from Boston. Be right back. (techno music) >> Narrator: Since the dawn of the cloud

(theCUBE theme music) >> Narrator: Live from Washington D.C. It's theCUBE, covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and its partner ecosystem. >> Welcome back here to the show floor at AWS Public Sector Summit 2017. Along with John Furrier, I'm John Walls. Glad to have you here on theCUBE as we continue our coverage here live from the nation's capital. Joining us now from Enlighten IT Consulting is John Eubank IV, Director of Program Management Office. John, thanks for joining us here on theCUBE, a CUBE rookie, I believe, is that correct? >> Yes, sir, yeah, thanks for the invite. >> Nice to break the maiden, good to have you aboard here. First off, tell us a little bit about your consulting firm for our viewers at home, to give an idea about your frame and why you're here at AWS. >> Absolutely, so we're a big data consulting company focused on cyber security solutions for the DOD IC community. What we jumped into about three years ago was a partnership with AWS. And seeing, just the volume, the velocity of data coming out of the DOD, that those on-premise server farms could not keep up, could not support it with the power, space and cooling needs. So we partnered with AWS and over the last three years we've been migrating our customers up to GovCloud, specifically. >> So what are you doing then for DOD specifically, then? When you said you solve problems, right? They've got reams and reams of data, trying to help them manage that process a little bit better, but, you know, drill down a little bit more specifically what you're doing for DOD. >> Absolutely, so we developed a proprietary technology called the Rapid Analytic Deployment and Management Framework, RADMF, it's available on RADMF.com, R A D M F dot com. >> John Walls: True marketer. >> Yeah, true marketer at heart. So that's our, sort of governance framework for DOD applications that want to move to the cloud. It automates the deployment process to get 'em out of their existing systems up to the cloud. One of the real problems inside the DOD that we've encountered is the disparate data sets to enable effective analytics when it comes to cyber security solutions. So, I like to think back to the day one conversation about, sort of the data swamp, not the data lake. That's exactly what we have inside the DOD. There's so many home-built sensors, paired with COT sensors, that it's created this absolute mess, or nightmare of data. That swamp needs to be drained. It needs to be, sort of refined in a way that we can call it a data lake, something understandable that people can-- >> I hate the term data lake, I, you've been listening, I, John knows I hate the term data lake. Love the term data swamp, because it illustrates exactly that, there is, if you don't watch the data, and don't share it, it's just stagnant, and it turns into a swamp. And I think, this is a huge issue. >> John Eubank IV: Absolutely correct. >> So I want you to just double down on that, just give some color. Is it the volume of the data, is it the lack of sharing, both? (laughs) >> It's really every, it's everything under the sun, there's, you know, sharing issues all across the federal government right now and who can see what data, Navy doesn't want to share with Army, inside the IC-- >> John Furrier: Well that'll never happen. >> Agencies don't want to share with each other. (laughs) I think we're, we're breaking down those walls. We're seeing that, when it comes to cyber security, no one person can defend an entire nation. No one agency can defend an entire nation on their own. It has to be a collaborative solution. It has to be a team effort. Navy, Army, Air Force, IC, etc., have to work together, in tendem, in partnership, if we're ever going to just, defend our nation from cyber hackers. >> I want to ask you a philosophical question, because, you know, as someone who's been online all my life, computer science, you've seen, there's always the notion of trolling, the notion of online message boards, back in the day when I was running, is now main stream now, >> John Eubank IV: Right. >> I mean people trolling each other on Twitter, for crying out loud, main stream. So, the culture of digital has an ethos, and open source is a big driver on that cyber security, there's a huge ethos of sharing, and it's kind of an honor among practitioners. >> John Eubank IV: Mm-hmm. 'cause they know how big the threat is. How is that evolving? Because this seems to highlight, your point about sharing, that it's, the digital world's different than the analog world, and some of the practices that are getting traction can be doubled-down on. So everyone's trying to figure out what's, what should be double-down on, and what are the good practices from the bad? Can you just share some cultural... >> Well, I think you hit the nail on the head with the open source model there. That is the key right here. It's not even within the government we need to share. It's industry and government, in partnership, need to approach these problem sets together and work on 'em as one cohesive body. So, for example, our company, our platform, it's entirely an open source platform. It's government-owned solution. We don't sell, it's the big data platform, it's provided by DISA right now. We don't sell that product. It's available to any government agency that wants it for free. We have 1500 different software developers and engineers from across the government community that collaborate together to evolve that platform. And that's really the only way we're going to make a significan difference right now. >> That creativity that could come out of this new process that you're referring to, I'm just kind of thinking out loud here on theCUBE, is interesting because you think about all those people on Twitch. >> John Eubank IV: Uh-huh. >> 34 million, I think, a day or whatever the big number, it's a huge number. Those idle gamers could be actually collaborating on a core problem that could be fun. So if you look at a crowd sourcing model of attacking data, this is kind of a whole new mindset of culture. To me, this is the kind of doors that open up when you start thinking like this model. Because the bad guys are already ahead of the game. I mean, so, how do you, how do you guys talk about that, 'cause you guys have to kind of keep some data masked, and you have to kind of, maybe not expose everything. How do you balance that secretive nature of it, and yet opening it up? >> That's a question that the DHS is struggling with, sort of day in and day out right now. They're going through a couple different iterations of different efforts. There was the ESSA program, there's the Automated Indicator Sharing program going on right now with DHS and some of the IC partners of what do we share with industry, because we're recognizing as a government we can't defend this nation on our own. We need an industry partnership. How do we open that up to the general public of the United States to do that crowd sourced mentality. Threat hunting is a lot of fun if you know what you're doing, and if somebody will guide you down the path, it's an endless world and a need for threat analysts to study the data sets that are out there. Indicators of compromise point you in a general direction, but they're a wide-open direction, and... >> They're already playing, it's like lagging in a video game, they're, gamers are already ahead of, the hackers are already ahead of you. Interesting point, Berkeley, University of California at Berkeley has a new program, they call it the quote Navy Seals of cyber. It's an integrated computer science and engineering and Haas business school program. And it's a four-year degree specifically for a special forces kind of thinking. Interdisciplinary, highly data driven, computer science, engineering and business so they can understand, again, hackers run a business model. These are organized units. This is kind of what we're up against. >> Absolutely agree. >> John Furrier: What are your thoughts on that? You think that's the, the right direction, we need more of it? >> We need more of it, absolutely. DOD is moving in the same direction with the cyber protection teams or CPTs. They're beginning to do sort of the same formal training models for the soldiers. Unfortunately, right now a lot of the cyber protection teams are just scavenged resources from other branches of the military. So you have guys in EOD that are now transitioning into cyber, and they're going from diffusing bombs to diffusing cyber threats. It's a totally different scenario and use case, and it's a tough struggle to transition into that when your background was diffusing a bomb. >> And you brought up the industry collaboration, talking about private, you know, private sector and public sector. I know, you know, personal experience in the wireless space, there was a lot of desire to share information, but yet there was a congressional reluctance. >> John Eubank IV: Mm-hmm. >> To allow that. For different concerns. Some we thought were very unwarranted at the time. So how do you deal with that, because that's another influence in this, is that you might have willing parties, but you've got another body over here that might not be on board. >> I think we're going to start seeing more of a shift as private industry acknowledges their need for government support and that government collaboration, so data breaches like the Target breach and massive credit card breaches that, you know, these private industries cannot keep up with defending their own network. They need government supoort for defending very large corporations. Walmart, Target, Home Depot, the list goes on of breaches. >> Final question as we wrap up here, but what's the coolest tech that you're seeing that's enabling you to be successful, whether it's cool tech that you're looking at, you're kicking the tires on. From software to Amazon, hardware, what are you seeing that's out there that's really moving the needle and getting people motivated? >> So a surprising thing there, I'm going to say the Snowball Edge. And people go, it's just a data hard drive. Well, not really. It's way more than a data hard drive. So when you come to Amazon you think enterprise solutions, enterprise capabilities. What the Snowball Edge provides is a deployable unit that has processing, compute, storage, etc., onboard that you can take into your local networks. They're putting it so you can run any VM you want on the Snowball Edge. What we're doing is we're taking that inside DOD tactical spaces that don't have connections to the internet. We're able to do computation analytics on threats facing that local regional onclave using a hard drive. It's really cool technology that hasn't been fully explored, but that's uh, that's where we're-- >> You can tell you're excited about it. Your eyes light up, you got a big smile on your face. >> Drove the new Ferrari that came out. >> Yeah, right. >> When I saw it, I just jumped all in. >> John Walls: You loved it, right. >> So, three months ago... >> You knew right away, too. >> Right. >> John Furrier: The big wheel. >> John, thank you for being with us. I think they're going to kick us out of the place, John. >> Hey, they got to unplug us. We're going to go until they unplug us. >> Alright, John, again thanks for being with us. >> Well, thank you guys for your time, much appreciated. >> Thank you for joining us here from Washington, for all of us here at theCUBE, we appreciate you being along for the ride at AWS Public Sector Summit 2017. (theCUBE theme music)