The Database Dilemma: Why AI’s Appetite is Breaking the Foundation

Hey guys, thanks for the throw. We're here with Ryan McCarty, who's vice president of marketing for Liquibase. And we're having a chat about a new report that they have about, well, the state of database management and automation.

Ryan, welcome to the show. Thanks, Mike. It's great to be here.

Mm. Appreciate it. I think we have a newfound appreciation for databases again.

I think for a while there we took them for granted, but in the age of AI, suddenly everybody's paying a lot more attention. And, well, to be honest, we don't really manage them in a way that a lot of folks would say was, automated or even remotely automated. Maybe they're just manual most of the time.

But what's your assessment of what's going on here, and what are you seeing in this report that you guys did? Yeah, you know, so, for those that aren't familiar with Liquibase, we have our community, which is wildly popular. You know, we help, companies automate database change, in our community edition.

And then, our Liquibase Secure platform, also provides, automated database change and self-service for developers, as well as the control of policies and security compliance a-across our 65, databases that we support. So really creating a standard of excellence. And, you know, what we're seeing in our report, we, you know, we put out a survey for the last couple of months to our broader, broader community, which has, you know, over 100 million downloads of our community edition.

And, it was actually really compelling what we're seeing, and we're seeing it also show up in, a lot of the, prospect, conversations that we're having. 5%, or let's just call it 97%, of our community, has some form of AI touching production databases. Um, obviously, there's a fair amount of risk to that.

And Amazon just, you know, had a power outage the other day where I think there was something similar that happened where they had some, AI assist that caused some downtime. And, you know, in, in parallel to the amount of AI or LLMs potentially touching, production databases, we found that only roughly 27 or 28%, of the community has some form of enforced governance. So really controlling what goes into the database, what tou-touches production, which of course, you know, is...

There's a cause for concern there, especially as it relates to AI investments and, uptime and really reputation. Uh, 'cause what, what we've found in a lot of our conversations and, you know, we work with nine of the ten largest banks in the world, a lot of heavily regulated, industries. Um, what we found though is for the, you know, the larger community, if you have variation at the foundation level, at the database level, you obviously have your data models that sit on top of that or your data platforms, and then you have your AI models on top of that.

If there's variance in the foundation, that's actually gonna impact the top, which, you know, every company in the world has some, some investment right now, in that. And, we're finding that, the database, and specifically standardization of database change across these environments is becoming critical to reduce the variations in their AI models. Um, so it's actually a, a really incredible time here at Liquibase.

Is it your sense that people are looking to automate this more, and how do I do that? 'Cause I feel like there's more at stake than ever. The database is kind of serving as the memory for the AI system now, but it's also the mechanism by which I might apply some governance policies to AI agents that, shall we say, have a voracious appetite for data, for better or worse.

So how are we rethinking the whole approach to databases? Well, so, you know, in our report, we found that, you know, most companies kind of on average, they have five different database types. Let's call it, like, data lakes and warehouses and, you know, your Oracles.

They have a, a lot of different database types. Some companies have 10. And then the amount of change that's happening is pretty substantial, too.

Uh, you know, the league companies, it's twice a day. Uh, I think on average, usually it's, like, once a, a week, couple times a week. Um, but that's...

You know, you think about trying to standardize that change or that amount of change across those different types, that's a lot of nuance to get your arms around, as an organization. Um, so, you know, what we allow our, our customers to do and we enable them to do is allow developers to self-service that change. Um, and then we're able to have automated guardrails, in addition to that.

So we have custom policies that we provide as well as, a form of schema lineage so they can see, you know, the who, what, when, where, and why of change. And, we provide that for auditability purposes, but also for, their AI models as well. Um, which in a lot of senses, I think pretty profound, where some companies are still trying to do this manually through DBA tickets and scripts, which effectively does not scale.

Um, so we really empower DBAs to take control while allowing developers to self-service. That to us is effectively status quo. Where we really shine is when you're able to standardize all your database change across your enterprise and have the consistent policy and the visibility to ensure, you know, really this level of standard-standardization, creating, let's call it a center of excellence for our customers.

And again, we're doing that for, nine of the ten largest banks in the world today, a lot of retail, healthcare, et cetera, all the-- all of which are very regulated. So in the report itself, what surprised you or what leapt out at you the most that you didn't expect to see? Well, you know, I think there's, there's two bits of it.

There's the piece where we looked at the community and kind of said, "All right. What's the community doing? Um, what are their challenges?

" Um, and then we also looked at our customers. We surveyed our Liquibase Secure customers, and they're on polar opposites of the spectrum, as you can kind of imagine. So when we spoke to our community, you know, I think hearing that like, well, you know, you're...

have some type of AI assist, agentic DDL or LLM touching your production databases, like that's a... I didn't expect that number to be so high actually. I thought that that would be lower.

Um, so that was somewhat of a concern. Um, the amount of change, you know, was like all right, that feels about right. Like, we know that your elite teams are kind of releasing daily in some cases.

Um, certainly there's a need for innovation that is always pushing developers, which is critical, and we of course support that. Um, I think the, the thing that was really interesting though is that the community said the number one thing for preventing our AI initiatives from happening or really putting our AI initiatives at risk is data quality, and that comes down to the variance at the foundation influencing what's happening, the AI models at the top. Um, and I think that to me is like very profound and, you know, really compelling, in the research.

And then when you juxtapose that to the Liquibase Secure, customers, you know, really being a Liquibase Secure customer means, you already have governance turned on. So, you know, right out of the gate, you already have some form of policies already set where you have guardrails. Immediately you have visibility.

Uh, we have structured logging that you can send right into your SIM or, we work with you to create different dashboards for, reporting purposes and whatnot, a- as well as, you know, various, kind of like risk scoring as well as drift detection. 2% of them have this immediately. Um, so they're in, very different states when you think about their environments and, kind of the maturity of accomplishing their AI initiatives, and this is something I believe every CIO will care about as they're under pressure to accomplish these things.

To your point, as we get further into this whole AI era, are we establishing some greater appreciation now for the fundamentals of data management that, you know, arguably we've kind of ignored for far too long? 100%. Um, you know, you see it, y- we definitely see it more so I think on the Snowflake, the Databricks sides of the house where it's very much the Wild West.

You know, we've been working in the application CICD, side of the world for a really long time. Um, that is a very... Let's just call it i- it's been around long enough where there's a lot of process typically there.

But for these, these newer platforms, these newer data platforms, it's data, data engineers effectively not having a DVA always trying to manage all of this, and of course there's a lot of risk associated with that. Um, but, you know, from a, a CIO or CDO's standpoint, just that level of standardization is almost incredibly impossible to achieve without a Liquibase. Uh, you, you can't do it through humans.

Um, so I think, you know, as the need for speed increases, and obviously we're seeing that pushed 10X by AI, the need for control increases, and ultimately that's what we're helping, some of these top enterprise companies achieve. You know, and to your point, it seems like, you know, we all complain a lot when the AI is wrong or the output's off base, but I can't help but wonder to what degree is that really the fault of the AI model or is it just simply we were unable to get to the right data at the right time and the right place and therefore the AI model then went off the rail? Yeah.

Cer- certainly you got... You know, there's, there's modeling things that need to be accurate. Um, but I, I...

You know, it's really easy to go point to the model. Where we're finding is that the underlying infrastructure has to be working in parallel, or those models will never be accurate. Um, and I, I think as companies continue to walk down this path, and you could argue run down this path, they're probably gonna find that out the hard way, through downtime and audits and everything else that comes along with this.

Uh, there's certainly a lot of pressure on these AI initiatives, as well as, you know, you could argue there's more investment than ever in them. So just think, we're in a really interesting time in technology, and, we're seeing, you know, the, the need for control over your infrastructure, to be as accurate as, you know, really your, your models are. So, it's an interesting time.

Are there things that you see people doing today that you kinda just shake your head a little bit and go, "To your point, that's gonna be the school of hard knocks"? Well, you know, we, we talk to a lot of community customers and, you know, what, what we find is, or, or maybe they're using some type of, database change management tool, but they're really invested in the speed part of it. They're like, "Well, we have to, you know, automate this change," and that's really important.

We believe in that. I mean, that effectively is our religion as kind of the creators of, of, you know, that. But, I-But what they don't always understand is the inherent risk that comes without having controls.

I kinda said, like, you can automate more database change, but ultimately that creates more risk, and at a certain point that tips over and there's real consequences that comes along with that. Could be a fine, it could be a security threat, could be the reputation. Um, I mean, I think Cloudflare is a really great example.

They had, they had- they took down half the internet in the fall, because of a database change. And, you know, there's things like that, and they're probably one of the most resilient companies on the planet. Um, so this can happen to anybody.

Uh, so I think it's really, you know, thinking about, all right, so how are we gonna make sure there's automated guardrails, we're not prohibiting speed, we're really helping our developers self-service, ensuring there's visibility, ensuring there's lineage, ensuring that we can go accomplish our initiatives so, you know, CIOs can continue to build, you know, what they're, being held to go build and doing that safely and, upholding, you know, the reputation of the company. Uh, we're helping our customers achieve that. I also feel like we're overestimating the ability of our platforms to handle all this data as it comes in because not only are we accessing more data than ever, but the AI agents or whatever it may be will be creating more data than ever.

So do we kinda have to have a fundamental rethink of the underlying in- infrastructure? Yeah, absolutely. Um, o- of course, you know, as AI, whether it's AI assist or a- agentic AI is, Copilot, for example, supporting more development, the amount of code that it creates is pretty significant.

And if they go q all- QA all of that, obviously takes time and, just in terms of process, I think people have to really rethink that. Um, but again, it's not, it's not achievable through humans and tickets and documentation. It's, it's achievable through automation.

Um, and I really believe that's policy as code, and it's ensuring that you have the right lineage as part of that. Um, so that's, that's what I think companies will, um... We, we saw that this last year.

Um, we actually, you know, had some pretty incredible achievements this last year, in terms of growth. Lot of it was supported by exactly what we're talking about. Every company in the world has some type of modernization initiative.

Uh, definitely the, the Fortune 1000 and above, which is largely who we work with. 0, you know, internal development platform really, where they're pressure testing all their infrastructure to support phase two. So, it's an incredible time.

Is it your sense that organizations are being proactive about this, or are we gonna stumble our way into this and usually until there's something bad happens and then we're gonna wake up and go, "Hey, you know what? " Well, the stumbling's already happening. Um, you know, just last year alone, there was a few instances, a few headlines where we saw, agentic AI take down some production databases.

Um, you know, it's March, and there's already been multiple stories of this. The, the, I think the most recent one was, you know, the Amazon outage that happened, believe it was two days ago. Um, so these things are certainly happening.

They're happening to, again, some of the most resilient companies. You know, Amazon's one of the most incredible companies on the planet. Um, so I just think, everyone is susceptible to this, and it's something that, you know, whether you're a CIO or a CDO, you really have to get your arms around quickly.

And, again, it's not something you can solve through documentation and humans. You really need automation to, achieve this level of kinda compliance and governance at scale. Ultimately then, you know, if you get your crystal ball out a little bit, where do you think we're gonna be a year from now?

Well, what's, what's kinda like, you know, the, the journey ahead look like from where you sit? I believe regulation is gonna have a big impact on, AI initiatives and the trust of these models. We're already seeing a few AI mandates.

Uh, I know there's a couple in the EU. Um, there's been guidance put out in North America too. I believe this is gonna put more pressure on leaders to really, ensure that, customer trust is supported.

They really have, you know, really the proof in place to show that they're in control. Um, that's ultimately what it comes down to, and I think that's, you know, a year from now where we're gonna be at. There's gonna be so much pressure on leaders to achieve this, because of regulation.

All right, folks. You heard it here. I think we can all agree that AI provides great powers, but as always, when you get great power, there's more responsibility.

Ryan, thanks for being on the show. Thank you. Appreciate it.

All right. And back to you guys in the studio.