Why building an AI-ready foundation doesn't stop at the technology
by Aly McGue
Kraken, the AI-powered operating system behind some of the world's largest utilities, manages over 90 million customer accounts across 27 countries for clients including EDF, E.ON, National Grid, and Tokyo Gas. Kraken uses Databricks as its internal data platform and partners with Databricks to help clients maximize the value of the data they receive via secure, scalable data distribution.
Kristy Mayer-Mejia is the Global Head of Data Transformation at Kraken, where her team helps utility clients understand, adopt, and extract value from the data Kraken provides. Her mandate is twofold: speed up the time it takes clients to use the data, and increase the value they get from it.
I sat down with Kristy to understand how data functions as a business asset and is the foundation of a successful AI strategy. A key point from our conversation is that becoming data-driven is as much about clean, unified data as it is about deep business context and ownership. Platforms like Kraken and Databricks solve what Kristy calls the foundational unification problem, the prerequisite that makes everything else viable. But once that foundation is in place, the part most leaders underestimate is the business context that makes unified data usable.
Aly McGue: In your experience, why do siloed data and fragmented systems remain a big hurdle for organizations trying to extract value from their investments?
Kristy Mayer-Mejia: What we see repeatedly with our clients is that low-quality, siloed data is the single biggest blocker to getting value from any other investment. Until the data is in one place, nothing else works at scale, and solving that is exactly the problem Kraken’s platform is designed to address. And I've lived this as a data leader in all my prior roles, too. Your team spends 80% of their time cleaning data, and that's just not valuable work. It's not necessary.
The real unlock is self-service, but it’s only possible once the underlying data is clean, unified, and accessible. Especially in the age of AI, self-service is possible at scale. You're never going to move quickly as a business, innovate or make day-to-day data-driven decisions if every question has to be answered by the data team. But when the data is scattered across systems with no documentation and no clear way to join it, self-service is impossible. Unification is this foundational unlock that makes everything else viable: the analytics, the AI, the speed of decision-making. It's table stakes.
Aly: We’ve all been in meetings where leadership spends more time debating 'which number is right’ than actually making a decision. What is the hidden cost of that lack of trust in the data?
Kristy: I give this example all the time, and it's been true at every company I've ever worked at. Before you have unified data, the classic question is: how many customers do we have? And no one totally knows. You know the rough magnitude. But when I give that example, every time people laugh because they know it's true.
What it leads to is a lack of trust in the data. And one of the primary early values that unified data provides is the speed of decision-making, the ability to embed data-driven thinking in the company's DNA. You can't move quickly if every time you pull a number, you're thinking, ’ Am I sure this is right? ’ Let me check five other places. Let me ask someone. And then it's different. And then you have to run down why it's different. Suddenly, it's two weeks later or a month later, and you might as well have just picked a random direction and kept moving.
Aly: We often talk about data fueling AI, but you’ve suggested that AI might actually be a 'forcing function’ for better data. How is the push for AI changing the way organizations approach documentation and context?
Kristy: AI has actually been a forcing function. The inputs AI needs are the same inputs humans need: clear data, documentation, context on what columns mean, and how things join together. When data is hard to use, self-service analytics feels like a nice-to-have because the value is hard to pin down. It's a few hours saved here and there on individual decisions, which doesn't feel compelling in isolation. But accumulated across the organization, it's huge. It's just hard to see.
AI has made that value visible and has made clean data & documentation table stakes. It takes what everyone always knew was needed and makes it non-negotiable. And then on the other side, AI itself provides the tools to unlock analytics. Things like conversational interfaces that let people query data without writing SQL. So it's both the forcing function that drives unification and the payoff that comes out of it.
Aly: You've talked about the need to unify and document data. But when it comes to AI specifically, is documentation in a knowledge base or a PDF enough?
Kristy: It used to be. We shared our data documentation the way most companies do: a PDF, or a page on a website that a data analyst could reference when they needed context. That works well enough for humans. It does not work for AI.
Every client I talk to now is asking the same question: can you share the metadata in context, alongside the data itself, so we can actually feed it into models and have them understand what they're working with? That shift, from documentation as a reference artifact to documentation as a live input, is one of the more underappreciated changes AI is forcing. With Unity Catalog and Delta Sharing, we can share that context with the data rather than separately from it. For our clients, that is often the difference between AI that can reason about the data and AI that cannot.
Aly: What does 'data unification’ look like in practice? How does near-real-time visibility change day-to-day operations?
Kristy: A few examples from our clients stand out. One is call center operations, which is a massive function for utilities. We had a client go from monthly reporting on call volume, which was so painful to put together, to dashboards that update every couple of hours, with a predictive model layered on top of what calls they're likely to see going forward. That ability to fine-tune operations in near real time, rather than looking backward once a month, is a completely different way of running the business.
Another area is product innovation. In the utility space, clients are determining which products and tariffs to offer to attract and retain customers. That's a decision that can be deeply optimized with data. Clean, clear data give clients easy insight, and rapid test-and-learn cycles to optimize their product offers – and then Kraken's platform lets them quickly launch those new tariffs.
Aly: The 'analyst bottleneck’ is a classic pain point for leadership. How do natural language interfaces, like Databricks Genie, shift the culture from waiting weeks for a report to getting answers in minutes?
Kristy: Most of our Genie clients are still in the early stages. But what we're seeing is that it's accelerating their time to get started by weeks or more. They don't need to deeply model the data the way you would to feed it into a traditional BI tool. They need clear documentation, they need the context, they need the data in one place, but they don't have to structure it so precisely that a user can explore it through a rigid interface.
But beyond the speed, there's a really clear cultural knock-on effect. One of the bigger barriers to data value is the cultural shift of making data part of your DNA. And I firmly believe one of the keys to that is making it incredibly easy and intuitive. When the barrier is low, and people can get in quickly, the culture, and the compounding value, follows.
Aly: What is the biggest misconception C-level leaders have when they task their IT departments with 'getting the data ready’ for AI?
Kristy: Data is a business asset. And the biggest mistake I see leaders make is treating it like an IT platform. They disconnect it from the business and say, "Okay, IT, go prepare our data.” But the key to building a solid data foundation is the deep business context. How is the data generated? How is it used? How do people interpret it? What does this field actually mean? Once the technical foundation is in place, the hardest part becomes that deep business context. And the vast majority of that work sits with the business, not the data team.
So my advice is to embed data within the business. The roadmap to getting your data ready for AI is a shared roadmap. It's a business roadmap as much as it is a technical one.
Aly: Kraken sits across a large share of the utility industry's data. Where do you see AI and data taking your clients over the next three to five years?
Kristy: What I find most interesting is how quickly AI is raising the ceiling on what clients can do once they have a solid data foundation. For a long time, the question was: how do we get our data into a usable state? That work is still real, and it still takes time. But the question is shifting toward: now that the foundation is there, what becomes possible? And the answer to that keeps expanding. AI is changing where clients start from and what good looks like. Clients who would have considered a monthly report a success two years ago are now running hourly dashboards with predictive models layered on top and looking quickly toward broad use of agentic AI.
The ones who invested early in their data capabilities – and not just their tech but their skills and culture – are the ones moving fastest now, and the gap between them and everyone else is only going to widen.
Kristy's perspective adds an often-missing layer to the data infrastructure conversation. The platform and the unification it enables are the foundational unlock. But where she sees most organizations stall is in the work that comes after: the business knowledge that makes data usable, the documentation that makes AI possible, and the cultural shift that makes self-service real.
As you develop your roadmap to embed AI across your organization and products, download the Databricks State of AI Agents to help you benchmark your investments.
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