On this episode of The Six Five – On The Road, hosts Daniel Newman and Patrick Moorhead welcome Cloudera’s Cindy Maike, VP Industry Solutions, during Cloudera Evolve NYC for a conversation on the customer perspective on generative AI, adoption, and planning for the future of their data.
Their discussion covers:
- How Cloudera is talking to customers about AI adoption, generative AI solutions, and the benefits from the data that Cloudera has under management
- What customers have identified as barriers to adoption and operationalizing generative AI
- The biggest shifts in Cloudera’s customer interactions from traditional analytical AI phase to the generative AI phase
- What customers should be considering in the next two to three years, when it comes to AI adoption and their data
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Transcript:
Patrick Moorhead: The Six Five is live here in New York City, at the Cloudera Evolve Event 2023. Dan, we are on the road. We’re talking about many of our favorite topics here. Big data, a lot of data, but the thing that some people think was invented nine months ago but wasn’t, and that is generative AI. So we have talked about so many things here at the show, technologies, products, partnerships. But of course, as you know, everything comes together. If it’s not for the end users, the people actually pay money and try to solve problems with all of this. It’s all pretty much for naught.
Daniel Newman: You mean customers?
Patrick Moorhead: I know. Imagine that.
Daniel Newman: Wild. Now, I got to tell you, when you said nine months ago people thought generative AI was invented, I think there’s some people that think nine months ago AI was invented.
Patrick Moorhead: Yeah. Back in the ’60s, little algorithms.
Daniel Newman: I think it’s been a minute. And I think a lot of the things when people are like, “Can you imagine life without the ability to, I don’t know, punch in a question and get an answer?” It’s like, “Yeah, I’ve been doing that. It’s called Google.” I mean, we’ve been doing that for quite a while. Yes, I get it. It’s different in the way it formats the text. But for people like us, sometimes we just have to take a breath and realize that we’re in it. But there’s really exciting things coming on. But back to that customer lens thing, I definitely believe, Pat, that every company on the planet that really wants to win a market has to have the view of the customer in mind. It has to really be able to articulate their value proposition.
Patrick Moorhead: That’s right. Technology for technology’s sake is worthless. But when applied to customer problems and solving, it’s a big deal. There’s nobody else at Cloudera I’d rather talk to about this than Cindy. Cindy, welcome back to The Six Five. It’s great to have you on. We must have maybe said something right or not offended too badly because you’re back. And we appreciate that.
Cindy Maike: Yeah. Thank you.
Patrick Moorhead: Thank you.
Cindy Maike: Thank you.
Patrick Moorhead: Yes.
Cindy Maike: Well, thank you for having me back.
Daniel Newman: The no offended list, the not offended list, and they come back. Because I like that. We should have that on our site.
Patrick Moorhead: I know. It’s a big marketing tagline.
Daniel Newman: Yeah. But we do appreciate you joining us.
Cindy Maike: Thank you.
Daniel Newman: Look, I think we’ve talked some ecosystems here. We’ve had the opportunity to spend some time with your CEO here. But I continue to think that generative AI in these customer use cases are really the barrier to mass adoption and growth in the business. And a lot of the effort here at Cloudera Evolve 2023 has been really talking through the eyes of the customers, operationalizing these use cases. Talk a little bit about how you, at Cloudera, are talking to customers about operationalizing these, so that you can obviously gain the adoption and benefit from all the data that Cloudera has under management.
Cindy Maike: Yeah. So that’s a good question. And I think a lot of times, especially technologists or technology companies, we always talk about the how. And it’s like, “Here’s how you go do it.” And it’s like, “Yeah, but why and what are you trying to do?” And my premise is always we’ve got to be able to solve a problem with it, or maybe you’re truly creating a new business model or something like that. But it’s really about, when you think about GenAI and everything data related, it’s like you’re using it to solve for something. And so what problem are you trying to solve for? What type of questions are you trying to answer? And so from an operational perspective, companies are actually saying, “If I can use data and I can use data differently, is it truly giving me new insights?” And just from a GenAI perspective, it’s also, “Yeah, this is cool. This is the latest buzzword.”
We’ve heard people say, “It sounds interesting, but we’re not sure yet and how would I actually use it?” But more importantly, it’s like, are there still problems that are out there that you haven’t been able to solve for? And so, when you start to think about operationalizing on GenAI, it’s fundamentally about, okay, what problem can we solve with it? And I think that’s where a lot of companies are trying to look at it, and they’re struggling with how do I actually do it? And then AI in general, business has always said, “That’s why were… I’m a former accountant or recovering accountant,” is what I like to say. It’s like, “Let me prove it.” People used to take the… Here’s the report and they’re like, “Hey, you know what? Let me 10 key that because I want to prove the math.” So it’s kind of like GenAI is the next true generation of some of the things that were the barriers to adoption. People got to trust the data. And so yeah, and it’s like where did it come from? What’s the source and how can I actually truly use it?
Patrick Moorhead: So once customers get to the point where maybe they have a thesis on the hey, you know what, generative AI or machine learning can help solve their problems in some unique way, that quite frankly either increases revenue or cut costs, that’s what businesses do. What are some of the barriers that they’re finding to adopting that or even operationalizing it? And maybe talk a little bit about, because on stage I saw a bunch of customers talked about in the context of AI. How are they overcoming these challenges?
Cindy Maike: Yeah. I think one of the things, and we’ve always kicked the can down the road when it comes to data governance and the data quality issues. And I think that’s one of the key… We’re still continuing to see that as a barrier to adoption. It’s like, we continue the cost of bad data. And now you’re actually saying, “I’m going to use advanced AI techniques.” And if it’s bad data, I’m like, “Okay, so what are you going to get? A bad answer multiplied.” And so you got people who are going, “I’m still worried about that data. Is it quality data? Can I actually trust it to do the right business decision?” And then you start talking about the fact that we have techniques around, I’ve got all this new type of data. And it’s like, I think where we’re starting to see people say, I can get business value is, it’s not just that traditional type data. I’ve got stuff that’s been… Your data landscaping, you guys know I like to use that word, but it’s like I got stuff in content management systems. I now have stuff in videos, I’ve got voice files. And when you’re starting to talk about that type of data, what are the techniques that I use to go after some of that unstructured data? And that’s critical.
And people are like, “Yeah, but where’d that come from?” And so you see a lot from a data governance perspective, it’s like, “Where’s the data? Where’d it come from? Who touched it last? Has it been manipulated?” And for certain industries, “Are the regulars going to let me use that data?” So that’s some of the aspect. And then one other element, had conversations with folks is, how do I actually make these things happen and why will business want to adopt it? And as soon as you can start to say, they can answer, “Hey, is my data honest? Is it helpful? And is it harmless?” Because responsible AI, especially for some of the key industries, that harmless component is huge. And given what we see in GenAI and the ability to hallucinate, it’s going to be one of the challenges. You got to address that from a data governance perspective.
Patrick Moorhead: Cindy, I have to ask. So I went to… I’m not going to tell you exactly when I went to college, but-
Cindy Maike: Oh, I probably went there before, you did.
Patrick Moorhead: … but it was in the mid ’80s and literally in one of my computer classes it was garbage in, garbage out. It’s something that we’ve known for a long time. So it’s a consistent theme, isn’t this?
Cindy Maike: Yes.
Patrick Moorhead: But I believe that as we’re spreading the data out and it’s moving from structured to unstructured, and the amount of data has gone up so much, it’s been even harder to manage all of that data. Am I hallucinating here as Pat bot or have we been talking about this challenge for ever?
Cindy Maike: Decades. Yeah, no, I mean, it goes back to, I think the problem’s just gotten bigger. I don’t think it’s gone away. And I think it’s, people start to have to address that. And it’s like how do you actually have capabilities that say, I’m not going to have data in one central location, it’s just not going to happen. And so how do I get a governance process in place? And also dealing with data that’s anywhere and bringing that type of mentality in. And one of the other things from a garbage in, garbage out is between business and IT, we got to rationalize, business owns the data and IT is the custodian of the data. And that goes into, you got to work together and it’s no longer you can say business and IT, it’s like, no, it’s we. We collectively as a company, this is our responsibility.
Patrick Moorhead: That’s good.
Daniel Newman: And I think the big hairy problem is companies are actually, the ability to operationalize data within an application has been done pretty quickly. And in fact, if you started a generative AI project early in the year, you probably felt pretty overwhelmed by midyear when you saw all the kind of off-the-shelf solutions that started to come out. CRMs and HCMs and SCMs, all offering generative-
Patrick Moorhead: Domain specific stuff.
Daniel Newman: The problem is, is that A, is that data fabric and having all that data together in a format that’s usable. And then of course managing that whole data state to then have what generative AI can be, which is ultimately a business driver, productivity and efficiency. And of course, everything you mentioned about risk and safety and privacy, huge. And by the way, the Biden executive orders and in Europe, you’re seeing more and more. I mean, we heard about it this morning in the keynote 30, 40, whatever of these types of things. But I started this conversation talking about AI is not new.
Cindy Maike: Yeah. It’s not.
Daniel Newman: It’s not. And so every company on the planet’s doing and has been doing, not every company, every large company, and most even mid-size companies are doing something with advanced analytics, machine learning, and probably some version of AI. What are you seeing as the biggest shifts in your customer interactions from this kind of traditional analytical AI phase that we’ve been in to this generative AI phase, which we’re now really full tilt in?
Cindy Maike: Yeah, I think you can look at GenAI in a couple different fashions. One, are you actually generating new data? But also some of the new techniques that we have around large language models and the ability to actually leverage that type of technique to summarize, to synthesize. And that I think is what’s fundamentally different between our original analytical AI, is now we are actually have more advanced techniques that actually allow us to work with the data and bring it together, where historically we haven’t. And the fact that every organization’s data landscape is different, some of these newer techniques. Now from a GenAI perspective, the fact of the generative piece, there’s going to be certain industries and we’ve seen it already that are adopting it much faster than something that’s in a regulated industry. And so is it an external facing type usage of it or is it an internal facing?
But the fundamental difference, I think, is it’s the technological techniques that we’re using. And now, as long as from a business perspective, we can go back and say, what’s the source of the data? What’s the, going back into can I trust the data? That’s going to be the key thing. And the other aspect is, I hate to harp on governance and quality and so forth, but you sit there and it’s like, if you don’t know those data quality attributes about the data, you can sit there and ask all the questions, but you know what? What’s the age of that data? You can get back a bad answer because it looks right, but what was the date of that data? That might’ve been the right answer in 2010. But it didn’t read when was the data.
Patrick Moorhead: We saw that with the first GPT. A lot of that because that was like three-year-old data, right?
Cindy Maike: Exactly.
Patrick Moorhead: I think it was. So yeah, I mean, it’s a pretty substantial problem, it’s keeping the data modernized and making sure that all the data that needs to be considered is being considered.
Cindy Maike: Yeah. And that aspect, that goes back into the business has to be the ability to, is it helpful, honest, and harmless?
Patrick Moorhead: It’s been a great conversation, but I did want to end and talk about what’s next? And it might come off as a non-question because I feel like the industry is pushing enterprises faster than they could even take this. But I do feel that businesses should have strategies that encompass the future because what they adopt today might not be what they need in the future. Can you talk about from a customer point of view, what should they be thinking about two, three, four years out?
Cindy Maike: One, it’s fundamentally thinking about where they headed at from a business perspective. So what are your business objectives? And do your business strategy with the knowledge of, or with the fact that I’ve got these new technological capabilities. And your AI strategy, your data strategy are all fundamental to your business strategy. And those are the things that we have to look at and making sure that it’s incorporated and they can’t be done in isolation because we see too many POCs that go off, things don’t get operationalized. Money’s being spent, and people go, “Where was the return?” And so do the three in concert with each other. Don’t do them in isolation. Is that the future? I think that’s the hurdle that we have to get over to get to the future.
Patrick Moorhead: I appreciate that.
Daniel Newman: Yeah, I think we’ll see it move slower and then faster and slower and faster. And those hurdles will keep coming up and we’ll get more natural at how we overcome them. But the proliferation is very fun and exciting to keep track of.
Cindy Maike: Most definitely.
Daniel Newman: And Cindy, thanks again for joining us. Welcome.
Cindy Maike: Thank you for having me.
Daniel Newman: All right. Everybody, you heard it here. We’re at Cloudera Evolve 2023 in New York City. We’re talking AI, generative AI, and of course managing your data estate and so much more. Hit subscribe. Join us for all of the episodes here at Evolve 2023, and then of course, all The Six Five shows with Patrick Moorhead here and myself. But for this one, we got to say goodbye. See you all later.
Author Information
Daniel is the CEO of The Futurum 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.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
