Sergio Gago discusses Cloudera’s State of Enterprise AI Survey Report

Hey, everyone. Welcome back. It's Alan Shimmel here on Text Trunk tv.

I've got a, a, actually, I think it's his first time on, so let's welcome Sergio Ggo. Sergio is the CTO Chief Technology Officer over at c Cloudera. Sergio, welcome to Tech Drunk tv.

It's great to have you on here. Hi, Everyone. Thank you very much, Alan, for having me.

It's really a pleasure, and, and I'm honored to be part of the show finally. Thank you very much. I appreciate it.

Sergio, you know, you weren't always the chief technology officer at Cloudera. You've had a rather distinguished career already. Why don't you, if you don't mind, share a little bit of, of your details, your journey with the audience?

Sure, a hundred percent. Um, so my name is Sergio. I am, I'm a Spanish, uh, chief Technology Officer by trade or engineer.

Uh, I started my career as a software developer, and I was always trying to be on the consumer side, effectively building things that were cool and created value. Uh, so life took me through all trades and industries from hospitality, e-commerce, technology in general, but always trying to tinker with what is, what technology is giving us for us to play and, and, and create more value. I've been in quantum computing as well.

I've been in small startups, large ones, and scale apps all the way to enterprises. Um, and I joined Cloudera seven months ago as, as the, as the CTO where we are building, and from that perspective of being very customer centric and product led, building the data platform of the future. Excellent, excellent.

Um, let's talk about Cloudera, if it's okay, Sergio, right. I, I think a lot of people in our audience over the years have heard the name Cloudera, you know, and, and as, as the cloud has matured, as technology has changed, the mission's changed a little bit too, though I, I think it's still stays true to their original mission of, of, of, you know, helping customers, helping their customers navigate. But why don't, if you don't mind, give us, if you would, a brief history of Cloudera and then let's really talk about today's Cloudera and what that's about.

Absolutely. Uh, and, and that is actually one of the reasons why I joined the company and, and, and what makes me super happy to be, to be here. Cloudera was at the end of the day, the father of Big Data and Hortonworks the mother, right?

Back then, may, may many years ago, more than a decade ago, these two companies were competing for being the ones who manage the data at scale. That enterprises and any company, uh, for that matter, needed to use these platforms to effectively manage, ingest the data and create insights out of that data, right? Then the companies merge one public then when private, uh, and Cloud Vena has been forever building the data platform as the world evolve from these big Hadoop clusters, uh, HDFS clusters, all that technology that allowed companies to ingest gigabytes, terabytes, petabytes of data, all the way to the cloud days when the cloud came in, and then was able to create these environments both on the data center and on any of the cloud providers out there.

And today in what we call the era of convergence, what is this? Effectively every enterprise keeps ingesting tons of data every day. Every single person moving across a city, driving, taking a bicycle, making a phone call, consuming any service, generates terabytes and terabytes of data every day.

How do we manage all that and effectively power the new feature of AI that we are seeing bloom all around ourselves, AI agents and so on, and most importantly, in a well governed way so that we have true private AI, explainable AI systems that we can manage and govern properly. That is what Cloudera does today in the head of convergence. Whatever your data lives, it doesn't matter where your company is, whether you use one cloud provider or another.

If a Cloud pro provider is down for any reason, if you have data centers, cloud data is the data platform that helps you orchestrate and convert your data and extract insights to power ai. Love it. That was great.

That was, thank you. Good works. Um, I I almost didn't rehearse it.

No, no, I get it. One last thing. com is the website.

Yes. com. That's where anybody can go.

You can join webinars, you can join training sessions. Data is an amazing place to be. Today we are seeing how AI is reshaping the world, and in order to do that, especially at the enterprise level or public institutions, sobering clouds, companies that need to have, well go burned and well, a, a IGN systems, essentially, you need to make sure you do your, your homework right.

Um, sir, how are you going to access your data? Which systems and humans are going to do that? I am passionate about showing people what are the different things that you need to do in order to architect this AI agent enterprise of the future?

And that is we're building our data platform for. Excellent. Okay.

Let's, let's turn to, to a recent, uh, report that Cloudera did based on their annual, uh, state of enterprise AI survey. And, you know, as, as, uh, you know, I don't know how any annual state of enterprise ai we, there was state of enterprise and so forth, but certainly everything is AI today. But, um, let you know, give us a little background here.

What, what was the report like, what's the mission of the report? And then maybe we could dive into some of the findings this year. Um, absolutely.

So we've been a, a doing this report in order to understand better what are the main challenges that enterprises have in, in adapting to new this new world. And you say that very right? It seems that AI is new, but it is not.

AI is decades old. Um, so for a long time, why did you need data as a, as a company, you wanted to create business intelligence passwords. You wanted to create insights effectively.

You wanted to know how your company performed in the past and tried to build models that would help you forecast what things would happen in the future with machine learning and, and, and things like that. Of course, now we all went crazy with generative ai and, and, and with the advent of JGPT and the, and all the different companies that are popping up today, and every single, literally every single enterprise is trying to get a hold on on that by every single, I mean, 96% are trying to get fully into ai. And that's to your point, everyone is talking about ai, but it's not just a generative AI elements, but using your previous systems, your data management in order to build, uh, these elements.

Now, this is already almost four years old, and we've gone through different phases. First, virtually every company created a small team, uh, innovation teams and such to build a small chatbot, a small system that would provide answers. And I'm sure many of you have seen all those problematic chatbots that gave you the wrong answer, that gave away products that started misbehaving, right?

Because we didn't have the right guardrails in place. So we've seen how enterprises have been maturing both on how we integrated these systems and how we are effectively now doing the full cultural change and, and, and, and, and effectively industrial chains of our own internal processes, whichever the use case is. But for, with our survey, we're trying to understand what are enterprises falling short?

What are the challenges they face, and are they looking more into a gentech workflows? Are they looking to, into looking at the past into chatbots for customer support, use cases, technologies? And with that information, we can influence obviously, our own products and the way we support our customers, but the industry in general and how we imagine the world is, is changing in in the next few years.

Absolutely. I, and it is, I mean, I did some days it feels like the world is changing in the next few weeks or months, right? Let alone years.

But Sergio, so as part of this survey, you guys interviewed over 1500 IT leaders mm-hmm. Right? 1500.

That's a decent sized sample. And, and, and as you said, it's no longer an option. It's not just a priority.

It's really do or die, if you will, AI or die. And, um, and that of course is having repercussions up and down, not just it up and down the whole business. Um, give us some of, if you will, some of the key findings here.

Yeah. About, you know, how how orgs are, are responding. You, you, you, you're absolutely right that the main biggest outcome of the survey and biggest change from previous years is that now it is not a priority anymore.

Now, for companies is a mandate for their teams to use AI and embed it in their own systems. So now it is expected for your employees to use AI in the best way possible to improve their systems or the, their workflows or the way they work. One example is a, a for software developers, which is very close to, to my heart.

Now, it is expected that a software engineer, a programmer uses coding agents for their work. It's not okay to not use them. And effectively the less, uh, uh, have less throughput or, or be less effective that your counterparts.

So companies are taking this as a pure competitive advantage. And now we've moved into the mandate stage, almost like when computers were optional many decades ago in the, in the companies. And obviously no company understands someone still handwriting letters and, and sending them by post.

Right? So we, we are reaching that, that point. Um, the second point that I think it was really relevant is how enterprises need access to their different data states.

Now, we saw, uh, how six 63% say that they had data stored in data centers on-prem, and they need their agents to be able to access that data instead of treating that as two different universes. Um, and I think the third data point that is really relevant to mention is that, uh, more than half of the, um, uh, budgets going into, into AI or gene ai, or going straight into AgTech enterprise AgTech ai. So it's not anymore about, Hey, I want to create a little chat bot that allows you to access my documentation or my manuals.

Now, we are building systems that can have reasoning capabilities, that can use tools within your system, and that effectively are digital colleagues, uh, that help you or your, or your clients do more things in a more effective way, Sort of generative ai, you know, nice to know you. I har I hardly knew you, right? Generative AI was here for, you know, when we look back in retrospect 24, 20, 25 years from now, you know, generative, the age of generative AI was relatively short, two, three years.

Yep. And then we immediately, you know, are head rushed headlong into agentic ai, which, you know, and who knows how long that's gonna be, right? You mentioned Quantum before.

What, what effect is when we get to Q Day and quantum combined with ai, what, what is that going to look like and, and how does does that, you know, work into it? Um, but I mean, it, it's interesting. We have this whole sort of AI economy, if you will, where AI is affecting everything, consumers business up and down.

And then where we live here at Techstrong, and where maybe CLA Cloudera plays a lot is specifically within the IT stack. And though the, the influence is profound everywhere, it's especially profound in this, in the IT stack. Whereas you said, if you're a developer today and you're not using ai, your job's at risk, frankly.

Right? A hundred percent. As I always, I tell people this, it's AI's not gonna take your job.

Someone who uses AI better than you is going to take your job. Yeah. And, and so that to me is the, the real, the the, the question here, um, I'm, I'm wondering like, how does this, you know, so within ai, uh, within IT AI is paramount then, but we also know that within IT and within development and data management and so forth, you know, so the DORA principles, if you will, of high performing IT teams, right?

They, they generally adopt these newer technologies earlier, and then the gap widens right? Between the high performers and the not so high performers. I wonder if that somehow shows up in this, in this report, We, we, we didn't, uh, delve too deep into the specific of the, of the use cases, more about the platform that you use to, uh, run that in a, in a secure way.

However, what we do see specifically on software development and IT and, and whatnot, is, um, that that is the, the, the paramount use case, right? It is the most obvious first because it's easy to measure. So the ROI is very obvious.

Uh, and at the same time, it has a very good, uh, penetration point into the industry because of that being the, the typical early adopters into the, into the new technologies and, and so on. Also, it's really important to see when, when you imagine the development pipeline on, on an IT t and for example, typically you always think about, hey, there is someone just typing words in a, in a weird language on a computer. But that is only a small fraction of, of the job, right?

There is design, there is a, a, a code builds. There's testing and, and building the test. There is infrastructure.

There are many different elements like in any other, uh, uh, uh, supply chain. And these systems are helping in many of those aspects as well. For example, when when we review code from our peers, that process is now exponentially faster when we need to migrate a, a code from one old language into another.

And this is really relevant for Cloudera as well. We are, for example, building tools and platforms that help you move and migrate pipelines that were created 15 years ago by an employee that hasn't been in the company for 10 years. And those pipelines use older frameworks and languages and have embedded a lot of domain knowledge of your company.

Imagine one pipeline that calculates the, uh, uh, uh, operational margin of a given store. And that is, that knowledge is embedded in a script, in a piece of code that is 200 lines. No one has touched that in 10 years, and the employee who built that is not even in the company anymore, right?

And now you have to move that to a newer system that your IT team is forcing you to upgrade for cybersecurity reasons, for example. So these agents are allowing your IT team to A, understand that code better, much, much quicker, or b, doing the upgrade or the migration pretty much automatically. So in data, we typically deal with, uh, ETL jobs, right?

Extract, transform load, or ELT jobs. All those things are arguably valueless because they are not working for the actual value, the insights that you get from today, the data. But it's a fundamental part.

So it's a, it's a key element that supports your job, but it's not the one thing at the end that, that generates the value on, on of the data. So can we use these agents to automate all those pipelines and then use the, the human that leverages ai that's going to take the job to be the one that brings the creativity, the curation capabilities of discerning what really matters for the company. But all the nitty gritty work of, of typing words on a keyboard, that's something that we have systems that can do that much faster and, and, and essentially, um, better and than you.

So yeah, most of the, uh, most of companies are saying, look, health at least of this is going to be for IT systems. Cybersecurity is a great addition as well. Things like automatically check in for, uh, cybersecurity vulnerabilities, uh, or identifying, uh, uh, potential outliers or potential problems in your network, for example, um, now you have an basically infinite set of eyes looking at outliers in the, in the data, and then being able to take action and use the skills, use tools to make decisions, right?

So, um, it's a fantastic time to be alive. Maria, you know what? It's so funny you say that.

I, I was just telling someone that again the other day. It really is, I mean, you know, first of all, I've always felt you look over the course of human history, right? The course, the, the history of homo sapiens, the fact that you're born when I was born, for instance, and have seen these such big changes.

But now, right now we're on the cusp of, I like this whole other era in age of, of, especially if you're in it, it's right. And it is a convergence of, of technologies because e every single industrial revolution that we had so far came from one big technology chains, whether it was the, the steam engine, the internet, the iPhone. But now we're having the convergence of several technologies that by themselves could have created a full industrial revolution, but we are actually getting them to, to power each other.

You mentioned quantum computing, ai, robotics, just Those three things. Yeah. Or just those three.

Just those three. And then how, as you mentioned, each one of them, And then you add more things from bio computing, nanomaterials, a space exploration, right? It's, uh, fusion energy maybe a bit further away.

Um, so we are seeing a transformation that the humankind has never seen before, uh, that is net positive for humankind. At the same time, there are many pitfalls that we have to avoid. I, I was just gonna say that if we don't kill ourselves first, or, you know, we gotta remember about freedom and, and, and other things and Why on, on GN ai, it came so fast that many companies just went right on without any controls, right?

Shooting from the hip. And that is okay for experimentation. But all those good practices, best practices that we created in the era of machine learning, your data governance, your, uh, guardrails, your explainability and interpretability, your catalogs, all that was well said.

And many companies forgot about it just to go straight into creating, uh, uh, chat bots using LLMs and so on. So we are in the moment where, and and that's part of the survey as well. Companies are now saying, Hey, alright, stop for a second.

We did the experimentation. We see massive return of investment, but now we have to bring all these back into our well governed systems and our, and our data quality controls and, and, and, uh, checks and balances essentially. And now we have that, that challenge ahead of us on bringing AI back to our data pipelines, which I think is beautiful.

Absolutely. Sergio, we're over time, but for people who want to maybe download and di d dive in deeper to this Cloudera state of enterprise AI report, um, where can they go? com.

And, uh, and, uh, we can share the link in there. Um, and also happy to share that anybody can contact me on, on, on LinkedIn and, and find me. We can share that report and many more insights on how we see data going and, and the future of humankind with AI essentially.

Absolutely. Sergio, I wish we had more time to dive deeper. Maybe you come back sometime.

We'll continue the conversation. Until then, good luck with Cloudera and your relatively new position. Looking forward to hearing more great things coming out of Cloudera.

Thank you. Thank you very much, Alan, and thank you everyone for, for listening. All Righty.

Time. Alright. Sergio Ggo, chief Technology Officer of Cloudera here on Textron tv.

We'll be back in a minute.