How Databricks Revolutionize Intelligent Enterprise AI in ASEAN with Patrick Kelly
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We did a survey with The Economist globally, obviously including Europe and APAC as well. And we asked the question, my organization's current architecture supports the unique demands of AI workloads. And basically 85% said, no, we don't have the architecture to support it. Some partially does, but it needs lots of modifications. So we can still feel a lot of people are still in the early stage. And that kind of data point ties back to 85% of Gen AI has not gone into production yet.

And I think another interesting point is, does your architecture connect AI application with your relevant business data, which is probably nearly even more important for me. And again, it was still about 80%. We don't have that because that business data is all over the place. Without the clean data, you cannot get good AI.

Welcome to Analyze Asia, the premier podcast dedicated to dissecting the pulse of business technology and media in Asia. I'm Bernard Leong, and I often inform the decision makers in businesses that data is important for artificial intelligence to work. How do we ensure enterprise AI applications power the businesses in Southeast Asia? With me today, Patrick Kelly, Senior Director for Digital Natives, Startup and Enterprise Commercial Sales in Southeast Asia from Databricks to discuss this subject,

Patrick, welcome to the show. Thanks, Bernard. Great to be here. Great to talk to you again. I also probably should also inform you, we were ex-colleagues and you were probably my mentor and boss when I first joined AWS and really guided me through the launch process. So thank you very much, Patrick, for doing this with me. Yeah, those are fun days. We can talk about that today for sure. Sure. Of course, without doubt, we always start with the origin story of our guests. So with your origin story, how did you start your career?

Yeah, actually, surprisingly, I've been in sales now quite a long time, but actually I did start in technology. I was a network engineer when I first started my career, working a lot in enterprise networking, Cisco, Juniper, kind of doing banking systems and stuff like that as well. So it was pretty interesting.

I was really into that. Then I moved into telecom. So I joined the Ericsson, which is the big Swedish telecom equipment vendor. That was, I'm Irish, right? So it was based in Ireland. But then I joined like the professional services team, global services. So I did projects all over the world. It was fantastic. Like in Brazil, in Jordan, in Australia, and then landed up into Japan. As I say, I went to Japan on like a four week assignment and I ended up staying five years. So that was pretty, pretty fun.

I think in Japan was interesting because I went from then being engineering into consulting. So network consulting, it's like for SoftBank. So back then we, LTE was just launching right back in like 2009. And SoftBank had the exclusive rights for the iPhone. And the iPhone was a new beast on the network, right? It was all the signaling. We didn't know how to handle it, right? So we did a lot of consulting work for SoftBank, how to manage the load in Tokyo and really high densely populated areas. That was pretty cool.

And from there, then I moved into sales, like selling the services, selling the hardware, selling the software. And then from Japan, then that brought me over to Singapore because in telco, it was like Japan and Korea are always number one. They're all first with 5G, first with like all the new technologies. And then we took that when we started doing virtualization on telecom networks, we then brought that over to Southeast Asia with like Singtel and TelcomSell and yeah, brought that all around the region as well.

And from there, after about nine years, I had a great time. But then I went to join the startup world. So I joined the startup doing IoT, which was Jasper at the time. That was a really interesting role. And that then led me into AWS, which just spent about five years doing different roles. Started with the IoT business,

Then we did the analytics business together, machine learning business, Bernard, and then took on the ISV business, which is our B2B software sales, like in the sales motion. And then we collaborated a lot. Yes. A lot. Yes. I remember in Malaysia, we did a lot of good stuff for Malaysia with case studies as well. Yes. And then finally, the role was both ISV, but also digital native as well. So the likes of Grab and Traveloka and other iconic B2C ASEAN customers.

And then it led me to Databricks, where I'm kind of doing a similar role in that digital side. But then I also have more of the, we call it commercial, right? Emerging enterprise. So traditional companies, but very big across Southeast Asia who are trying to understand how to use data and AI to solve business problems. So how do you actually come to this present role with Databricks?

Yeah, I actually was thinking about it when I was in the process of it. And I thought back to our days when back in 2019, when we had the DNA team, right? Data and machine learning, right? Where we were...

ticket talking about those problems right how do we solve problems with data like customers a lot of data but a lot of them was in data lake and a data swamp couldn't get real insights into it and we helped a lot of customers with that right and especially with our machine learning solution lab like solving really tough problems through data science right and when i was in the process with databricks i was thinking about

How do I get the back to something that's really specific and into a certain technology domain? Amazon is fantastic. You have all of the services and all of the technology, but you become super broad, right? Because you are selling 200 services. But at Databricks, there's a very defined focus on it's a data platform. We're building intelligence on top. It's AI as well. And that was something that really excited me of getting there. And also exciting about building a team and building a business again, because you're

I think data and AI now or even last year was probably at the same stage as cloud was maybe six, seven years ago in certain stages. So it's in that early stage of

Customer mindshare and transformation. I totally agree with you. I think the essential parts of AI is actually to do with data. And I find that the integration where we work together between data analytics and machine learning, or even what we call generative AI, tends to now integrate quite seamlessly once the customers actually have a pretty good understanding of where their data structures are. So before I'm going to get to the most exciting part of today's conversation, I definitely must ask you this.

From a career journey, what lessons can you share with my audience? I always think in anything, it's personal over professional, right? So no one's ever going to remember you worked long hours. No one will remember you worked long weekends when you retire, right? So personal goals are super important because your personal goals are going to drive your professional goals down the line, right? So I think that's super important to you as well. I know you're so passionate around NUS and education, right? It's a huge part of it.

of your life as well and I think yeah like a lot of I work with a lot of my team I've got an acquisition team are pretty newer newish in career like early in career and I always say to me you don't need to be in a rush like your time is now the right time don't be looking at other people and thinking about you know I should be like that person enjoy the moment it goes super fast right and then focus on your profession right if you're an engineer if you're a data scientist you have to be expert in what you do coding your skills they need to be practiced I think there's a lot of

Noise around AI will change. Like you won't need software developers anymore. I don't buy that at all, right? Software development is an art in the way of how you create software. And I don't think AI can get there so fast. And definitely more so in sales. Sales is a profession. You need to work at it. You need to work on your discovery, understand customer problems.

go deep to understand that technical pain, translate the business objectives, and then show how your solution can actually differentiate and help them solve those problems. And you only get good at that by practicing, right? I think delivering results is super important. We know that from Amazon, right? So we start with customer obsession, we end with deliver results, and we have all the leadership principles in between. But really, it's a trailing indicator of success. And it really shows that the strategy you have, the tactics you execute on, they worked, right? And then you get the results at the end.

And then finally, coming back to the whole personal piece, just be kind and be a nice person, right? Karma can come around and bite you. So yeah, just be a nice person and I think things will happen. Patrick, you're one of the best people that I've worked with. I'm definitely saying this in public because I think I really enjoyed how you actually guide me through some of the process of when we are thinking about how to actually do sales with the ISVs and what is the mental models behind. So with that to do, get to the main subject of the day, which I want to talk about data breaks in

ASEAN and also within the age of data and AI. Maybe to start, can you talk about now the total market opportunity of AI and data in the Asia Pacific or maybe even Southeast Asia, specifically now for business enterprises and why now Databricks is poised to actually capture this market? Yep.

I think before we even look at the data, I want to look at the cloud market, right? I think in Southeast Asia, talk to different people like Gartner and IDC and everyone else, but roughly it's about $20 billion, right? And probably about 8% to 10% of that IT spend is in the cloud, you know, with hyperscalers, AWS, GCP, Azure, Alibaba, et cetera, right? The Chinese clouds.

For specifically data AI workloads, we think it's about a 1 billion market for Southeast Asia. That's excluding the compute and GPUs. Here we're talking about the SaaS pass piece, right? The analytics software angle. So it's a huge market, right? And it's grown very fast. Like for Databricks globally, we're growing like 70%. And in the region, we're actually growing faster than that. So it's like pretty hyper growth, right, for us. Databricks globally, we're the fastest growing enterprise software company ever, right? And we're still containing that clip

Last run rate globally, 2.4 billion, like a north of 60% growth rate. And really at Databricks, our mission is really to democratize data and AI, helping data teams solve the world's toughest problems. And you notice there I mentioned data. Data is core of everything, right? If you don't have data, you cannot do AI, right?

And where Databricks came from, right? We invented Spark, which a lot of people know, right? Which really revolutionized how data processing worked at scale from the Hadoop days. We pioneered the lakehouse concept, which means you have your data lake and your data warehouse put together, which then drives down TCO and enables self-service analytics. So you get analytics from the cloud cost storage, right? From S3, you don't have expensive data warehouse to manage and deploy. Right.

And then lastly, I think most importantly now, we are talking about data intelligence. So we're talking about

How customers can democratize insights with natural language and build AI on top of their data, their own private enterprise data. They're not giving that away data to anyone. We always say, don't give your data to Databricks. Your data, drive your insights on top of it. That will really differentiate your business. Just to also help my audience, TCO just means total cost of ownership. And I think one of the major milestones was Databricks acquisition of Mosaic as well for

for the AI side. So maybe we should just baseline our audience, given that they are from very different walks of businesses. Can you explain the concept of generative AI and data lakehouses and how they helped business enterprise to actually achieve their business goals? I think one of the things that you have already alluded to is the use of the lakehouse concept that actually drives down this total cost of ownership and the sales analytics very quickly. Yeah.

So with Lakehouse, so traditionally, before we had cloud, we had on-premise data systems, right? So we had a warehouse, right, on cloud. But that was very structured data. Columns, tables, think Excel, right? So you could ask questions, okay? You go in and filter and say, okay, what was my sales report for January? And they're very easy to see, right? So that was the warehouse world. As the internet became prevalent, like across the world, we had...

websites and pictures and images and video and social media and all this data, unstructured data, right? And the cloud came around and we said, okay, how do we store this cheaply? And that was for Amazon. The first service was Amazon S3, simple storage, right? So that storage became very cheap and we put everything in there.

We created Data Lake. So we had a Data Lake and then we had a data warehouse on-prem. So we were like, okay, now I think we can put a data warehouse into the cloud. So data warehouses came into the cloud. We have, you know, Amazon had Redshift, Google have BigQuery, Azure have Synapse. So they've built all these data warehouses in the cloud for the structured data. But we still have the same problem. We still have data silo. So unstructured is in your lake.

all your structured data is in your warehouse and trying to find some commonality between both. It still needs a lot of work from the engineering team, from the data analyst team. They're still churning through creating reports and doing a lot of manual effort. So that was a real problem statement. And also the cost of both you're paying for

storage in your data lake, you're also paying for storage within the data warehouse. And we kind of found, okay, there has to be a better way of doing this. So the founders of Databricks are from Berkeley. They wrote a paper, academic paper, defining how this architecture should look like, what are the key principles around it, and what does it mean for customers? And that's where the lake house concept started a couple of years ago.

So with that, how does the strategy of Databricks actually, with our current architecture, is it dependent on this house concept and how does it evolve into the AI side?

So Lakehouse underpins everything, right? So we have all of your data sitting in low-cost cloud storage, and then all your analysts and data science can work off the same copy of data. So one copy of data, right? That's underpinning everything. Now, as you know from a long time, you're an OG in the ML world, right, Bernard? You were talking about AI back when it was spelled ML. So now, with that single copy of data, now you can...

run a machine learning model you can train it with different data sets but it's not a data set sitting in this environment or another data set here with data drift and then the model you're like you're double guessing the model you're saying okay is that a good outcome because i'm not sure if the data is clean or not so once you have that then you can add intelligence on top of that lakehouse architecture and we're calling that data intelligence so jenny i obviously is

a little bit of the world when GPT came around. We are not forgetting classic AI, you know, those great use cases around, you know, predicting customer churn, forecasting demand, optimizing customer experience. Those are huge benefits for companies today, right? Gen AI obviously will generate content and you'll have smart advisors and in, you know, financial services, robo advisors, which is fantastic. But I think that we should talk about AI in the wider sense of generative, but also classic.

I think it's a very good point. I think whether currently a lot of people just focus on Gen-AI, but they forgot actually there's a lot of very classical use cases that already been able to be coped by basic AI in those use case that you have mentioned. I'm probably very curious. I know that Databricks is a pretty well-known company funded by Anderson Horowitz. And I've listened to a couple of panels done by your CEO, Ali. What is the current business footprint of Databricks in ASEAN?

Yeah, I'll start with APJ. So we've got really five defined markets across the area, starting with India, and then we've got ASEAN, Greater China, we've got that in America, Japan, Korea, and then down to A and Z.

So in ASEAN GCR or for APJ, Singapore is their headquarter. So last year when we did our Data Night World Tour, which is a flagship event in Singapore, we announced that Singapore is now the regional hub for APJ. And we had all our plans to increase Singapore-based workforce, working with Singapore EDB, adding critical roles in field engineering to help customers unlock problems, our professional services, strategy ops, learning enablement, et cetera. So we're building

building out a whole team across all the different functions. And over across APJ, we have over 800 employees with about 150 who are based in Singapore. And part of that investment is really, we are really committed to democratization, right? And what that really means is we're going to upskill greater than, you know, 10,000 data and AI talent within Singapore. And that's a partnership with IMDA, training partners, NTUC Learning Hub and NUS ICT Academy.

And I think you also do some parts with startups as well, specifically with Gen EF. Can you talk a little bit about that? Yeah, super passionate part of what I do. So yeah, back in the middle of last year, we were thinking about, we've got a Databricks for Startup program. You know, we, a lot like the hyperscalers, we invest in them, we give them credits, we give them go-to-market expertise and help them, you know, think about how to build a product on, on

on Databricks. And with ASEAN Genii fund, when that was set up to invest specifically in Genii startups, you know, set up by ex-Amazonians as well, you know, you've got Dan, Laura and Kai there. So we know them pretty well. And we did like a six city tour last year. It was great. It was great for us because we kind of found 500 new companies that we didn't know about that weren't in our purview. So that was fantastic.

And then we've got a lot of them like building with us now. We're investing in them. Obviously, Jenny, I are investing equity in them as well. And now next week and our next two weeks,

April 10th, we're going to do a startup matching. So the startups we've identified as high priority with enterprises. And we're doing that with EDB in Singapore with Google on April 10th. So that's going to be a great event to see how these companies grow. Yeah, I probably also want to highlight that I think now Databricks is actually quite multi-cloud, right? It's definitely not just working Amazon Web Services, also Google GCP and even Microsoft Azure as well. Correct.

Yeah. So how are customers now using Databricks in ASEAN? Can you share some really quite interesting key studies? Yeah, sure. I think it's super varied, right? We've got everything from highly regulated industries like FSI and Telco to some of the largest digital native customers like Grab across the region.

I think Grab is a great one because they really, they work with Databricks for many years building customer data platform. As you can imagine, they've got millions of data points coming in across customers, across ride hailing, across food delivery, across all the different signals of their advertising. So how do they manage all those touch points and build a customer centric experience and then personalize recommendations for all our millions of customers?

I think that's one good example. Another one I think is GetGo in Singapore. I think you've probably used before. Largest car sharing platform in Singapore. That really helped improve customer satisfaction and their fleet utilization. So some key data points, you know, they really accelerated time to insights by 66% for their fleet maintenance. And now they can deliver insights seven times faster, making, you know, really next business day decision making. And also they...

wanted to really figure out, okay, how do customers actually use the car? So analyzing booking behavior, refueling patterns, they could actually reduce fuel theft by 50%.

which was really, really impactful for them and their business by minimizing misuse of food cards and really enhancing like overall customer trust. So that's like, I think two of the more kind of digital type customers. And then I think more and more regulated like GovTech, for example, which I think you work a lot with as well and you know all them. So really in charge of, you know, the public sector, digital transformation, using Databricks to empower sales service analytics and through our

data security and governance because all the different government agencies and being able to all agency just have the access to the data that they need. They really achieved, you know, dashboards could create a three times faster. They could democratize data, 50% of data across corporate divisions and actually saved 8,000 labor hours annually, which is massive for a government agency. Wow. So they actually actually had

as such, it's like taking the entire Databricks at scale for the entire Singapore government. Totally. Like productivity gains is fantastic because you are, the platform is taking away a lot of that manual tedious work. I hear a lot about multi-regional companies like Graph and then the local companies in Singapore. Can you talk about, say, maybe other use cases in other parts of, I think Siam Commercial Bank is also one of your customers, right? Yeah. Yes. I think Siam Commercial Bank is a great customer of ours.

And of course, in the FSI space, you know, being a huge bank and having a lot of data estate and IT real estate. So what we really worked with them was how do we create a seamless and personalized digital banking experience? But the big thing they wanted to do is how can they do a customer 360, which is AI powered?

And that really means, okay, you engage with the bank through the website, the mobile app or in person. It's all those data points are tied together. So if you access in through the web, that is all logged and maps you around and you're not duplicating data. A lot of paper is removed from the process. I think the real game changer for them was how they can do instant loan approvals. You know, even a lot of times today,

You put in the loan, you fill in the paperwork, you sign it in, you have to scan it, and then you send it off and wait two weeks, right? For it to come back. But that is like now it's one click process because SCB have built a profile of you, built a risk profile, know your income, know your spending, and be able to predict, will you qualify for this based on predictive analysis? And out of this, it becomes,

like the customer experience was fantastic, but they've seen the twofold increase in approval rates for their digital lending products. Yeah. So I think this kind of AI credit score, I remember in those days when in AWS, we talk about it, but now I think I see in action is quite a, quite interesting outcome. So what are like unexpected insights or maybe challenges that you have learned from your customers in the ASEAN region now? I think like customers are an ultimate source of feedback, right? So they really, one of our principles at Databricks is really obsessed over customers and,

And we are founded by academics who build products and are scientists. So when we build the products, we really take into the requirements of the field and what customers are telling us. And we build that back into the product capabilities and features. So we get feedback on our streaming service. We get feedback on our warehouse. We get feedback on our UI.

So for example, Databricks Assistant, which is our UI, which will have coding pieces and the tables, a lot of that is feedback from our community and from our customers. Okay, that's how you get the feedback. So I'm going to switch gears a bit, but given now we have proliferation of foundation models and AI agents, I guess, how does now Databricks think about its position within the market itself?

Yeah, I think the acquisition of Mosaic AI for us was really a game changer in how we think about AI and also how we think about, you know, the talent that we brought into the company. We have a very deep and talented research team at Mosaic AI who are really solving our problems at scale, right? Especially in the science part, right? And what that really brought us was thinking deeply around enterprise quality,

And when I say enterprise quality, something that has to be really robust on security, governance, and be able to deliver that at a very low cost to serve.

So really our position is really that you should maintain full control over your data and your model. You should not put that away to any low SaaS model or model that is out in the market because we feel that then the data that you're using to train someone else's model, if you're in retail industry, then someone else can use that model and can potentially benefit from the data that you've used to train that model. So we really should really maintain control. The

The next thing is really production quality at scale, right? So scale in an enterprise is you need to have that capability, but you also need to manage the quality, hallucination and toxicity, right? That's super important. And a lot of that is then refined from what is your governance practice within the enterprise so you can control that as well. Cost I mentioned already, right? Really drive that.

cost of that. Obviously, we're a big partner of all the GPU providers and NVIDIA as well, so we can help on that as well, and obviously with the cloud providers as well. And then like native support, we've built out a Gentic framework end-to-end. And the idea is that we want to, like everything in technology, abstract away a lot of technology. Hopefully, you don't have to think about RAG and vector databases and embeddings and weightings. You can never look to a person that, I've got a business problem. I'm trying to develop a

internal knowledge chatbot for my HR, I should be able to just roll that straight away and then the platform will take it underneath. That's the framework that we're building. So it's actually in the Databricks viewpoint, it's actually those layers are being extracted away from the customer so that they can basically just focus on getting what they need specifically out of their data. Could be insights, could be specific kind of analysis on their

all that and also am i right to say that the large language model you have the db rx are also currently being deployed as well on the data breaks the architecture as well yes yeah so dbx was dbx was the best performing model i think for about 10 days until the next model no it's going to come back again so every foundation model every other week i'm getting like this model performs better than the other model so i'm going to expect a better model from db rx at some point

Yeah, yeah. Well, the purpose of it was not to show that the best performing model, it was to show that you could do it at a cost effective way. Yeah. Ali, when he talked about it, when we launched it, we trained that model from scratch and did a lot of optimizations, especially with the mixture of experts model, especially how you call an expert for coding or an expert for English or an expert for math. Yeah. We did it all for $10 million, which is pretty amazing at the time.

It's pretty impressive for enterprise AI application. I talked to various enterprises who are using Databricks and I think it's pretty interesting that, I think it's what I call a full enterprise driven AI model. And I think very few foundation models are thinking about

because I think they're trying to cover all things for all people and such. So what's the one thing you know about Databricks in Asia that very few do? I think, like, obviously, the technology is there and we presented our events and we obviously show the tech and we work with customers and partners, et cetera. But I think one thing that

We are extremely diverse company, right? With people, lots of different cultural backgrounds. So for my team, like I've got obviously Singaporeans, Indian, Thai, Vietnamese, Indonesian, French, Irish, very diverse, very diverse background of culture, which is fantastic, I think for the team.

But then from company experience, we've hired people from all sorts of backgrounds. Like, of course, we've got hyperscalers, you know, myself from AWS, we've people from GCP and Microsoft. But also we've got a lot of people from like application vendors, like software, Salesforce, Workday, of

Observability vendors like New Relic. We've got people from system integrators who show, who bring experience of building solution end-to-end and implementation plans. I think that diversity really helps us learn from each other and help to serve our customers better. So for example, a lot of people may have been selling CDP solution and know all those use cases inside out. Or someone has come from an SI and implemented really complex on-premise data warehouse migration and we can learn from that as well. So it really helps us

be a very holistic team around delivering the data intelligence platform for customers. So I think one curious question now I really have is thinking about the trends in generative AI and data. What are the trends that are globally or even locally that you have seen that are becoming important for business applications, thinking about their enterprise AI deployments? Yeah. I think the main thing with GenUI now is, of course, agent is a new buzzword, right? It's a new buzzword. It's a new buzzword.

We'll call it a buzzword. Yeah. Well, we're seeing actual deployments, right? So MasterCard have just deployed the agent framework for their digital payment assessment after doing like 300 POCs, which they openly talk about because it was the last 18 months of years of testing, seeing what works and

Like our last report with The Economist, we did found that still about 85% of Gen EI projects were still POCs and didn't go production. But I think we're seeing the first stages of actually production now with Agent, because Agent is end-to-end. Where Gen EI to start was very point, okay, it's LLM and I'm just doing this. You need to have the whole end-to-end framework. I think another trend we're seeing, and actually I was speaking to a big GSIS yesterday about this,

A lot of customers, they want to access all their data under management. So they want it all in one place, right? They've got data everywhere. It's in the data lake. It's in data warehouse. Some is in SAP. Some is in Salesforce. Some is in different platforms, marketing platforms.

They want it all in one place to see how can they actually get better insights. Actually, that's something we're working hard on, which is something called Lakeflow Connect, where we connect into all these systems and pull only the pertinent data to get the answers that the business wants. It's basically a data connector layer, very similar to, I think, what Entropi now calls the Model Context Protocol, MCP or something like that. Yeah. And then super important is governance and security. It's like, everything, you have to start...

That is key. That is at the start. It's not a bolt-on. If you don't have your government set up on your data to figure out, okay, who can look up what is the lineage, who can access what at what time and be able to trace that back to your system, especially in enterprise and a regulated industry, if you have any breach, you're going to be in trouble, right? So we think that the

Governance is such an important part and that is non-negotiable. And we always say that at AWS as well, security, job zero. Yeah, governance is very important. Definitely. And I think a lot of the senior executives when I train in AWS focuses a lot on how to think about guardrails with the data. I think that is probably one of the key concerns for enterprise customers. And I think a lot of companies are not thinking, who are serving these customers, don't really think about how important that element is on there. But how about locally then? Is

in terms of trend lines for generative AI and data? Yeah, I think different stages of adoption, right? So I think like really advanced big digital native businesses, right? You know, they built data platforms over many years and they built AI stacks, variation of self-built or built with cloud providers or built with Databricks as well. So they really help us, really challenge us on delivering better performance, better price performance.

better outcomes, challenges on the agent frameworks, which is fantastic. Again, giving us feedback about our product as well.

I think on the enterprise side, I think a lot of them are just, they're really fed up with their legacy on-prem warehouse. It's a really cost on the business and getting data out is super laborious. So a lot of them are looking to migrate to the cloud, build their lake house and build a lake house on the cloud, which will then drive those insights. I think the key part of how we're helping customers there is we did announce an acquisition of BladeBridge. So BladeBridge is a data warehouse analyzer tool. So analyze your on-prem data warehouse and

look at, okay, what that would look like, and then it'll be able to convert the code into the cloud. So it's a really great way of moving into a cloud-based type lake house. And then like, I think as we move into mid-market SMB, they really just want access to their data to drive their business without having a heavy lift. So they're looking for a cost-effective BI solution. How can they really get a really insight to their data, allow self-service analytics at

ask questions of it. And there we're providing, we have our genie assistant, which, you know, asks a natural language question. And then we enable to build dashboards. So we call it AI BI. So we're kind of rethinking the world of BI where it's dashboard. It should be dashboard and ask questions. And you can actually, should be able to draw a dashboard and pull in data point that you like.

Right. So essentially, this AIBI is kind of the fusion of the earlier iteration of, say, business intelligence to be able to get your analytics, but then now having the AI to power the insights on top of that.

Is that how I understand it correctly? Yeah, correct. So you could just put in a prompt and say, okay, give me the sales report for the last 12 months and graph it in the bar chart and away you go. Yeah. That's all your data, right? Correct. I was looking at some of the reports out there from, I think I saw one of them on Databricks. I think you also talk, I think you also have

talk about there's some data relating to ASEAN specifically on the current architecture and also how the ASEAN countries are viewing Gen AI as strategically important. Can you talk elaborate about that or maybe give me more color about the matter? We did a survey with The Economist globally, obviously included Europe and APAC as well. And we asked the question, my organization's current architecture supports the unique demands of AI workloads. And

basically 85% said no. We don't have the architecture to support it. Some like it partially does, but it needs lots of modifications. So we can still feel a lot of people are still in the early stage. And that kind of data point ties back to, yeah, 85% of Gen EI has not gone into production. So it kind of tops off.

with that as well. And I think another interesting point is does your architecture connect AI application with your relevant business data, which is probably nearly even more important for me. And again, it was still about 80%. We don't have that because that business data is all over the place. And like as we've

we talked about this for many years. Without the clean data, you cannot get good AI. So it's the same infrastructure question I think hasn't been really addressed, basically. I would agree, yes. Yeah, I think more business owners should talk to you then. They should talk to me, yes. How about like perception of ASEAN countries in terms of the strategic importance of Gen AI then? Yeah, I thought this was super interesting. So,

So along with like UK and Japan, ASEAN is up there at around 70, high 70, 80%. Gen EI is critical to their long-term strategic goals, which is really interesting. Whereas compared to Korea, Korea was in about 65%, which is a little bit surprising for me, being Korea being a very high-tech company. So it really shows that in ASEAN, a lot of companies are thinking about how do I use Gen EI to really differentiate business, grow my business, and actually drive growth

Yeah, it drives growth for my company, but I think it also drives a lot of growth for the countries as well. You know, we'll drive GDP, we'll have startups, we'll have businesses, we'll have training. So I see like the societal impact is going to be pretty huge. I think, so I think just now we talked a bit about the large language models. I think more from the perspective of now which new model comes out and et cetera. But let me flip the question a little bit. From your perspective,

What should businesses be thinking about with foundation AI models? For example, we talk about Databricks, DBRX, and then how do they actually think about the agentic AI workflows as well? Because I think there's still a lot of education that we need to help decision makers to think about these workflows as well. There's always going to be a new model, right? We launched DBRX and all that intent we could show.

A leading LLM could be trained and tuned like for $10 million. Yeah. Through that deep seek, which kind of rocked the world a bit, which was fantastic, right? Yeah. Showing how you can innovate with technology limitations. And then we have Manus, which is showing...

an agent working across multiple different platforms, which is super cool as well. But our belief is that you should have a platform with your clean data. You should have access to all of these models, which we provide. So we've got an API gateway, which you can then decide, okay, I've got my data set here. I want to...

Run a GenAI application. Should I use GPT? Should I use Cloud? Should I use DeepSeq? We provide you that capability. We think you should choose the best model fit for that use case. We provide you that opportunity to use that. Yeah. I'm sure that now we've seen everything is going to go to the applications there. So I'm going to ask you this question, Ed. What would be your advice for business owners in thinking about implementing AI applications in their organizations?

For the AI application, it really depends on who the

the product team is in your company, right? I think a lot of companies now, if you think of the digital native, they have teams, right? They develop products. There's a chief product officer and there's product managers. Probably a bit less so in the enterprise where it was probably sitting under an IT team or some application team or customer service team. I think they need to really think about what is the product and what is it doing? And with a product design, then you'll start thinking about what the technology maps underneath. Right.

So it's kind of like you think about there's a lot of proof of conceptions over the last two years, right? And actually, how much is the percentile that's really in terms of production and how can we drive them towards the production workloads then?

Yeah, I think over the last, it was, I think it was 85% from the last time we seen from Conrad's report. And I'm seeing that as well in the field. We did a lot of, a lot of POCs last year on internal knowledge, chat spots, external, some customer service assistance, content creation, you know, for marketing and things like that as well. A lot of that has actually gone into production, which is interesting because it's, I think it's a little bit, obviously sales augmentation, you know, preparing sales outbound and messaging.

messaging. A lot of that is done by AI today because I think that's super useful. But I think the real external facing Gen AI use cases we haven't seen at scale yet. But I think it's coming there because I think people were still concerned and customers were concerned about the hallucination and giving the wrong information. You know, we had the Air Canada case and the Chevy case and those giving wrong information from the chatbot to the customer. But I think with guardrails and security and being able to

train the model, but train the system what you should say and what you cannot say. Again, that's all tied in your enterprise governance and your security posture, right? I think that's where we can help a lot of companies get to production. So what is the one question that you wish more people would ask you about Databricks? Yeah, I'd love them to ask me, like, what is data intelligence? Okay, then what is data intelligence then? People always ask me what the lake house is, but we say the lake house is the architecture. But with data intelligence, as I said before, it's like democratizing

access to your data to derive intelligence so you think about this data and it's your ai right so with data intelligence you have clean data and you know where it is and you know what you can do about it but you can derive intelligence from it so you can ask a question about your customer you can ask questions about your operations and a lot of it is very natural language and being able to get answers immediately that for us data intelligence and for the smallest

company in the world to the largest enterprise, we think everyone's going to adopt this approach. That's so concise and so simple. I think I'm going to try to use that tagline too. Please do, please market it for me. So my traditional closing question, what does great look like for Databricks in ASEAN from your perspective? Yeah, great question. I think we alluded at the start, I think we're really at the start of something special, especially around AI.

It's like a once in a lifetime opportunity to change how people work, how people live, how people connect with each other through the concept of data intelligence, right? It's for the smallest company, for the largest enterprises, for regulated industries, for government, like to really unlock a lot of data and like solve a lot of really hard problems. Like we're really here to solve hard problems. And then for people, it's all about creating a career defining experience, right?

We're the fastest growing software company ever. We're going north to 60%. And we're really investing in our people. We're investing in resources. We're investing in the tech to really deliver on the data intelligence strategy. And then super important for me, especially in certain stages, building that ecosystem with our partners. You mentioned before, we work with all the cloud providers. We work with system integrators. We work with ISVs. We work with country associations like IMDA, MDEC Malaysia, Venasa in Vietnam. How do we really...

drive the ecosystem to upscale. Millions of people? 10,000 people in Singapore, but millions, right? Millions of people across Southeast Asia. That's going to be great for us. Wow. That's a very good way to conclude this. So Patrick, many thanks for coming on the show. And of course, if you are recruiting, I strongly recommend anyone to join you because Patrick has been a very fair and great boss to me when I was working with him and taught me a lot about that. So in closing, I have two quick questions. Any recommendations that have inspired you recently?

Yeah, I was thinking about this. So I travel around ASEAN a lot, right? And, you know, Singapore, we're super spoiled with how we come in and out of the airport, right? Place of recognition and everything else. And usually every other airport, I need a visa because I'm like Irish passport and I need to apply before and stamp and all this kind of stuff. But I got to Jakarta two weeks ago and I was going through my normal queue, right? I was going to go and the guy comes over and said, no, auto gate. And I'm like, can't be true, right? Auto gate for me.

He says, yeah, go up. Okay, I have my visa, like my QR code. I scan, facial recognition me, and I'm through. It was the fastest time I've ever been through Jakarta airport in my life. And all of that is underpinned by AA, right? You know, facial recognition, mapping back to the day there where I have my QR code, mapped to my passport number. So that was like super amazing for me. And that's like an innovation that's just going to grow tourism for Indonesia like crazy.

Yeah, I had the same experience in Taipei and Kuala Lumpur as well recently. Also the same thing, AutoGate, just go right through. You don't even need to get that fast track, a special discount to get through. So my final question then, how do my audience find you? Yeah, LinkedIn does best. You can get me on LinkedIn or patrick.kelly at databricks.com. Yeah, welcome. Any conversation you have, anything you're looking for around Lakehouse, data intelligence, yeah, happy to talk.

So you can definitely subscribe to us everywhere from YouTube to Spotify. It's all in video now. And of course, share with us your feedback and definitely, totally give us a five-star rating from any of the podcast platform, et cetera. Patrick, many thanks for coming on the show and totally really enjoyed this conversation. And I wish you all the best and definitely we will talk again soon. Yeah. Bernard, one last plug. Data and AI Summit, Databricks.

In San Francisco, June 9th to 12th. If you're interested, please register. It's going to be awesome. We're looking to have more than 20,000 people this year. Great forum to learn, great experiences, all sorts of customers, industries, etc. So yeah, we'd urge you to join that. Definitely. So if you're interested, go and take part in this event. Patrick, many thanks. Thanks, Bernard.