 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 Gillan. We have two guests for this session. We have Rebecca Shockley. She is the executive consultant at 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 the info. Shh, don't tell. You're giving a speech about the AI ladder. Right. What is the AI ladder? So, when we think about artificial intelligence or augmented intelligence, you know, it's very pervasive. We're starting to see it a lot more in the organizations, but the AI ladder is basically says that you need to build on a foundation of data so that data and information architecture is 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 the ladder really quickly and then realize they need to come back down and do some foundational work with their data. You know, I've been doing data and analytics with IBM for 21 years and data governance is never fun and it's hard. And people would just as soon go do something else than do data governance, data security, data stewardship and especially as we're seeing more business side use of data. So, 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 and whether it's virtual or physical. And 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 bakers, 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? Talk to our viewers about that. Yeah, first, I'd sort of like to start with laying some of the context of what we do. So McGraw Hill Education has been traditionally a textbook publisher. We've been around for over 100 years. I started with the company over 100 years ago. You 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. So we're trying to create intelligent software based on Spark 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 US, but internationally. It used to be we were born, went to school, got a job, raised a family, retired, and then we died. Well now, education is not episodic. People need to be educated. It's lifelong learning, right? And it's survival, but also flourishing. So that's created a massive problem and a challenge. So 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. But 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 people who have largely been neglected. So 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. 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 the 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 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, now 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 lifelong. So 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. And so what we're trying to do when 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 specific needs. And just 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 does 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 gonna be very different. And then, so our algorithms also help route the auxiliary of information and knowledge at the right time to the right person. I mean, you're talking about these massive social challenges. I mean, education as solving global inequity and not every company has maybe such a high-minded purpose. I mean, but does it take that kind of mission, that kind of purpose to unite employees? I mean, both of you, I'm interested in your perspectives here. I don't think it takes 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 global. You want to extract that mission to a higher level than making the world a better place. Exactly, or at least your little corner of the world. And again, what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that, right? So 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, what a 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 click this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, 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 CDO's face is getting buy-in. And what you're proposing about, you're this new model really appending the old stage on the stage model. Is there a lot of pushback or 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. That's what you do. But 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, because 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 for diversity and bringing talented people. I think they're out there. I think we could do a much better job with that. Recruiting them, great. 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 Gilland. We will have more from theCUBE live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.