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The Six Five at Cloudera Evolve 2022 with Carolyn Duby & Santiago Giraldo
by Daniel Newman | October 31, 2022

The Six Five “On The Road” at Cloudera Evolve NYC. Hosts Daniel Newman and Patrick Moorhead are joined by Carolyn Duby, Field CTO, & Santiago Giraldo, Sr. Director, Hybrid Data Services, Cloudera. Their conversation centers on Cloudera’s mission to provide their customers with tangible AI, ML, and analytics solutions that drive real, actionable insights.

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Transcript:

Daniel Newman: Hey everyone. Welcome to another Six Five On The Road here in New York City at Cloudera Evolve. I’m joined here by my wonderful co-host, Mr. Patrick Moorhead. By the way, the MC and Keynote here at Cloudera Evolve. Good morning, Pat. How are you buddy? You’ve been at it.

Patrick Moorhead: I’ve been at it. Been on camera already. I got to tell you, they must have gone through a long list of presenters before they got to me. But I got to tell you though, I had so much fun. Josh Wordle was up there, and by the way, I really stink at that game. But it was great to hear from IBM. It was great to hear from CEO Rob Bearden as well.

Daniel Newman: Absolutely.

Patrick Moorhead: Yeah.

Daniel Newman: Yeah, it was great to hear from Rob and I did Wordle while the Wordle guy, while Josh Wordle was up there talking about Wordle. I did my wordle and I sent it to you because I wanted to give you ammo and thank you so much for then using it to kind of give you a little comedy relief.

Patrick Moorhead: Anything I can do.

Daniel Newman: The third or fourth time you got back up on stage now, really excited. We have some great guests today. We’ll get them on in just a minute. Just excited to be here though, Pat. Your keynote kicked off the event. Very, very impassioned conversation. You told everybody their cloud sucks. Now I’m sure when you got a room full of enterprise executives, CTOs, data leaders, I’m sure they really appreciated that.

Patrick Moorhead: I’m sure they did and Rob Bearden didn’t yank me, which I appreciated.

Daniel Newman: Yeah, no, you know what? I think sometimes provocation works. I think we’ve learned that from social media and whatnot. So without further ado, we’ve got Carolyn and Santiago here, some great guests we’ve come across in the past. Quick opportunity for both you. Just introduce yourselves and talk a little bit about your role at Cloudera.

Carolyn Duby: Sure. I’m Carolyn Duby. I am Field TTO here at Cloudera. I work with customers across the Americas and across the world on general use cases as well as a specialty in cyber security and streaming.

Santiago Giraldo: My name is Santiago. I lead product marketing for Cloudera’s hybrid data services. My background is generally in machine learning and data science. That’s how I started off in this career. It turns out that when you can write and talk and or can do tech, you end up in product marketing. So here I am.

Patrick Moorhead: So this is great. We have Carolyn, you directly meet with customers every day and your team, and Santiago product marketing. I love it. Did that in product management for over 20 years before I did the analyst gig. I’m definitely a product person.

But hey, instead of talking about myself longer, now I want to talk about the important stuff. And I want to hone in on AI and ML. It’s been a really interesting journey, right. Where, is AI really AI? What is it? Is AI just fancy analytics? But regardless of whether it’s analytics, whether it’s AI or whether it’s ML, it’s all about getting insights, actual insights out of the data for organizations to do some awesome stuff. So the question I have for both of you and Santiago, I’ll start with you is, what are some of the challenges that you’re seeing out there right now regarding with your customers getting AI and ML going?

Santiago Giraldo: Yeah, and I think you touched on something that’s really interesting. What is machine learning? What is AL, right? A lot of organizations are trying to adopt these strategies. And there’s sort of like a certain way that people are thinking about it, that generally in my opinion, needs to be kind of rethought. And I think it kind of approaches it in two specific areas. One is a technology that enables you to make predictions. And then how you use those predictions. And two, it’s the culture of an organization. And I think between those two, that’s where you often see a lot of the challenges where they lie.

On the technology side, I would say that it’s really about understanding that machine learning and artificial intelligence are really not the same thing. AI is how you essentially get things done, how a business can use it, applications, things like that. Whereas machine learning technically is just an algorithm. But as a collective to get to AI, it’s so much more. You have to do so many different things in terms of data ingestion and data engineering and getting those workflows into your machine learning models effectively. On the cultural side, which I think can be sometimes the biggest hurdle, it really has to do with essentially how an organization adopts it and how they change the way that they function at a fundamental level to actually make use of those predictions effectively.

Patrick Moorhead: Interesting. Carolyn, how about you? I mean, I said your team and you talk to customers every day, it’s probably every hour. What are you seeing out there? What are some of the challenges to actually get in and do AI and ML?

Carolyn Duby: So I think there are a number of different hurdles, technical hurdles that you have to cross over in order to do AI and ML.

Patrick Moorhead: Are they different from analytics or similar?

Carolyn Duby: They’re different because of when you start to use AI and ML, you have to look and see how it fits into the process. So AI and ML are just another tool in a toolkit and you have to look at how are you using that AI and ML.

But to back up a step, you have to look at an automated decision and how that will be managed by the organization. So it might be something different than the organization has done before. So they’re making a decision using humans, the people that are working at the organization, and then how do you incorporate an automated process into that whole flow? And then how do you put in the logistics and the fairness behind that process. So you’re making a decision, is it fair? Is the data that you’re working with biased or does that automated process need breaks on it? Some friction in order to make sure that it’s staying on track? How do we have the care and the feeding? So that’s the -building the model and doing the actual technical work of building the model is just one step in the process. You have to consider the whole flow.

Daniel Newman: It’s interesting, I listened to Rob Bearden, your CEO speak, we listened to IBM’s, Rob Thomas speak, I listened to you. And then yesterday, Pat, by the way, we spent a day at Google Cloud Next and we listened to a number of what their customers as well as their executives are saying. And I’m hearing a lot of the same thing. And Carolyn, I want to kind of ask you this. One of the most probably valuable data points that our audience could get right now from you and as well I think from you is what are your customers sort of coming to you asking for help with? Because what I got as a whole after listening to all these people is this is hard. It’s a hard problem. And so when they come to you, are you seeing consistency in what they’re asking or what are some of the big questions that are regularly coming your way?

Carolyn Duby: So I think a lot of the challenges are around change management. So navigating how do I go from where I am now to where I want to be in the future? So again, we’re in the midst of a big digital transformation, a rapid acceleration, and everybody’s just trying to figure out where are we now and where are we going? So looking at that and then looking at some of the technical challenges of moving forward with, I have a lot of data. How do I secure it? How do I govern it? How do I make it available for other teams to use? Because the security and the governance are the glue that goes in the foundation to help other people manage and share the data. And if you can’t share the data, you can’t do the next step, which is the AI and the ML and the analytics.

Daniel Newman: Yeah. In our research we’ve seen one of the biggest challenges is around simplicity. It’s kind of funny at one level, you have data scientists and the folks who go all the way down to the algorithm level saying, hey, I don’t need it to be simplified. But more times than not, when I ask enterprises why they’re not widely using AI or ML, it’s about simplicity. It’s just too hard and they don’t have the right people. And if they do, these people are really expensive. And I’m curious, Santiago, this question’s for you. What is Cloudera doing to simplify through the entire data life cycle, AI and ML?

Santiago Giraldo: Yeah, I think that there’s a couple different ways of thinking about this question. So Cloudera in general, because we’re a hybrid data cloud company, you can actually do machine learning and artificial intelligence pretty much wherever you need it. As I mentioned before,

Daniel Newman: Now, what do you mean by that, wherever? Do you mean for everybody? Anywhere?

Santiago Giraldo: Yep, exactly. So basically whether you’re a giant organization that’s trying to run a multi-cloud environment or whether you have some data that’s required to be on premises and some that’s required to be in some kind of cloud or whether you have a line of business strategy, Cloudera essentially enables you to have the same quality experience in an N10 workflow for machine learning, in any of those environments, in any combination of those. Also, portability is really important for those workloads.

And I think that one of the most important pieces is how you enable that N10 flow. So we tend to think that machine learning, AI, that’s just these models that are making predictions. The reality is the model can sometimes be the smallest part of the project. It’s about how you get that data where it needs to be. Oftentimes you’re talking about petabytes of data across hundreds of different sources. You have to be able to automate those, that engineering pipelines, that then inform a model that then enable you to automate those workflows in an effective way. And you need to have explainability within, essentially baked into it so that it’s not a black box and that the business teams can feel confident about the decisions that they end up making.

Patrick Moorhead: Will CDP One simplify things even further?

Santiago Giraldo: Absolutely. So CDP One, as a fully managed software as a service product, it actually enables you to do streamline machine learning without any of the IT essentially management on the backside. And it’s going to offer a completely streamlined, agile and very fast way of getting to end results very quickly. And the cool thing about CDP One is that it really encompasses that entire machine learning life cycle that I mentioned. It’s not just about the writing the code, but everything else is kind of packaged really neatly so you can deliver results quickly.

Patrick Moorhead: Excellent.

Carolyn Duby: Yeah, I think that’s a huge benefit of the CDP One because it looks completely different than any other product we’ve built before. And it’s built with that kind of end user in mind. Looking at, okay, I want to go in and work with some data. Now how do I get there? It’s a different way of organizing the product and the interface.

Patrick Moorhead: Daniel and I both wrote papers on that and I know we’re pretty excited about that and it really opens it up to a brand new market. Listen, I’ve never heard anybody say I didn’t get the capability that I needed out of Cloudera. That they could get there. They’ve told me that, hey, it’s hard for a certain set of people that I want to use this. And once CDP One gets out to more people, I really think that that’s going to go away. And big investment on your part. I know you made some acquisitions in there and you pulled that together. So sorry to interrupt.

Daniel Newman: No, I was just thinking to myself, one of the big opportunities for Cloudera is truly winning the market perception that it has that data life cycle from on prem to cloud. And of course up on stage today, we’re talking hybrid. We’re talking hybrid. I believe, and Carolyn, I’ll direct this your way, that Cloudera is still, the majority of its workloads are still on prem, but there’s a shift going on. And as I’ve kind of hit you on that customer response, do you find the customers right now are very open and receptive and are moving forward with Cloudera’s journey? Cause it’s kind of like a journey together. I mean, you went from very prem focused to talking a lot more about cloud, but again, the guts, the Hortonworks guts, the Cloudera guts, these were the best and the most robust on-prem data solutions. And now you’re changing that story. How are the customers sort of reacting and are you able to get them to come along?

Carolyn Duby: So it’s a journey and each one of our customers has goes at a different pace. Some of our customers are going very quickly to the cloud, some of them are looking at more of a hybrid strategy. And some customers will probably stay just on premise due to just the restrictions of their business environment or the way that they operate. So as far as that journey, we’re giving customers the flexibility to make the choices that they need and to run their workloads where they want to run them. And this is hugely powerful and it resonates really well with the folks that I’ve been talking to because they don’t really know. Things are still very uncertain in the marketplace and they don’t know, is this the right place that I want to run my workload, and do I want to spend a lot of time re-engineering something from a proprietary system if I want to take it to a different place. So the customers like the flexibility of being able to run the workload where they want to run it.

Patrick Moorhead: Yeah, it’s interesting. In my talk this morning I talked about hybrid infrastructure and how you need a hybrid data strategy and that hey, it’s probably safe to go in the water right now, just watch it. There’s still some sharks out there. By the way. I give Hannah Smalltree credit for that. I stole that directly from her. But the main point here being is, you have to off your customers the wide variety.

And it’s an interesting psychological thing, and you can appreciate this from a marketing standpoint. If you didn’t have CDB Cloud and you didn’t have CDP One, people would think, okay, they’re trying to hoard my data on prem. But what’s pointed out to me as I got ready for the keynote is somebody told me, hey Pat, not everybody is in the cloud. I’m like, okay, that’s super interesting. And it is the big challenge, which is how do I manage this data? How do I protect this data? What’s the lineage of that data when a lot of your customers who quite frankly are very regulated businesses, come up and the regulators show up and they want to know what’s happening with all the data. Does the game change at all? And Santiago, I’ll start with you. Does that game change at all between what your customers are doing today and moving into AI and ML? Or is that consistent flow good for all data workloads?

Santiago Giraldo: Yeah, I think that the way that a lot of organizations are evolving is oftentimes yes to the cloud, but oftentimes not. So one of the things that we really focused in on at Cloudera was providing a cloud native experience on premises as well. So you mentioned for example, that it seems like a lot of our messaging is changing and the identity of Cloudera is evolving, which is definitely true. But at the same point, we’re realists in terms that the largest organizations in the world fundamentally may have requirements to keep stuff on premises. And we need to offer the best possible experience on that. And something you would expect from what you get in the cloud, you should be able to get that same quality on premises so that you can deliver AI and ML or advanced analytics or consumer applications or whatever it might be.

And I think kind of underpinning all that is this notion that we architected the Cloudera data platform in such a way that it enables truly a true data fabric with all of the interconnected data services that work together to deliver results with the SDX and security that sits underneath. It delivers essentially that open data lake house experience with unified analytics across the board so that you can solve those problems quickly.

Patrick Moorhead: Excellent.

Daniel Newman: So as we take this interview home, Carolyn, I’d love to ask you as the customer-facing side, and by the way, you’ve both been great guests.

Patrick Moorhead: Yeah, thank you.

Carolyn Duby: Thank you.

Daniel Newman: What is the advice and recommendations for the AI and ML journey for the enterprise? Do you have a couple of things that you’re sort of telling them consistently? Cause I know every case is unique, but product management, it’s all about having a consistent story. So what’s the story you’re telling customers in terms of getting their journey started?

Carolyn Duby: So I would say focus on three things. One is making sure that you’re paying attention to what you’re doing and making sure that you’re AI and ML again, it’s a technology, it’s a particular technique. But look at how am I delivering business value?

And then the second point is looking at the data and making sure that you have a really good strong data foundation because that is the foundation that will help you to build on all of your data products. So thinking about what kind of data products am I delivering to my customers, whether that be internal customers or external customers, am I delivering the kinds of insights that they can actually use? Not just random data, but actually products that they can actually use.

And then the third thing is, how am I thinking about my AI and ML project and how they fit into that larger process throughout the organization, whether that be a customer workflow or some internal optimization processes. How do we use it and how do we make sure that it’s doing the right, playing the right role in that process?

Daniel Newman: No, I appreciate it. That’s super advice here. And with that, I think we’re at our final minute here, but I just want to thank both of you for coming on the Six Five and sharing what I consider very practical advice. Listen, I love unicorns too, but I think most people here at the show appreciate how to really get things done. And what I love about the show is that you’re talking about practical implementations. That doesn’t mean it’s not visionary, it doesn’t mean it’s not going to result in billions of revenue increased or cost reduced or improved stickiness. But anyways, thanks for coming on the show. I really appreciate that.

Patrick Moorhead: I swear you said unicorn stew.

Daniel Newman: Did I?

Patrick Moorhead: I think he said unicorn stew.

Daniel Newman: Wow, okay.

Patrick Moorhead: Almost sounded like lunch.

Daniel Newman: No, I appreciate it. It’s probably a great place to cut here, but-

Patrick Moorhead: Absolutely.

Daniel Newman: So hey everyone, thank you so much for tuning in to this Six Five on the Road here in New York City at Evolve NYC, if you’re doing the hashtag. Thanks for tuning in. More to come. See you all soon.

About the Author

Daniel Newman is the Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise. Read Full Bio