- Water Stem was just saying
data is more valuable than ever.
And that was also recently shown starkly
by the $28 billion valuation of the machine learning
and data engineering company, Databricks.
Well the US firm's data platform is used by companies
like H & M, Nationwide, Sky's parent company, Comcast.
And 40% of the fortune 500 is just partnered
with Google Cloud to expand its global reach.
And I spoke a little earlier
to its Chief Executive, Ali Ghodsi.
It turned out that all those things that have been
around many, many decades,
they actually didn't work very well.
But around 2010 or '12, people figured out if they use
much, much more data on modern computers
you get fantastic superhuman results.
- Now central to what you do is something called
lakehouse architecture.
For the uninitiated, what is that?
- Well, it means people are already storing all their data
in the cloud in something called data lakes.
But they can't actually make use of it, there.
If they wanna do machine learning on it,
they have to move it somewhere else
in these things that are called data warehouses.
So we've cracked the code on enabling these enterprises
do machine learning right there, on the data lake
so they don't need to necessarily move it
to a data warehouse.
Therefore, we call it lake house.
So a port montage of those two things.
- Right, and what are the sectors where you're seeing
the most growth demand, right now?
- You know, that's a great question
'cause when we started the company, I was trying
to figure out what's the sector we should focus on.
And I was always surprised.
It turns out this stuff is applicable
in every vertical in every sector.
You know, we have people in health care,
we have people in mass media,
we have customers in, you know, retail,
we have customers in the public sector,
we have, you know, customers that are
in the IoT equipment space.
So this technology is applicable everywhere.
- I think I'm right in saying
you've done some very interesting things
in the health care sector, in particular.
Tell me about those.
- Yeah, so you know, a lot of the vaccine development
that's been going on, a lot of the pandemic fighting,
behind the scenes, they use Databricks.
And they use AI in particular, to look at the genomics
and find genes and gene markers that are responsible
for, you know, diseases and you know,
phenotypes as they call them.
- And this is taken away a lot of sort of man hours,
physical labor that previously wouldn't have just
made this work impossible to do.
- Yeah, in the genomic space, it would probably be
hard to to at all if you didn't have the machine learning
'cause this is huge DNA sequences
and you're looking for that needle in the haystack
that potentially is responsible for some disease.
So it wouldn't be even possible with this.
- Now you're just raised $1 billion from investors.
How are you going to be putting that to work?
- Yeah, so you know, expanding internationally
is very costly, it's almost like
starting the company all over again.
You know, every country, different legal code,
different partners, different HR.
So we're gonna aggressively expand in Europe, EMEA,
in Asia, ASIAPAC, and also in Latin America.
So a lot of it will go to international expansion
but we're also investing it a lot of it back into R & D.
So our percentage of R & D spend as total company spend is
actually increasing year over year.
So a lot of it is just continuing investing
in research and development.
- And the fact that you are investing so much
outside the United States, is that because
the valley's become too costly a place to do business,
or is it just your going where the customers are?
- No, I mean, look,
Europe is a huge market.
Asia is a massive market.
You know, cloud adoption is sometimes bigger
in some of those places so, you know,
we're just going where those are.
US is not the only place, you know,
where people do business.
- Some of your shareholders it's some of the
biggest names in tech, the likes of Alphabet,
Amazon, Microsoft, Salesforce.
I mean, some of these are your customers,
as well as your shareholders.
How do you manage those two, separate relationships?
- Yeah so look, this is very early days in the AI market.
We're super excited.
These cloud vendors, they're now seeing that
all this data is moving from these on-premises deployments
into the cloud and we're a killer app
for those cloud data lakes where all the data resides.
So it makes a lot of sense for them to invest in us.
You know, we drive lots of consumption.
We launch five million machines every day in the cloud.
But, you know, they're also customers,
they also use our framework
so you know, we try to be as customer obsessed
as possible and follow whatever our customers need.
- And you also obviously have a lot of traditional
financial investors, the likes of BlackRock,
Franklin Templeton, and so forth.
Are their interests in you and their demands on you
different from some of those trade investors?
- Yeah, I mean I think these firms that you named,
they are very longterm, right?
And you know, frankly speaking, when they invested
in this round, we took $1 billion investment,
but the truth is they wanted to deploy
really three, $4 billion.
Now we didn't want to dilute that much
so we, you know, shaved it down.
But they're thinking the long game.
And I think these kind of investors,
they are thinking AI, machine learning AI
is gonna be a huge market over the next 10,
you know, 20 years.
Let's get in now and let's just invest
and buy more and more, and more.
If Databricks stock goes up, we'll buy more.
If Databricks stock goes down, we buy more.
I think that's different from maybe
if you're more shortsighted.
- That's an astonishing thing you've just said, there.
You could have raised even more money
than you actually did.
And presumable this means you're in no rush
to go to the public markets.
- Well, you know, I get a lot of questions around that.
We're gonna assess that when the timing is right.
But yeah, we're not in a hurry.
You can get a lot of these benefits of IPOing
already in the private markets, today.
When the time is right, we'll also execute an IPO.