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In today's episode, Ryan Zauk is joined by Junta Nakai, Global Vice President at Databricks. Junta leads the Financial Services, Cybersecurity, Sustainability, and Public Sector Go-To-Market (GTM) for one of the world's largest and most exciting startups at the beating heart of AI.

Databricks has become a household name in tech, with 60% of the F500 and well over 10,000 customers worldwide, plus a robust open source suite. They recently raised an unprecedented $10Bn Series J financing at a $62Bn valuation - The round was led by a who's who of investors including Thrive, a16z, DST, GIC, ICONIQ, and more.

But did you know that financial services is their largest business line? Databricks has become critical to some of the world’s largest financial institutions, insurance firms, and fintechs helping power their AI, cloud, machine learning, and big data initiatives. Some clients include Capital One, S&P, Mastercard, Coinbase, HSBC, Morgan Stanley, KPMG, and more.

Junta is a phenomenal speaker and we cover a lot of ground in today's episode. Here were 6 of our favorite quotes (without giving away too much):

1. "Databricks helps banks take advantage of probably their two most important assets today - their data and their people. 10 years ago, their two most important assets were probably their capital and scale." Databricks helps democratize access to big data and AI to folks all across major FIs. Usage has been incredible - Junta cites 40% of Block's team uses Databricks, and bank clients like MUFG have tens of thousands of users on Databricks.
2. "The easiest part of AI is the AI itself. The hardest part is everything that happens right before." The biggest challenge enterprises face isn't necessarily AI deployment, it's the data infrastructure required. Is your data accessible? Stored compliantly? Able to be streamed? Who can access it and where is it going? The list goes on...especially for massive FIs. This is why Databricks is so powerful.
3. "The quickest way to lose credibility in front of the C-suite is saying something that is just a little bit off....And that shuts the door. So we spend an enormous amount of time enabling our salespeople." Databricks is a highly technical product, requiring a technically savvy sales team. They invest heavily in GTM education, making sure their team is fluent in this powerful product. Otherwise, they won't succeed.
4. "[After deploying GenAI] one client found that the bottom 25% of engineers got worse, and the top 25% got better than ever." AI is supercharging those willing to put in the hours to master it / augment their weaknesses, while bottom performers are learning less than ever and blindly copying AI code.
5. "I believe personally, the next decade is going to be the most prosperous in human history. Not because of population growth, but because of massive productivity gains from AI." Macroeconomic prosperity is largely driven by 1) population growth and 2) productivity gains. Junta explains the demographic slowdowns in large markets across Asia and now globally, and how AI's rapid efficiency gains will still push us toward macroeconomic prosperity.
6. "As AI grows, the characteristics that make us human become more important, not less...the foundation for the most successful people is going to be something super intangible - self-awareness." Similar to the engineering example, AI can serve as an accelerant to double down on strength / improve weaknesses, or a debilitating crutch that stunts development. There will be no blanket usage - applying it strategically in your career will be invaluable.

Quotes were lightly edited for brevity or clarity.

Episode Timeline

• 3:04 - Junta's path to Goldman Sachs and how he gained the conviction to join Databricks
• 7:45 - What exactly Databricks does and how it helps global enterprises scale
• 12:45 - How Junta and his team think about GTM
• 16:20 - How Databricks works with the world's largest financial institutions
• 23:35 - Junta's role, how he tracks his team, and the importance of training his salesforce for a technical sale
• 29:05 - A few quotes from prior interviews that Junta extrapolates on
• 32:30 - AI's potential effect on the global economic future. Trends in population decline and productivity gains and how they may affect the macro environment
• 39:27 - His passion project, Brooklyn Kura, a Brooklyn Sake Brewery
• 42:23 - Rapid fire round including his must-try restaurants, his news diet, the value of network, the Brazilian startup most exciting to him, and more.
  

Enjoy the show.

Junta Nakai is a Global Vice President at Databricks, leading the Financial Services, Cybersecurity, Sustainability, and Public Sector Go-To-Market (GTM). In this role, he is responsible for the global adoption of the Databricks Data Intelligence Platform across capital markets, banking, insurance, and federal, state, and local governments. Additionally, he oversees the GTM strategy for sustainability and cybersecurity solutions across all industry verticals. Before joining Databricks, Junta spent 14 years at Goldman Sachs, holding various leadership positions within the Equities Division. Junta has a B.A. in Economics & International Studies from Northwestern University.

Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on X, LinkedIn, and Facebook.

Ryan Zauk is the Host of the This Month in Fintech Podcast and Bay Area lead for the broader This Week in Fintech platform. In his day job, Ryan is an investor at OMERS Ventures, the direct investing arm of one of the world’s largest pension plans with $130Bn+ in net assets. OMERS Ventures focuses primarily on Series A through C direct investments in Software, Fintech, and AI. Prior to OMERS, he worked in Morgan Stanley’s Tech Investment Banking team focused on M&A and capital markets across technology. He is based in the Bay Area. You can find him on Linkedin or Twitter.

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Transcript

[00:00:00] Ryan Zauk, TWIF: The views expressed in this podcast are the speaker’s own and are not the views of This Week in Fintech or any other person or entity. The content provided in this podcast is for informational purposes only and should not be construed as legal, business tax, or investment advice or recommendation, solicitation endorsement, or offering by me or anyone else for the sale subscription or purchase of securities, or for investment advisory services of any kind.

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Hello everyone and welcome to today's episode of the This Month in FinTech podcast. I'm your host, Ryan Zauk, Investor at OMERS Ventures, and today I'm joined by Junta Nakai of Databricks. Junta is a global vice president at Databricks leading GTM for financial services, cybersecurity, sustainability and the public sector.

Databricks has become a household name in technology with 60% of the Fortune 500 and well over 12,000 customers worldwide on their platform, plus a robust open source suite. But did you know that financial services is actually their largest business line? Databricks has become critical to some of the world's largest FIs.

Insurance firms and fintechs helping power their AI cloud machine learning and big data initiatives. Some of these clients include Capital One, S&P, MasterCard, Coinbase, Morgan Stanley, KPMG, and many more. June two comes on the show today to discuss the four big trends that gave him conviction to join Databricks so early, how Databricks specifically helps financial institutions, why it will be such a great decade to be a bank customer, how careers will evolve in AI, what might be in store for global productivity and population growth, his sake brewery side hustle, and much more. Let's get started.

Junta, welcome to this month's episode of the This Month in FinTech podcast. It's great to be joined with you live in a tiny booth in your New York office just off of Bryan Park. It's a beautiful office buzzing with energy today. Welcome to the show.

[00:03:04] Junta Nakai, Databricks: I'm excited to be here in this very tiny room.

[00:03:06] Ryan Zauk, TWIF: Yeah, we're about two feet apart, and so Junta, let's kick off with a little bit on your career story. You had a long career at Goldman Sachs, then left after about 13 years for a brief stint in FinTech, and let's just start there.

What drew you into Goldman and Markets and then how'd you leave in 2017 at such a great time for the firm to go join the startup world?

[00:03:26] Junta Nakai, Databricks: So I grew up in Connecticut, not too far from New York City, and around middle school I started to notice that my friends live in the much larger houses than I do. And I would ask people like, what do your parents do?

And oftentimes they would say, oh, my dad works on Wall Street, or My dad or mom does this. And then I said, well, you know, Wall Street is right if I want to live in a house like that. And as you know, simple and materialistic that sounds, that was kind of a 12-year-old brain thinking, you know, that's what I want to do in the future.

And that's kind of been the goal that I've had when I was growing up is one day I wouldn't want to work on Wall Street and not that I really understood what that was. Right. And when I started, it was fantastic times. Mm-hmm. Things were booming. It was pre financial crisis. And you know, as as years went on, I started to really notice that automation was creeping into this business.

And specifically I was in equities. So if you can imagine buying and selling stocks Yeah. Is arguably a job more suited for algorithms.

So I looked around and. I started noticing that things are being done cheaper, faster, better than machines. And there was this one statistic that the CTO I think of Goldman said at the time, which I have to double check.

He said something along the lines of. At the peak we had maybe 600 stock traders.

[00:04:49] Ryan Zauk, TWIF: Mm-hmm.

[00:04:51] Junta Nakai, Databricks: And about a decade later, there was only two left

[00:04:53] Ryan Zauk, TWIF: Two?

[00:04:54] Junta Nakai, Databricks: Two. Because so much of has of it was automated. Now, I'm not sure if this was a very specific type of trader, right. That he was referring to, but you know, I really felt that looking around and to.

To see that things can be done better by automation. Yeah. You know, I, I wanted to kind of be part of the future. Mm-hmm. And, you know, I, I thought buying and selling stocks probably is not the future. Right. And I kind of had this existential crisis of what is the point of a career anyway? Right. Is it to maximize your short term earnings, cash flows, or is it to maximize the NPV right.

Of your career? And I thought the latter. And, you know, what is the defining technology of our lifetime? Probably going to be AI. Yeah. So as simple as that, I said, Hey, I'm gonna go work for an AI company. So I took a massive pay cut. Um, luckily my wife was okay with that, and I joined a small FinTech, and the thought process there was, you know, I didn't view, uh, as a step back or step back in prestige or step back in pay or anything like that.

You know, I was very cognizant that I did have gaps to kind of, you know. Move into Silicon Valley, into tech. So this is a way for me to kind of get my foot in the door, and that's why, you know, I made that drastic decision and, uh, ended up in, in the

[00:06:09] Ryan Zauk, TWIF: tech world. And then after that stint, if most of our listeners had a time machine, they probably would've wanted to go and join Databricks in 20 18, 20 19.

You actually, at the time ended up joining Databricks. The company though well-funded and was growing quickly, was still in its early days, and the New York City office I heard was nothing but a little WeWork. How did you find this company and what gave you conviction to join at this time? We'd all love to know.

[00:06:34] Junta Nakai, Databricks: Yeah. One, one of my friends had joined Databricks and prior to that he was a equity research analyst.

[00:06:42] Ryan Zauk, TWIF: Mm-hmm.

[00:06:42] Junta Nakai, Databricks: And when he joined this company, you know, he was telling me a little bit about it and I thought, well that sounds interesting. And then, you know, the more research I did, I kind of had four, four thoughts actually, which is.

What are the four things that you kind of need to believe in the future for tech?

[00:06:59] Ryan Zauk, TWIF: Mm-hmm.

[00:07:00] Junta Nakai, Databricks: And I thought those four things were cloud data, ai and open source prescient. Yeah. And you know, those are kind of secular, uh, tailwinds. When I looked around the market and the more research I did, there was very few companies that ticked all four of those boxes.

Right. And as I spoke to more people at Databricks, I realized, hey, like. Sticks, all four of these boxes. Right. And this is a very unique position to be in. And that was kind of the bet that, that I made. And at the time, we were probably maybe four or 500 people. Mm-hmm. And six years later we're probably eight to 9,000.

Wow. At this point. So it's grown, uh, quite a bit. So been lucky. Um, and, uh. It's been a great journey.

[00:07:46] Ryan Zauk, TWIF: Great. And now I'm gonna ask, speaking of ai, probably the hardest question possible in AI today. What exactly does Databricks do? So, in

[00:07:54] Junta Nakai, Databricks: the simplest form, I think Databricks gives you the ability to trust your data.

[00:08:01] Ryan Zauk, TWIF: Mm-hmm.

[00:08:02] Junta Nakai, Databricks: And then democratize data and AI to your workforce. Okay. The easiest part of AI is the AI itself. The hard part is everything that happens before that, right? Right. So where is the data coming from? How are you transforming it? Who is changing things? Yeah. Where is it going? Because especially in a regulated entity like banking or telecommunications or healthcare.

Healthcare, yeah. You really need to understand the governance and lineage of that data before you could. Use it for productive purposes. Databricks enables you to do is a single platform that end-to-end helps you bring data in, transform that data to analytics on that data. Mm-hmm. All the way down to building the most advanced agent systems and gene AI models on that data.

Hmm. So it's a one-stop shop for everything related to big data. Ai. Mm-hmm. And that's one of the reasons it's been so successful over the past few years is because now every CEO in every company, right, understands the value of becoming a data and AI driven organization.

[00:09:16] Ryan Zauk, TWIF: Right? And so what did it look like then before, or maybe some potential customers now, how might they be trying to capture all of these disparate data sources?

Let's say a bank and you have. Consumer data, you have credit data, business data, and then it's also split across maybe mortgage departments, loan departments, equity research, public data sources. What does Databricks do to remedy that situation? Dealing with, so

[00:09:38] Junta Nakai, Databricks: right now, for a company to try to bring all that data together and do stuff with that data, you would have to stitch together potentially dozens and dozens of different types of technology.

Right? Right. So you might have a. Data lake, a database mm-hmm. To, to store all your unstructured data.

[00:09:58] Ryan Zauk, TWIF: Mm-hmm.

[00:09:58] Junta Nakai, Databricks: You might have a data warehouse where you take a subset of that data, clean it up and use it for reporting purposes. You might have what we call an ETL tool that helps you kind of bring, move that data around.

You have a data science tool, right? You have a streaming tool for real time data, right? So then you kind of think about this and it becomes a complex mess very, very quickly. And that complexity is one challenge. There's two other bigger challenges that arise from that. One is cost, right? Because it's very costly to maintain and and stitch together all these different systems.

The other one, which is very pertinent and perhaps more important is the data governance nightmare. Mm-hmm. That arises from that. How do you ensure that the data is fresh between two systems? That you know, that you're, you know, the data that you're using in one section is the same data that you're using in another section.

There, it becomes a very, uh, big data governance nightmare when you try to stitch all those things together. And this is kind of the, the main thing that Databricks help to solve for, especially for a financial service customers. Is an ability to say, Hey, what if we can bring all your data together right in a single place and work from that single source of truth and do everything from business intelligence to artificial intelligence from that single place.

Mm-hmm. And governed. That's what we offer. And that's why it was so hard to do that before technology like Databricks came along.

[00:11:26] Ryan Zauk, TWIF: Right. And then so you think about having to do that at a large company, a major bank, let's say JP Morgan, Morgan Stanley, something like that. What does implementation then look like?

Right? Is this a couple days plugging in? Do you have boots on the ground deployed at a bank? 'cause this is a lot of handholding and careful data that you're working with.

[00:11:44] Junta Nakai, Databricks: Yeah. You know, it depends on the use case and it depends on the customer. Mm-hmm. Naturally, but. The bare minimum for Databricks is to have data on the cloud.

[00:11:53] Ryan Zauk, TWIF: Mm-hmm.

[00:11:53] Junta Nakai, Databricks: So if you do have data already on the cloud and you're leveraging AWS Right. Azure gcp, uh, Databricks works on all three of those platforms and, um, you could get started right away. You could get started pretty quickly. Now when you start thinking about enterprise wide deployment, right, right.

Because some of these companies have thousands of databases. Or data sets or maybe millions of data sets, right? Millions of data pipelines. Then it becomes really complex and you know, we have 12,000 customers around the world. 60% of the Fortune 500 uses Dataverse already. That's incredible. So we have a deep bench of expertise where we've seen best practices.

We helped large complex organizations. Become more data and AI driven companies. So we provide that kind of help along the way to get our customers up and running,

uh,

[00:12:45] Ryan Zauk, TWIF: as quickly as they can. Right. And so you mentioned 60% of the Fortune 500, probably a massive long tail of customers after that. You've helped lead go to market for so long.

It seems from what you've described, many, many, if not almost any company could be a Databricks customer in some way. How did you as a go-to-market leader think about segmenting this market and prioritizing in your early days?

[00:13:07] Junta Nakai, Databricks: You know, at, at this point in 2025, I think, you know, any company, right, can be a customer of Databricks because every company or any company has data and AI use cases.

Yeah. But back in 2019, when I started, we had to take a little bit more of a tactical approach, right? Which is. Taking the first lens of, okay, well which customers are cloud ready? Right. Which ones are on the cloud? And the second question becomes, well, what data sets do they have on the cloud? Mm-hmm. Right.

And typically, I'll give you an example in financial services, some of the early movers into the cloud or asset management firms.

[00:13:45] Ryan Zauk, TWIF: Interesting.

[00:13:46] Junta Nakai, Databricks: Why is that? Well, a lot of the data sets they tend to deal with. Are not as sensitive as a retail bank. Mm-hmm. Right. A retail bank has to think about PII data. Yeah.

Right. If you're an asset manager, you might be dealing with market data. Mm-hmm. That is a, a publicly available dataset. Right. So if you think about, well then what are the use cases, which is the third lens, right? So it's like, are they cloud ready? What kind of data sets are on the cloud? What can they do with that data?

What is the use case? So for example, things like transaction cost analysis for an asset manager, market risk calculations, right. For a sell side. Right, right. Those are fairly, um, robust use cases that require data sets that perhaps, uh, some customers were not comfortable moving to the cloud yet. And that's the different slices.

Of the thinking that we had in the early days, it was less about segmentations. Mm-hmm. It was more about what is kind of the practical steps that these customers are gonna take? What is the journey that they're going to take and how can we align ourselves to that journey, um, so we could help them along?

[00:14:50] Ryan Zauk, TWIF: And, and thinking about journey alignment, I think a big. Challenge in 2010 to 2020 was this migration to the cloud moving from on-prem to the cloud. What was the balance going to be? It seems cloud has won in that trend, and at the beginning of 2020, A-K-P-M-G study found that 67% of CEOs expressed concern about migrating so much of their business to the cloud.

That number's probably closer to zero than it is 67 today.

Mm-hmm.

[00:15:15] Ryan Zauk, TWIF: Do you know what has changed after COVID in enterprise architecture and how that manifested in your business?

[00:15:22] Junta Nakai, Databricks: When there's a moment of great volatility or change? You know, crisis tends to bring the future faster. So all the things that the cloud had promised, so resilience, agility, right?

Right. All those kind of buzzwords, all those became tangible. Because CEOs and CTOs had to really pivot their business on the fly, right? They had to understand what was happening across the full spectrum of their business. They had to pivot their business model. They had to assess new risks and do new simulations that maybe they didn't think they needed to do all of a sudden, and that kind of made concrete the need for both cloud and the scalability of the cloud.

Um, as well as a data platform like Databricks mm-hmm. Where you need to be able to, again, like I said before, trust your data and democratize that data so that you can make smart decisions.

[00:16:20] Ryan Zauk, TWIF: And then let's, uh, let's have this, the Databricks story come to life a little bit for our listeners. You have some phenomenal customers, especially in financial services.

Capital One, JP Morgan, Nubank, Intuit, TD Bank, others. What have you enabled them to do now, right? Like they have the AI products that they're trying to deploy, they're using Databricks to harness their data. What has that led to for the customers? Right.

[00:16:45] Junta Nakai, Databricks: So if you think about a financial service company, there's actually been very little innovation there across the board, right?

So think about how you bank relative to how you watch a movie or how you buy things, right? It's probably the sector overall has had the least. Amount of innovation, probably amongst most of the major sectors out there.

[00:17:08] Ryan Zauk, TWIF: Insurance is the only one that's probably

[00:17:10] Junta Nakai, Databricks: insurance. What Databricks really helps unlock for these customers is taking advantage of the two most important assets that they have today, which are data and their people.

And 10 years ago, the most important assets that, let's say a bank had was. Probably it's capital and scale, right? So how much money do you have? How many branches that you have today? It's about data and people. And that's critically important because especially if you look at banking, CEOs and CFOs operate their business with their efficiency ratio in mind, right?

Right. So in other words, like how productive can you be with the assets that you have to generate the most amount of revenue? Right. For any of these companies, uh, personnel costs. Compensation cost is the single biggest cost. Oh, always. Yeah. So what Databricks really unlocks is productivity gain. That's the main thing.

So I'll give you an example. Um, block. Mm-hmm. Cash App Block. Um, they use Databricks, and today 40% of their workforce uses Databricks. 40%. 40%. Wow. Across 15 different job profiles. So these are not just data scientists and data engineers. These are marketers, for example, right? Call center people. Interesting for example.

And what they found is that on average, people are getting 30% more productive. Or in other words, they're saving 30% of their time by having a platform and the age agentic workflows that Databricks empowers. But what they've also found is that efficiency gains don't. Stop at a hundred percent. Right? Right.

Because you could actually start doing more than what you can do with a hundred percent of your time. Right. So the productivity gains are tremendous, and that's kind of like the FinTech, more tech forward companies, but we're seeing this in very traditional incumbent organizations. Right. And I'll give you an example is MUFG in Japan, which is the largest bank in Japan, a top 10, top 10 bank in the world.

There. Deploying Databricks to 30,000 people, uh, which is about a third of their workforce. Wow. In Japan and doing things like fraud detection and risk management and automation. And why are they doing this? Well, if you look at a company like MUFG, their efficiency ratios are probably in the low to mid sixties.

Right. So for those listeners that don't know what that is, financial Yeah. You know, the lower the number, the better. Right. Okay. So a very good company. Maybe JP Morgan, as a example, has an efficiency ratio about 50%. Mm-hmm. Cost income ratio of about 50%. Right? Meaning if you're MUFG and you're sitting in the mid sixties, you wanna figure out how to use technology to lower your efficiency ratio, because that has tremendous re uh, returns in terms of profitability and your share price.

Databricks or more broadly, data and AI is the, the biggest boost to productivity that you could probably have for your workforce today. Mm-hmm. And that is, again, because people are the biggest expenses, getting your people more productive has tremendous economic returns. It has tremendous implications to your customers as well, right?

So I, I always say it's gonna be a great decade to be a banker, but probably a better decade to be a customer of a bank, a bank.

[00:20:33] Ryan Zauk, TWIF: Love that. And, uh, that's kind of a perfect segue. 30,000 people at a bank. I mean, that's product usage probably as high as anything. Obviously behind Microsoft Office sweep, probably second.

What did you do as a GTM leader and as a company to break through into these fis and have success in a notoriously hard market? Right. The trust, I mean, at this point, right? Databricks has such an incredible logo list and brand and technology. But in the earlier days, actually getting these fis to trust you rolling out such a big part of the organization, it's, it's little by little of course, but do you remember what Databricks is?

First customer? It was,

[00:21:12] Junta Nakai, Databricks: I do not.

[00:21:13] Ryan Zauk, TWIF: Uh,

[00:21:13] Junta Nakai, Databricks: but when I, when I joined DataWorks, we had very, very few financial service customers that I remember. Um, and today we have well over 1500, um, financial service customers. I God, uh, around the world. But it's really about focusing ultimately on the use case that our customers are going to be able to deploy using Databricks.

Um, I think that is kind of the path to the ultimate success. Of course, like I said before, the earlier on, there's more kind of tactical slices that you need to, to think about and the best new to make about customer readiness, et cetera. If you look at it today, it's all about the end use cases and the business outcomes that I, you can drive.

I was talking to this CTO a couple months ago and he said it jokingly, but he said, Hey. My CFO is yelling at me all the time. He's like, why is that? Well, he says, I gave you all this money two years ago for J ai. Where's my return? Right. And that return cannot be, look at this great chatbot we built, or, right.

Look, I am a little bit more efficient here. That No. He's like, what is the dollar amount you are earning me? Right. With all the investments. That we've made in AI over the past two years, and the answer is for most people, very little.

[00:22:23] Ryan Zauk, TWIF: Mm-hmm.

[00:22:24] Junta Nakai, Databricks: Right. So now there's this like incredible focus on what are the outcomes that we're driving.

Mm-hmm. Right? Software is sold, it's not bought. Right? Right. So we have to really understand the outcomes that our customers are trying to achieve and align ourselves there. And show them what is the unique advantages that Databricks provides that enables you to achieve those use cases, achieve those business outcomes?

So we've spent a lot of effort building content, technical content. Mm-hmm. That helps our customers bridge the gap between our technology and the solution or the use case they're trying to do. And because our DNA as an open source company. We make that code available to a lot of people. Wow. Right. So if you're trying to do market risk calculations, right?

Right. Here's how you bring that data in. Here's some contribution analysis aggregations. You know, this is how you might do a value at risk calculation using databricks and scale out simulations using GPUs and all that code is publicly, um, available so our customers can now feel. And see exactly what data risk can do for them in an area like market risk as an example.

Right.

[00:23:35] Ryan Zauk, TWIF: And now let's talk a little bit about your role as head of go to market for financial services. 'cause we have a lot of founders and or mid stage growth stage executives that listen to this podcast and try to understand how should they hire for this role, what should they be doing day to day. Can you talk about your day-to-day role, how you prioritize your time, and kind of how you define success as a GoTo market leader?

I think.

[00:23:59] Junta Nakai, Databricks: This goes back to what I said before, which is software is sold. Yeah. Um, and not bought. Right? And just put it bluntly. Customers are tired of hearing vendors talk in generalities, right? They really want to understand what specifically are you going to do for my business and how are you going to help me?

And the quickest way to lose credibility in front of a C-suite or an executive decision maker is saying something that is a little bit off. That shows that you just quite don't understand that business enough and that shuts the door. So we spend an enormous amount of time enabling our salespeople and our field, our sales engineers.

Right? So I. What are the segments that we're going after? Um, I cannot be the bottleneck for Databricks. If, if Junta is the bottleneck for, uh, solutions engineers and, and reps to go talk to customers, then we would have failed as a company.

[00:24:57] Ryan Zauk, TWIF: Mm-hmm.

[00:24:57] Junta Nakai, Databricks: So our mission is to uplevel and to train as many people in the field as possible.

That can have these meaningful conversations with executives. So one of the things that we've done is build something called outcome, outcome map for all verticals. So I'll give you an example in Fs, it's very simple. We said, okay, there's three large segments we care about. Okay?

Roughly speaking, which is banking, right? Capital markets in insurance. Okay? Okay. So when you're gonna go talk to them, what are they actually trying to do? These customers, right? They're largely trying to do three things. Which is to drive revenue, revenues, manage risk, or become more efficient, kinda like the efficiency ratio.

Okay. And then we said, okay, what are the buckets of use cases that drive each and every one of these imperatives? That we have seen in our customers where we believe we have a distinct advantage. So the technical advantage of using Databricks. Mm-hmm. And how can we help them achieve those use cases as quickly as possible.

So again, an example, if you're thinking about driving revenues for a retail bank, for example, personalization, cross sell, upsell, right? Investment advice, like those are very, very important things. So we spend a lot of time enabling our field about having these conversations. Building technical assets that help our customers get started on those use cases.

Um, build partnerships with software vendors and consulting firms that help accelerate the implementation of those use cases. And now this has become kind of the north star in which the go-to market function operates and how we can have differentiated conversations with our customers again and not talk in generalities.

Right. So that's the, that's a big, big focus for me. And because what's gonna happen I think is with Perplexity and Chat, JBT, everybody's gonna sound like a genius. I

[00:26:50] Ryan Zauk, TWIF: was just talking about this yesterday, right?

[00:26:52] Junta Nakai, Databricks: Yeah. Like how do you differentiate yourself if everybody seems to have deep insights, right?

Seems to kind of know your business and sounds brilliant over email. And I think the answer is face-to-face conversations. Mm-hmm. So if our reps at scale, if the field at scale. Can have very deep, meaningful conversations. Again, not talk in generalities, but explain and articulate and deliver use cases and outcomes for our customers.

I believe that's how a company like Databricks will win.

[00:27:21] Ryan Zauk, TWIF: Yeah, that's great. And I always wonder that when I see highly technical products and I see the backgrounds of some folks that go and be BDRs, SDRs, I have to think how are they handling that conversation right, with the key decision maker on the other side.

And then last question on your role. What are the metrics that you track yourself toward as a GTM leader?

[00:27:40] Junta Nakai, Databricks: Yeah, so I track, we use Databricks on Databricks all day.

[00:27:44] Ryan Zauk, TWIF: Meta.

[00:27:45] Junta Nakai, Databricks: I have a lot of insight about trends and mm-hmm. You know, all this stuff. But at the end of the day, we are a consumption driven business.

Okay? It's like the electricity meter, right? So unless our customers use Databricks, we don't get paid. In other words, our incentives are extremely aligned to our customers. Meaning, unless you are successful. Whatever you're trying to do, we simply do not get paid. Right. Right. So one of the key metrics that, that I look at is how many use cases, again, these business outcome use cases, are we driving at each of our customers?

If you look at similar customers, what are the use cases they do? So for example, if the top three use cases for, let's say a, a retail bank. Our cybersecurity risk management and, and hyper-personalization. Let's look at the cohort of all of our customers in this segment. Which ones are we doing only one or only two and not three?

And for those, how can we operate into to accelerate those use cases and, and have our customers see the value of using Databricks for those use cases? Right? So I spent a lot of time thinking about. Different segments of our customers. Mm-hmm. The use cases they could be doing and how we could accelerate those use cases.

[00:29:05] Ryan Zauk, TWIF: And then our next section, I want to talk, put out a few quotes that you've said over the last few years. Yeah. Nothing bad. Don't worry. I'll professional. And have you just elaborate on them a little bit on the audience. One of the first one, actually, in our initial call you had said that 90% of AI is unsexy.

I think you've hit on that a little bit already, but can you elaborate on what you mean by that?

[00:29:26] Junta Nakai, Databricks: You know, like I said. With bad data, you make bad decisions with high confidence. So the most important part about ai, you know, you hear about hallucinations and all this stuff, right? The fundamental thing you have to get right before you do any of this stuff is ensure that a, can I even use this data?

Is it accurate data, right? Is it up-to-date data? These are kind of the foundational building blocks before you could do what, you know, what we say? I guess sexy ai, right? That people kind of are focused on. That's what we mean by that, and that is the, the vast majority of how our platform is used, especially in financial services because like I said, very few people have figured out how to do the sexy AI stuff, right?

Especially in financial services. So that's what we mean by that is really helping customers. Do that. Right? AI is not a magic box that you turn on and all of a sudden everything is one of them, right? There's a lot of hard work.

[00:30:18] Ryan Zauk, TWIF: Right?

[00:30:18] Junta Nakai, Databricks: It's like going to the gym and doing reps

[00:30:19] Ryan Zauk, TWIF: have to, and then so following up on that, you had said, you know, data governance is the most important thing for a regulated institution.

Over the last couple of years we've seen seemingly dozens of companies spin up going after. AI and LLM observability, evals tracing, performance building GRC and compliance frameworks. Where does Databricks play in that space? What can you offer to customers?

[00:30:43] Junta Nakai, Databricks: At the end of the day, everything comes down to, do you know where data is coming from?

Who is touching it? Where is it going, and how is it being used? And then can you go back to it? Can you discover it? Can you explain it? Can you have the lineage? That's the key, right? So what Databricks is providing is, is specifically a technological called Unity catalog that helps you govern all data assets, right?

Not just tables, or not just specific data sets, but AI models, right? Dashboards, right Data feeds, lots of different things. So we have a open source, uh, we open source, a big chunk of it, of something called unit catalog that enables you to do that seamlessly. Bring AI and gen AI to the governance process.

Mm-hmm. Right? I'll give you an example. What if generative AI can look at tables and explain what's in there, create the metadata, right? For that would help tremendously with discoverability of that data, right? So that's just an example how you could actually bring AI into the governance process to make it easier.

Mm. For companies to do. So it's like, it's kind of the foundation for, for all of this is making sure that the data is trustworthy. Mm-hmm. And that's what we provide.

[00:31:58] Ryan Zauk, TWIF: Got it. And then last one, if the period of low rates and high growth that characterize the last decade are truly gone, cloud, open source and AI become must haves no longer nice to haves?

Yeah.

[00:32:09] Junta Nakai, Databricks: Everything is

[00:32:10] Ryan Zauk, TWIF: easier when money

[00:32:11] Junta Nakai, Databricks: is cheap.

[00:32:12] Ryan Zauk, TWIF: Yeah.

[00:32:12] Junta Nakai, Databricks: And when rates are high, growth is a little bit lower. There's more volatility in the marketplace. You know, again, going back to what I said, it's become productivity becomes the game changer. Right? So if you look at the course of human history, two things have really driven prosperity for us.

[00:32:32] Ryan Zauk, TWIF: Mm-hmm.

[00:32:32] Junta Nakai, Databricks: Right? It's population growth and there's productivity gains.

[00:32:36] Ryan Zauk, TWIF: Yeah.

[00:32:37] Junta Nakai, Databricks: Now, one of those, actually, both of those levers, at least for the last two decades or so. Have fallen off a cliff, right? We have a productivity crisis in most countries, right? Productivity has grown, not grown, uh, or has grown almost half the rate as it has been, let's say in the previous two decades.

In the last two decades. Um, if you look at what I was reading, this McKinsey report, they call f first wave countries, so some parts of Western Europe, Japan, Korea, et cetera, China, um. For this, what will they call first wave of demographics countries. Populations already peaked, right? A few years ago. Right.

So this kind of natural driver of population growth that we had in the developed world forever is now turning right. And productivity is also very low. Right. So you could have one of, you could do one of two things in a situation. Hope people have lots of kids, but unlikely. Right, right. I was in Korea a few weeks ago.

The total fertility TFR, total fertility rate in Korea is 0.75. One of the lowest in the world, and that means

[00:33:34] Ryan Zauk, TWIF: per couple, it's 0.7 funds. You need

[00:33:37] Junta Nakai, Databricks: 2.1 to sustain a population. Right. So Right. You know, Japan is often thought of as a poor demographic country, but Jesus, people don't realize in East Asia, Japan has the best demographics, which is

[00:33:48] Ryan Zauk, TWIF: great.

'cause

[00:33:48] Junta Nakai, Databricks: I feel

[00:33:49] Ryan Zauk, TWIF: like there is you viewed as

[00:33:49] Junta Nakai, Databricks: the case study as the worst.

[00:33:51] Ryan Zauk, TWIF: Yeah.

[00:33:51] Junta Nakai, Databricks: So the us, Taiwan, China, South Korea. Okay. A much worse, uh, fertility rate. Of course, Japan is a little bit earlier on how the demographic trends shift. Mm-hmm. So what that means is that productivity is the only thing that you have, right?

Right. And this is what data and ai at the end of the day, if you think about why are people so excited about generative AI agentic systems, it really comes down to the word productivity. Mm-hmm. Right? I believe personally, the next decade is going to be the most prosperous decade in human history. Not because of population growth, but because of the massive productivity gains that we're gonna see.

Mm-hmm. From. Governments from educational institutions, from companies, from healthcare companies to academic research, medical research, et cetera. And, and that is because. Like I said before, using kind of block as an example, right? We are finding out that people are gonna be significantly more productive

[00:34:45] Ryan Zauk, TWIF: mm-hmm.

When

[00:34:46] Junta Nakai, Databricks: they are augmented with ai.

[00:34:48] Ryan Zauk, TWIF: But then how do you, I guess, compare that to the existential fear of how efficient are we going to get? How many, how fewer humans will be needed for tasks? Right. And the potential employment crisis that gets pulled. I think so.

[00:35:00] Junta Nakai, Databricks: I don't know in the long run, to be honest with you, but in the short run, I have not seen.

The need for people to go down. But I do think what has happened is the type of skill sets that you need to be successful has drastically changed.

[00:35:16] Ryan Zauk, TWIF: Mm-hmm.

[00:35:16] Junta Nakai, Databricks: Right? So it's not that you need less of them, you need a different type of person. I'll give you an example. I was meeting with the chief technology officer of a large financial service institution.

I spend a lot of time with these CTOs at large, and they had rolled out. Kind of chat GBT functionality to their engineers. Mm-hmm. Okay. And the expectation was everybody's gonna get better, and maybe one day they're gonna be so much better, we'll need less of them. Right. And he was wrong. And it was true that on average people got better.

That is true. But he also found that the bottom 25% of coders actually got worse. Wow. Because why? Well, they're just blindly copy and pasting things without really understanding, you know, what, what was happening. The top 25% probably got tremendously better. Yeah. And it's not necessarily because of the reasons that you think.

It's not just because they're, you know, coding or doing us, it's because they figured out that Gene AI was not a replacement for doing their job. Just a way to tackle some of the gaps that they might be having. So if you're bad at writing, you use it to help you write emails. Right. You know, if you're bad with creativity, you help you to kind of nudge you and, and kind of think about things in new ways.

These are the people that got ahead. So the number of people they need to do the job still remains, right? But then they kind of looked at them and said, Hey, these are the type of people, and these are kind of more like soft skills almost, that we want more of because we found that these folks, when coupled with technology can be extremely productive.

Yeah. Right. So it's change their hiring process, change. How they change, how they do interviews, change how they evaluate people. So the total number of people remain the same. But the desired skill sets of a successful employee has changed.

[00:37:02] Ryan Zauk, TWIF: Mm-hmm.

[00:37:03] Junta Nakai, Databricks: And I think that's what's happening, um, today

[00:37:05] Ryan Zauk, TWIF: across the board.

Mm-hmm. So what would you suggest then? We mentioned you have a young children at home, they're trying to future proof their careers at some point, obviously, you know, toying with ai, integrating it however they can, or they're near their skills that you can think of. For the younger generation,

[00:37:19] Junta Nakai, Databricks: it's very hard to predict like what skills are gonna be the most in demand, right?

Yeah. Because for the last. Two 20 years, you computer science, encoded computer science and Right. Maybe it's questionable whether that was the best advice to give somebody. Right. But what I do think happens is as AI becomes all around us, the characteristics that make us human become way more important.

Mm-hmm. Not less. So think about, you know, creativity, communication, coordination. Right. But the foundation of the most successful people going forward, I believe is going to be something super. Intangible, which is self-awareness. You have to be able to look yourself in the mirror and be very honest about what am I good at?

What am I not so good at? What are the gaps that prevent me from going where I want to go? And that serves the foundation of using AI to help, like I said, close those gaps. Right? So what happens is these kind of human things become. Incredibly more important and kind of like what said, how do you get ahead when everybody else sounds like a genius over email.

Right, right. Or for phone call. So true. Right? So these are kind of things and, and kind of corollary to that ultimately becomes things like your reputation, right? Because. There's gonna be a lot of disruption in careers going forward. Right. Just because if you're, you're good at one thing, doesn't mean you're gonna be good at the next thing and you're gonna have to switch jobs and pivot careers because the pace of disruption is going to accelerate.

Yeah. And each and every one of those times, you have to be aware enough to understand what's happening in your business. Right. And then you have to have this kind of reputation where people are willing to take a bet on you that says, Hey, you know, this person did this in the last three years. The world is moving this.

I think he or she can also do this. Right. And reputation is a currency that's traded among people that make decisions about you. And those people are gonna remain people, I believe, right? So these are kind of the soft skills that I think, you know, they're gonna become more important. So I guess the, a very long-winded answer would be master the things that make you human and

[00:39:27] Ryan Zauk, TWIF: then last.

Question before we get to the rapid fire round. More on the personal side, outside of work, you run a sake distillery out in Brooklyn called Brooklyn Kura. Am I pronouncing that correctly? Brooklyn Cora. It's a, it's a brewery. Brewery sake is brewed, not distilled brew. Oh man. My ignorance is showing. So I would love to understand what was the story behind this saki brewery and inspiration?

Sure. So

[00:39:50] Junta Nakai, Databricks: when I was having my existential crisis working at Goldman and thinking, Hey, this job is gonna disappear, um. I kind of thought to myself, well, I could take one of two paths, right? It's like I could try to be the disruptor instead of disrupted, right? So maybe I should go work for something like ai, right?

Which I did. The other thought I had is maybe I do something that cannot, cannot be automated. Yeah. With ai, and it turns out sake making is an incredibly manual process. It's a labor of love, right? So how I got there is I met these folks. Their name is Brian and Brandon. We're incredibly talented and. They were Goldman or out in the wild?

No, they were two, uh, two hipster dudes in Brooklyn. And how did you meet them, if you can share? So I got introduced them, um, through one of my neighbors who said, Hey, I know two guys who are starting a sake brewery. I said, great. Are they Japanese? No. I said, sounds said. That sounds like a terrible idea. Uh, but then I met them and I was like, wow, they're a very talented, and, and BI think this is a very interesting market, right?

Like it's growing. And I thought sake can do what Tequila did. Tequila used to be cheap and available at Mexican restaurants. Right. 20, 30 years. Today it's ubiquitous and it's high-end and sake is the same. Right. People maybe get introduced to sake when they're sake bobbing in college. But you know, sake is an incredibly complex drink.

It has three times the range of white wine. Wow. So it could be paired with sashimi that's very light or. Kobe beef, that's very, you know Right. Wagyu, that's very heavy. Right. So it's, it's like extremely wide range. And I thought, oh, what an incredible, uh, product that is probably underappreciated in the market.

And, um, that's how I got involved and became an investor. And uh, it's been great. So we have a big sake brewery in Brooklyn. We have a tap room. And it's expanded quite a bit, um, over the

[00:41:39] Ryan Zauk, TWIF: past seven years. That's awesome. And a lot of our audience is in New York. So where exactly in Brooklyn is it? It's in industry city.

Industry city. In Sunset Park. Yeah. When is it open? When, uh, the

[00:41:48] Junta Nakai, Databricks: tap room is open? Thursday, Friday, Saturday, Sunday. Okay. And you can do tastings. There's multiple types of sake that, that we create, again, sake is an extremely manual labor of love. Right. That's awesome. So when Brandon, who's the master brewer, when he produces sake.

He stays overnight. Right. And he has to wake up every few hours to tinker with the temperature and, and all this, it's a very precise production process. Um, and I think the out output and the quality is, um, as good as anything. Mm-hmm. From Japan, if not better in some cases.

[00:42:23] Ryan Zauk, TWIF: Wow. Well, I'm excited to hopefully try it soon.

All right. June two, you've made it to the last part of the episode, which is the rapid fire question, round a bunch of questions, more on the personal side rather than professional. Usually 10 to 32nd response Max per question. First off, what was the first job that you ever had at Goldman? Even summer

[00:42:40] Junta Nakai, Databricks: jobs.

All that is a yes. That makes me sound very privileged, but yes, uh, Goldman

[00:42:45] Ryan Zauk, TWIF: was the first job. All right. I never had coming outta college. What about the worst? I guess you've only had three jobs where? I was gonna say, worst job.

[00:42:53] Junta Nakai, Databricks: I would say the best thing that's happened to my career was getting a job at Goldman Sachs.

The second best thing that's happened to my career was leaving Bo Casac.

[00:43:04] Ryan Zauk, TWIF: Love it. All right. New York food. What is your favorite restaurant in New York City? I love a lot of restaurants in Brooklyn.

[00:43:11] Junta Nakai, Databricks: There's a particular restaurant in Greenpoint that, uh, I go to quite often called Rule of Thirds. Um, they have, they like the

[00:43:18] Ryan Zauk, TWIF: photography, uh, rule.

[00:43:19] Junta Nakai, Databricks: That's right. You know. Yeah. They have, it's a Japanese restaurant. They typically have Brooklyn Kura, and, uh, I just think that the space is beautiful. I think the food is great. Just whenever I go back to Brooklyn.

[00:43:30] Ryan Zauk, TWIF: Yeah. All right. I tend to go there, adding it to the list. And then what about books? Are there any books that you reference Both business wise and then personal side?

I recently

[00:43:38] Junta Nakai, Databricks: read a book called Pachinko. It's about these Korean immigrants to Japan. Um, and it's a fiction, but kind of the hardships that they go through and and their lives, and I thought was super. Fascinating. Mm-hmm. Um, because people think of Japan as a very homogeneous country, but it's actually in some cases fairly diverse.

Right? There's lots of, um, Korean descendants in Japan who've been there for generations and it chronicles that story. So, um, that's a book that I read. I think. I thought that was a fantastic. In terms of nonfiction business books, gosh, I read a lot of those, but I don't know. People have polarizing opinions.

Some read a lot, some zero I read them, but like. At the, at the end it's like, can you really put them into practice because it's colored by such a unique set of circumstances that probably cannot be replicated. Yeah. So like to me, I spend a lot more time reading the FT and Wall Street Journal, right?

Trying to understand what's happening and then trying to figure out my own opinion about things and tailoring it to what I think is, is the business or what's most tailored to my business. And you know, my biggest philosophy. Right now is to just make big bets. All right. You know, when you, you have very limited time, you just have to make big, big bets and you know, focus on the things that matter and try to distill the noise as much as you can and make those two or three things as successful as you possibly can.

[00:45:03] Ryan Zauk, TWIF: You talked about Wall Street Journal and ft. What is your news diet? Are there any sources that you find highly differentiated in extracting that signal from noise? So

[00:45:12] Junta Nakai, Databricks: I read. A lot of news. I do not read social media very much. Um, actually deleted most of them because I realized that that's why you look so happy all the time.

Exactly. Um, but you know, really the best source of information you could have is your network. So they say network is network worth. Right. So I try to keep up with and connect with and maintain relationships with people who are. In my field, but also vastly different from my field because the things they care about and the things they think about are totally different.

So I'll give you an example. When I started getting involved in Brooklyn Co, it was fantastic because I started to meet all these people in the food and beverage industry. And before that, most everybody I knew worked on Wall Street, and it was such an enriching experience to. Engage with people and learn about new things.

And it's the same thing in tech. I also look after our public sector go to market and our cybersecurity go to market. So I spend a lot of time meeting people in those places and of, and of course going down to DC and talking to customers and people about what they think about and what they care about.

Vastly different. Right? And you talk to our customer in banking, like I said, what do they wanna do? Right? They wanna drive revenues, reduce risk. You talk to a defense agency, what do they want to do? What, what is their mission? Vastly different things they think about, right? Yeah. So that's been an enriching thing.

So my thing really is to try to meet as many people in various fields as possible, both domestically and internationally. Maintain relationships with them and exchange ideas. And I think that's the the best way to learn things that you know, you can't learn. Because if you're reading what everybody else is reading, then there's no edge, right?

[00:46:51] Ryan Zauk, TWIF: All consensus. All right, last two questions. First one. What are any startups or emerging companies that you've been most inspired or excited by recently? You can't say Mosaic ml or any, any other recent acquisitions?

[00:47:04] Junta Nakai, Databricks: I was in Brazil a few weeks ago, and I met with, uh, a FinTech called PicPay. Oh, yeah.

Yeah. It's not really like a early startup as you, you know, as, as you, it's huge. That would explain it, but I was just floored just. What can happen when you build a financial service organization that is data and AI forward, and I was in a room with a few people and it's so ubiquitous there. Everybody in the room took out their took and opened it and they all tried to borrow money.

Same amount of money, right? A few hundred, a few thousand. I forgot what it was. And almost everybody had a different installment plan. Wow. Right. Based on their transaction history and based on this. And there are billions and billions of possible personalization combinations that are available on their application for people.

And I just thought about that and saw that and just compare that to a place like the United States. Where, you know, you kind of have these blunt instruments, so blunt, right? Yeah. It's like, hey, you, here's the 30 year mortgage, you know, chunks. Exactly right. And, and I was just like floored by what they can do, um, when you have data and, and ability to use that and do mass personalization, right?

Mm-hmm. And that's kind of like the, the main thing I was super impressed by. And the reason for that is, you know, if you look at NPS scores, so, so how happy customers are with their financial product. There's usually a very tight correlation between how many customers you have and how happy they're meaning.

The retail bank typically has the worst NPS score, but the high net worth institutional banking has the best one. Right? Why is that? Well, institutional banking can, and high net worth banking can be solved with bodies, right? You could hire lots of people who are seasoned, who are very smooth. They could take you of golfing, all this stuff in retail.

You cannot do that to the masses, right? So it's about how do I use technology to figure out how to do personalization at scale and meet, make people feel important and make, make them feel like their product, they're tailored to them. And when I met with Pick Bay is one of those rare examples when I said, wow, this is the power of what data and AI can do for customers.

Like I said, I think it's gonna be a. Awesome decade to be a customer.

[00:49:36] Ryan Zauk, TWIF: Mm-hmm.

[00:49:36] Junta Nakai, Databricks: Of financial services, not just working in financial services. And that was

[00:49:39] Ryan Zauk, TWIF: one of the, um, the great moments that I've had. That's awesome. And last question here. Who is your dream guest to hear on the, this month in FinTech podcast?

[00:49:47] Junta Nakai, Databricks: I think you should try to get, show Ani to, uh, come on your podcast there. You sponsored by DraftKings. Yeah. I mean, I mean, he's just, uh. Incredible. Yeah. That's like once in a, once in a lifetime type of person. I'm a huge fan and, and I've never actually heard him, like I've actually seen him in Japan do interviews in Japanese.

He is, you know, it's really interesting. I've never heard him do a real interview for a US media outlet. Okay. And, uh. I think it'd be awesome if you could get him. That would be getting me autographed. Then. Even better,

[00:50:16] Ryan Zauk, TWIF: I'll see what I can do and uh, maybe if he ends up Angel investing in a ton of FinTech apps like the last generation, uh, I'll see if we can get him on.

Well, Junta, thank you so much for coming on today's episode this month in FinTech podcast. Great conversation. Excited to share it with our global audience.

Thank you for listening to today's episode of the This Month in FinTech podcast. If you enjoyed today's episode, please like, follow, subscribe, or rate us across your preferred podcast and social media platforms.

Lastly, I'd like to thank our editor Evangelo Markous, for his great work on our episodes. Signing off. I'm your host, Ryan Zauk.