 Live from New York, it's theCUBE, covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Welcome back to New York City as theCUBE continues our coverage here at IBM as machine learning everywhere. Build your ladder to AI along with Dave Vellante. I'm John Walls. We're now joined by Vitaly Sivan who is Executive Vice President at AMC Networks. And Vitaly, thanks for joining us here this morning. I don't know how this interview is gonna go, frankly, because we've got a die-hard Yankee fan in our guest and a Red Sox fan who, you know, bleeds Red Sox Nation. Can you guys get along for about 15 minutes? Maybe about 15. I'm glad there's a little bit of space between us. It's given us the off-season and the Yankees have done so well. I'll be humble. Okay, we'll wait and see. Just in case, I'm ready to jump in if we have to separate here. But it is good to have you with us this morning. Thanks for making the time. Let's just first off talk about AMC Networks a little bit. So five U.S. networks, you said multiple international networks and great presence there. But you've had to make this transition, right? To becoming a data company, in essence. You have content and you're making this merger and the data and how's that gone for you? And how have you done that? First of all, you are making me happy when you say that AMC Networks have made a transition to be a data company. So we haven't. We are using data to help our primary business, which is obviously broadcasting our content to our viewers. But yes, we use data to help to tune our business to follow the lead that viewers are giving us. As you can imagine, in the last so many years, viewers have actually dictating how they want to watch. Whether it's streaming video rather than just turning their set of boxes or TV boxes on and pretty much dictating what content they want to watch. So we have to follow, we have to adjust and be at the cutting edge of our business. And this is what data come into play. So how did you get there? You must have done a lot of testing, right? I mean, I remember when binge watching wasn't, didn't even exist. And then all of a sudden now everybody drops 10 episodes at once. Was that a lot of A.B. testing, just analyzing data? How does a company like yours come to that real estate? Or is it just, wow, the competition is doing it, we should too, explain. Interesting, so when I speak to executives, I always tell them that business intelligence and data analytics for any company is almost like an iceberg. So you can actually see the top of it and you enjoy it very much, but there's so much underwater. So that's what you're referring to, which is that in order to be able to deliver that premium thing that's the tip of the iceberg is that we have to have state-of-the-art data management platforms. We have to curate our own first-party data. We have to acquire meaningful third-party data. We have to mingle it all together. We have to employ optimization predictive algorithms on top of that. We have to employ statistics and arm business with data-driven decisions. And then it all comes to fruition. Now, your company's been around for a while. You've got an application developer, you're an application development executive, so you've sort of made your personal journey. Curious as to how the company made its journey. How did you close that gap between, say, the data platforms that we all know, the Googles, the Facebooks, et cetera, which are data is the central part of their organization, to where you used to be, which probably was building, looking back, doing a lot of business intelligence, decision support, and a lot of sort of asynchronous activities. How did you get from there to where you are today? Makes sense. So I've been with AMC Networks for four years. Prior to that, I've been with Disney, ABC, ESPN for six years, doing roughly the same thing. So number one, we're utilizing ever-rapidly changing technologies to get us to the right place. Number two is during those four years with AMC, we've employed various tactics. Some of them are called data democratization. So that's actually not only get the right data sources, not only process them correctly, but actually arm everyone in the company with immediate, easy access to this data. Because the entire business, data business, is all about insights. So the insights, and if you think of the business, if you for a minute separate business and business intelligence, then business doesn't want to know too much about business intelligence, what they want, insights on the silver plate, that will tell them what to do next. Now that's the hardest thing you can imagine, right? And so the search and drive for those insights has to come from every business person in the organization. Now obviously you only expect them to build their own statistical algorithms and see the results and employ even machine learning, but if you arm them with that data and the tip of their fingers, they will make many better decisions on their daily basis, which means that they're actually coming up with their own small insights. So they're small insights, big insights, and they're all extremely valuable. A big part of that is cultural as well, on that mindset. Many companies that I work with, their data is very siloed. I don't know if that was the case with your firm, maybe it was prior to your joining, would be curious as to how you sort of achieve that cultural mindset shift with people, because a lot of times people try to keep their own data, they don't want to share it, they want to keep it in a silo, gain political power. How did you address that? Absolutely, one of my conversations with the president, we were discussing the fact that if we were to go, make recordings of how people talk about data in the organization today and go back in time and show them what they will be doing three years from now, they would be shocked, they wouldn't believe that. So absolutely, so culturally, educationally, bringing everyone into the place where they can understand data, they can take advantage of the data, it's an undertaking, but we are successful in doing that. Help me out here, maybe I just am requiring a little translation here of some sort of simplification. So you think about AMC, right? If you've got programming, you've got your lineup, I come on, I click, I go, I watch a movie and I enjoy it or watch my program, whatever. So now in this new world of your habits changing, my behaviors are changing, what have you done to, what have you looked for in terms of data and telling you about me that has now allowed you to modify your business and adapt to that? So I mean, how's data, driving that on a day-to-day basis in terms of how I access your programming? So a good example to that would be something we called TV everywhere. So you said to yourself, obviously, users or viewers have used to watching television as when the shows were provided via television. So with new technologies, with streaming opportunities today, they want to watch when they want to watch and what they want to watch. So one of the ways we accommodate them with that is that beyond just television, so we are on every available platform today and we are allowing viewers to watch our content on demand digitally when they want to watch it. So that is how we are, one of the ways how we are reacting to it. And so that puts us in a position as one of the sort of B2C type of businesses where we're now speaking directly to our consumers, not via just the television, so we're broadcasting they are watching, which means that we understand how they watch and we try to react accordingly to that. So which is something that Netflix is bragging about as to that they know the patterns, they actually kind of promote their business, so we are in that business too. Can you describe your sort of innovation formula, if you will? How do you go about innovating? Obviously there's data, there's technology, presumably there's infrastructure that scales, you have to be able to scale and have massive speed and infrastructure that heals itself, all other things, but what's your innovation formula? How would you describe it? So, unfortunately it's simple, it starts with business when I'm fortunate that business has desire to innovate. So formulating goals is something that drives us to respond to it. So we don't just walk around the thing and look around and say, let's innovate. So we follow the goals, business goals of innovation. A good example is when we promote our shows, so the major portion of our marketing campaigns falls on our own air. So we promote our shows to our AMC viewers or we TV viewers. And when we do that, we try to optimize our campaigns to the highest level possible to get the most ROI out of that. And so we've succeeded and we managed today to get about 30% ROI on that and either just do better with our promotional campaigns or reallocate that time for other businesses. I mean, you were saying that after the first question or responding to the first question, we're really not, we're a content company still and we're incorporated data, but you really are, Dave and I have talked about this a lot about everybody's data company now in a way because you have to be in order, because you've got this hugely competitive landscape that you're operating in, right? In terms of getting more eyeballs. So it's got to be no longer just a part of what you do or a section of what you do, it's got to be embedded in what you do. Does it not? Oh, it's absolutely is. And I still think that it's a bit premature to call AMC Networks a data company, but to a degree every company today is a data company. And with the culture change over the years, if I was, if I used to solicit requests and go about implementing them today, it's more of a prioritization of work because the company and every department of the company got educated to the degree that they all want to get better and they all want those insights from the data, they want their parts of the business to be improved and we're venturing into new businesses and it's quite a bit in demand. So is it your aspiration to become a data company or is it more a data-driven sort of TV network? How would you sort of view that? I'd like to say data-driven TV network, of course. It's more in tune with reality. And so I want to talk about aligning with the business goals that's kind of your starting point. You were talking earlier about a gut feel, we were joking about baseball, moneyball for business. So you're a data person, so the data doesn't lie, et cetera, but insights sometimes are hard. They don't just pop out. Is that true? Do you see that changing? Is the time to insight, from insight to decision going to compress? What do you see there? So the search for insights will never stop, right? And the more advanced we are in that journey, I mean, the better we are going to be as a company. But the data business is so much depends on technologies so that when technology is mature and we manage to employ them on a timely basis, so we just simply get better from that. So a good example is machine learning. So there are a ton of optimization algorithms, forecasting algorithms that we put in place. So for a while, it was a pinnacle of our deliveries. Now with machine learning maturing today, we are able or trying to be in tune with the audience that is changing their behavior. So the patterns that we would be looking for manually in the past, machine is now looking for those patterns. So that's the perfect example of what we're trying to catch up with the reality. What I'm hoping for, and that's where the future is, is that one day we won't be just reacting, utilizing machine learning to the change in patterns in behavior. We are actually going to be ahead of those patterns and anticipate those changes to come and react. I was gonna say, yeah, what is the next step? Because you said that you are reacting. I was ahead of your question. Yeah, right, you were. So I'm gonna go ahead and re-ask it. Data guy. But you've got to get to that next step of not just anticipating, but almost creating, right, in your way, creating new opportunities, creating, use data to develop these insights into almost shaping viewer behavior, right? Totally, so like I said, optimization is one avenue that we pursue and continue to pursue. Forecasting is another, but I'm talking about true predictability. I mean, something that goes beyond just to say how our show will do, even beyond, so which show would do better. Yeah, can you do that? Yeah, I mean, even to the point and say, these are the elements that have been successful for this genre and for this size of audience, and therefore, as we develop programming, whether it's in scripting, casting, whatever. I mean, take it all the way down to that micro level to developing almost these ideal, these optimal programs that are going to be better received by your audience. Look, this is not a big secret. Every company that is in the content business is trying to get as many walking debts as they can on their portfolio. Is there a direct path to success? Probably not, otherwise, everyone would have been super scared. Everyone would do it. Yeah, we're doing that. But yeah, those are the most critical and difficult insights to get a hold of when we're working towards that. Are you finding that your predictive capabilities are getting meaningfully better? I mean, maybe you could talk about that a little bit in terms of predicting those types of successes or is it still a lot of trial and error? I'd like to say they are meaningfully better, but look, we do obviously interesting findings. There are sometimes setbacks and we learn from it and we move forward with it. Okay, as good as the weather or better or worse? It depends on the morning and the season. Vitaly, how have your success, or have your success measurements changed as we enter this world of digital and machine learning and artificial intelligence? And if so, how? Well, they become more and more challenging and complex. Like I gave an example of data democratization. It was such an interesting and telling company-wide initiative. And at the time it felt as a true achievement when everybody would get access to their data on their desktops and laptops. When we look back now a few years, it was a walk in the park to achieve. So the more complex data and objectives we set in front of ourselves, the more educated people in the company become, the more challenging is to deliver and take the next step. And we strive to do that. I want to ask you a question from a developer's perspective. You obviously understand the developer mindset. We were talking to Dinesh earlier. He's like, yeah, you know, it's really the data scientists that are loving the data, taking a bath in it, the data engineers and so forth. And I was kind of pushing on that, saying, well, but eventually the developers have to be data oriented. The data is the new development kit. What's your take? I mean, granted the 10 million Java developers, most of them are not, you know, focused on the data per se, will that change? Is that changing? So first of all, I want to separate the classical IT that you just referred to, which are developers. Just because this discipline has been well-established, whether it's a waterfall or a gile, so every company has those departments and they serve companies well. Business intelligence is a different animal. So most of the work, if not all of the work we do is more of an R and D type of work. It is impossible to say in three months I'll arrive with the model that will transform this business. So we're driving there. That's the major distinction between the two. So is it the right path for some of the data oriented developers to move on from, let's say, IT disciplines and then to BI disciplines? I would highly encourage that because the job is so much more challenging, so interesting, this very little routine as we said, it's actually just challenge, challenge and challenge. And as you look at the news the way I do and you see that data scientist becomes the number one desired job in America, I hope that there'll be more and more people in that space because as every other department we're struggling to find good people, right people for the space. And even with that space you have, as you mentioned, data engineers, you have data scientists or statisticians and now it's maturing to the point that you have people who are above and beyond that, those who actually can envision models not to execute on them. Are you investigating blockchain and playing around with that at all? Is there an application in your business? It hasn't matured fully yet in our hands, but we're looking into it. The reason I ask is that it seems to me that blockchain developers are data oriented and those two worlds, my view, are coming together, but it's early data. Look, I mean, we are in this space and like I said, we don't know exactly, we can't commit, fully commit to a delivery, but it's always a balance between being practical and dreaming. So if I were to say, let me jump into blockchain right now and be ahead of the game, maybe, but then my commitments are going to be sort of further ahead and I'm trying to be pragmatic. Well, before we let you go, I gotta give you 30 seconds on your Yankees. How do you feel about the season coming up? As for every season, I'm super excited and can't wait until the season starts. We're always excited when pictures and catchers show up. That's right. If I were a Yankee fan, I'd be excited too. I must admit that. Nobody's lost the game? Yeah. That's right. I could tell. Vitaly, thank you for being with us here. We appreciate and continue the success at ABC Network. Thank you for having me. Back with more on theCUBE right after this.