>>Okay. We're here with the rest of the child who was the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So rest, let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate, uh, their businesses with that in mind, you know, we know designer and are the products that Ultrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyperaware, um, now kind of renamed, um, Altrix auto insights. Uh, we even speak with the, uh, business owner of the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so Trifacta made so much sense for us. >>Yeah. Thank you for that. I mean, look, you could have built it yourself. Would've taken, you know, who knows how long, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birthed Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical and, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really helped to automate those. So, so a, a broader set of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I can use it with the right level of quality and I'm happy to be, but don't tell me to be more data driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company. And I think as, as we, um, you know, saw over the course of the last 5, 6, 7 years that, um, you know, a real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all of these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making. This is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making is we've looked, we've contextualized most of our operational systems, but the big data pipelines hasn't gotten there. And maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform, uh, for analytics automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely altereds has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics or AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. And so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's gets applied and so multiple personas. Um, and now we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now that at least 3% is the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that, how is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are, uh, in the line of business that are driving a lot of the decision-making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market has not cracked the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that that was painted and, and got us really energized about the acquisition and about the potential of the combination. >>Yeah. And you're really, you're obviously riding the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, snowflake is doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just, um, what Adam said resonates with me deeply, um, analytics is one of those, um, massive disciplines, an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was Alteryx's and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization, uh, because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altryx it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? >>Yeah, I think, I think you should think about them and, uh, um, as, as very complimentary right design a cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, that really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with Trifacta. Um, I think we have to get deeper inside to think about what does the data engineer really need what's business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the tri-factor on the amazing tri-factor cloud platform. >>You know, >>I was just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of, by factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but I, one of the reasons I always liked Altryx is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the current organization said, wow, there's big data stuff is taken off, but we need security. We need governance. And, and it was interesting because he got, you know, ETTL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like, uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Yeah. Um, thanks for asking about our sales kickoff. So we met for the first time and kind of two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was, uh, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we had a Trifacta to, um, the, the party such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for us, the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working Adam and I were working so hard on, on the deal and the core hypothesis and so on. And then you step back and you kind of share the vision, uh, with the field organization and it blows you away, the energy that it creates among our sellers, our partners, and I'm sure Adam will, and his team were mocked every single day with questions and opportunities to bring them in. >>But Adam, maybe he's chair. Yeah, I know it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended the story, that we was just, you have this opportunity to really cater to what the end-users, you know, care about, which is a lot about interactivity and self-service, and at the same time. And that's, and that's a lot of the goodness that, um, that Ultrix has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. >>And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication of that. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that Jensen. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube, your leader in enterprise tech coverage.

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

(upbeat music) >> Welcome back to Supercloud22. This is an open community event, and it's dedicated to tracking the future of cloud in the 2020s. Supercloud is a term that we use to describe an architectural abstraction layer that hides the underlying complexities of the individual cloud primitives and APIs and creates a common experience for developers and users irrespective of where data is physically stored or on which cloud platform it lives. We're now going to explore the nuances of going to market in a world where data architectures span on premises across multiple clouds and are increasingly stretching out to the edge. Paula Hansen is the President and Chief Revenue Officer at Alteryx. And the reason we asked her to join us for Supercloud22 is because first of all, Alteryx is a company that is building a form of Supercloud in our view. If you have data in a bunch of different places and you need to pull in different data sets together, you might want to filter it or blend it, cleanse it, shape it, enrich it with other data, analyze it, report it out to your colleagues. Alteryx allows you to do that and automate that life cycle. And in our view is working to break down the data silos across clouds, hence Supercloud. Now, the other reason we invited Paula to the program is because she's a rockstar female in tech, and since day one at theCube, we've celebrated great women in tech, and in this case, a woman of data, Paula Hansen, welcome to the program. >> Thank you, Dave. I am absolutely thrilled to be here. >> Okay, we're going to focus on customers, their challenges and going to market in this cross cloud, multi-cloud, Supercloud world. First, Paula, what's changing in your view in the way that customers are innovating with data in the 2020s? >> Well, I think we've all learned very clearly over these last two years that the global pandemic has altered life and business as we know it. And now we're in an interesting time from a macroeconomic perspective as well. And so what we've seen is that every company in every industry has had to pivot and think about how they meet redefined customer expectations and an ever evolving competitive landscape. There really isn't an industry that wasn't reshaped in some way over the last couple of years. And we've been fortunate to work with companies in all industries that have adapted to this ever changing environment by leveraging Alteryx to help accelerate their digital transformations. Companies know that they need to unlock the full potential of their data to be able to move quickly to pivot and to respond to their customer's needs, as well as manage their businesses most efficiently. So I think nothing tells that story better than sharing a customer example with you, Dave. We love to share stories of our very innovative customers. And so the one that I'll share with you today in regards to this is Delta Airlines, who we're all very familiar with. And of course Delta's goal is to always keep their airplanes in the air flying passengers and getting people to their destinations efficiently. So they focus on the maintenance of their aircraft as a necessary part of running their business and they need to manage their maintenance stops and the maintenance of their aircraft very efficiently and effectively. So we work with them. They leverage our platform to automate all the processes for their aircraft maintenance centers. And so they've built out a fully automated reporting system on our platform leveraging tons of data. And this gives their service managers and their aircraft technicians foresight into what's happening with their scheduling and their maintenance processes. So this ensures that they've got the right technicians in the service center when the aircrafts land and that everything across that process is fully in place. And previously because of data silos and just complexity of data, this process would've taken them many many hours in each independent service center, and now leveraging Alteryx and the power of analytics and bringing all the data together. Those centers can do this process in just minutes and get their planes back in the air efficiently and delivering on their promises to their customers. So that's just one of many examples that we have in terms of the way the Alteryx analytics automation helps customers in this new age and helping to really unlock the power of their data. >> You know, Paul, that's an interesting example. Because in a previous life I worked with some airlines and people maybe don't realize this but, aircraft maintenance is the mission critical application for carriers. It's not the booking system. Because we've been there before, we show you there's a problem when you're booking or sometimes it's unfortunate, but people they get de booked. But the aircraft maintenance is the one that matters the most and that keeps planes in the air. So we hear all the time, you just mention it. About data silos and how problematic they are. So, specifically how are you seeing customers thinking about busting the data silos? >> Yeah, that's right, it's a big topic right now. Because companies realize that business processes that they run their business with, is very cross-functional in nature and requires data across every department in the enterprise. And you can't keep data locked in one department. So if you think of business processes like pay to procure or quote to cash, these are business processes that companies in every industry run their business. And that requires them to get data from multiple departments and bring all of that data together seamlessly to make the best business decisions that they can make. So what our platform does is, and is really well known for, is being very easy for users number one, and then number two, being really great at getting access to data quickly and easily from all those data silos, really, regardless of where it is. We talk about being everywhere. And when we say that we mean, whether it's on-prem, in your legacy applications and databases, or whether it's in the cloud with of course, all the multiple cloud platforms and modern cloud data warehouses. Regardless of where it is, we have the ability to bring that data together across hundreds of different data sources, bring it together to help drive insights and ultimately help our customers make better decisions, take action, and deliver on the business outcomes that they all are trying to drive within their respective industries. And what's- >> You know- >> Go ahead. >> Please carry on. >> Well, I was just going to say that what I do think has really sort of a tipping point in the last six months in particular is that executives themselves are really demanding of their organizations, this democratization of data. And the breaking down of the silos and empowering all of the employees across their enterprise regardless of how sophisticated they are with analytics to participate in the analytic opportunity. So we've seen some really cool things of late where executives, CEOs, chief financial officers, chief data officers are sponsoring events within their organizations to break down these silos and encourage their employees to come together on this democratization opportunity of democratization of data and analytics. And there's a shortage of data scientists on top of this. So there's no way that you're going to be able to hire enough data scientists to make sense of all this data running around your enterprise. So we believe with our platform we empower people regardless of their skillset. And so we see executives sponsoring these hackathons within their environments to bring together people to brainstorm and ideate on use cases, to share examples of how they leverage our platform and leverage the data within their organization to make better decisions. And it's really quite cool. Companies like Stanley Black & Decker, Ingersoll Rand, Inchcape PLC, these are all companies that the executive team has sponsored these hackathon events and seen really powerful things come out of it. As an example Ingersoll Rand sponsored their Alteryx hackathon with all of their data workers across various different functions where the data exists. And they focused on both top line revenue use cases as well as bottom line efficiency cases. And one of the outcomes was a use case that helped with their distribution center in north America and bringing all the data together across their various applications to reduce the amount of over ordering and under ordering of parts and more effectively manage their inventory within that distribution center. So, really cool to see this is now an executive level board level conversation. >> Very cool, a hackathon bringing people together for collaboration. A couple things that you said I want to comment on. Again, one of the reasons why we invited you guys to come on is, when you think about on-prem data and anybody who follows theCube and my breaking analysis program, knows we're big fans of Zhamak Dehghani's concept of data mesh. And data mesh is supposed to be inclusive. It doesn't matter if it's an S3 bucket, Oracle data base, or data warehouse, or data lake, that's just a note on the data mesh. And so it should be inclusive and Supercloud should include on-prem data to the extent that you can make that experience consistent. We have a lot of technical sessions here at Supercloud22, we're focusing now and go to market and the ecosystem. And we live in a world of multiple partners exploding ecosystems. And a lot of times it's co-opetition. So Paula, when you joined Alteryx you brought a proven go to market discipline to the company. Alignment with the customer, playbooks, best practice of sales, et cetera. And we've seen the results. It's a big reason why Mark Anderson and the board promoted you to president just after 10 months. Summarize how you approached the situation at Alteryx when you joined last spring. >> Yeah, I think first we were really intentional about what part of the market, what type of enterprises get the most benefit from the innovation that we deliver? And it's really clear that it's large enterprises. That the more complex a company is, most likely the more data they have and oftentimes the more decentralized that data is. And they're also really all trying to figure out how to remain competitive by leveraging that data. So, the first thing we did was be very intentional that we're focused on the enterprise and building out all of the capability required to be able to serve the enterprise. Of course, essential to all of that is having a platform capability because enterprises require that. So, with Suresh Vittal our Chief Product Officer, he's been fantastic in building out an end to end analytic platform that serves a wide range of analytic capabilities to a wide range of users. And then of course has this flexibility to operate both on-prem and in the cloud which is very important. Because we see this hybrid environment in this multicloud environment being something that is important to our customers. The second thing that I was really focused on was understanding how do you have those conversations with customers when they all are in maybe different types of backgrounds? So the way that you work with a business analyst in the office of finance or supply chain or sales and marketing, is different than the way that you serve a data scientist or a data engineer in IT. The way that you talk to a business owner who wants not to really understand the workflow level of data but wants to understand the insights of data, that's a different conversation. When you want to have a conversation of analytics for all or democratization of analytics at the executive level with the chief data officer or a CIO, that's a whole different conversation. And so we've built very specific sales plays to be able to have those conversations bring the relevant information to the relevant person so that we're really making sure that we explain the value proposition of the platform. Fully understand their world, their language and can work with them to deliver the value to them. And then the third thing that we did, was really heavily invest in our partnerships and you referenced this day. It's a a broad ecosystem out there. And we know that we have to integrate into that broad data ecosystem. and be a good partner to serve our customers. So, we've invested both in technology integration as well as go to market strategies with cloud data warehouse companies like Snowflake and Databricks, or RPA companies like UiPath and Blue Prism, as well as a wide range of other application and all of the cloud platforms because that's what our customers expect from us. So that's been a really important sort of third pillar of our strategy in making sure that from a go to market perspective, we understand where we fit in the ecosystem and how we collectively deliver on value to our joint customers. >> So that's super helpful. What I'm taking away from this is you didn't come to it with a generic playbook. Frank Lyman always talks about situation leadership. You assess the situation and applied that and a great example of partners is Snowflake and Databricks, these sort of opposites, but trying to solve similar problems. So you've got to be inclusive of all that. So we're trying to sort of squint through this Paula and say, okay, are there nuances and best practices beyond some of the the things that you just described that are unique to what we call Supercloud? Are there observations you can make with respect to what's different in this post isolation economy? Specifically in managing remote employees and of course remote partners, working with these complex ecosystems and the rise of this multi-cloud world, is it different or is it same wine new bottle? >> Well, I think it's both common from the on-prem or pre-cloud world, but there's also some differences as well. So what's common is that companies still expect innovation from us and still want us to be able to serve a wide range of skill sets. So our belief is that regardless of the skill set that you have, you can participate in the analytics opportunity for your company and unlocking the potential of your data. So we've been very focused since our inception to build out a platform that really serves this wide range of capabilities across the enterprise space. What's perhaps changed more or continues to evolve in this cloud world is just the flexibility that's required. You have to be everywhere. You have to be able to serve users wherever they are and be able to live in a multi-cloud or super cloud world. So when I think of cloud, I think it just unlocks a whole bigger opportunity for Alteryx and for companies that want to become analytic leaders. Because now you have users all over the globe, many of them looking for web-based analytic solutions. And of course these enterprises are all in various places on their journey to cloud and they want a partner and a platform that operates in all of those environments, which is what we do at Alteryx. So, I think it's an exciting time. I think that it's still very early in the analytic market and what companies are going to do to leverage their data to drive their transformation. And we're really excited to be a part of it. >> So last question is, I said up front we always like to celebrate women in tech. How'd you get into tech.? You've got a background, you've got somewhat of a technical background of being technical sales. And then of course rose up throughout your career and now have a leadership position. I called you a woman of data. How'd you get into it? Where'd you find the love of data? Give us the background and help us inspire some of the young women out there. >> Oh, well, but I'm super passionate about inspiring young women and thinking about the future next generation of women that can participate in technology and in data specifically. I grew up loving math and science. I went to school and got an electrical engineering degree but my passion around technology hasn't been just around technology for technology's sake, my passion around technology is what can it enable? What can it do? What are the outcomes that technology makes possible? And that's why data is so attractive because data makes amazing things possible. I shared some of those examples with you earlier but it not only can we have effect with data in businesses and enterprise, but governments globally now are realizing the ability for data to really have broad societal impact. And so I think that that speaks to women many times. Is that what does technology enable? What are the outcomes? What are the stories and examples that we can all share and be inspired by and feel good and and inspired to be a part of a broader opportunity that technology and data specifically enables? So that's what drives me. And those are the conversations that I have with the women that I speak with in all ages all the way down to K through 12 to inspire them to have a career in technology. >> Awesome, the more people in STEM the better, and the more women in our industry the better. Paula Hansen, thanks so much for coming in the program. Appreciate it. >> Thank you, Dave. >> Okay, keep it right there for more coverage from Supercloud 22, you're watching theCube. (upbeat music)

(upbeat music) >> Okay, we're kicking off the program with our first segment. Jay Henderson is the vice president of product management at Alteryx. And we're going to talk about the trends and data where we came from, how we got here, where we're going. We got some launch news. Hello, Jay, welcome to theCUBE. >> Great to be here. Really excited to share some of the things we're working on. >> Yeah, thank you. So look, you have a deep product background, product management, product marketing. You've done strategy work. You've been around software and data your entire career, and we're seeing the collision of software, data, cloud, machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or a data executive at an organization, Jay, what's your north star? Where are you trying to take your company from a data and analytics point of view? >> Yeah, I mean, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust creating large volumes of data. Storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's drowning in data, but somehow still starving for insights. And so I think, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics and you know, let the business users and the whole organization get value out of all that data they have. >> And we're going to dig into that throughout this program. And data, I like to say is plentiful. Insights, not always so much. Tell us about your launch today, Jay. And thinking about the trends that just highlighted, the direction that your customers want to go, and the problems that you're solving. What role does the cloud play, and what is what you're launching, how does that fit in? >> Yeah, we're really excited today we're launching the Alteryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, and to take advantage of things like browser based access. So that it's really easy to give anyone access including folks on a Mac. It also lets you take advantage of elastic compute, so that you can do, you know, in database processing and cloud native solutions that are going to scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud. We've got Alteryx machine learning which helps up-skill, regular, old analyst, with advanced machine learning capabilities. We've got auto insights, which brings business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition which is Trifacta, that helps data engineers do data pipelining, and really, you know, create a lot of the underlying data sets that are used in some of this downstream analytics. >> So let's dig into some of those roles, if we could a little bit. I mean, traditionally Alteryx has served the the business analysts, and that's what designer cloud is fit for, I believe. And you've explained kind of the scope. Sorry, you've expanded that scope into the to the business user with Hyper Anna. And in a moment, we're going to talk to Adam Wilson and Suresh, about Trifacta. And that recent acquisition takes you as you said into the data engineering space and IT, but in thinking about the business analyst role, what's unique about designer cloud and how does it help these individuals? >> Yeah, I mean, really I go back to some of the feedback we've had from our customers which is, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product. Really as they look to take the next step, they're trying to figure out, how do I give access to that, those types of analytics to thousands of people within the organization. And designer cloud is really great for that. You've got the browser based interface. So if folks are on a Mac, they can really easily just pop open the browser and get access to all of those prep and blend capabilities to a lot of the analysis we're doing. It's a great way to scale up access to the analytics and start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >> Okay, great. So now then you add in the Hyper Anna acquisition. So now you're targeting the business user, Trifacta comes into the mix, that deeper IT angle that we talked about. How does this all fit together? How should we be thinking about the new Alteryx portfolio? >> Yeah, I mean, I think it's pretty exciting. When you think about democratizing analytics and providing access to all these different groups of people, you've not been able to do it through one platform before. It's not going to be one interface that meets the needs of all these different groups within the organization, you really do need purpose built specialized capabilities for each group. And finally today with the announcement of the Alteryx analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single end to end application. So, really finally delivering on the promise of providing analytics to all. >> How much of this have you been able to share with your customers and maybe your partners? I mean, I know all this is fairly new but have you been able to get any feedback from them? What are they saying about it? >> Yeah, I mean, it's pretty amazing. We ran early access and limited availability program, that let us put a lot of this technology in the hands of over 600 customers. >> Oh, wow. >> Over the last few months. So we have gotten a lot of feedback. I tell you, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data they've got. They're excited to be able to use analytics in every decision that they're making so that the decisions they have are more informed and produce better business outcomes. And this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >> That's good. Those are good numbers for a preview mode. Let's talk a little bit about vision. So if democratizing data is the ultimate goal, which frankly has been elusive for most organizations. Over time, how's your cloud going to address the challenges of putting data to work across the entire enterprise? >> Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes. And these are really kind of enduring themes that you're going to see us making investments in over the next few years. The first is having cloud centricity. The data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it to provide cloud solutions. So, the first one is really around cloud centricity. The second is around big data fluency. Once you have all of that data you need to be able to manipulate it in a performant manner. So, having the elastic cloud infrastructure and in-database processing is so important. The third is around making AI a strategic advantage. So, you know, getting everyone involved in accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. And then the fourth thing is really providing access across the entire organization, IT and data engineers, as well as business owners and analysts. So, cloud centricity, big data fluency, AI as a strategic advantage, and personas across the organization, are really the the four big themes you're going to see us working on over the next few months and coming years. >> That's good, thank you for that. So on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know monolithic organizations, very specialized, or I might even say hyper specialized roles. And your mission, of course, as the customer, you and your customers, they want to democratize the data. And so, it seems logical that domain leaders are going to take more responsibility for data life cycles, for data ownerships, low code becomes more important. And perhaps there's kind of challenges the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving, and what role will Alteryx play? >> Yeah, I think we'll see sort of a more federated system start to emerge. Those centralized groups are going to continue to exist, but they're going to start to empower in a much more decentralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model result in millions of dollars a day for the business. And then by pushing some of the analytics out closer to the edge and closer to the business, you'll be able to, you know, apply those analytics in every single decision. So I think you're going to see both the decentralized and centralized model start to work in harmony in a little bit more of a, almost a federated sort of way. And I think the exciting thing for us at Alteryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, and drive business outcomes with the analytics they're using. >> Yeah, I mean, I think my take on it, I wonder if you could comment is, to me the technology should be an operational detail. And it has been the dog that wags the tail or maybe the other way around. You mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operational systems that then it somehow eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, those line of business experts get more access, that's your goal. And then even go beyond analytics, start to build data products that could be monitized. And that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >> Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications, and to enable somebody who's an analyst or a business user to create an application on top of the data and analytics layers that they have, really to help democratize the analytics, to help pre-package some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more of. >> Yeah, and to your point, if you confederate the governance and automate that... >> Yep. Absolutely. >> Then that can happen. I mean, that's a key part of it, obviously, so... >> Yep. >> All right, Jay, we have to leave it there. Up next, we take a deep dive into the Alteryx recent acquisition of Trifacta with Adam Wilson, who led Trifacta for more than seven years, and Suresh Vittal, who is the chief product officer at Alteryx, to explain the rationale behind the acquisition, and how it's going to impact customers. Keep it right there. You're watching theCUBE, your leader in enterprise tech coverage. (upbeat music)