April 6, 2025 • 41 mins
" We did a survey with The Economist globally which obviously included Europe and APAC as well. And we asked the question, 'Does my organization's current architecture supports the unique demands of AI workloads.' 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 stages and that data point ties back to: 85% of GenAI [proof of concepts] has not gone into production. I think that another interesting point is, 'Does your architecture connect AI application? -your relevant business data.' which is probably nearly even more important for me. 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." - Patrick Kelly
Fresh out of the studio, Patrick Kelly, Senior Director for Digital Natives, Startups & Enterprise and Commercial Sales in Southeast Asia at Databricks, joins us to discuss how data intelligence is powering enterprise AI applications in ASEAN. Beginning with his career journey from network engineering to tech leadership across Asian markets, Patrick explained how Databricks pioneered the Lakehouse architecture and integrated generative AI into enterprise workloads. Emphasizing the critical role of data quality in AI success, he showcased compelling customer case studies from across ASEAN and revealed striking generative AI trends in Asia - notably that 85% of organizations lack proper architecture to support AI workloads, reinforcing that clean data remains foundational for effective AI implementation. Patrick concluded by sharing his vision of what success looks like for Databricks in Southeast Asia.
Audio Episode Highlights:
[00:46] Quote of the Day #QOTD
[01:49] Introduction: Patrick Kelly from Databricks
[02:28] Career journey from network engineering to technology sales leadership
[06:43] Lessons from Patrick's career journey
[09:10] The Data & AI total market opportunity in Southeast Asia and How Databricks is poised to capture the market.
[10:08] How Databricks pioneered the Lakehouse concept, combining data lake and data warehouse capabilities.
[15:24] The One Thing that Patrick know about Databricks that very few do.
[17:52] Customer success stories from Grab, GetGo, GovTech to Siam Commercial Bank
[22:50] How Mosaic AI positioned Databricks to develop enterprise-quality AI solutions with customers.
[27:29] Key Trends in Asia Pacific on Generative AI.
[32:23] The Importance of Data Architecture for Enterprises adopting AI.
[35:13] Advice for Businesses on Implementation of AI
[37:25] What does great look like for Databricks?
[41:40] Closing remarks and invitation to the Data and AI Summit.
Profile: Patrick Kelly, Senior Director for Digital Natives, Startups & Enterprise and Commercial Sales in Southeast Asia at Databricks.
LinkedIn: https://www.linkedin.com/in/patrick-kelly-aab6168/
Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
We did a survey with the Economist globally, obviously
including Europe and APEC 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
(00:21):
stage. And that kind of data point ties
back to 85% of Jenny, I has not gone into production.
And I think another interesting point is does your architecture
connect AI application, 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
(00:41):
cannot get good AI. Welcome to Analyse Asia, the
premium podcast dedicated to dissecting the pulse of
business, technology and media in Asia.
I am Bernard Liang, and I often inform the decision makers in
businesses that data is important for artificial
intelligence to work. How do we ensure enterprise AI
(01:01):
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 Data breaks to discussthis subject.
Patrick, welcome to the show. Thanks, Bernard.
Great to be here. Great to you know again.
I also probably should also inform you ex colleagues and you
(01:22):
are probably my mentor and boss when I first joined AWS and
really guide me through the launch process.
So thank you very much, Patrick,for doing this for me, 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 got in sales in quite a long
(01:44):
time. But actually I did start in
technology. I was a network engineer when I
first started my career, workinga lot in enterprise networking,
Cisco, Juniper, kind of doing like banking systems and stuff
like that as well. So it's 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. But I was, I'm Irish, right?
So that was based in Ireland. But then I joined like the
(02:06):
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 because it's, I went to Japan in
like a four week assignment and I ended up staying five years.
That was pretty, pretty fun. I think in Japan was interesting
because I went from then being engineering into consulting.
(02:27):
So network consulting, it's likefor 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 signals. We didn't know how to handle it,
right. So we did a lot of consulting
work for SoftBank, how to managethe load in Tokyo and really
high densely populated areas. That was pretty cool.
(02:48):
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, in telco, it was like Japan and Korea are always
#1 they're all first with 5G, first with like all the new
technologies. And then we took that when we
started in virtualization on telecom networks.
(03:08):
We then brought that over to Southeast Asia where like
Singtel and telecom sell. 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 that led me into Ada Best, which just spent about
five years doing different roles, started with the IoT
(03:28):
business, then we did the analytics business together and
machine learning business Bernard and then took on the ISV
business, which is our B to B software sales like in the sales
motion and then. We collaborated a lot on that
as. Well, yes, I remember in
Malaysia we did a lot of good stuff in Malaysia, good case
studies as well. And then finally, the role was
both ISE but also digital nativeas well.
(03:48):
So the likes of Grab and Travelocca and other iconic B to
C ASEAN customers. And then it led me to data
bricks where I'm kind of doing similar role in, 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 howto use data night AI to solve
(04:09):
business problems. So how do you actually come to
this present role with data bricks?
Yeah, actually I was taking a battle when I was 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 team
and machine learning, right, where we were talking about
those problems, right. How do we solve problems with
(04:31):
data? Like customer has 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. And when I was in the process
with data bricks, I was thinkingabout how do I get some get the
backs to something that's reallyspecific and into a certain
(04:54):
technology domain. Like Amazon is fantastic.
You have all of the services andall of the technology, but you
become super broad, right, because you are you're selling
200 services. But at Databricks is a very
defined focus on is 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
(05:14):
building a team and building a business again because I think
data and AI now or even last year was probably at the same
stage as cloud was maybe 6-7 years ago in Southeast Asia.
So it's in that early stage of customer mind share and
transformation. Yeah.
I totally agree with you. I think the essential parts of
AI is actually to do with data. And I find that the integration
(05:35):
where we work together between data analytics and machine
learning or even, well, we call it the generative AI, tends to
now integrate quite seamlessly once the customers actually have
a pretty good understanding of where their data is, 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?
(05:57):
I was, I always think in anything it's personal over
professional, right? So no one's ever going to work.
Remember you work long hours nowwill remember you work long
weekends and you're retired, right?
So 3 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
(06:18):
around NUS and education, right?It's a huge part of, 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 new, 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?
(06:39):
And then focus on your profession, right?
If you're an engineer, if you'rea 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
and way of how you create software.
And I don't think AI can get there so fast and definitely
(07:02):
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?
(07:22):
So we started with customer obsession, we end with deliver
results. 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
(07:43):
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 we have 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 ISPs and what is the
mental models behind. So with that to do get to the
main subject of the day, which Iwant to talk about data breaks
(08:04):
in ASEAN and also the with university in 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 whynow data Bricks is poised to
actually capture this market? Yeah, I think before we even
(08:25):
look at the data, AII 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 hyperscale as
ADA, best, GCP, Azure, Alibaba, etcetera, right?
The Chinese clouds for specifically data AI workloads,
(08:48):
we think it's about a 1 billion market for Southeast Asia.
That's excluding the compute andGPUs here.
We're talking about the SAS passpiece, right?
The analytic software angle. So it's a huge market, right?
And it's grown very fast. Like for data bricks globally,
we're growing like 70% and in the region we're actually
growing faster than that. So it's like pretty, pretty
hyper growth, right? For us, it's globally, we're the
(09:08):
fastest growing enterprise software company ever, right?
And we're still containing that clip, a last run rate globally,
2.4 billion at like a yeah, north of 60% growth rate.
And really at database, our mission is really to democratize
data in AI, helping data teams solve the world's toughest
problems. And you noticed there I
mentioned data. Data is core of everything,
(09:29):
right? If you don't do data, you cannot
do AI, right? And why where database came
from, right? We invented Spark, which a lot
of people all right, which really revolutionized how data
processing worked at scale from the Hadoop days.
We pioneered the lake house concept, which means you have
your data lake and your data warehouse that together, which
then drives down TCO and enablesself-service analytics.
(09:50):
So you get analytics from the cloud cost storage right from
S3. You don't have expensive data
warehouse to manage and deploy. 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.
(10:13):
They're not giving that away data to anyone.
We always say don't give your data to data bricks, your data
drive your insights on top of it.
That will really differentiate your business.
Just also have my audience. PCO just means total cost of
ownership and I think you I think one of those so major
muscles was a bricks acquisitionof Mosaic as well for the AI
side. So maybe we should just baseline
(10:34):
our audience given that they arefrom a very different walks of
businesses. Can you explain the concept of
generative AI and data lake houses and how they helped
business enterprise to actually achieve their business goals?
I think one of the things that you have really alluded to is
the use of the lake house concept that actually drives
down this total cost of ownership and the sales of
(10:54):
analytics very quickly. Yeah.
So with lake house, so traditionally in the before we
had cloud, we had on premise data systems, right.
So we had a warehouse right on on cloud, but that was very
structured data volumes tables think Excel, right.
So you could ask question, OK, you go in and filter and say,
OK, what was my sales report foryou know, January and a very
(11:16):
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 wesaid, OK, how do we store this
cheaply? And that was, you know, for
Amazon, the first service was Amazon S3, simple storage,
(11:38):
right? So that storage came very cheap.
And we put everything in there. We create a data lake.
So we had a data lake and then we had a data warehouse on Prem.
So then we're like, OK, 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 Big Query,Azure have Synapse.
So they've built all these data warehouses in the cloud for the
(11:59):
structure data. But we still have the same
problem. We still have data silo.
So unstructured is in your lake,all your structure data is in
your warehouse. I'm 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
(12:19):
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, OK, there has to be a better way of doing
this. So the founders of data bricks,
they're 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
(12:41):
for customers? And that's where, and that's
where the lake house concept started.
Yeah, a couple of years ago. So how about with that what how
does the strategy of data bricksactually with that current
architecture? Is it dependent on this lake
house concept and how does it evolve into the AI side?
So Lake House underpins everything, right?
So we have all of your data sitting in low cost cloud
storage and then any, all your analysts and data science can
(13:03):
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 OG in the ML world,right, Bernard, you were talking
about AI back when it was spelt ML.
So now with that like single copy of data, now you can run
machine learning model, you can train it with different data
sets, but it's not a data set sitting in this environment or
(13:26):
another data set here with data drift.
And then the model you're like you're double guessing the
model. You're saying, OK, 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 Lakers architecture and
we're calling that data intelligence.
So Jenny, I obviously is when GPT came around, we are not
(13:46):
getting 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, Jenny? I obviously will generate
content and you'll have smart people advisors and in, you
know, financial services robo advisors, which is fantastic.
But I think the we should talk about AI and the wider, some
(14:08):
generative but also classic. I think it's a very good point.
I think whether currently a lot of people just focus on Jenny M,
but they forgot actually there'sa 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 fundedby Anderson Horowitz and I've
(14:30):
listened to a couple of annals done by your CEO Ali.
What is the current business footprint of data breaks in
ASEAN? Yeah, I'll start with ABJ.
Looks, we've got really 5 defined markets across the area,
you know, starting with India and then we've got ASEAN,
Greater China, we've got that inthe market, Japan, Korea and
then down to ANZ. So in the ASEAN GCR or for ABJ,
(14:51):
Singapore is their headquarter. So last year when we did our day
to night world tour, which is a flagship event in Singapore, we
announced that that Singapore isthe regional hub for APJ.
And we had plan all our plans toincrease Singapore based
workforce working with SingaporeEDB, adding critical roles in
field engineering, you know, to help customers unlock problems,
our professional services strategy, OPS, learning
(15:13):
enablement, etcetera. So we're 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 in AI talent within Singapore.
(15:35):
That's a partnership with IMDA training Partners, NTUC Learning
Hub and NUSICT Academy. And I think you also do some
parts with startups as well, specifically with Jenny F and
can you talk a little bit about that?
Yeah, super passionate part of what I do.
So yeah, back in middle of last year, we were thinking about,
we've got a date with for startup program.
You know, we a lot like the hyper scales, we invest in them,
(15:57):
we give them credits, we give them go to market expertise and
help them, you know, think abouthow to build a product on on
Databricks and with ASI and Jenny iPhone when that was set
up to invest specifically in Jenny, I start-ups, you know,
set up by X Amazonians as well. You know, you 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
(16:18):
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 themlike building with us now we're
investing in them, obviously Jenny Ire fund and investing
equity in them as well. And now next week and our next
two weeks, April 10th, we're going to do a start up matching.
So the start-ups we've identified as high priority with
(16:39):
enterprises and we're doing thatwith EDB in Singapore in with
Google on April 10th. So that's going to be a great
event to see how these companiesgrow.
Yeah, I probably will still wantto highlight that.
I think now database is actuallyquite multi cloud, right?
It's definitely just not just working in Amazon websites.
This is also Google, GCP and even Microsoft Azure as well,
correct? Yeah.
So how are customers now using data bricks in ASEAN?
(17:02):
Can you just share some really quite interesting case 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 data bricks like for many years building customer data
platform. As you can imagine, they've got
millions of data points coming in across customers, across ride
(17:26):
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 personalized recommendations, you know, for
their, all their millions of customers.
I think that's one good example.Another one I think is get go in
Singapore. I think you've probably used
before largest car sharing platform in Singapore that
(17:47):
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 7 times faster, making,you know, really next business
day decision making. And also they wanted to really
figure out, OK, how do customersactually use the car?
(18:09):
So analyzing booking behaviour, refilling patterns, they could
actually reduce fuel theft by 50%, which was really, really
impactful for them in their business by minimizing misuse of
fuel cards and really enhancing like overall customer trust.
So that's like, I think 2 of themore kind of digital type
customers. And then I think more and more
regulated like Gov tech, for example, which I think you, you
(18:29):
work a lot with as well. And you know all them.
So really in charge of, you know, the public sector digital
transformation, using data bricks to empower self-service
analytics and through data security and governance.
Because all the different government agencies and being
able to all agency just have theaccess to the data that they
need. They really achieved, you know,
dashboards could create a three times faster.
(18:51):
They could democratize data, 50%of data across corporate
divisions and actually saved 8000 labour hours annually,
which is massive for for a government agency.
Wow, so they actually actually had as such is like taking the
entire data bricks at scale for the entire Singapore government.
Totally like productivity gains is fantastic because you are the
platform is taking away a lot ofthat manual tedious work.
(19:14):
I hear a lot about from 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 that Siam Commercial Bank is a great customer of ours
and of course in the FSI base, you know, being huge bank and
having a lot of data estate and IT real estate.
(19:37):
So what we really worked with demos, how do we create seamless
and personalized digitized digital banking experience.
But the big thing they want to do is how can they do a customer
360, which is AI powered and that really means OK, you engage
with the bank through the website, the mobile app or in
person. It's all those data points are
(19:57):
tied together. So if you access in through the
web, that is all logged and every and maps you around and
can. You're not duplicating data.
A lot of papers removed from theprocess.
Well, I think a 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
(20:18):
two weeks, right, for it to comeback.
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 like
the customer experience was fantastic, but they've seen the
two fold increase in approval rates for their digital lending
(20:38):
products. Yeah.
So I think this kind of AI credit score, I remember in
those days when when AWS we talked about it, but now I think
can see nation is quite a quite interesting outcome.
So what I like insights or maybechallenges that you have learnt
from your customers in the ASEANregion now I think like.
Customers are ultimate source offeedback, right?
So they really, one of our principles of database is really
(21:00):
obsessed over customers and we are founded by academics who
build products and they're scientists.
So we really are, 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, you know,
our streaming service, we get feedback on our warehouse, you
(21:22):
know, we get feedback on our UI.So for example, Databricks
Assistant, which is AUI, which will have, you know, coding
pieces and the tables. A lot of that is feedback from
our community and from our customer.
OK, that's how. You get the feedback.
So I mean, given I'm going to switch gears away, but given now
we have proliferation of foundation models, you know, and
AI agents, I guess, how does nowDatabricks think about its
(21:45):
position within the market itself?
Yeah. I think the acquisition of
Mosaic AI for us was really a game changer and 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 were really solving our problems
at scale, right, especially in the science part, right?
The science of and what that really brought us was thinking
(22:08):
deeply around enterprise qualitysolutions.
I'm going to 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 reallythat you should maintain full
control over your data on your model.
You should not that away to any low SAS model or model that is
(22:31):
out on 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 really soon should really
maintain control. The next thing is really
production quality at scale, right?
So scale in an enterprise is youneed to be have that capability,
(22:53):
but you also need to manage the quality.
Hallucinate, 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 the cost of that.
Obviously we're a big partner ofall the GP providers, NVIDIA as
well. So we can help on that as well.
And obviously with the cloud providers as well and then like
(23:15):
native support, we've built out a genetic framework end to end.
And the idea is that we want to like everything in technology,
abstract away a lot of technology probably that you
don't have to think about RAG and vector databases and
embeddings and weigh things. You'll never look to the person
that I've got a business problem.
I'm trying to develop a internalknowledge Chapel from my HR.
(23:38):
I should be able to just roll that straight away and then the
platform will take it underneath.
That's it framework that we're building.
So it's actually. In the Databricks viewpoint is
actually those layers are being extracted away from the
customers so that they can basically just focus on getting
the what they need specifically out of their data.
Could be insights, could be specific kind of analysis on
their or that. And also am I right to say that
(24:00):
the large language model you have the DBRX are also currently
being deployed as well on the data breaks the architecture as
well? Yes, yes.
So. DBRX was DBRX was the best
performing model I think for about 10 days until the next
LAMA 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 toexpect a better model from DBRX
(24:21):
at some point. Yeah, yeah.
But the purpose of it was not toshow that the best performing
model, it was so 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
with especially with the mixtureof experts model, especially how
you call an expert for coding oran expert for English or an
expert for math. We did a lot for $10 million,
(24:43):
which is pretty amazing at the time.
It's it's pretty. Impressive.
For enterprise AI application, Italked to the various
enterprises who's using data breaks and I think it's pretty
interesting that it's, I think it's what I call a full
enterprise driven AI model. And I think very few relation
models are thinking about that because I think they're trying
to cover all things for all people and such.
So what's the one thing you knowabout data breaks in Asia that
(25:05):
very few do, I think. Like obviously the technology is
there, we presented our events and we actually show the tech
and we work with customers and partner etcetera.
But I think one thing that we are extremely diverse company,
right, with people from lots of different cultural backgrounds.
So for my team, like I've got obviously Singaporeans, Indian,
Thai, Vietnamese, Indonesian, French, Irish, a very diverse,
(25:29):
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 background.
Like, of course, we've got hyperscalers, you know, myself
from a the best of people from GCP and Microsoft, but also
we've got a lot of people from like application vendors, like
software Salesforce, work day, observe observability vendors
like, you know, New Relic, you know, people from system
(25:50):
integrators who show, who bring experience of building, you
know, solution end to end and implementation plans.
I think that diversity really helps us learn from each other
and how to serve our customers better.
So for example, a lot of people may have been selling, you know,
CDP solution and know all those use cases inside out or someone
has come from an SI and implemented, you know, really
complex on premise data warehouse migration and we can
(26:12):
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?
(26:36):
Yeah. I think the event thing with
Jenny I know is of course agent is a newborn, right?
It's a. New busword.
It's a new Yeah, we'll call it A.
Buzword yeah, but we're seeing actual deployments, right?
So Mastercards have just deployed the agent framework for
their digital payment assessmentafter doing like 300 POC's,
which they openly talk about because it was the last 18
months of years of testing, seeing what works.
(26:58):
Like our last report with the Economist, we did found that
still about 85% of Jenny I projects we're still PO CS and
didn't go production. I think we're seeing the first
stages of actually production now with agent because agent is
end to end. I think Jenny I had to start was
very point, OK, it's LLM and I'mjust doing this.
You need to have the whole end to end framework.
I think another trend we're seeing and actually speaking
(27:20):
through a big GSI yesterday about this, a lot of customers,
they want to access all their data under management.
So they want to 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 tosee how can they actually get
(27:40):
better insights. Actually, that's something we're
working hard on, which is something called Lake Flow
Connect, where we connect into all these systems and pull only
department data to get the answers that the business wants.
It's basically the. Data connector layer, very
similar to I think what entropy now called the model contacts
protocol MCP or something like that, yeah.
And then super important is governance and security like
(28:04):
it's like everything you have tostart 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, OK, who can look up what is the lineage, who can
access what, at what time and beable to trace that back through
your system, especially in enterprise in a regulated
industry, if you have any breach, you're going to be in
trouble, right? So we we think that the
(28:25):
governance is such an important part and that is, is it's non
negotiable. You always say that a the best
as well as security job 0. Yeah, governance very important
definitely. And I think a lot of the senior
executives when I train in AUS focuses a lot on how to think
about guard rails 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 we're serving
(28:47):
these customers, don't really think about how important that
that element is on there. But how about locally then in 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, very variation of self built or built
(29:10):
with cloud providers or built with data bricks as well.
So they really help us really challenge us on delivering
better, you know, performance, better price, performance,
better outcomes like challenges on the agent framework, 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
(29:31):
business and getting data out issuper 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 iswe did announce an acquisition
of Blade Bridge, so bridges datawarehouse analyzer tool.
So we'll analyse your on Prem data warehouse, look at OK, what
(29:51):
that would look like and then we'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 together as we move at the mid market SMB, they
really just want access to theirdata 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, ask
(30:13):
questions of it. And there we're providing, we
have our Genie assistant, which,you know, ask the natural
language question and then we enable to to build dashboards.
So we call it AIBI. 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,
(30:33):
so. What essentially this AIBI is
kind of the fusion of the earlier iteration of say,
business intelligence to be ableto get your analytics, but then
now having the AI to power the insights on top of that.
Is that how I understand it? Correct.
So you could just put in a prompt and say, OK, give me the
sales report for the last 12 months and graph it in the bar
(30:54):
chart and away you go. Yeah, that's all your data,
right? I.
Was looking at some of the reports out there from I think I
saw one of them, but on data breaks I think you always talk.
I think you also have talked about there's some data relating
to ASEAN specifically on the current architecture and also
how the ASEAN countries are viewing Gen.
AI strategically important. Can you talk elaborate about
(31:17):
that or maybe give me more colour about the matter?
We did a survey. With the economist globally,
obviously including Europe and APEC as well.
And we asked the question. My organization's current
architecture supports the uniquedemands of AI workloads and
basically 85% said no, we don't have the architecture to support
it. Some like a partially does, but
(31:37):
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% ofJenny I has not gone into
production. So with that as well.
And I think another interesting point is does your architecture
connect AI application, your relevant business data, which is
probably nearly even more important for me.
And again, it was still about 80%.
(31:59):
We don't have that because that business data is all over the
place. And like as we talked at the
start, Bernard, and we talked about this for many years,
without the clean data, you cannot get good AI.
So that's 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 I should talk to you, yes.
(32:19):
How about like? Perception of ASEAN countries in
terms of the strategic importance of Gen.
EI, then yeah, I thought. This was super interesting.
So along with like UK and Japan,ASEAN is up there at around, you
know, 70 high 7080% Gen. EI is critical to their long
term strategic goals, which is really interesting.
Whereas you compared to Korea, Korea was in about 65%, which is
(32:40):
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 Jenny I to
really differentiate business, grow my business and actually
drive, yeah, drive growth from my company.
But I think it also drives a lotof growth for the countries as
well. You know, we'll drive GDP, we'll
have start-ups, we'll have businesses, we'll have training.
(33:03):
So I see like the societal impact is going to be pretty
huge. I, I think so.
I think just now we talk a bit about the large language models.
I think more from the perspective of know which new
model comes out in etcetera. But let me flip the question a
little bit from your perspective, what should
businesses be thinking about with foundation AI models?
(33:24):
For example, we talk about data breaks, 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 leading LM
could be trained and tuned like for $10 million through that
(33:46):
DeepSeek which kind of rocked the world a bit, which is
fantastic, right. Showing how you can innovate
with technology limitations. And then we have Manus, which is
showing an agent working across little different platforms,
which is super cool as well. But our belief is that you
should have a platform which areclean data.
You should have access to all ofthese models and which we
provide. So we've got API Gateway, which
(34:07):
you can then decide, OK, I've got my data set here.
I want to run a Gen. AI application.
Should I use GPT? Should I use Clod?
Should I use DeepSeek? 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're going to
(34:28):
all the big, 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 youradvice for business owners in
thinking about implementing AI applications in their
organisations 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 aid
(34:49):
of theme teams, right, they develop products as the chief
product officer and as product managers.
Probably a bit less on the enterprise where it was probably
sitting under an IT team or someapplication team or customer
service team. I think they need to really
think about what is the product and what is it doing, and with
the product design, then you start thinking about what the
technology maps underneath. So it it sound like you think
(35:12):
about there's a lot of proof of consensance over the last two
years, right? And how much actually, how is
the percentile there's really interms of production and how can
we drive them towards the production workloads then?
Yeah, I think over. The last it was, I think it's
85% from the last time we've seen from Connor's report and
I'm seeing that as well in the field.
We did a lot of a lot of POC's last year on yeah, internal
(35:35):
knowledge, chat bots, 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 to outbound and messaging.
A lot of that is is done by AI today because it's super useful.
But I think the real external facing general use cases we
(35:58):
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 thatwas giving wrong information
from the chatbot to the customer.
So, but I think with guard railsand security and being able to
(36:19):
train the model, but train the system, what you should say and
what cannot, 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 helpa lot of companies get
production. So what is the one?
Question that you wish more people would ask you about data
breaks? Yeah, I'd love to.
Ask me like what is data intelligence?
OK, then what is data? Intelligence then people always
(36:39):
ask me what the. Lake house is, but we say the
lake house is dark. But with data intelligence, as I
said before, it's like democratizing access to your
data to derive intelligence. So you think about it as 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 canderive intelligence from it.
(36:59):
So you can ask a question about your customer, you can ask
question about your operations and a lot of it is very natural
language and be able to get answers immediately.
That for U.S. data intelligence and for the smallest company in
the world to the largest enterprise, we think everyone's
going to adopt this approach. They're so.
Concise and so saying, I think I'm going to try to use that
pipeline too. Please do better.
(37:21):
Please market it for me. So my traditional closing
question, what does great look like for data breaks in ASEAN
from your perspective? Yeah, great question.
I think we, I'll leave that at 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 peoplework, how people live, how
people connect with each other through the concept of data
(37:42):
intelligence, right? It's for the smallest company,
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 hard to solve hard problems.
And then for our 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%.
(38:03):
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 ourpartners.
You mentioned before, we work with all the cloud providers, We
work with system integrators, wework with Isvs, We work with
country associations like IMDA, MDEC, Malaysia, Vanessa in
(38:23):
Vietnam. How do we really drive the
ecosystem to upscale 10,000 people in Singapore, but
millions, right, millions of people across Southeast Asia
like that? 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 recommendanyone to join you because
(38:45):
Patrick has been a very fair andgreat boss to me when I was
working with him and taught me alot about that.
So in closing, I have two quick questions.
Any recommendations that have inspired you recently?
Yeah, I was thinking about this.So 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? Face of recognition and
everything else. And usually every other airport
(39:06):
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 2 weeks agoand I was going through my
normal queue, right? I was going to go and the guy
comes over and say no, autogate.And I'm like, can't be true,
right? Autogate for me he says yeah, go
up OK, what all the gay I have my visa like on my QR code I
scan facial recognition me and I'm true it was the fastest time
(39:28):
I've ever been through Jakarta airport in my life so and all of
that is underpinned by a a rightyou 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
(39:50):
recently. Also the same thing.
Autogate just go right through. You don't even need to get that
fast track a special discount toget through that.
Yeah, yeah, yeah. So my.
Final question then how do my audience find you?
Yeah, LinkedIn. Does best.
You can get me on linkedinorpatrick.kelly@databricks.com.
Yeah. Welcome any conversation you
have, any anything you're looking for around Lake House
(40:10):
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, etcetera.
Patrick, Many thanks for coming on the show and totally really
enjoy this conversation and I wish you all the best and
definitely we will talk again soon.
(40:30):
Yeah. Bernard, one last plug data in
AI Summit Data bricks 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, etcetera.
So yeah, we'd already join that,definitely so.
If you're interested, go ahead and take part in this event,
(40:52):
Patrick. Many thanks.
Thanks, Bernard.
We did a survey with the Economist globally, obviously
including Europe and APEC 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
(00:21):
stage. And that kind of data point ties
back to 85% of Jenny, I has not gone into production.
And I think another interesting point is does your architecture
connect AI application, 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
(00:41):
cannot get good AI. Welcome to Analyse Asia, the
premium podcast dedicated to dissecting the pulse of
business, technology and media in Asia.
I am Bernard Liang, and I often inform the decision makers in
businesses that data is important for artificial
intelligence to work. How do we ensure enterprise AI
(01:01):
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 Data breaks to discussthis subject.
Patrick, welcome to the show. Thanks, Bernard.
Great to be here. Great to you know again.
I also probably should also inform you ex colleagues and you
(01:22):
are probably my mentor and boss when I first joined AWS and
really guide me through the launch process.
So thank you very much, Patrick,for doing this for me, 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 got in sales in quite a long
(01:44):
time. But actually I did start in
technology. I was a network engineer when I
first started my career, workinga lot in enterprise networking,
Cisco, Juniper, kind of doing like banking systems and stuff
like that as well. So it's 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. But I was, I'm Irish, right?
So that was based in Ireland. But then I joined like the
(02:06):
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 because it's, I went to Japan in
like a four week assignment and I ended up staying five years.
That was pretty, pretty fun. I think in Japan was interesting
because I went from then being engineering into consulting.
(02:27):
So network consulting, it's likefor 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 signals. We didn't know how to handle it,
right. So we did a lot of consulting
work for SoftBank, how to managethe load in Tokyo and really
high densely populated areas. That was pretty cool.
(02:48):
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, in telco, it was like Japan and Korea are always
#1 they're all first with 5G, first with like all the new
technologies. And then we took that when we
started in virtualization on telecom networks.
(03:08):
We then brought that over to Southeast Asia where like
Singtel and telecom sell. 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 that led me into Ada Best, which just spent about
five years doing different roles, started with the IoT
(03:28):
business, then we did the analytics business together and
machine learning business Bernard and then took on the ISV
business, which is our B to B software sales like in the sales
motion and then. We collaborated a lot on that
as. Well, yes, I remember in
Malaysia we did a lot of good stuff in Malaysia, good case
studies as well. And then finally, the role was
both ISE but also digital nativeas well.
(03:48):
So the likes of Grab and Travelocca and other iconic B to
C ASEAN customers. And then it led me to data
bricks where I'm kind of doing similar role in, 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 howto use data night AI to solve
(04:09):
business problems. So how do you actually come to
this present role with data bricks?
Yeah, actually I was taking a battle when I was 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 team
and machine learning, right, where we were talking about
those problems, right. How do we solve problems with
(04:31):
data? Like customer has 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. And when I was in the process
with data bricks, I was thinkingabout how do I get some get the
backs to something that's reallyspecific and into a certain
(04:54):
technology domain. Like Amazon is fantastic.
You have all of the services andall of the technology, but you
become super broad, right, because you are you're selling
200 services. But at Databricks is a very
defined focus on is 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
(05:14):
building a team and building a business again because I think
data and AI now or even last year was probably at the same
stage as cloud was maybe 6-7 years ago in Southeast Asia.
So it's in that early stage of customer mind share and
transformation. Yeah.
I totally agree with you. I think the essential parts of
AI is actually to do with data. And I find that the integration
(05:35):
where we work together between data analytics and machine
learning or even, well, we call it the generative AI, tends to
now integrate quite seamlessly once the customers actually have
a pretty good understanding of where their data is, 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?
(05:57):
I was, I always think in anything it's personal over
professional, right? So no one's ever going to work.
Remember you work long hours nowwill remember you work long
weekends and you're retired, right?
So 3 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
(06:18):
around NUS and education, right?It's a huge part of, 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 new, 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?
(06:39):
And then focus on your profession, right?
If you're an engineer, if you'rea 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
and way of how you create software.
And I don't think AI can get there so fast and definitely
(07:02):
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?
(07:22):
So we started with customer obsession, we end with deliver
results. 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
(07:43):
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 we have 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 ISPs and what is the
mental models behind. So with that to do get to the
main subject of the day, which Iwant to talk about data breaks
(08:04):
in ASEAN and also the with university in 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 whynow data Bricks is poised to
actually capture this market? Yeah, I think before we even
(08:25):
look at the data, AII 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 hyperscale as
ADA, best, GCP, Azure, Alibaba, etcetera, right?
The Chinese clouds for specifically data AI workloads,
(08:48):
we think it's about a 1 billion market for Southeast Asia.
That's excluding the compute andGPUs here.
We're talking about the SAS passpiece, right?
The analytic software angle. So it's a huge market, right?
And it's grown very fast. Like for data bricks globally,
we're growing like 70% and in the region we're actually
growing faster than that. So it's like pretty, pretty
hyper growth, right? For us, it's globally, we're the
(09:08):
fastest growing enterprise software company ever, right?
And we're still containing that clip, a last run rate globally,
2.4 billion at like a yeah, north of 60% growth rate.
And really at database, our mission is really to democratize
data in AI, helping data teams solve the world's toughest
problems. And you noticed there I
mentioned data. Data is core of everything,
(09:29):
right? If you don't do data, you cannot
do AI, right? And why where database came
from, right? We invented Spark, which a lot
of people all right, which really revolutionized how data
processing worked at scale from the Hadoop days.
We pioneered the lake house concept, which means you have
your data lake and your data warehouse that together, which
then drives down TCO and enablesself-service analytics.
(09:50):
So you get analytics from the cloud cost storage right from
S3. You don't have expensive data
warehouse to manage and deploy. 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.
(10:13):
They're not giving that away data to anyone.
We always say don't give your data to data bricks, your data
drive your insights on top of it.
That will really differentiate your business.
Just also have my audience. PCO just means total cost of
ownership and I think you I think one of those so major
muscles was a bricks acquisitionof Mosaic as well for the AI
side. So maybe we should just baseline
(10:34):
our audience given that they arefrom a very different walks of
businesses. Can you explain the concept of
generative AI and data lake houses and how they helped
business enterprise to actually achieve their business goals?
I think one of the things that you have really alluded to is
the use of the lake house concept that actually drives
down this total cost of ownership and the sales of
(10:54):
analytics very quickly. Yeah.
So with lake house, so traditionally in the before we
had cloud, we had on premise data systems, right.
So we had a warehouse right on on cloud, but that was very
structured data volumes tables think Excel, right.
So you could ask question, OK, you go in and filter and say,
OK, what was my sales report foryou know, January and a very
(11:16):
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 wesaid, OK, how do we store this
cheaply? And that was, you know, for
Amazon, the first service was Amazon S3, simple storage,
(11:38):
right? So that storage came very cheap.
And we put everything in there. We create a data lake.
So we had a data lake and then we had a data warehouse on Prem.
So then we're like, OK, 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 Big Query,Azure have Synapse.
So they've built all these data warehouses in the cloud for the
(11:59):
structure data. But we still have the same
problem. We still have data silo.
So unstructured is in your lake,all your structure data is in
your warehouse. I'm 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
(12:19):
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, OK, there has to be a better way of doing
this. So the founders of data bricks,
they're 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
(12:41):
for customers? And that's where, and that's
where the lake house concept started.
Yeah, a couple of years ago. So how about with that what how
does the strategy of data bricksactually with that current
architecture? Is it dependent on this lake
house concept and how does it evolve into the AI side?
So Lake House underpins everything, right?
So we have all of your data sitting in low cost cloud
storage and then any, all your analysts and data science can
(13:03):
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 OG in the ML world,right, Bernard, you were talking
about AI back when it was spelt ML.
So now with that like single copy of data, now you can run
machine learning model, you can train it with different data
sets, but it's not a data set sitting in this environment or
(13:26):
another data set here with data drift.
And then the model you're like you're double guessing the
model. You're saying, OK, 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 Lakers architecture and
we're calling that data intelligence.
So Jenny, I obviously is when GPT came around, we are not
(13:46):
getting 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, Jenny? I obviously will generate
content and you'll have smart people advisors and in, you
know, financial services robo advisors, which is fantastic.
But I think the we should talk about AI and the wider, some
(14:08):
generative but also classic. I think it's a very good point.
I think whether currently a lot of people just focus on Jenny M,
but they forgot actually there'sa 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 fundedby Anderson Horowitz and I've
(14:30):
listened to a couple of annals done by your CEO Ali.
What is the current business footprint of data breaks in
ASEAN? Yeah, I'll start with ABJ.
Looks, we've got really 5 defined markets across the area,
you know, starting with India and then we've got ASEAN,
Greater China, we've got that inthe market, Japan, Korea and
then down to ANZ. So in the ASEAN GCR or for ABJ,
(14:51):
Singapore is their headquarter. So last year when we did our day
to night world tour, which is a flagship event in Singapore, we
announced that that Singapore isthe regional hub for APJ.
And we had plan all our plans toincrease Singapore based
workforce working with SingaporeEDB, adding critical roles in
field engineering, you know, to help customers unlock problems,
our professional services strategy, OPS, learning
(15:13):
enablement, etcetera. So we're 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 in AI talent within Singapore.
(15:35):
That's a partnership with IMDA training Partners, NTUC Learning
Hub and NUSICT Academy. And I think you also do some
parts with startups as well, specifically with Jenny F and
can you talk a little bit about that?
Yeah, super passionate part of what I do.
So yeah, back in middle of last year, we were thinking about,
we've got a date with for startup program.
You know, we a lot like the hyper scales, we invest in them,
(15:57):
we give them credits, we give them go to market expertise and
help them, you know, think abouthow to build a product on on
Databricks and with ASI and Jenny iPhone when that was set
up to invest specifically in Jenny, I start-ups, you know,
set up by X Amazonians as well. You know, you 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
(16:18):
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 themlike building with us now we're
investing in them, obviously Jenny Ire fund and investing
equity in them as well. And now next week and our next
two weeks, April 10th, we're going to do a start up matching.
So the start-ups we've identified as high priority with
(16:39):
enterprises and we're doing thatwith EDB in Singapore in with
Google on April 10th. So that's going to be a great
event to see how these companiesgrow.
Yeah, I probably will still wantto highlight that.
I think now database is actuallyquite multi cloud, right?
It's definitely just not just working in Amazon websites.
This is also Google, GCP and even Microsoft Azure as well,
correct? Yeah.
So how are customers now using data bricks in ASEAN?
(17:02):
Can you just share some really quite interesting case 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 data bricks like for many years building customer data
platform. As you can imagine, they've got
millions of data points coming in across customers, across ride
(17:26):
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 personalized recommendations, you know, for
their, all their millions of customers.
I think that's one good example.Another one I think is get go in
Singapore. I think you've probably used
before largest car sharing platform in Singapore that
(17:47):
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 7 times faster, making,you know, really next business
day decision making. And also they wanted to really
figure out, OK, how do customersactually use the car?
(18:09):
So analyzing booking behaviour, refilling patterns, they could
actually reduce fuel theft by 50%, which was really, really
impactful for them in their business by minimizing misuse of
fuel cards and really enhancing like overall customer trust.
So that's like, I think 2 of themore kind of digital type
customers. And then I think more and more
regulated like Gov tech, for example, which I think you, you
(18:29):
work a lot with as well. And you know all them.
So really in charge of, you know, the public sector digital
transformation, using data bricks to empower self-service
analytics and through data security and governance.
Because all the different government agencies and being
able to all agency just have theaccess to the data that they
need. They really achieved, you know,
dashboards could create a three times faster.
(18:51):
They could democratize data, 50%of data across corporate
divisions and actually saved 8000 labour hours annually,
which is massive for for a government agency.
Wow, so they actually actually had as such is like taking the
entire data bricks at scale for the entire Singapore government.
Totally like productivity gains is fantastic because you are the
platform is taking away a lot ofthat manual tedious work.
(19:14):
I hear a lot about from 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 that Siam Commercial Bank is a great customer of ours
and of course in the FSI base, you know, being huge bank and
having a lot of data estate and IT real estate.
(19:37):
So what we really worked with demos, how do we create seamless
and personalized digitized digital banking experience.
But the big thing they want to do is how can they do a customer
360, which is AI powered and that really means OK, you engage
with the bank through the website, the mobile app or in
person. It's all those data points are
(19:57):
tied together. So if you access in through the
web, that is all logged and every and maps you around and
can. You're not duplicating data.
A lot of papers removed from theprocess.
Well, I think a 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
(20:18):
two weeks, right, for it to comeback.
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 like
the customer experience was fantastic, but they've seen the
two fold increase in approval rates for their digital lending
(20:38):
products. Yeah.
So I think this kind of AI credit score, I remember in
those days when when AWS we talked about it, but now I think
can see nation is quite a quite interesting outcome.
So what I like insights or maybechallenges that you have learnt
from your customers in the ASEANregion now I think like.
Customers are ultimate source offeedback, right?
So they really, one of our principles of database is really
(21:00):
obsessed over customers and we are founded by academics who
build products and they're scientists.
So we really are, 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, you know,
our streaming service, we get feedback on our warehouse, you
(21:22):
know, we get feedback on our UI.So for example, Databricks
Assistant, which is AUI, which will have, you know, coding
pieces and the tables. A lot of that is feedback from
our community and from our customer.
OK, that's how. You get the feedback.
So I mean, given I'm going to switch gears away, but given now
we have proliferation of foundation models, you know, and
AI agents, I guess, how does nowDatabricks think about its
(21:45):
position within the market itself?
Yeah. I think the acquisition of
Mosaic AI for us was really a game changer and 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 were really solving our problems
at scale, right, especially in the science part, right?
The science of and what that really brought us was thinking
(22:08):
deeply around enterprise qualitysolutions.
I'm going to 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 reallythat you should maintain full
control over your data on your model.
You should not that away to any low SAS model or model that is
(22:31):
out on 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 really soon should really
maintain control. The next thing is really
production quality at scale, right?
So scale in an enterprise is youneed to be have that capability,
(22:53):
but you also need to manage the quality.
Hallucinate, 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 the cost of that.
Obviously we're a big partner ofall the GP providers, NVIDIA as
well. So we can help on that as well.
And obviously with the cloud providers as well and then like
(23:15):
native support, we've built out a genetic framework end to end.
And the idea is that we want to like everything in technology,
abstract away a lot of technology probably that you
don't have to think about RAG and vector databases and
embeddings and weigh things. You'll never look to the person
that I've got a business problem.
I'm trying to develop a internalknowledge Chapel from my HR.
(23:38):
I should be able to just roll that straight away and then the
platform will take it underneath.
That's it framework that we're building.
So it's actually. In the Databricks viewpoint is
actually those layers are being extracted away from the
customers so that they can basically just focus on getting
the what they need specifically out of their data.
Could be insights, could be specific kind of analysis on
their or that. And also am I right to say that
(24:00):
the large language model you have the DBRX are also currently
being deployed as well on the data breaks the architecture as
well? Yes, yes.
So. DBRX was DBRX was the best
performing model I think for about 10 days until the next
LAMA 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 toexpect a better model from DBRX
(24:21):
at some point. Yeah, yeah.
But the purpose of it was not toshow that the best performing
model, it was so 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
with especially with the mixtureof experts model, especially how
you call an expert for coding oran expert for English or an
expert for math. We did a lot for $10 million,
(24:43):
which is pretty amazing at the time.
It's it's pretty. Impressive.
For enterprise AI application, Italked to the various
enterprises who's using data breaks and I think it's pretty
interesting that it's, I think it's what I call a full
enterprise driven AI model. And I think very few relation
models are thinking about that because I think they're trying
to cover all things for all people and such.
So what's the one thing you knowabout data breaks in Asia that
(25:05):
very few do, I think. Like obviously the technology is
there, we presented our events and we actually show the tech
and we work with customers and partner etcetera.
But I think one thing that we are extremely diverse company,
right, with people from lots of different cultural backgrounds.
So for my team, like I've got obviously Singaporeans, Indian,
Thai, Vietnamese, Indonesian, French, Irish, a very diverse,
(25:29):
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 background.
Like, of course, we've got hyperscalers, you know, myself
from a the best of people from GCP and Microsoft, but also
we've got a lot of people from like application vendors, like
software Salesforce, work day, observe observability vendors
like, you know, New Relic, you know, people from system
(25:50):
integrators who show, who bring experience of building, you
know, solution end to end and implementation plans.
I think that diversity really helps us learn from each other
and how to serve our customers better.
So for example, a lot of people may have been selling, you know,
CDP solution and know all those use cases inside out or someone
has come from an SI and implemented, you know, really
complex on premise data warehouse migration and we can
(26:12):
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?
(26:36):
Yeah. I think the event thing with
Jenny I know is of course agent is a newborn, right?
It's a. New busword.
It's a new Yeah, we'll call it A.
Buzword yeah, but we're seeing actual deployments, right?
So Mastercards have just deployed the agent framework for
their digital payment assessmentafter doing like 300 POC's,
which they openly talk about because it was the last 18
months of years of testing, seeing what works.
(26:58):
Like our last report with the Economist, we did found that
still about 85% of Jenny I projects we're still PO CS and
didn't go production. I think we're seeing the first
stages of actually production now with agent because agent is
end to end. I think Jenny I had to start was
very point, OK, it's LLM and I'mjust doing this.
You need to have the whole end to end framework.
I think another trend we're seeing and actually speaking
(27:20):
through a big GSI yesterday about this, a lot of customers,
they want to access all their data under management.
So they want to 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 tosee how can they actually get
(27:40):
better insights. Actually, that's something we're
working hard on, which is something called Lake Flow
Connect, where we connect into all these systems and pull only
department data to get the answers that the business wants.
It's basically the. Data connector layer, very
similar to I think what entropy now called the model contacts
protocol MCP or something like that, yeah.
And then super important is governance and security like
(28:04):
it's like everything you have tostart 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, OK, who can look up what is the lineage, who can
access what, at what time and beable to trace that back through
your system, especially in enterprise in a regulated
industry, if you have any breach, you're going to be in
trouble, right? So we we think that the
(28:25):
governance is such an important part and that is, is it's non
negotiable. You always say that a the best
as well as security job 0. Yeah, governance very important
definitely. And I think a lot of the senior
executives when I train in AUS focuses a lot on how to think
about guard rails 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 we're serving
(28:47):
these customers, don't really think about how important that
that element is on there. But how about locally then in 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, very variation of self built or built
(29:10):
with cloud providers or built with data bricks as well.
So they really help us really challenge us on delivering
better, you know, performance, better price, performance,
better outcomes like challenges on the agent framework, 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
(29:31):
business and getting data out issuper 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 iswe did announce an acquisition
of Blade Bridge, so bridges datawarehouse analyzer tool.
So we'll analyse your on Prem data warehouse, look at OK, what
(29:51):
that would look like and then we'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 together as we move at the mid market SMB, they
really just want access to theirdata 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, ask
(30:13):
questions of it. And there we're providing, we
have our Genie assistant, which,you know, ask the natural
language question and then we enable to to build dashboards.
So we call it AIBI. 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,
(30:33):
so. What essentially this AIBI is
kind of the fusion of the earlier iteration of say,
business intelligence to be ableto get your analytics, but then
now having the AI to power the insights on top of that.
Is that how I understand it? Correct.
So you could just put in a prompt and say, OK, give me the
sales report for the last 12 months and graph it in the bar
(30:54):
chart and away you go. Yeah, that's all your data,
right? I.
Was looking at some of the reports out there from I think I
saw one of them, but on data breaks I think you always talk.
I think you also have talked about there's some data relating
to ASEAN specifically on the current architecture and also
how the ASEAN countries are viewing Gen.
AI strategically important. Can you talk elaborate about
(31:17):
that or maybe give me more colour about the matter?
We did a survey. With the economist globally,
obviously including Europe and APEC as well.
And we asked the question. My organization's current
architecture supports the uniquedemands of AI workloads and
basically 85% said no, we don't have the architecture to support
it. Some like a partially does, but
(31:37):
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% ofJenny I has not gone into
production. So with that as well.
And I think another interesting point is does your architecture
connect AI application, your relevant business data, which is
probably nearly even more important for me.
And again, it was still about 80%.
(31:59):
We don't have that because that business data is all over the
place. And like as we talked at the
start, Bernard, and we talked about this for many years,
without the clean data, you cannot get good AI.
So that's 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 I should talk to you, yes.
(32:19):
How about like? Perception of ASEAN countries in
terms of the strategic importance of Gen.
EI, then yeah, I thought. This was super interesting.
So along with like UK and Japan,ASEAN is up there at around, you
know, 70 high 7080% Gen. EI is critical to their long
term strategic goals, which is really interesting.
Whereas you compared to Korea, Korea was in about 65%, which is
(32:40):
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 Jenny I to
really differentiate business, grow my business and actually
drive, yeah, drive growth from my company.
But I think it also drives a lotof growth for the countries as
well. You know, we'll drive GDP, we'll
have start-ups, we'll have businesses, we'll have training.
(33:03):
So I see like the societal impact is going to be pretty
huge. I, I think so.
I think just now we talk a bit about the large language models.
I think more from the perspective of know which new
model comes out in etcetera. But let me flip the question a
little bit from your perspective, what should
businesses be thinking about with foundation AI models?
(33:24):
For example, we talk about data breaks, 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 leading LM
could be trained and tuned like for $10 million through that
(33:46):
DeepSeek which kind of rocked the world a bit, which is
fantastic, right. Showing how you can innovate
with technology limitations. And then we have Manus, which is
showing an agent working across little different platforms,
which is super cool as well. But our belief is that you
should have a platform which areclean data.
You should have access to all ofthese models and which we
provide. So we've got API Gateway, which
(34:07):
you can then decide, OK, I've got my data set here.
I want to run a Gen. AI application.
Should I use GPT? Should I use Clod?
Should I use DeepSeek? 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're going to
(34:28):
all the big, 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 youradvice for business owners in
thinking about implementing AI applications in their
organisations 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 aid
(34:49):
of theme teams, right, they develop products as the chief
product officer and as product managers.
Probably a bit less on the enterprise where it was probably
sitting under an IT team or someapplication team or customer
service team. I think they need to really
think about what is the product and what is it doing, and with
the product design, then you start thinking about what the
technology maps underneath. So it it sound like you think
(35:12):
about there's a lot of proof of consensance over the last two
years, right? And how much actually, how is
the percentile there's really interms of production and how can
we drive them towards the production workloads then?
Yeah, I think over. The last it was, I think it's
85% from the last time we've seen from Connor's report and
I'm seeing that as well in the field.
We did a lot of a lot of POC's last year on yeah, internal
(35:35):
knowledge, chat bots, 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 to outbound and messaging.
A lot of that is is done by AI today because it's super useful.
But I think the real external facing general use cases we
(35:58):
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 thatwas giving wrong information
from the chatbot to the customer.
So, but I think with guard railsand security and being able to
(36:19):
train the model, but train the system, what you should say and
what cannot, 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 helpa lot of companies get
production. So what is the one?
Question that you wish more people would ask you about data
breaks? Yeah, I'd love to.
Ask me like what is data intelligence?
OK, then what is data? Intelligence then people always
(36:39):
ask me what the. Lake house is, but we say the
lake house is dark. But with data intelligence, as I
said before, it's like democratizing access to your
data to derive intelligence. So you think about it as 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 canderive intelligence from it.
(36:59):
So you can ask a question about your customer, you can ask
question about your operations and a lot of it is very natural
language and be able to get answers immediately.
That for U.S. data intelligence and for the smallest company in
the world to the largest enterprise, we think everyone's
going to adopt this approach. They're so.
Concise and so saying, I think I'm going to try to use that
pipeline too. Please do better.
(37:21):
Please market it for me. So my traditional closing
question, what does great look like for data breaks in ASEAN
from your perspective? Yeah, great question.
I think we, I'll leave that at 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 peoplework, how people live, how
people connect with each other through the concept of data
(37:42):
intelligence, right? It's for the smallest company,
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 hard to solve hard problems.
And then for our 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%.
(38:03):
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 ourpartners.
You mentioned before, we work with all the cloud providers, We
work with system integrators, wework with Isvs, We work with
country associations like IMDA, MDEC, Malaysia, Vanessa in
(38:23):
Vietnam. How do we really drive the
ecosystem to upscale 10,000 people in Singapore, but
millions, right, millions of people across Southeast Asia
like that? 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 recommendanyone to join you because
(38:45):
Patrick has been a very fair andgreat boss to me when I was
working with him and taught me alot about that.
So in closing, I have two quick questions.
Any recommendations that have inspired you recently?
Yeah, I was thinking about this.So 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? Face of recognition and
everything else. And usually every other airport
(39:06):
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 2 weeks agoand I was going through my
normal queue, right? I was going to go and the guy
comes over and say no, autogate.And I'm like, can't be true,
right? Autogate for me he says yeah, go
up OK, what all the gay I have my visa like on my QR code I
scan facial recognition me and I'm true it was the fastest time
(39:28):
I've ever been through Jakarta airport in my life so and all of
that is underpinned by a a rightyou 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
(39:50):
recently. Also the same thing.
Autogate just go right through. You don't even need to get that
fast track a special discount toget through that.
Yeah, yeah, yeah. So my.
Final question then how do my audience find you?
Yeah, LinkedIn. Does best.
You can get me on linkedinorpatrick.kelly@databricks.com.
Yeah. Welcome any conversation you
have, any anything you're looking for around Lake House
(40:10):
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, etcetera.
Patrick, Many thanks for coming on the show and totally really
enjoy this conversation and I wish you all the best and
definitely we will talk again soon.
(40:30):
Yeah. Bernard, one last plug data in
AI Summit Data bricks 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, etcetera.
So yeah, we'd already join that,definitely so.
If you're interested, go ahead and take part in this event,
(40:52):
Patrick. Many thanks.
Thanks, Bernard.
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