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>> Hey, welcome to this "CUBE Conversation".
I'm Lisa Martin today talking to a CUBE alumni
who's been on many times talking about data,
all things data.
Please welcome Satyen Sangani
the Co-Founder and CEO of Alation.
Satyen, it's great to have you back on theCUBE.
>> Hi Lisa, it's great to see you too.
It's been a while.
>> It has been a while.
And of course in the last year
we've been living in this virtual world.
So, I know you've gotten to be
on theCUBE during this virtual world.
Hopefully someday soon,
we'll get to actually sit down together again.
There's some exciting news that's coming out of Alation.
Talk to us about what's going on.
What are you announcing?
>> So we're announcing
that we are releasing our Alation Cloud Service
which actually comes out today,
and is available to all of our customers.
And as a consequence are going to be the fastest,
easiest deploy and easiest to use data catalog
on the Marketplace,
and using this release to really double down
on that core differentiation.
>> So the value prop for Alation
has always been about speed to deployment, time to value.
Those have really been,
what you've talked about as the fundamental strengths
of the platform.
How does the cloud service double down on that value prop?
>> Well, if you think about data,
our basic premise and the reason that we exist is that,
people could use data
with so many of their different decisions.
People could use data to inform their thinking.
People can use data in order to figure out
what decisions, the best decision
at any given point in time.
But often they don't.
Often gut instinct,
or whatever's most fast or easy to access
is the basis off of which people decide to do what they do.
And so if you want to get people to use data more often
you've got to make sure that the data is available
that the data is correct,
and that the data is easy to find
and leverage.
And so everything that we can do at Alation
to make data more accessible,
to allow people to be more curious,
is what we get excited about.
Because unlike,
paying your payables or unlike,
figuring out whether or not
you want to be able to have greater or lesser inventory,
those are all things that a business absolutely has to do
but people don't have to use data.
And to get people to use data,
the best thing you can do is to make it easy
and to make it fast.
>> And speaking of fast,
that's one of the things
I think the last year has taught us is that,
real-time access to data is no longer a nice to have.
It's really a competitive differentiator.
Talk to me about how you enable customers
to get access to the right data fast enough,
to be able to do what so many companies say,
and that is actually make data-driven decisions.
>> Yeah, that's absolutely right.
So,
it really is a entire continuum.
The first and most obvious thing that we do
is we start with the user.
So, if you're a user of data,
you might have to hunt through a myriad of reports,
thousands of tables in a database,
hundreds of thousands of files in a data lake,
and you might not know where to find your answer
and you might have the best of intentions
but if you don't have the time
to go through all of those sources,
the first thing you might do is,
go ask your buddy down the hall.
Now, if your buddy down the hall
or your colleague over Zoom
can't give you the time of day
or can't answer your question quickly enough
then you're not going to be able to use that data.
So the first thing, and the most obvious thing that we do
is we have the industry's best search experience
and the industry's best browse experience.
And if you think about that search experience,
that's really fueled by our understanding
of all of the data patterns in your data environment.
We basically look at every search.
We look at every log within a company's data environment
to understand what it is
that people are actually doing with the data.
And that knowledge just like Google has page rank
to help it inform which are the best results
for a given webpage.
We do the exact same thing with data.
And so great search is the basis of what we do.
Now, above and beyond that,
there's a couple of other things that we do,
but they all get to the point
of getting to that end search experience
and making that perfect
so that people can then curate the data
and leverage the data as easily as possible.
>> Sounds like that's really kind of personalized
based on the business, in terms of the search,
looking at what's going on.
Talk to me a little bit more about that,
and how does that context help fuel innovation?
>> Yeah.
So,
to build that context,
you can't just do,
historically and traditionally what's been done
in the data management space.
Lots of companies come to the data management world
and they say,
"Well, what we're going to do is we're going to hire...
"We've got this great software.
"But setting the software up is a journey.
"It takes two to three to four years to set it up
"and we're going to get an army of consultants
"and everybody's going to go and assert quality of data assets
"and measure what the data assets do
"and figure out how the data assets are used.
"And once we do all of that work,
"then in four years we're going to get you to a response."
The real key is not to have that context to be built,
sort of through an army of consultants
and an army of labor that frankly nine times out of 10
never gets to the end of the road.
But to actually generate that context day one,
by understanding what's going on inside of those systems
and learning that by just observing
what's happening inside of the company.
And we can do that.
>> Excellent.
And as we've seen the acceleration in the last year
of digital transformation,
how much of that accelerant was an accelerator revelation
putting this service forward
and what are customers saying so far?
>> Yeah, it's been incredible.
I mean, what we've seen in our existing accounts is that,
our expansions have gone up by over 100% year over year
with the kind of crisis in place.
Obviously, you would hypothesize that these catalogs,
these, sort of accessibility and search tools
and data in general,
would be leveraged more when all of us are virtual
and all of us can't talk to each other.
But, it's been amazing to see that
we've found that that's actually what's happening.
People are actually using data more.
People are actually searching for data more.
And that experience and bringing that to our customers
has been a huge focus of what we're trying to do.
So we've seen the pandemic,
in many cases obviously been bad for many people
but for us it's been a huge accelerant
of customers using our product.
>> Talk to me about Alation with AWS.
What does that enable your customers to achieve
that they maybe couldn't necessarily do On-Prem?
>> Yeah, so, customers obviously don't really care anymore,
or as much as they used to,
about managing the software internally.
They just want to be able to,
get whatever they need to get done
and move forward with their business.
And so by leveraging our partnership with AWS,
one, we've got elastic compute capability.
I think that's obviously,
something that they bring to the table,
better than perhaps any other in the market.
But much more fundamentally,
the ability to stand up Alation and get it going,
now means that all you have to do
is go to the AWS Marketplace
or call up an Alation rep.
And you can, within a matter of minutes,
get an Alation instance that's up and running
and fit for purpose for what you need.
And that capability is really quite powerful because,
now that we have that elasticity
and the speed of deployment,
customers can realize the value,
so much more quickly than they otherwise might've.
>> And that speed is absolutely critical
as we saw a lot last year
that was the difference between the winners
and those that were not going to make it.
Talk to me a little bit about creating a data culture.
We talk about that a lot.
It's one thing to talk about it,
it's a whole other thing to put it in place,
especially for legacy institutions
that have been around for a while.
How do you help facilitate
the actual birth of a data culture?
>> Yeah, I mean, I think we view ourselves as a technology,
as a catalyst,
to our best customers and our best customer champions.
And when we talk to chief data officers
and when we talk to data leaders
within various organizations that we service,
organizations like Pfizer,
organizations like Salesforce,
organizations like Cisco,
what they often tell me is,
"Look, we've got to build products faster.
"We've got to move at the speed and the scale
"of all of the startups that are nipping at our heels.
"And how do we do that?
"Well, we've got to empower our people
"and the way that we empower our people
"is by giving them context.
"And we need to give them the data
"to make the right decisions,
"so that they can build those products
"and move faster than they ever might've."
Now those are amazing intentions
but those same leaders also come and say,
"I've just been mired in risk
"and I've been mired in compliance,
"and I've been mired in
"doing all of these data janitorial projects.
"And it's really hard for me to get
"on the offense with data.
"It's really hard for me to get proactive with data."
And so the biggest thing that we do,
is we just help companies be more proactive,
much more easily,
because what they're able to do,
is they're able to leave a lot of that janitorial work,
lead a lot of that discovery work,
lead a lot of that curation work to the software.
And so what they get to focus on is,
how is it that I can then drive change
and drive behavioral change within my organizations
so that people have the right data at their disposal.
And that's really the magic of the technology.
>> So I was reading the
"Alation State of Data Culture Report"
that was just published a few weeks ago.
This is this quarterly assessment that Alation does,
looking at the progress that enterprises have made
in creating this data culture.
And the number that really struck out at me
was 87% of respondents say,
data quality issues are a barrier
to successful implementation of AI in their organizations.
How can Alation help them solve that problem?
>> Yeah, I think the first is,
whenever you've got a problem,
the first thing you've got to do is acknowledge
that you've got a problem.
And a lot of the time people,
leaders will often jump to AI and say,
"well, hey, everybody's talking about AI.
"The board level conversation is AI.
"McKinsey is talking about AI, let's go do some AI."
And that sounds great in theory.
And of course we all want to do that more,
but the reality is that many of these projects
are stymied by the basic planning.
You don't necessarily know where the data's coming from.
You don't know if people have entered it properly
in the source systems
or in the systems that are online.
Those data often get corrupted
in the transformation processes
or the processes themselves don't run appropriately.
And so you don't have transparency.
You don't have any awareness of
what people are doing, what people are using,
how the data is actually being manipulated
from step to step,
what that data lineage is.
And so that's really where we certainly help
many of our customers by giving them transparency
and an understanding of their data landscape.
Ironically, what we find is that,
data leaders are super excited to get data to the business
but they often don't themselves have the data
to understand how to manage the data itself.
>> Wow, that's a conundrum.
Let's talk about customers
because I was looking on the website
and there's some pretty big metrics-based business outcomes
that Alation is helping customers drive.
I wanted to kind of pick through some examples
from your perspective.
First one is 364% ROI.
Second one is 70% less time for analysts
to complete projects.
Workforce productivity is huge.
Talk to me about how Alation is helping customers
achieve business outcomes like that.
>> Yeah, so if you think about a typical analytical project
you would think that most of the time
is spent inside of the analytical tool,
inside of your Excel,
inside of your Tableau,
that where you're thinking about the data
and you're analyzing it, you're thinking deep thoughts.
And you're trying to hypothesize
you're trying to understand.
But the reality is going back to the data quality issue
that most of the time is spent with
figuring out which are the right datasets.
Because at one of our customers, for example,
there were 4,000 different types
of customer transaction datasets,
that spoke to the exact same data.
Which data set do I actually use
out of a particular database?
And then once I figured out which ones to use,
how do I construct the appropriate query and assumptions
in order to be able to get the data into a format
that makes sense to me.
Those are the kinds of things that
most analysts and data scientists struggle with.
And what we do is we help them
by not having them reinvent the wheel.
We allow them to figure out what the right dataset is fast,
how to manipulate it fast,
so that they can focus most of their time on doing that
end analytical work.
And that's where all the ROI
or a lot of the ROI is coming from
because they don't know how to reinvent the wheel.
They can do the work
and they can move on with the much faster business decision
which means that that business moves significantly faster.
And so what we find is that
for these very highly priced resources,
some data scientists who make 200, 300, $400,000
fully load it for a company,
those people can do their job 74% faster
which means they can get not only the answer faster
but they can get many more tasks done,
for over a given period of time.
>> Well, that just opens up a potential
suite of benefits that the organization will achieve,
not just getting the analyst productivity cranked up
in a big way,
but also allowing your organization to be more agile
which many organizations are striving to be.
to be able to identify new products, new services,
what's happening, especially,
in a changing chaotic environment
like we've been living in the last year.
>> Yeah, absolutely.
And they also can learn...
Not only can they help themselves figure out
what new products to launch,
but they can also help themselves figure out
where their risks happen to be,
and where they need to comply,
because it could be the case
that analysts are using datasets
that they ought not to be using
or the businesses using the data incorrectly.
And so you can find both the patterns
but also the anti-patterns,
which means that you're not only moving faster,
but you're moving forward with less risk.
And so we've seen so many failures with data governance,
regimes, where people have tried to assert
the quality of data and figure out the key data elements
and develop a business glossary.
And there's that great quote,
"I wanted data governance but all I got is a data glossary."
That all happens because,
they just don't have enough time in the day
to do the value added work.
They only have enough time in the day
to start doing the data cleaning
and all of the janitorial work that we, as a company,
really strive to allow them to completely eliminate.
>> So wrapping things up here, Alation Cloud Service.
Tell me about when it's available, how can customers get it?
>> So it's available today,
which is super exciting.
Customers can get it either through the AWS Marketplace
or by calling your Alation representative.
You can do that coming to our website.
And that's super easy to do
and getting a demo and moving forward.
But we try to make it as easy as possible.
And we really want to get out of the way,
of allowing people to have
a seamless frictionless experience
and are super excited to have this cloud service
that allows them to do that,
even faster than they were able to do before.
>> And we all know how important that speed is.
Well, Satyen, congratulations
on the announcement of Alation Cloud Service.
We appreciate you coming on here
and sharing with us the news
and really what's in it for the customers.
>> Thank you, Lisa.
It's been phenomenal catch up and great seeing you.
>> Likewise.
For Satyen Sangani, I'm Lisa Martin.
You're watching this "CUBE Conversation."
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