Greg Kihlström

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S6 | 527: Optimizing data operations for AI in marketing, with Eric Madariaga, CData

Basil Ali
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About the Episode

Artificial intelligence (AI) has become a pivotal tool for organizations seeking to enhance their marketing strategies and operational efficiency. Yet, adopting AI effectively presents a set of challenges and opportunities. Today, we're exploring how organizations can optimally set up their data operations for AI, the implications of the maturity gap in AI adoption, and envisioning the future that involves a collaboration of sorts between AI and the human touch in sales and marketing.

To navigate these topics, I’d like to welcome Eric Madariaga, Co-Founder of CData.

About Eric Madariaga

Eric Madariaga is a co-founder at CData, a leading provider of data virtualization solutions. He was previously the company's Chief Marketing Officer for over 8 years. Prior to helping start CData, Eric spent 15 years at CData's parent company, /n software, serving as Vice President of Marketing & Business Development for over 10 years.

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Transcript

Please note: this isn’t perfect; it was AI-generated and we did some light error-checking.

Greg Kihlstöm:
Artificial intelligence has become a pivotal tool for organizations seeking to enhance their marketing strategies and their operational efficiency. Yet, adopting AI effectively presents a series of challenges as well as opportunities. Today, we're going to explore how organizations can optimally set up their data operations for AI. the implications of the maturity gap in AI adoption, and envisioning a future that involves a collaboration of sorts between AI and the human touch in sales and marketing. To navigate these topics, I'd like to welcome Eric Madriaga, co-founder of CDATA. Eric, welcome to the show.

Eric Madariaga: Thanks, Greg. Excited to be here today.

Greg Kihlstöm: Yeah, looking forward to talking about this topic with you. So why don't we get started with you talking about your role at CDATA and a little bit about what CDATA does for those less familiar.

Eric Madariaga: Sure, I'd be happy to. So I'm one of the co-founders here at cData Software. I ran marketing for the organization up until 2023 and kind of shifted into a strategy and advisory position of the marketing team. As a business, cData really lives in the data integration space. We develop the world's most comprehensive platform for data connectivity, for real-time data access. So we focus on standards-based connectivity, which essentially means that we take all the applications and databases and files and APIs and other systems and make them all look and speak the same language, essentially Transact SQL. So as a result, we make it super easy to connect any application or tool, including AI processes with live data from just about anywhere.

Greg Kihlstöm: Great, great. Well, yeah, so let's dive in here. First, we're going to start by talking about setting up data operations for AI in marketing. So for those organizations looking to use AI and use it more effectively in their marketing, the foundation of effective data operations is critical, as many of you probably know. We're going to discuss some of the key components here. So let's get started with what, in your view, what are the critical steps that organizations need to take to ensure their data operations are properly set up to adopt AI and marketing effectively?

Eric Madariaga: Yeah, that's a great question. So I think the number one thing, and this is kind of obvious, I think with most undertakings, is really to define a clear goal. AI can improve a lot of processes, but you need to be very clear with what you want it to do. The marketing side of things, are you looking to improve forecasting? Lean conversion, improve retention, support self-service. What is it that you're looking to augment your current marketing operations with in order to really take advantage of those AI technologies? Second to that, I'd say that you really need to make all of your data, as much of it as humanly possible, available for consumption. So AI technologies are extremely good at pattern recognition. But in order for them to really work their magic, they need to be able to operate on data from a diverse set of systems. Now, in the industry, a lot of businesses are trying to do warehousing or lake house implementations and consolidating. That's not necessarily required, don't necessarily need a huge IT implementation to get started and get working with AI. So I can give you an example. We implemented a kind of an AI driven customer health system internally. So it was designed to identify customers in Mars that might be at risk. In order to make that work, we obviously needed to get information and data from our CRM system. So their type, their history, account data, demographics, all those things. as well as connectivity with our support systems. Have customers been engaging with our support team? Have they been happy, unhappy? What's the setup look like? As well as integration with internal databases for customer usage and telemetry. So all of that data connectivity was required in order for these AI models to build, maintain, grow, and work in order to tell us if a customer may be more likely to try

Greg Kihlstöm: Yeah. And so in that example, you know, which parts are, you know, what is CDATA's role exactly in that and how, you know, how do you partner with internal teams then? You know, what does that relationship look like?

Eric Madariaga: Yeah. So absolutely. So it's very straightforward. So, and as I mentioned from CDATA's perspective, we simplify data connectivity. So we make everything in your organization, your CRM system, your IP system, your databases, your flat files, all those things speak the same language, which is basically structuring those systems for consumption by AI. That's one common usage. I'll give you an example. For that as well, we have a product called CDA Connect Cloud. And Connect Cloud is a consolidated SaaS-based connectivity solution that uses our connectivity technologies internally to to connect all your different systems and things, right? The benefit to that is that you have one platform to use, and we were able to layer AI technology on top of that and implement things like text to SQL interfaces. So that allows customers to ask questions of their data. Give me how my North American performance is over the past six months for sales, right? So you can ask those questions in text, and it translates that into SQL, goes, queries Salesforce, goes, queries some other CRF system, and returns that data. So by having a common interface, a common language for all these systems, it really sets you up for success on the AI implementation side.

Greg Kihlstöm: And so Let's talk then about some of the challenges facing organizations. Obviously, it's hard to escape conversations about AI, and a lot of organizations want to adopt it more in beneficial ways. But as it's transforming a lot of how marketers work, there there is a divide emerging between companies that are, you know, let's say they're leading in AI adoption and those that are struggling to keep pace. So from your perspective, where do most organizations require the most assistance in adopting AI and, you know, what are some of the common challenges that they're facing?

Eric Madariaga: Yeah, that's another good question. So let me start out by saying that as long as you have a good ops team, I think AI is pretty easy to get started with. We're very fortunate to have some brilliant sales marketing operations team members here that have made the integration relatively painless. There are tons of solutions on the market that will do things like embed predictive capabilities in your marketing processes. Things like Salesforce Einstein or Sixth Sense that are looking at intent data, looking at patterns to provide scores and things like that. I think one of the biggest barriers for adoption is really validating the outputs of AI across your business. So what I mean, like by that is, you know, let's take, take, for example, at Salesforce, Einstein or six cents, they evolve and they're built on models that train themselves over time. And they essentially give you a predictive score. Right. Um, so, but, but how and when do you trust. the thresholding of that predictive score. If you're trying to build an MQL model where you decide that a lead is a good lead, how do I know when the predictive model is good and is actually giving me the right insight about a lead? Sixth Sense, for example, is a self-training model that takes cues from customer touchpoints that are all over the place, all kinds of different systems, all kinds of different touchpoints. So anyway, your data systems internally really need to be set up so that you can validate the outputs of AI and continually test side-by-side, validating its historic data before trusting the outputs. So I think it requires a certain maturity to really be successful there.

Greg Kihlstöm: Yeah. And along those lines then, to talk about the maturity gap, do you see or do you believe that there is a growing maturity gap between those leaders and the laggards in AI adoption? And is this something that is temporary? Do you see it widening over time? And if so, what implications might that have for the industry?

Eric Madariaga: Sure. So, David, this is by saying I'm not an analyst or industry expert. I've been with C-Data since 2014, since its inception. So we have a very specific view of the industry. But I think there's definitely a growing tree gap between leaders and laggards. I read a McKenzie study some time ago that said that those companies that have built out leading AI capabilities are outperforming the laggards by two to six times or something like that. So I think that kind of performance has a lot of marketing teams scrambling to learn how they can best incorporate AI technologies into their processes. That performance is kind of undeniable. As these organizations are understanding these benefits, definitely leads to challenges. Same in the industry today, and there's obviously talent scarcity, there's issues with integration complexity internally, and then of course, challenges with IT and adoption of AI in terms of privacy and the data that, how much of that are we exposing to AI and what are the implications of doing that, right? So, All those things are barriers that you need to overcome in the process, but the performance is there. People are going to continue that adoption and move forward.

Greg Kihlstöm: Last topic I wanted to talk about with you is, I think in the early days of the recent AI boom, I think there was a lot of talk about AI is going to replace all our jobs and all sort of like doom and gloom. And I think some of that has subsided. Doesn't mean that AI is not going to replace parts of our jobs. I think that's been pretty widely documented and forecasted and so on and so forth. But I think more realistic view is yes, some jobs may be changed and or replaced, but mostly it's going to be a combination of humans working with AI almost collaboratively or you could think of it as teams or however you want to couch it. First, do you agree with this scenario and how do you envision the role of AI in human personalization evolving in sales and marketing specifically, you know, is, is this hybrid approach the future?

Eric Madariaga: I mean, I think that that's exactly right. I mean, AI will continue to evolve and play a bigger, bigger role in sales and marketing, but I think humans are safe for now. The focus really shouldn't be to replace people, but to kind of develop AI empowered sales and marketing people and teams. AI excels at automating repetitive tasks and personalizing experience and experiences at scale, predicting outcomes based on large data sets, but it still falls short in terms of the human touch, which I think is really critical for businesses and customers and connectivity with brand you know, those things are influenced so much by individuals and people. I don't think you can remove that element from the sales and marketing process.

Greg Kihlstöm: Yeah, yeah. And, you know, so so along those lines, I mean, can you having worked with a lot of companies as you have, you know, can you share some examples or maybe some insights on how our organizations doing this well now, in this collaboration between AI and humans, and how might they do some of this more in the future as well?

Eric Madariaga: Yeah, absolutely. I mean, there's dozens of ways this can be implemented successfully. I think that, I don't know if it's the easiest, but a very common path is that AI-empowered customer insights for your sales team, right? So giving sales folks the ability to get insights from data and provide that to them in real time so that they can be better at doing their job. I think there are huge benefits here for businesses like our own, where we have kind of a highly technical products or very complex going market processes. being able to augment sales teams with these kind of tools can help them do a lot more without having to ingest all of the different variations of ways that a product can be sold or of ways that we're integrating and talking to customers, right? So, I mean, I think it's kind of something that can live alongside of the sales team and help with that process. Beyond that, there's, AI driven A B testing optimization, right? So you can, you can use AI to test marketing messages and content variations and things, but ultimately it requires kind of a marketing team to look at that, the data that comes out of those tests and evaluate what kind of campaigns and things you want to run moving forward to align everything still to, you know, your, your company goals.

Greg Kihlstöm: Yeah, that's great. Well, Eric, thanks so much for joining us here. One last question before we wrap up. Looking ahead, whether that's months or years down the road, you certainly mentioned and gave a lot of great ideas of how AI and marketing and sales can be combined today and even into the future. But what should companies be thinking about? What should their mindset be? And what should they be focusing on so that not only they capitalize on some of those available opportunities, but to make sure they're remaining competitive and effective in this changing landscape, as well as staying ahead of that potential maturity gap?

Eric Madariaga: Yeah, well, great. Thanks again for having me as a guest today. I've really enjoyed the opportunity to spend time with you and kind of dig into some of these technologies. It's been great. In terms of the future, you know, the march towards AI adoption is really unavoidable. You know, at this point, as I mentioned, I think the benefits are really improving up the market. For businesses that are just starting with AI, technology has never been more approachable than it is today. Identify a pain point in your marketing. like improving lead conversion or retention or what have you. Adopt a solution, test, and continue to iterate. Even at a small scale, AI can make a huge impact on your marketing performance. For more established leaders, look for opportunities to connect additional systems and data to AI. The more data that you can feed those AI models, the more accurate and powerful they become. So yeah, I think that's a good basis for a path forward.

Greg Kihlstöm: Love it. That's great. Well, again, I'd like to thank Eric Madriaga, co-founder of CDATA, for joining the show. You can learn more about Eric and CDATA by following the links in the show notes. Thanks again for listening to the Agile Brand, brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.GregKihlstrom.com. That's G-R-E-G-K-I-H-L-S-T-R-O-M.com. While you're there, check out my series of best-selling Agile brand guides covering a wide variety of marketing technology topics, or you can search for Greg Kihlstrom on Amazon. The Agile brand is produced by Missing Link, a Latina-owned, strategy-driven, creatively-fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging, and informative content. Until next time, stay Agile. the Agile brand.

Eric Madariaga, Co-Founder, CData