What Keeps Creative Work Moving at Scale with Lisa Watts, CEO and Founder, CREE8

In the debut episode of Unleashing Genius, NetApp’s Chief Marketing Officer, Gabie Boko, is joined by Lisa Watts, Co-Founder and CEO of CREE8, to explore how creative teams keep work moving in real-time across distributed environments. 

Together, they examine the real-world pressures on workflows, including data access and movement. Lisa shares how her teams reduce bottlenecks and rethink data management to make real-time collaboration feel effortless.

In creative production, fast and reliable data access is essential. Drawing on CREE8’s experience, the episode shows what it takes to build consistent foundations for real-time collaboration at scale, helping organizations move from possibility to execution and deliver stronger creative outputs and better business results at scale. 

Host: Gabie Boko

Produced By: Kenya Hayes

Transcript

SPEAKER_02 0:06

Hi there and welcome to Unleashing Genius. I'm Gaby Boko, the Chief Marketing Officer at NetUp, and this series is really a set of executive conversations about what's changing in the industries across AI and data, what's shifting in terms of business demands, and how that's reshaping how organizations are running. In each of these episodes, we're gonna zoom out and look for patterns that leaders are running into right now, and where the pressure might be building, where assumptions might be breaking, and what it actually takes to respond in a way that holds up over time. A lot of people can talk about what's possible. This series is about the shift from possibility to action and what it takes to turn new ideas into systems and workflows that keep performing at scale. We're gonna use customer experience as a lens, but the goal is obviously a much broader takeaway because this is about leaders and what they should learn, what they should do differently as these shifts start to accelerate. So today I'm really excited because we're looking at a trend that's actually showing up everywhere. When teams are starting to collaborate across locations and systems, the pressure shows up in one place fast, and that's data access and data movement. Because if the data isn't available where it needs to be and when it needs to be there, the workflow itself and the people start to slow down and their coordination starts to break. Create is a strong, strong storyline for this because their feedback loop is immediate. In real-time creative production, small delays don't stay small. Rather, they interrupt momentum and install collaboration. Today, in our very first episode, I'm joined by Lisa M. Watts. She's the co-founder and CEO of Create. Lisa, I am so excited you're here. Thank you so much for joining me.

SPEAKER_01 2:09

I'm excited to be here, Gaby, and congratulations on your new podcast. It's going to be amazing, and I'll be tuning in.

SPEAKER_02 2:17

I am super excited that you're my first guest because there's no better guest to start off a conversation about workflow and data than a creative guest. Um, I get it you're a CEO. I get it that you've built something amazing, but this environment that you lead is different. So I just want to start with the basics, maybe. Tell me a little bit who create is and and then maybe what does a typical kind of creative workflow look like in your environment?

SPEAKER_01 2:48

Create is a cloud production platform, content production platform. So our value proposition is what we call studio in the box. So any production team from uh uh somebody working on movies in television at the big studios to live sports um and live events, podcasts like this one, um, marketing teams, uh, you know, Fortune 100 companies that are now moving into being their own brand studios as well as the games development environment. The all of these things are now extremely distributed, and content and uh content production pipelines need to be real time. So, what Create has done is is essentially we used to call it democratizing access, but that's it tends to be much more about creative control and being able to hand that to the people that are doing the creative work and making sure that they have access to the best of the best, which normally would have been locked behind the walls of larger corporations. So create's responsibility is to bring the tools that can do high velocity content creation to our users. Um, and we do that through um several different mechanisms. First of all, being cloud first, cloud native, um, second of all by leaning into what we think is the best in class as far as infrastructure, bringing that infrastructure together. And third, by really paying attention to what it does mean to move data and move content, because um a lot of corporations have a lot of data, you know, data movement that they do to get the content around the world or could get files around the world, but you know, they're very used to moving spreadsheets and databases and those sorts of things. But in our case, we have to make sure that we can move 4K, 8K video files um in that same method. And uh because teams are so distributed, yeah, now, especially in the media entertainment business, um, you know, we we allow for teams to optimize for where the work is, where where the shoot is happening, and where the talent might be located, and try to do that without, you know, at the best cost possible at the lowest latency. But you know, science is still a thing, right? Um, speed, speed of light and all that, um, it these are all things that, you know, we've been partnered with um, you know, great companies like um NetApp uh and others who are really used to working in that enterprise space and do understand that it's not just about the time that the database can get to where it needs to be, but the content itself, time is of the essence. When you get your story out to the market matters. And these are the kind of workflows um that you know we support and um um make sure that we've got the best technology against every day.

SPEAKER_02 5:52

You know, it's it it's you're one of my favorite customer stories at NetApp, mostly because I think I'm drawn to the creative side of it, but also because data as content and data as um the creative motion is is is something that I think is becoming ever more prevalent. But my question is, what was the why? Why did why did create get founded? What was the ultimate solve you were trying to solve?

SPEAKER_01 6:19

That's an interesting question because um yeah, I'm an old IT person, right? I was in IT at Intel for many, many years. And the one thing that we used to always support was you know being able to remote into the office to get to your mail or or whatever. And I won't really date myself to say how long ago it was that we supported those environments. It was the hot second, Lisa. It was just the huck second. But you know, this experience that we all have through all these technical transitions and you know, the fact that, okay, we do know what it means to be able to move computing and and and experience closer to the data, right? Um, this was really the main uh key key breakthrough, key learning, which was that by centralizing data and content in a way that was globally accessible, that that makes a lot of sense to people, right? It's there's IP protection, there's um centralized uh encryption and access that you can manage. There is also then the idea that if it is in the cloud, it is in one of the big hyperscaler data centers or on you know, the the backbone, shall we say, that the data movement is much, much faster. I mean, we get transfer rates at times between four to five gigs per second, which you would never get from your house or from the even from the office. That's you know, those are you know black black fiber um types of connections. So once you make that decision and you decide that it really does make sense to have a single source of truth, to have your media in a single place, right? Um then you need to think about all of the other pieces of the workflow and the technology that need to wrap around that. So then by nature, then you become um aware that computing and people need to go to that content. There's a lot of people trying to do CDN and push the content and replicate it all over the place. But if you think about it, it's really not a very good model at the end of the day. There's a lot of risk to that. Um, and you know, we're with technology, you think about memory prices, which SSDs are memory at the end of the day, that that's that constraint is going to continue to um uh to be there in the marketplace. You've seen companies like Sand Disk and others with the small drives that prices are just going up. So even if it even if it wasn't going up, the model by which you can centralize your data and content information has so many different benefits, right? But then you then again, as I was uh was um mentioning, you have to put a system of technology and people around that. And so, therefore, by nature, that pushed us into saying, okay, what are the methods by which we bring those people to that content? So we do that in multiple ways, which is virtual machines as well as web-based workflows that you know respect where that content is and leave it there. And and you know, our goal really from a create perspective is if somebody ingests or creates a terabyte of of content, our job is to bring them the tools and the methods by which they can easily turn that into 10, because that's really what we're seeing in the content explosion. Um, we now have the capacity to deal with the large the large media that we are getting, and and and now we don't have to throw it away. This is really important in sports, for example. Of course. You know, there's oh there's you know, the idea of being able to go back and do a documentary of something and oops, I deleted everything the disk to save space. I mean, that's really a shame, right? Um, from from an opportunity perspective.

SPEAKER_02 10:10

It so is. You know what? Let's take the I'm gonna stick on the tech side and then I want to pivot to the people side, but let's keep on on tech for a minute. When you think about the data infrastructure that you've had to deploy and and really uh how and where you're looking at, uh I don't know if you're considering latency, I don't know if you're considering um access and security, but when you think about that, that data infrastructure system, what's your first indicator that something isn't right? And then what do you do to address it? I think the main thing for us is latency, right? Because that would seem right with a being a content kind of creator device.

SPEAKER_01 10:53

Yeah. And so we're addressing that in multiple different ways. One is is that you know, price becomes the second factor for us because a lot of production, content production, and media production, with AI in the picture, there's an assumption by leadership that things are just going to be less expensive to do. Um, there's uh cost pressure, and they're looking to offshore or outsource the work. All of those things start to, when you think about content production, the velocity needed, the anything that really increases costs, they're really taking a hard look at. There's there's just a lot of pressure in the system for doing things more efficiently. So when we think about addressing the latency question, there's big media companies out there that have the dollars to replicate their content to, let's say, if you're thinking about an Amazon Web Services uh scenario, there you people tend to forget that the cloud is data centers. You know, we're talking about buildings, big iron racks of things. And there's this concept called an availability zone, which is actually a building. And the I I'm oversimplifying, but it's think about it as a building. So you have you have a certain amount of stuff in one building and a certain amount of stuff in another building, a certain amount, and the you know, Amazon will encourage you to distribute those workloads for the maximum availability. But in the case of a media company where you're dealing with petabytes of files, just to replicate that data to three different places, not to mention 17 different data centers like we support, um that's physical cost, right? Because storage has weight, it eventually needs to sit on a piece of meat of hardware and it's taking up space. So that costs something, right? So the two factors that we deal with are latency and cost, but our users or our our clients typically are not going to be able to afford, you know, some of the big studios budgets as far as solving that problem with brute force, right? And just copying it everywhere. Again, that kind of goes a counter to our thesis. So, what do we do about that? Well, we look at different technologies, we do look at co-location of things where we can get them, and then we call NetApp and we tell them we have this problem. Right. How do you think we should solve it? Now, why do why would I, you know, there's lots of technologies out there that claim to be able to do um high speed access at a low cost on um say an S3 compatible type of storage, but that requires a lot of um programmatic and um um algorithmic work and putting things in in databases and chopping it into pieces and trying to reassemble it later. Those things do work to some extent, but what we find is that having access to that real content right on the highest speed storage possible is really important. And we cannot actually do those things on a local hard drive. So what we try not to do is replicate what you would see in the office, where you have a big gaming machine or a big, you know, uh render machine on your desk and you've got a hard drive attached to it. These have to be um, you know, shared storage um entities of some sort, right? So, so that's you know, we're constantly pushing the envelope on innovation on what is what what kind of access and um performance can we get out of raw storage, out of the best storage there is in the world. Um, and yes, we do have to wrap, you know, more technologies around that. Sometimes we have to change workflows so that you know the team's like, okay, well, if you want to do it, you're gonna you're gonna have to do some manual or some automated moving of files between uh certain systems to be able to get to that latency because you don't want to pay for the replication. Right. So that's a constant conversation for us. What are you willing to pay for? How much is the performance and what can we squeeze out of both of those?

SPEAKER_02 14:51

And I think what you're saying then is that it's it's it's not just the performance isn't just staying contained into one department anymore. It's really affecting that entire data continuum. And so how you're thinking about it is is really an integrated approach, not just connecting the silos after the fact.

SPEAKER_01 15:08

That's correct. And that's really important. I mean, I think when we talk to um AI companies who are who are so excited that they're transforming the entire content pipeline, um you're con you're transforming a content pipeline for one person or one role. But when we think about the studio, the studio as a whole, we use a a with the team, I use a um uh an analogy that is of an F1 team. I'm a huge F1 fan, so this works for me. Um I'm a huge fan. Um, so uh to the point where I might even get up at you know, middle of the night to watch race. Um, but the idea of the pit wall of that telemetry of the team working together, the garage itself, and all the different roles that are really important to get that car out on the track and then actually make sure that it wins, that's how we think about it. And then when you wrap the production, and if you look at the other side of F1 and the production that it takes and the different types of um roles, everything from the reporter on the side, you know, on the side of the track to the telemetry coming in off of a race that shows up in the broadcast to the social media teams that need to be able to get that content out real time. And then on be from there on to the influencers that take that content and bring it out to the world, circle back to Netflix and doing Drive to Survive. So that is such an interesting, it's one of the largest production and production engines in the world. You can then go and then add on the F1, the movie, which was actually shot at you know during races. That I think is the best example for people to make it real on the fact that when you're thinking about us, you know, stepping up to the plate to be able to provide a studio infrastructure that is cloud-backed and backed by some of the best technologies in the world to do those tasks. Right now, that what I just described for F1 is a huge amount of fragmented tools all over the place. Everybody has their own thing that they're doing for their own pipeline. But if we think about the fact that the content coming in as a single source of truth, as create, for example, being the hub of where that comes in and that can be managed, the opportunity we then have to empower the rest of those teams across that same core source material in live, it really becomes really obvious on why that is um so important. And then if you pivot to VFX workflows for movies, etc., studios are pushing out the the production out to their production partners. So the the the idea to be able to securely bring those things back together and um deliver the final product. Again, it's it's an orchestration of a production that is very similar to you might have live, but it's you know, you know, doing something else. You can repeat the same model for a corporation like yourself who has to go out to an event. I know you guys, you know, you're out um uh at the your at the events that you do trying to record and get content back and get it out. It's it takes you longer than should, right? Um, so that that's those are the problems that we're trying to solve, and we we see them repeating in multiple content industries.

SPEAKER_02 18:23

You know what? I love a good sports analogy because I think it makes it so real for people to experience that. Thank you for bringing that in because I think that not only are we an F1 fan and also just you know, full disclosure, we're a we're a sponsor of Aston Martin. Um, but but I think that the flow of data that you described is so so apropos to when people can see data in that environment, both from a, I'm having fun watching this, but then applying it back to the problems that they might have in their own day-to-day job. Um, I think that that's kind of the storytelling and the idea of how to how to change the paradigm of thinking about data. And so from that, it's a little bit of a two-parter, but a big one, kind of riffing off of that sports analogy. Um you're really talking about something that is delivering a consistent experience everywhere, right? So when you can no longer just talk about scale in silos and you have to have that consistent experience, how do leaders like you or others try to fix or modernize their approach to that consistency? Um, and and maybe what's like a one pitfall that you can say, oh, don't don't go there, don't do that, in trying to accomplish that change?

SPEAKER_01 19:41

Well, I it's a two-part answer for me because as we are, you know, as we are building the best F1 pit stop team for productions to land, the best garage and the best pit wall for them to land on.

SPEAKER_02 19:55

I am never gonna let you forget this, Lisa, by the way. I'm gonna the next time we talk, I'm gonna come to create and you are gonna take me through the your your garage.

SPEAKER_01 20:03

That sounds amazing. Um anytime. Um, but if you think about the uh customers that we, you know, the clients that we support are mutual clients in some cases, right? And what their demands are, as we bring in this studio in the box, this pit wall, this telemetry, this experience um that the teams have with each other, right? So collaborative, collaborative environment, um, everything that you need in one place, that doesn't mean we sell everything that we have under the hood. It means that, you know, you know, if if if Perelli is uh uh providing the tires, we make sure the tires go on the car appropriately, right? We're not necessarily building the tires, just make sure the tires get to the right place. In doing that, we also, as a con as a provider of a content production platform, um we also have to use it, we do use it for our own production. Now, that has been probably the most if I were to tell other leaders, you know, on the in the tech side, what you should be doing, you should be making sure that your tool, your platform is adding value to your own company. By doing that exercise, and we say either drink your own champagne or eat your own dog food or whatever analogy we've used in tech forever for for the consumption of our own product for that portion of our workflows, we've learned an incredible amount. Our customers love our platform. They love o, you know, but we've also made sure that we have people along alongside that. I mean, I think that's a downfall for SaaS platforms, you can never get a hold of it. But right, right. We actually understand that when you're in the hot seat, we've been in the hot seat. We have people that have been editors or you know, have been in live production, they've been in the chair, and now they're in the chair for that person on the other end. So those two. Things I think are super important, making sure that you're leveraging your own cop platform and understanding and being in your own customers' shoes. And we've learned an incredible amount. Many of it's been disappointing to ourselves to say that okay, we're trying to use this platform for this sort of production. And um, my head of design is like introspectively saying, Well, why do I not default to that? Right. And solving those particular problems for himself to say, what do I how as I develop these, you know, as I as I develop out the user experience, what are the things that make sure that I love my own experience that it can, it is actually a tool that is helping me get to from point A to point B. And um, you know, it's interesting to have such great feedback from clients and then still be introspective about what you're doing yourself and have to have that honest conversation and said, Yeah, I'm glad everybody loves it, but it's not enough. You got to be constantly curious, you got to be constantly paranoid. I, you know, I'm from Intel, so handy grows, only the paranoid survive, right? You have to be paranoid about enough about those things, and you have to care enough about them to be honest with yourself about what's working, what's not working, and then you have to push your partners to come with you to the party. I mean, I think, you know, we've um we uh we, you know, being a small and and quick growing startup, um, we've sometimes are surprised that you know, large companies like yourself are or like, oh, we're gonna talk to Creator, we're gonna bring them into um, and we're in a room with giants, and we're like, you know, okay. But then we'll say something and the giants will be like, yes, we totally agree. So I think we have that opportunity being out on the bleeding edge to see things in in a way that um that maybe others don't have the luxury of seeing, and we're nimble enough that we can move fast. And I would just encourage all leaders, brand leaders, agency leaders, production leaders, don't just accept what you've always done because um, you know, the that's that's been that's the safe path. There's no safe path anymore, and the only safe path is innovation. So you have to kind of get out there and be curious and be adventurous and be willing to um take the risk to to try what might be next.

SPEAKER_02 24:15

Yeah, I I say this, I've started saying this to my team. We don't want to be disrupted, we want to be our own disruption in terms of how we think about things. Absolutely. In that line, you know, you're right. I think create is is young in age, right? But nimble and building things that are really, really consumable by large enterprises who have gotten, right? You have gotten piece parts, siloed, disparate. I mean, it's still stunning to me, uh, probably to you too, that um at this point in my career I'm still talking about uh the value of unstructured and structured data and where it lives. So I mean, that's that's crazy to me, but the the aspects of it still say stay the same. So inside of your foundation, what you're building, what are you thinking about next? Like what's the one of kind of the big things you are choosing to standardize on? Um, and and how are you kind of gonna take your own medicine of what you just talked about with leadership to drive those homes and to not um become what the big companies have become?

SPEAKER_01 25:26

Yeah, super a super interesting question. Um, you know, being a being a young company and a startup, but also my team, I call them kind of the Ocean's 11 team, right? The expertise, my CTO, long time Microsoft, myself, long time Intel, head of customer um experience, long time in the media business, head of marketing, was a you know, producer in the chair with big, big uh brands out of um out of of Europe, right? Uh and we we so as we think about as we've assembled, there's a tricky part of that. You have people with these big careers and corporate and we get a lot of feedback that says, Oh, are you sure because you guys have all this experience that you'll be nimble enough? Right.

SPEAKER_02 26:23

It's such a hard question when you get it, right?

SPEAKER_01 26:25

It's like I'm nimble, I could be and I like to say that we were the drivers of every last transition that I've seen, and we are the drivers of this one. I love that. Yes, you can have a brave college student come out and maybe they're they're they're thinking you know differently. So that's the other part on the leadership pieces don't forget that your experience matters, but keep that curiosity and understand what they're what those other people's points of view are because they do have a fresh look. We all had the opportunity back in the day to have fresh look. We've had a fresh look at each one of these cycles. So we can't forget that that fresh look still matters, right? So the things that we're tackling in in media, for example, media manage media um asset management systems have been around for a really long time, right? Right. And so, you know, I'll you know, talk about something on this that I normally normally I I like to wait until I see the whites of their eyes, I'll work on something. And I don't want to have the anybodies come out and try to kill me before I get something to market, right? So, so but and this has been a conversation with your team with Amazon, et cetera, which is um, you know, we're getting really clever on how do we manage content and data and storage. But what we're missing is that opportunity for the integrations, for the aggregation across different types of storage. We can't just cram everything into one thing. And when we do that, the indexing or the um the media understanding, we keep saying, well, we're gonna put AI on it and AI is gonna look at it, and the computer vision of AI is gonna figure it all out. But the issue really becomes that, you know, again, you know, as as my British friend says, it's you know, ones and knots. Uh um, you know, it is it is down to ones and zeros at the end of the day. And how do we how do you know where do we prioritize our understanding of that information? Right now, we oftentimes have to have a piece of storage and then another, you know, database that we you know need to have in the mix, then we have to have an index server that's over here, and then that index server is is um you know has to mount that storage, which could be limited by some other network connection, you know, that that has to happen. So what we're working with is our partners to say, okay, let's let's just step back. You know, we keep trying to do the same thing, or keep trying to use the old technology or old mentality on how to do this, and we're just complicating it for ourselves, we're just glom gloming on new things to do X, Y, Z things. So, what we're trying to take a look at in the ma'am space is really just okay, okay, stop. We're gonna start over, right? And say, what are we actually trying to accomplish here? And then rebuild that from the ground up with just the things that we need. Instead of trying to bring along the box of rocks carrying all the stuff from forever and ever a go. We and we had the luxury of doing that, right? Because when we bring customers on, oftentimes, yes, they're bringing some of their existing workflows, et cetera. But when we're ingesting that content fresh, it's not like we already had like a hard drive that you've got to collect it in that's already like we have that opportunity. And I think that's the one of the most exciting things for me. If you say, okay, let's take a fresh look at it, how and where do we have access to, then what do we need to do with it? And then where is AI, computer vision, and all of these sorts of things um helpful to us in the understanding of that, you know, and the context and the understanding of that information. I think we still just keep taking the the I'm gonna index all this content um as a brute force mechanism, and we try to use AI to actually go do that thing. We're not making any innovation, anyway. We're not making any changes to the core function of indexing, per se, uh outside of uh adding on a few other features like oh, I can listen to it and or I can see it with you know with with can with computer vision. So that's the one thing that I think is a is a really good opportunity for all of us in storage and you know, media management place to go off and focus on.

SPEAKER_02 30:23

I love your clean sheet. That's exactly what you should be doing. Do you think that's what everybody should be doing, quite frankly. Do you think that AI is demanding everybody clean sheet? Is it what's driving you? And just this is not a deep answer, but just quickly, like everybody is labeling themselves as an AI company or their function as AI. Are you feeling inclined to to embed more AI in your work? Um, use more AI, describe yourselves as an AI company. How are you feeling about that as you clean sheet um how you think about things?

SPEAKER_01 31:00

That's a really, really good question. Um bad question.

SPEAKER_00 31:06

Well, I'll be candid, right? Which is I love candid. We're I think we're there. We're we are candid be with each other.

SPEAKER_01 31:13

The this conversation demands it. So, to my investors, I have resisted being labeled as an AI company. However, in the rubric of getting somebody to talk to you, unless you are AI forward right now, you can't get the resources, you can't get the investment that you need. To me, it's laziness on their part. I can understand that in the realm of the acceleration of development and what you know, programmatically speeding up all this information is and aggregating it together to be able to do something about it, really does push the envelope to say if you're not using it or doing it or producing it, it's you're not an innovative company. So for us, you know, we're AI native in our development cycle. We haven't touched a line of code since last year, right? But I kind of end up getting that dragged out of me. It's like we're, you know, as far as AI as an assisted or um, you know, assistive technology, it's really is uh very, very helpful. It is also exhausting, right? Uh the amount of information and data and the cognitive load that we see, not only in our own development teams, but also in our clients, is a real thing. So that's where we start thinking about okay, we all know from the bay, the dawn of time, the garbage in, garbage out, right? And so I think a thoughtful approach to where to start, and I don't encourage people to step back and redo their whole data schema and their whole data dictionary and do the whole like la la la la, that's you'll never get anywhere, and by the time you get to the end of it, everything will change anyway. Again, right so so what we try to do is then just small bite-sized pieces, right? And go quickly, iterate quickly, do it fast, but check, put tus in, put checks in, make sure that you know, as you're as you're making those changes, that they are they're logical and they make sense. Because if you try to give too much context to an AI engine, or you you you you make the scope too big, you actually will hurt yourself. You'll get come to conclusions that aren't correct, or it'll wander off and it'll have too much data. And it's it's like any if you had to think about it as a teenager, right? No, look at it it's its brain is developing, and it's likely to be incomplete or overloaded very easy, as easy as a person gets overloaded. So, so you have to break things down more logically. And this is where I think experienced developers really are winning, and this is also where I think in experienced production people, people who know how to run a camera, who I know how to run a shook, that's they're gonna win in the Gen AI space as well for the exact same reason because they know they have the information. I have this video that I did um last year on Vio just for fun when the tariff stuff started happening. So I was like, Oh, I'm gonna make a funny, a funny ad about, you know, can't ship hard drives because you know, you know, can't get a hard drive because tariffs. Yeah, I decided to have the production team panic and then take the hard drive out to the runway in London and try to ship it via pigeon, right? It's a cute, it's a really it's a really funny video. And actually, people asked me where I shot it and it was all AI, right? Um, but I noticed I wasted so much time and effort. I think it was like 10 hours of prompting. Where if I would have been less lazy and more precise or had more experience on how to direct the cameras, the lighting, the angles, the whole thing that a person in real life would know how to do, I would not have, you know, four hours of pigeon bloopers. Like pigeons are just as hard to direct in AI as they are in real life, maybe worse. But, you know, um I'll I'll I'll I'll share it with you. It's actually uh pretty, pretty funny. But it's excited to see that one. But it makes the point that says expertise matters. So people need to become students of the craft that they want to do, not be rushed into like you have your vike coding, so therefore you should be able to spit something out overnight. Well, yes, of course. But if you've done a really good job of understanding what problem you're trying to solve and the data and the insights that you're trying to include in whatever it is you're trying to do, take the time. You can still walk and chew gum at the same time. You can still show a prototype and show your investors or show your partners what you're working on, but don't make that the shipping product. Make that the the way to you know get feedback. So the normal product cycles really don't change, they just accelerate. Um, and it's you know, the we try to reduce the amount of garbage that it has to deal with because it's not only um not helpful, but it's expensive.

SPEAKER_02 35:59

Yeah, yeah. You know, what that is a perfect segue to our very last question, which is really the premise of this this podcast, um, and everything that we've been talking about. Everything that we've been talking about is um is how how and where data sits, how important is it to kind of think about your data architecture, data infrastructure. We call it intelligent data infrastructure at NetApp. But the the thought is is that as your data goes, so goes um how your company starts to think and how it wants to act, but it requires that human angle to put on top of it, right? The action comes from from the humans. Um, and that's really what unleashing genius is about. What how much have you used technology to create actions in your in with your employees or your customers or your partners to take action and unleash that genius? So my question is, how are you seeing it doesn't have to be all of them, but what's your favorite outcome that that your changes in how your company has been thinking about ma'am and and you know, media asset management and how you create content, um, what's what does that success look like, that single outcome that you're just super proud of?

SPEAKER_01 37:15

Well, I think at the end of the day, and this is kind of goes across the board, you get what you measure, right? And the one of the things that we're most excited about is that visibility and then that correlation of data and information that gives you that pit wall, right? That says, where is your stuff? How big is it? You know, what is it, right? And we're not finished on that journey by any stretch. Um, we're trying not to rush into it, although there's incredible pressure from our customers to improve the the kind of the feature set there. But I think that's the thing I'm most proud of from a team perspective, is that starting to get to that point where we're really providing that visibility. And then the second thing is, is as we see, you know, we kind of have of an unfair advantage of you know, several years of front row seat to many different kinds of productions. So, you know, it's important to anonymize that data, of course. You know, you want to protect people's information, their their intellectual property, et cetera. But the usage patterns themselves, understanding um how long does it take to get to bring content in? Are there different different methods? What happens when it gets there? Where does it go? Who uses it? How do they use it? That that is as much a really thing that I'm proud of us being able to understand, and actually is pushing new generations of our product line to say, you know, we're gonna do uh a model around that. It was always a debate is it a model, is it an agent, it what is it? And right and the uh conversations with AWS is like that's you're on your way to a foundational model that is information that that is very important for the customers on your platform to say, what did I do the last time, what should I do the next time, what worked, what didn't work. And that is as important to me as what is the content, where is it, show me all the cows at sunset and the build. So it's all about data, data to action, right? Data to action, first of all, recommendations, and then you know, human-centered action, and then uh eventually autonomous action on on parts of the workflow. But for us, it is about continuing to bring that information into the platform and in a way that humans understand and keeping humans at the center of that experience always, wherever we possibly can. And if they make a choice to, you know, to set it up for uh for automated action, that's but we want that to be in their control, right? Uh there's we're gonna see a whole lot of garbage content out there where people went to AI tools are just unleashing, oh, I'm gonna, you know, do the pigeon video from end to end and and just ship it out to socials. But that content isn't really gonna resonate with users or with people at some point. It's just a great thing for you and me. It's just gonna fill up our hard drives um with lots of data. But is it really important data? Uh was it is it valuable media? And then the second part of that, the other side of that coin is that the intellectual property around those things, intellectual property is seen as as valuable as real estate, right? As valuable as jewelry and luxury items. And so without the ability to understand what has happened with that content, the ability to protect that is intellectual property that's monetizable, or likeness and rights and all those sorts of things, we have to pay attention to that because uh that that asset class, it will become more valuable because of the people that are gonna put the effort into actually doing that piece of it, or it'll become more difficult to monetize it because you're competing against a bunch of content that wasn't really didn't really cost very much to produce, so therefore people aren't as worried about getting their money about uh back or supporting it, right? So I think it's a really interesting time when it comes to content, content productions, then not to mention the brands are becoming movie studios in and of themselves. So um they have a different type of intellectual property that they're trying to uh to protect. So I think the intellectual property space um and how that unpacks and our uh front row seat to the data and information that we think uh we have in our proprietary uh databases and and and back-end systems to help people protect and monetize that um intellectual property. I think that's an area to that's a yeah, watch this space. I think it's gonna be really interesting.

SPEAKER_02 41:47

That's a that's a great insight there. You know what, Lisa, thank you so much for inviting us into your garage. Um sorry to continue to go down that analogy. Um I think what I've heard from you multiple times is that success for you isn't is your team's not waiting on the system and you're clean sheeting everything and and really looking to be the agile kind of company that can change not just pieces of your industry, but aspects of the whole industry and make people successful along the way. Um, I really, I really love what you as a company are standing for and how you're how you're doing it, because it's very clear from everything we've just talked about that uh you want collaboration to be natural and you want the data to to really reinforce that and the data infrastructure to support that, not to replace it. So that to me is what this this whole first podcast was all about, unleashing genius, right? From intelligence and data to action. So Lisa, thank you again from Create for joining us, and thanks everyone for tuning in to the first episode of Unleashing Genius. We look forward to seeing you again.