In this AI and Data Trust CUBE Conversation, Larry Hunt, field chief data officer at Ataccama, joins theCUBE’s Rob Strechay to unpack why trusted data, not just tools or talent, is the critical path to real AI adoption. Citing Ataccama’s Data Trust Report and his financial services background, Hunt highlights the gap between ambition and outcomes: while ~99% of firms are piloting AI, only ~3–4% are seeing results, with data trust as a primary blocker. He explains how governance succeeds when it’s “compliance by design,” tied to CEO/board-level KPIs, and focused on enabling business outcomes, rather than “selling governance.” He also notes where leadership buy-in and cross-functional alignment matter most, and why 46% of leaders call out data quality as a top priority.
Hunt gets candid about today’s hybrid reality: legacy debt is worsening at large, federated institutions, making sustainability and scale the hardest challenges. He outlines how data products/domains can help de-risk modernization while balancing the CDO’s defensive mandate (regulatory compliance, risk) with offensive value creation (improving efficiency ratios). The takeaway: we may be in an AI hype cycle, but value will arrive faster than past waves – for organizations that ground their programs in trusted data and embed governance from the start.
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Larry Hunt, Ataccama
In this AI and Data Trust CUBE Conversation, Larry Hunt, field chief data officer at Ataccama, joins theCUBE’s Rob Strechay to unpack why trusted data, not just tools or talent, is the critical path to real AI adoption. Citing Ataccama’s Data Trust Report and his financial services background, Hunt highlights the gap between ambition and outcomes: while ~99% of firms are piloting AI, only ~3–4% are seeing results, with data trust as a primary blocker. He explains how governance succeeds when it’s “compliance by design,” tied to CEO/board-level KPIs, and focused on enabling business outcomes, rather than “selling governance.” He also notes where leadership buy-in and cross-functional alignment matter most, and why 46% of leaders call out data quality as a top priority.
Hunt gets candid about today’s hybrid reality: legacy debt is worsening at large, federated institutions, making sustainability and scale the hardest challenges. He outlines how data products/domains can help de-risk modernization while balancing the CDO’s defensive mandate (regulatory compliance, risk) with offensive value creation (improving efficiency ratios). The takeaway: we may be in an AI hype cycle, but value will arrive faster than past waves – for organizations that ground their programs in trusted data and embed governance from the start.
In this AI and Data Trust CUBE Conversation, Larry Hunt, field chief data officer at Ataccama, joins theCUBE’s Rob Strechay to unpack why trusted data, not just tools or talent, is the critical path to real AI adoption. Citing Ataccama’s Data Trust Report and his financial services background, Hunt highlights the gap between ambition and outcomes: while ~99% of firms are piloting AI, only ~3–4% are seeing results, with data trust as a primary blocker. He explains how governance succeeds when it’s “compliance by design,” tied to CEO/board-level KPIs, and focus...Read more
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What has changed in the perception and role of data in banking over time?add
What factors are contributing to the lack of actual adoption of AI in financial services despite the interest in piloting AI programs?add
What are the key considerations for implementing AI from a compliance and governance perspective?add
What are the key considerations for financial services leaders regarding data management and its importance in the future?add
>> Hello and welcome to this CUBE conversation where we will explore the heart of what makes AI tick, the data, and we'll gather some insights from Ataccama's Data Trust report. Today I'm excited to be joined by Larry Hunt, who's the field chief data officer at Ataccama. Welcome on board, Larry.
Larry Hunt
>> Hey Rob, really happy to be here. Thanks for hosting this today and looking forward to the discussion.
Rob Strechay
>> I've been excited about this for quite a bit because both of us spent some time on the financial services side of the things you much more than I did. I had a little stint there, but that must give you some unique perspective and opportunity to share benefits of AI and data and data trust and governance with customers. What's changed most in how banks treat data as a strategic asset?
Larry Hunt
>> I think it's just that, I think when I first started in banking and really first started my career data wasn't a career path, nor was it considered an asset for the banks. Most of the banks historically have focused on people, process and technology, but over time data is elevated to the same level as those three particular focus areas. Data is used as a utility to ensure compliance and resilience. It's also now being seen as an asset to fuel innovation. The biggest thing is the data conversation has shifted from historically, which was an IT black box that nobody didn't understand to a conversation with CEOs and boards of what we can do with our data on value for the company. And a lot of that has been accelerated in recent months with the evolution of AI, what companies think they can do with it to further drive value for their companies.
Rob Strechay
>> I think that's a great stepping off point, data fuels AI to put it mildly, and I think as you said, with banks having been there and we had more than just different financial instruments, we had different separations, we had to have different governance over different pieces of it, and AI muddies the water a little bit I would assume, but it's one of these things that you can't ignore AI, so why is adoption happening so fast now, especially in financial services in the banking industry?
Larry Hunt
>> It's a great question and not to be controversial, I don't know if adoption actually is happening. I believe that there is an ambition to drive AI usage, but to the earlier conversation, there are key components to make that real that are blocking the adoption for AI. Because my definition of adoption is actually implementation, and I think we've seen in some of our own internal analytics and our data trust report, 99% of financial services companies are looking to pilot some sort of AI program, but only three to 4% are actually seeing an outcome. And one of the key blockers and the ability to do that is trust the data. In addition to my tying back to what I said earlier, having the right people, the right infrastructure and the right technology, but even in my own experience, one of the biggest lack of data causes not to be able to deliver key use cases that we were trying to deliver. Something as simple as scripts for a call center that an element could use to summarize that the data didn't exist, so we weren't able to achieve those outcomes for those models.
Rob Strechay
>> I don't know that you're being that controversial about it because there's definitely reports out there that people are getting stuck in POC and trying to figure out the ROI of AI to put it mildly. But you also brought up a really good point here with how leaders are trying to figure out how to balance AI ambition with governance and risk and regulation. How do you see them approaching that governance risk and regulation when it comes to that AI ambition?
Larry Hunt
>> I think it's a really good question. I think the leaders that are being successful see data management as a . Embedding how they manage their data as a key dependency on delivering the value not only from an innovation perspective but also from a compliance perspective. That means compliance by design is part of what you're rolling out from an AI perspective, ensuring that what you have is adaptable as we move to more modular technology and infrastructure and it needs to be part of the strategy. Your governance is an enabler to build trust. The other piece of that is, is leveraging the AI to do the governance aspect of it. Because the one thing that the AI narrative has over some of the other things that we've tried to adopt historically is people have already accepted that AI is going to happen. Most of us have used ChatGPT. They introduced some of the prompt made AI real for everybody, and so everybody wants it. Some companies aren't ready to do it because they don't have the governance in place and the trusted data in place. And so being able to embed that and do it as part of the process, which we've learned in other technology rollouts that if you don't do it playing catch up later is not the right strategy. It's going to be something you have to pay for at the end. The leaders who are thinking about that now are the ones that are going to be successful and deliver value and business outcomes for their CEOs and their boards.
Rob Strechay
>> I think that to me makes a lot of sense in the fact that governance, you have to have it there to begin with. You need to be able to have those guardrails set in place to help people and guide them where they're going. But again, you're out there talking to these companies and these organizations all the time. Where do governance programs fail first from what you've been seeing?
Larry Hunt
>> Maybe another controversial statement, most governance programs fail by trying to sell a governance program. The governance program is really to enable business outcomes, so if you're not connecting that governance program to a business outcome, the audience that's receiving it is not going to understand the why and the why is super important. Secondarily, sometimes when you are able to do the governance program to the business outcomes, you're still lacking a larger data strategy and that data strategy more holistically needs to talk about the roadmap of the company in addition to governance, thinking about data management, execution, the architecture that's required and how you're going to enable your larger roadmap for AI and analytics. But even with that, those programs don't always find success because the third piece of it is sustainability. And I think one common narrative, whether it's data management or whether it's AI or it's any sort of theory or practice or new technology that we're looking at, being able to scale in sustainability in the current environment and just the amount of data that is out there is probably the biggest impediment. Making sure you have the tools that enable you to scale so you can actually do that from a data management perspective is a critical unlock to be able to have a successful program. The program itself is good, the tools that actually sustain it and make it real so you can actually execute on it, those are the leaders that are going to win in it's private state environment.
Rob Strechay
>> I like how you put that, the sustainability of the program really is the key to people wanting to use it and like you said, if it makes my life easier versus makes it... Governance again, always has had that wrap where hey, it's always the place of, no, you can't do this. It has to be that enabler versus hey being a no out of the gate to put it mildly. Let's shift a little bit here because I think there's definitely a lot of legacy debt out there. Is that getting better or is there still a lot of triage that's going on to bring things up to spec, to get enabled to be able to go and do these things?
Larry Hunt
>> The short answer is no. It's actually getting worse. I think the larger the company and the more acquisitions they've been through, I think the more federated and complicated our underlying environments are, and it's not for lack of trying to do the right things. I think it's just a byproduct of where prioritization has been, so if you're starting Evergreen today, then you're going to be in really good shape. Most companies are struggling half in half out on a legacy tech strategy that was on-prem and trying to figure out what their modernization strategy is going to be going forward moving to the cloud. AI is starting to expedite that shift so you can figure out how you can start to manage your storage and compute, again, scalability to manage AI. And I think, again, getting back to data trust and what we see in our data trust report, one of the biggest pain points that companies see when it comes to trying to stitch that data together. It's really hard to understand it, make sure where it's from, make sure it's fit for use. I do think companies are making a concerted effort to clean that up, but I still think it's going to be the foreseeable near future, they're going to have to manage that hybrid environment. And there's also a cost benefit analysis I think that's happening there too as they move to the cloud and start to see the real cost though their service charges as they start to move things to the cloud and what the right mix of that is. But holistically, I think most companies have an opportunity to de-risk their larger environment through their tech stack to get to a common set of data with a move to data products, data domains. I think that will start to then better inform how the aligned tech stack will start to align to that to start to eliminate some of that historical legacy tech.
Rob Strechay
>> I was having a conversation about data products just a little bit earlier today, and I think one of the things that's super interesting about this entire, what's going on with AI and everything like that is you still have to align the data. You still have to make the data ready for use for AI or you get, it's garbage in and garbage out. But one of the things that's been a more recent invention, and again given that you have this as part of your title, when you start to look at how CDOs, chief data officers, try to balance value creation with compliance, how do you think they're trying to make that balance happen? Because I think to me it's about giving the right data to the right people, but at the same time making sure you manage that risk and have it well governed. How do you see them balancing that while they're starting to try to figure out what value they can create out of all of these different new techniques?
Larry Hunt
>> I think the first thing is the CDOs that are going to be successful at it, it's part of their larger strategy and roadmap. And the core component of that strategy is that governance is an enabler and building data trust accelerates both what we'll call the defensive side of the CDO's job, which is managing the regulatory compliance items that are VAU table stakes. You have to get the data right, otherwise the regulators will have concern to start asking questions and then potentially opening issues through how you leverage data to drive efficiency internally. As you take a look at the latest, when I started data about 12 years ago in financial services, I was in the capital group and it was post the financial prices, the capital ratio was the number one thing. We quickly moved to liquidity, living will, can you wind down the banks and do that a liquidity to do it? If you listen to the bank's earnings calls most recently you hear about capital, you hear about liquidity, but they're all focused on their efficiency ratios. What does it cost me to generate each dollar that I bring in? And they all want to be at 50%, so the CDO's that can treat governance and trusted data to drive that efficiency ratio get to a business outcome that's going to be relevant at the CFO, CDO level. They've also addressed the risk side of it from a CRO perspective, which then embeds a foundation where you can then jump to driving business value to increase productivity, advantage for that particular firm. If you were focused on one of those three things and not thinking about it holistically with the right balance, those CDOs are going to have a tougher time trying to succeed in the current environment.
Rob Strechay
>> I love that. I think again, always starting from those key KPIs for the company or the organization, like very similar to same store sales in the retail sector is exactly what you were talking about with efficiency in the financial sector as well. I think that makes total sense. In all of the insights you've gathered and what you've been seeing in the report and what you've been seeing talking to customers, what do you think leaders should prepare for in the next two to three years?
Larry Hunt
>> That's a great question. I think about this time and time, I'm not really sure because I can't predict the future. There are a couple of things though that if I look at my crystal ball that seem there to be pretty consistent. One is, I currently my own personal opinion believe we are in an AI hype cycle. However, just like we were in an internet hype cycle in the 2000s before the dotcom bubble burst the reality of what the internet was supposed to be came to fruition and it came to fruition probably even greater than what we were stating then, it just took more time. I think on the AI front we're going to see exactly the same thing, but we're going to see it faster. As with most things, I think the speed to market as you continue to drive innovation continues to reduce. With that the need for trusted data, what we see is financial services leaders, 46% of them about seek data quality is a key sort of thing that they need to focus on. Compliance to data management discipline is also something that registers quite heavily, but their ability to meet that is something that they worry about. And so I do believe that data management focus and rigor to have trusted data to enable the key functions that financial services companies want to perform in the future will continue. Like I mentioned it earlier, I never thought I'd be in data career 25 years ago, but I'm very much in a data career. I'm pretty excited to what that's going to bring because I know without knowing the future, I know data's going to be a critical component of it, and I know trusted data is going to be a critical component of it. What we see in that the trust report solidified that for me, so I'm super excited to what's come. And honestly, I'm actually looking forward to what surprises that I might see too because I know data's probably going to be a key component to deliver IOs as well.
Rob Strechay
>> I totally agree with you. I think that the AI hype cycle has been a little bit crazy, to put it mildly. I think that definitely agents and how every agent's going to talk to another MCP server and another agent and all of this is still a little bit out there. I definitely think it'll come, but I think we're not going to get there as fast as I think we all thought we might about six months ago. But like you said, I don't have a crystal ball either. But I appreciate that because I think what you were talking about is really things people can look forward to and I think they can look forward to your report and taking a look at the trust report as well, because I think they'll be able to get some good stuff out of that, so thanks for coming on board, Larry.
Larry Hunt
>> Rob, really appreciate the time. Enjoyed the conversation. We'll do it again sometime, see what type of stuff does come in the future, see how close we were to what it could or could not be. But I guarantee you trusted data will be required for whatever's to come.
Rob Strechay
>> I couldn't agree more. And thank you for joining us on this CUBE conversation on theCUBE, the leader in analysis and news.
