 From theCUBE Studios in Palo Alto in Boston, it's theCUBE, covering empowering the autonomous enterprise, brought to you by Oracle Consulting. Welcome back everybody to this special digital event coverage that theCUBE is looking into the rebirth of Oracle Consulting. Janet George is here, she's Group VP Autonomous for Advanced Analytics with Machine Learning and Artificial Intelligence at Oracle, and she's joined by Grant Gibson, who's Group VP of Growth and Strategy at Oracle. Folks, welcome to theCUBE, thanks so much for coming on. Thank you. Grant, I want to start with you because you get strategy in your title. I'd like to start big picture. What is the strategy with Oracle, specifically as it relates to autonomous and also consulting? Sure, so I think Oracle has a deep legacy of strength and data. And over the company's successful history, it's evolved what that is from steps along the way. If you look at the modern enterprise, Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it, it's the how that's really tricky. And that most enterprises in order to really get an enterprise level ROI on an AI investment need to engage in projects of significant scope. And going from realizing there's an opportunity or realizing there's a threat to mobilizing yourself to capitalize on it is a daunting task for an enterprise. Certainly one that's, you know, anybody that's got any sort of legacy of success has built in processes, has built in systems, has built in skill sets and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs, as well as the data science needs to it. So wow, there's about five or six things that I want to follow up with you there. So this is going to be a good conversation. Janet, ever since I've been in the industry we were talking about AI, it sort of starts, stops, starts, stops. We had the AI winter, and now it seems to be here. It's almost feel like the technology never lived up to its promise. We didn't have the horsepower, the compute power, didn't have enough data maybe. So we're here today, it feels like we are entering a new era, why is that? And how will the technology perform this time? So for AI to perform, it's very reliant on the data. We entered the age of AI without having the right data for AI. So you can imagine that we just launched into AI without our data being ready to be training sex for AI. So we started with BI data, or we started with data that was already historically transformed. Formatted, had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly AI comes along and we say, take this data, our historical data, we haven't tested to see if this has labels in it, if this has learning capability in it, we just thrust the data to AI. And that's why we saw the initial wave of AI sort of failing because it was not ready for AI, ready for the generation of AI if you will. And part of I think the leap that clients are finding success with now is getting novel data types. And you're moving from the zeros and ones of structured data to image, language, written language, spoken language. You're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it, is different than what we would have understood under the structured data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. The language that we use today I feel like is going to change. And you just started to touch on some of it. Sensing, there are senses and the visualization and the auditory. So it's sort of this new experience that customers are seeing. And there's machine intelligence behind that. I call it the autonomous enterprise, right? The journey to be the autonomous enterprise. And when you're on this journey to be the autonomous enterprise, you need really the platform that can help you be. Cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud, right? Or doesn't end with the data lake. These are just infrastructures that are basic, necessary, necessities for being on that autonomous journey. But at the end, it's about how do you train and scale at very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous enterprise, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components, AI and machine learning to derive business intelligence and business value. So I want to get into a little bit of Oracle's role. But to do that, I want to talk a little bit more about the industry. So if you think about the way this, the industry seems to be restructuring around data, historically, industries had their own stack or value chain. And if you were in the finance industry, you were there for life. You know, so when you think about banking, for example, highly regulated industry, think about agriculture. These are highly regulated industries. It was very difficult to disrupt these industries. But now you look at an Amazon, right? And what does an Amazon or any other tech giant like Apple have? They have incredible amounts of data. They understand how people use or how they want to do banking. And so they've come up with Apple Cash or Amazon Pay. And these things are starting to eat into the market, right? So you would have never thought an Amazon could be a competition to a banking industry just because of regulations. But they are not hindered by the regulations because they're starting at a different level. And so they become an instant threat and an instant disruptor to these highly regulated industries. That's what data does, right? When you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous, if you will, capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry, it can be the cloud industry, the book industry, you know, different industries. So, you know, that's what I see happening with the tech giants. So Grant, this is a really interesting point that Janet is making that you mentioned you started off with like a couple of industries that are highly regulated, the harder to disrupt, you know, music got disrupted, publishing got disrupted, but you've got these regulated businesses, you know, defense, automotive actually hasn't been truly disrupted yet. Some of Tesla maybe is a harbinger. And so you've got this spectrum of disruption, but is anybody safe from disruption? I don't think anyone's ever safe from it. It's changed in evolution, right? That you, whether it's, you know, swapping horseshoes for cars or TV for movies or Netflix or any sort of evolution of a business, I wouldn't coast on any of it. And I think to your earlier question around the value that we can help bring to Oracle customers is that, you know, we have a rich stack of applications and I find that the space between the applications, the data that spans more than one of them is a ripe playground for innovations where the data already exists inside a company, but it's trapped from both a technology and a business perspective. And that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. Yeah, it's powerful. People always throw the bromide up. The data is the new oil and we've said, no, the data is far more valuable because you can use it in a lot of different places. Oil you can use once and it's, it has to follow the laws of scarcity. If you can unlock it. And so a lot of the incumbents, they have built a business around whatever, a factory or, you know, process and people. A lot of the trillion dollar start in us that become trillionaires, you know, I'm talking about data's at the core, their data company. So it seems like a big challenge for your incumbent customers, clients, is to put data at the core, be able to break down those silos. How do they do that? Grading down silos is really super critical for any business. It was okay to operate in a silo. For example, you would think that, oh, you know, I could just be payroll and expense reports and it wouldn't matter if I get into vendor performance management or purchasing, that can operate as a silo. But anymore we are finding that there are tremendous insights between vendor performance management, I expense report, these things are all connected. So you can't afford to have your data sit in silos. So grading down that silo actually gives the business very good performance, right? Insights that they didn't have before. So that's one way to go. But another phenomena happens, when you start to grade down the silos, you start to recognize what data you don't have to take your business to the next level, right? That awareness will not happen when you're working with existing data. So that awareness comes into form when you grade the silos and you start to figure out you need to go after a different set of data to get you to new product creation. What would that look like? New test insights or new CAPEX avoidance. That data is just, you have to go through the iteration to be able to figure that out. Stakes is what you're saying. So this notion of the autonomous enterprise, it won't help me here because I get kind of autonomous and automation coming into IT, IT ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. I think when AI is a technology problem, the company is at a loss. AI has to be a business problem. AI has to inform the business strategy. AI has to, when companies, the successful companies that have done, so 90% of our investments are going towards data. We know that and most of it going towards AI. There's data out there about this, right? And so we look at, what are these 90% of the company's investments? Where are these going and who is doing this right and who's not doing this right? One of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with Uber, right? So it's not like saying, okay, I'm going to have all these, I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market versus I'm going to do connected health, right? And so how does data serve the business model of being connected health rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now AI is informing drug discovery. AI is not helping you just put more drugs to the market, rather it's helping you come up with new drugs that would help the process of connected care. There's a lot of discussion in the press about the ethics of AI and how far can we take it from a technology standpoint? There's a long roadmap there, but how far should we take it? Do you feel as though public policy will take care of that? A lot of that narrative is just kind of journalists looking for the negative story. Will that sort itself out? How much time do you spend with your customers talking about that? We in Oracle, we're building our data science platform with an explicit feature called explainability of the model on how the model came up with the features, what features it picked. We can rearrange the features that the model picked. So I think explainability is very important for ordinary people to trust AI because we can't trust AI. Even data scientists can't trust AI, right? To a large extent. So for us to get to that level where we can really trust what AI is picking in terms of a model, we need to have explainability. And I think a lot of the companies right now are starting to make that as part of their platform. Well, we're definitely entering a new era. The age of AI, the autonomous enterprise, folks. Thanks very much for a great segment. Really appreciate it. Yeah, a pleasure. Thank you for having us. You're welcome. Thank you for having us. All right, and thank you. And keep it right there. We'll be right back with our next guest right after this short break. You're watching theCUBE's coverage of the rebirth of Oracle Consulting. Right back.