What if the key to successful AI initiatives lies not in advanced algorithms but in the very foundation of data itself? Join us as we sit down with Jacqueline, the dynamic CMO at Teradata, who brings her wealth of experience from industry giants like IBM, Oracle, GE, and Nielsen. Jacqueline passionately discusses the essential role of trusted data in AI, drawing a vivid analogy to Earth's water to explain the scarcity of high-quality data. She introduces us to Teradata's revolutionary Data DNA product, which ensures data reliability by meticulously tracking its lineage. This episode highlights the importance of clean data and trusted sources as the backbone of accurate AI outcomes, setting the stage for meaningful AI advancements.
Explore the transformative power of generative AI across a range of industries, from financial services and banking to telecommunications and marketing. Jacqueline illuminates how AI can streamline efficiency and enhance personalization, particularly in contact centers and multi-channel marketing efforts. Our discussion underscores the necessity of responsible AI governance and the irreplaceable role of human oversight in training and managing AI systems. As we transition to real-world applications, we uncover the excitement and potential of implementing AI use cases that drive tangible business outcomes. Get ready to be inspired by the groundbreaking work and future innovations in the AI landscape.
What if the key to successful AI initiatives lies not in advanced algorithms but in the very foundation of data itself? Join us as we sit down with Jacqueline, the dynamic CMO at Teradata, who brings her wealth of experience from industry giants like IBM, Oracle, GE, and Nielsen. Jacqueline passionately discusses the essential role of trusted data in AI, drawing a vivid analogy to Earth's water to explain the scarcity of high-quality data. She introduces us to Teradata's revolutionary Data DNA product, which ensures data reliability by meticulously tracking its lineage. This episode highlights the importance of clean data and trusted sources as the backbone of accurate AI outcomes, setting the stage for meaningful AI advancements.
Explore the transformative power of generative AI across a range of industries, from financial services and banking to telecommunications and marketing. Jacqueline illuminates how AI can streamline efficiency and enhance personalization, particularly in contact centers and multi-channel marketing efforts. Our discussion underscores the necessity of responsible AI governance and the irreplaceable role of human oversight in training and managing AI systems. As we transition to real-world applications, we uncover the excitement and potential of implementing AI use cases that drive tangible business outcomes. Get ready to be inspired by the groundbreaking work and future innovations in the AI landscape.
More at https://linktr.ee/EvanKirstel
Speaker 1:
Hey everybody, Fascinating chat today with Teradata around delivering trusted data towards trusted AI with Jacqueline, CMO at Teradata. Jacqueline, how are you? I'm great, Evan. How are you so inspiring, so impressive? I have lots of questions, but before that, I'm really intrigued by you and your background and bio. Maybe introduce yourself and your tenure with amazing companies like IBM and Oracle and others that led to your current role at Teradata.
Speaker 2:
Sure, I'd be happy to. I've been in technology probably for 25 plus years technology probably for 25 plus years and, as you said, evan, have been with Oracle, ibm and even I would consider GE somewhat trying to be in the technology space, and as well as Nielsen. So those are some of the companies that I've been with, in fact, all of the companies that I've been with, and really I have to say that for me personally, I love data. I always have. I'm a CMO that's very into math and actually started my career in finance.
Speaker 1:
Fantastic and I got your attention, caught my attention through the Fast Company article where you talk about the importance of data lineage and high quality clean data and data science, and you know maybe elaborate on that why this foundation of data is so important before starting and launching all these AI initiatives we're all excited by.
Speaker 2:
Sure, I really like to talk about data like water. If you think about this planet and I have spoken about this before and some of you may have already heard it, so for those of you who have, this is kind of me saying it again, because I think repetition is important when you're communicating on these kinds of important topics. When you're communicating on these kinds of important topics, for me, when I think about this planet and this beautiful planet we call Earth, it is mostly water. It is over 75, 70% water. But when you really think about it, is that usable? Because, as most of us know, particularly those who may be in geographies that are having water shortages, it doesn't feel like our planet is made up of 70% water. The fact of it is not usable. And so, when you really think about, well, what is usable?
Speaker 2:
About two and a half percent of all that water is actually clean, pristine, usable data by humans, and of that, only three-tenths of one percent is actually accessible.
Speaker 2:
So the two and a half percent is sitting in glaciers that we don't have access to, and the three-tenths of 1% is what we probably use every day. And so, when you think about it, if you actually can't consume it, use it for things that we need to live on as human beings. That means it is actually scarce and it means that it's not pristine, it's not clean. And I think you can think of data in the exact same way. There is a lot of data out there more and more. We talk about it every day the zettabytes, how much it's like multiplying, et cetera, et cetera but when you really think about it, do you know where that data is coming from? And is that data clean? And if the data isn't clean and it isn't pristine, what comes out of it when you use it will also not be clean and pristine and give you the kind of accuracy that you need to make the important decisions that I think every company in person has an ambition to do.
Speaker 1:
Well, I love that Such a great analogy. I'm going to take that as well and borrow it from you with attribution. Unlike some other LLMs out there, and speaking of which you know, how do leaders ensure that the outcomes they're looking for, the AI outcomes they're investing in, are trusted, are reliable? I assume you have something to do with that process at Teradata we do have something to do with that process.
Speaker 2:
We have a product actually called Data DNA, but that really says where is the lineage, where is the data coming from and can you trust it. Back in the day and back in the day maybe wasn't that long ago, but I like to talk about it like that Back in the day, even if you think about a decade ago, everyone was talking about systems of record, and the reason that they were talking about systems of record is because the record, or that kind of golden record, was the thing that you could actually trust, that you were using inside your organization. That even becomes even more important today. That is more important than it ever was.
Speaker 2:
What is the system, what is the record, where is it coming from and how did that record get created in the first place? And was it created with using trusted information, which is why the lineage becomes so important? Where did it come from? Where is it going to? Because as soon as in that process, in that cycle, as soon as you embed one particular element that actually maybe not be accurate, it will affect all the other outcomes that you have, because that element is going to be propagated throughout your systems and will be propagated throughout other records as well.
Speaker 2:
So it becomes critical, because AI is about pattern, it is about pattern recognition, it is about a system that learned, and it has to learn on something that's factual as soon as it's learning things that are not factual, which is why people talk about the data hallucinating. The data isn't necessarily hallucinating. It's bringing that information from somewhere, and so you have to be certain that the inputs that you are giving a system to work with, to build patterns on and to model your information and model your models on all of those things have to be really, really accurate.
Speaker 1:
Oh, such a great point and anything else business leaders should be aware of for launching an AI initiative. There's a key desire to get moving, get moving, let's go. But what else should we be aware of, technology-wise, people, process-wise, to ensure the results are explainable?
Speaker 2:
I think the first thing you have to think about, Evan right, is what is the value that you're going to get out of what you're doing? I think you've got to start with the end in mind. What is it that I want to achieve and how can I do that? So simple things, you know, if you have a subscription model for, you know, cable or any of the streaming services, they do care about churn. They do care about who's using their products. They do care about. How do I expand shared wallet If the person has one service, how do I get them to have more than one service?
Speaker 2:
So they're looking at all of those things that is incremental value to that organization. Can I expand someone using more of my services? Or can I ensure that people that are already using my services aren't becoming, you know, kind of passive users, and passive users tend to not stay users very long? So what are those patterns that I should be looking for, Because I want to ensure that I'm creating a better experience for people who may be considered passives. What is it that they're not getting out of my system and the reason that they're not using my app? Or what is it that I want to do to ensure that I provide these additional opportunities for people to consider other products and services that I may have that may meet their needs. Those are very specific questions that can be patterned, but it's not just that simple, Because if it was, everyone would be doing it. Everyone would be expanding share of wallet.
Speaker 2:
Right now you're talking about micro markets, greater personalization. What is it that you can truly provide that's unique to someone? That requires a much more sophisticated type of analysis than we probably have done in the past and it, you know, is going to be based on machine learning, it is going to be based on advanced analytics and ultimately, it will use some artificial intelligence. You can ingest different LLMs if you think that what they can contribute to you making a better decision. Of course, who wouldn't want to do that? But you don't just do it for that reason. You really have to start with the end in mind, and I think if there was anything that I would talk about to business leaders about it would be that Because if you're getting into AI because everyone's talking about it, that's actually the wrong reason to be getting into it.
Speaker 1:
A great point. And what about Teradata itself as a platform provider? How do you ensure your AI solutions are transparent and trustworthy and will work as defined for customers?
Speaker 2:
Well, one of the great things about Teradata is, for one thing, it's, you know, artificial intelligence.
Speaker 2:
As you know, evan has been around for a long time, 40 to 50 years.
Speaker 2:
When you really kind of think about what machine learning, the history of it right, it didn't just happen a couple years ago, even though its popularity because it's now accessible to more people it has grown in popularity. And what I would say is that the technology for Teradata and our analytics engine and the engine in the kernel for our data warehouse, has been using AI and machine learning for decades. This isn't a party that we just walked into and that we're, you know, excited to be joining, so I think that is extraordinarily important. I would also say that when I look at, you know, kind of the world's largest banks, you know, excluding the banks in China, 14 out of the top 15 banks are using Teradata and they're using it because of those trusted systems that we have and the ability to do the transactions at the speed and scale that they're looking for and, ultimately, to provide that what I would call value to the organization, and value is about growth right. Value is about gaining efficiency and ensuring that you have a higher return to your shareholders.
Speaker 1:
Very nice. So it's clear generative AI is going to transform so many industries. You're already sharing the impact in areas like financial services, banking. What other industries do you think are hot on the heels of adoption? I just had a call with a major player in the contact center customer experience space and there it's going to completely upend the way customer experience is managed and delivered. But what else? What are you seeing?
Speaker 2:
I definitely think telecommunications. I actually started my career in telecom, so I was at GTE before it was Verizon, and so when you think about CTI and telephony and really kind of what I would call aided opportunities in a call center and things that you can either help someone solve a problem more quickly, right, or really try to do a better job around personalizing that sales experience, I do think call centers and contact centers are going to be ones that could immediately take advantage of what's happening, particularly around generative AI. When I think about disciplines, I think that the marketing discipline is one that will and I shouldn't say that will, I should say that is embracing AI and not for this, the things that I think people are afraid about of images created that aren't real images and people having their IP abused and things like that when I think that AI and generative AI can help marketing, particularly on the efficiency side. We have many more channels today than we've ever had. You know, we have to talk to people everywhere, and that wasn't true five years ago, it wasn't true 10 years ago. It just really wasn't.
Speaker 2:
And now you have B2B businesses. Should I be on TikTok? Should I not be on TikTok? Now I'm, you know, okay, twitter was Twitter, but now it's X. How do like? What are the things? How do we show up there, maybe different than we showed up before? What? How do we ensure that we have the right presence on LinkedIn and Facebook and every place that you can think of that people are actually showing up and while the content is similar, so your message around your company is similar, the way that message gets delivered on those different channels is quite different. And what I think that particularly generative AI is good at it understands how things show up on Twitter, right Versus how they show up on LinkedIn versus how they show up on Facebook, and the simple analysis is to kind of create your content and then you need a shorter form content for LinkedIn or a shorter for content for X, and you say what? What does this? This is my story, this is what I want to say. Here's how I want to show up. I want it to sound have more energy on this channel versus this channel.
Speaker 2:
This channel and generative AI right now will give you quote pithier comments or more energetic comments. It will infuse those words instantaneously. So if you're the author, you could have written something. It's still your writing, right. And if you say to chat GPT, make this sound more enthusiastic. It throws some enthusiastic words in there and you, the author, still has control over how that shows up.
Speaker 2:
You know, maybe the language was too happy, you know, but you can. You get to tweak it, but without spending another three or four hours contextually changing it, it instantaneously does that and then you can kind of take it. Ai today are things that you can do that do not compromise the integrity of what you're doing as a company, because you are still in control and you are still the author. You're saying take this language and can this language be pithier? Can this language be more exciting? Can this language have higher energy? And if it took you a couple hours to create the original document, you know, if you put it into an LLM right now, it probably takes like less than two seconds to pitch out something that gives you some ideas that can spark your own creativity.
Speaker 1:
That's a great point. Well, there's a reason you've been called the AI CMO, which I think was meant as a compliment. I'll take it that way.
Speaker 2:
I hope so.
Speaker 1:
You're not an AI, but on that topic, I mean, how can businesses leverage AI in the way you're describing for efficiency and productivity without losing their brand authenticity or going off brand, really, which I see a lot of?
Speaker 2:
Yeah, one is you have to understand that this is a tool. It is not a replacement, and I think you know people started this conversation, you know, maybe in the wrong way. Oh my God, you know, if you look at some of the studies whether it's a McKinsey study or a study from Accenture it's like we're going to have these vast amounts of productivity improvements. Yes, we will. I just gave you an example of not having to rewrite seven things, but having some ideas given to you that help you streamline the time. You are still the author and still responsible. No one can are still the author and still responsible. No one can, you know, train your brand better than the people that have built the brand. The brand itself is an ethos, and you have to be able to kind of hone in to what that ethos is, as you're kind of preparing briefs or anything else, and so what becomes really important is to understand that the reason that I talk about trusted AI versus responsible AI is because people are the ones who have to be responsible.
Speaker 2:
An inanimate object cannot be responsible. You can't blame an inanimate object for something that happened. You have to look where did it start? It started with the people. It started with how the model was built. It started with how the model was trained, and if those models don't have the proper guide, rails and governance in them, you could have unintended consequences that you're not going to be happy with, which is why it's critical. Quote the people. Part of this conversation to me not only is foundational, but probably is the most important part of the conversation, and I think that, just in general, sometimes you know we've gotten away from that Really understanding. It's the people. They have to be trained properly. You have to train them. You have to have the right level of governance and compliance inside your organization and you, as leaders, are responsible for doing that.
Speaker 1:
Wonderful thought and speaking of people changing gears a little bit, you've been an advocate for underrepresented entrepreneurs and business leaders and you speak on that topic. What are your views there and what do we need to do to move forward?
Speaker 2:
together. It's interesting that you bring that up, because I think there are so many places. We have an opportunity now to have be much more vigilant about getting to the truth and getting the right information. So when it became clear that there were certain neighborhoods, cities, places that were being redlined by insurance and banks, then you know and most of those happen to be areas that were predominantly areas of either under representation, whether that was economic or whether it was racial I think that we have a responsibility to understand that, understand why that's happening and to curtail it. When you infuse bias, bias continues. So bias doesn't go away, with people hoping that bias goes away. Bias goes away when you actually have analytics that say we've done the analysis. This does exist. Now we have identified the problem, what are the steps that we need to do to resolve it?
Speaker 2:
I think it will be important for businesses, regardless of whether they are an underrepresented business or not, for them to understand the impact that AI can have on them, that they truly embrace educating themselves and understanding what can they do to potentially leverage this. I always think that there are many things that are equalizers. Education is an equalizer. When you have more education, you're able, generally speaking, to put yourself in a better position versus someone who may not be educated At the same time. I think AI will be similar. Can you use this technology in a way that gives you an opportunity to compete more effectively than you did previously? What is it that you need to say? How do you need to do it? And those questions can be broadly asked in many large language models today, and I just think informing yourself as a business owner is going to be important and also ensuring that we create opportunities, particularly for underrepresented groups who would not necessarily have exposure to the technology.
Speaker 1:
Such an important point, and I hope AI will act as a leveler in some ways and not making inequality even greater than it has been. So let's keep our eye on that. We're about halfway through the year now. What are you most excited about, both personally, professionally? What's on your mind for the rest of the year?
Speaker 2:
Well, I'm going on a cruise in two weeks, so I'm excited to do that.
Speaker 2:
So let's start there.
Speaker 2:
Let's start with going excited to be doing that, but I think for me for the rest of the year, it really is seeing some of the, I think, use cases that we've talked about around AI and generative AI really come to life. It's one thing to sandbox something, it's another thing to actually put some of these things into production so that they are facilitating the kinds of outcomes that I think you know kind of our customers and other companies are looking for. I think that's going to be really important and that's going to be. It's going to do two things. One, it will help, you know, kind of curtail the hype, so to speak. Right, so let's stop talking about the hype and let's kind of get some answers to questions that have been hard to answer so that we can drive better business outcomes. Once we're doing that and talking about it and demonstrating it, I think then the sky's the limit, because then the possibilities really do kind of unfold and manifest themselves in different ways, and I think the next six months is about companies really working hard to do that.
Speaker 1:
Well, on that great high note, thank you so much for joining. It's been really informative and insightful and thanks everyone for watching. Reach out, follow Jacqueline on social media, Teradata and the good work they're doing. Thanks, jacqueline.
Speaker 2:
Thank you, evan. It's been wonderful being with you today. Likewise, thanks so much, thank you.
