(techno music)
>> Live from Las Vegas.
It's theCUBE.
Covering Informatica World 2019.
Brought to you by Informatica.
>> Welcome back everyone to theCUBE's live coverage
of Informatica World.
I am your host Rebecca Knight
along with my co-host John Furrier.
We are joined by Suresh Menon.
He is the Senior Vice President and General Manager
Master Data Management here at Informatica.
Thank you so much for coming on the show.
>> Thank you.
It's great to be back.
>> Great to welcome a CUBE alum.
(laughing)
So a major theme of this conference is Customer 360.
It's about customers' need for trusted,
accurate data as they embark on their own
digital transformation initiatives.
Can you just talk a little bit
about what you're hearing from customers?
What their priorities are?
>> Yeah, absolutely.
You know with MDM,
the promise of MDM has always been creating a trusted,
a qualitative version of any business critical entity.
And who are the most important business critical entities
for any organization?
Customers.
Right?
So almost 80 to 90 percent of our customers
are talking about reinventing a new customer experience
because some of the things that they've been telling us
is that we've all learned that in the past
that a bad customer experience means that you know,
we've all had those experiences.
We go to a hotel,
we use a particular airline,
we have bad experiences and we say,
promise ourselves we'll never go back there again.
So organizations have always for years now understood
that there is a cost to not delivering a good enough
customer experience.
The big change that I'm hearing
at least over the last year or so now,
and especially at this event,
is that organizations have now been able to quantify
what great customer experience can mean
in terms of a premium that they can charge
for their products and services.
Now that is a big shift.
When you start thinking about saying,
if I deliver a better customer experience
I'm actually be able to charge 10 cents more
for a cup of coffee.
I can charge you know 20% more for an airline ticket.
That now has a direct impact on the top line.
>> Data drives is obviously datas a key part of it.
What's changed since last year?
I mean a lots happened we see on the regular (mumbles)
one-year anniversary of GDPR,
lot have pressure around regulation.
We see everyone sees Facebook and goes "Oh my god,
"maybe I don't want to fall in that trap of an enterprise."
Pressure to develop SAS-like applications
with data because we know what Cloud native
and born in a Cloud looks like.
We've seen companies come out of the woodwork
from a fresh start and use data as part
of that input with AI application for great software.
So now the enterprise will want to do that.
>> Exactly.
>> (laughs) It's hard.
>> It's hard and I think you know there's...
In a lot of organizations minds,
you know collective minds,
there's this push and pull.
Because in order to deliver that best possible
customer experience,
they realize they need to gather more data about us.
Right?
Every touch point,
every interaction,
if you can gain that complete 360 view,
it just means that you'll be able to deliver
a better possible experience.
But now we're gathering more data about customers,
and to your example about Facebook,
now means that we are now the custodians
of what was you know,
an explosion of data than what we used to have before.
And if you're moving those to the Cloud,
how do I make sure that I don't end up
you know in the front page of the Wall Street Journal.
You know like some of the other organizations have.
So there is great,
you know volumes of data being collected.
But how I do I manage it,
secure it,
govern it effectively so that we don't have those--
>> I want to ask to a question because we talk
a lot about fake news and Facebook lately.
Because you know we're a digital cubes
of digital distribution,
ten years been doing it,
putting out good payload with content.
Right, guest like yourself.
But there's really kind of two things,
that's where I want to get your reaction to.
There's the content payload
and then there's the infrastructure dynamics
of that (mumbles) effect.
So Facebook is an example
where there was no regulation obviously.
They were incentived to actually get more data
from the users but say you've got content or data,
and then you've got infrastructure kind of like dynamics.
You guys are looking at an end-to-end,
you got on-premises to Cloud,
that's infrastructure and that's going to be powering
the AI and the SAS.
Data becomes the paylood.
So what's your...
as a product management executive
and someone thinking about the customer,
and talking to customers,
how do you view that?
What's the customer's formula for success
to take advantage of the best use of the content
or data in digital while maximizing
the opportunities around these new kinds of infrastructure,
scale and technology?
>> Yeah,
I think you know they've come to the realization
that data is not entirely sitting on-premise anymore.
You know in the old world to get customer data,
you go to three applications,
a CRM and ERB and some kind of you know,
a couple of home grown applications that are on-premise.
Now for the same functionality
with its voice of customer,
customer experience applications,
what do we call it?
There's an app for it and it happens to reside in the Cloud.
So now you have about a 1100 on average
Cloud applications that store components.
So where do you start bringing all of that content together?
A lot of organizations have realized
that you know doing the Cloud for two reasons
because that's where bulk of this data is being generated.
That's where the bulk of this data is being consumed.
But the other aspect of it is we're not no longer
talking about hundreds of millions of records.
But I just start bringing in transaction data
and traction data becomes billions of records.
And where else can you scale with that much ease
other than the Cloud?
So,
but at the same time,
there is a hybrid that is extremely important
because those applications are sitting on-premise
are not going away.
You know they still serve up a lot of valuable
customer data and continue to be front line
operations systems for a lot of the users.
So a truly hybrid approach is being developed.
I think that thought process is coming around
where some domains live in the Cloud,
some domains live on-premise,
but a seamless experience across both.
>> That's great insight.
Now I want to then follow up and ask you okay how does
Informatica fit into that because you guys want to provide
that kind of horizontally scalable data layer depending on
where the customers' needs are in any given time,
you've got APIs out there,
there's things that are going on,
where do you guys fit in?
How do you make that a reality
with the statement you just made?
>> Yeah,
and the reality is already being you know,
being lived today with a few of our customers.
And it is that data layer that says you know,
we can you know bring data,
run workloads that are behind the firewall.
We can do those same workloads in the Cloud
if that's where you want to scale the new workloads.
But at the same time,
have a data layer that looks like one seamless bridge
between the Cloud and on-premise.
And there are a number of different experiences
that can you know help that.
We've invested in a Cloud you know designing
and monitoring you know capabilities that allow
you for a completely Cloud-like experience.
But all of the data still resides on-premise.
It's still being managed and you know behind a firewall.
Which is where a lot of the organizations are going as well
especially the more conservative,
more regulated organizations.
>> One of the things I want to get your reaction to as well,
great commentary by the way,
great insight.
Is...
Some success examples that might not be directly
to Informatica but kind of point to some of the patterns.
Let's take Slack for instance.
Been a great software,
it's basically an IRC message chat room with...
on the web with a great user experience,
but the adoption really kicked in when they built
integration points into other systems.
>> Yes
>> So this seems to be a fundamental piece
of Informatica's opportunity is to kind of do this layer
but also integrating it because although you might
have monitoring,
I might want to use a better monitoring system.
>> Yes
>> So you're now thinking about integration.
How do you respond to that?
What do you guys do with respect to integration?
What's the product touch points?
Can you share any commentary on that?
>> Yeah so you know
the openness of our entire data architecture
and all of the solutions is something that we you know,
I think we use the word Switzerland quite often.
But what it also means is that you know,
you are able to plug-in a best of breed execution engine
for a particular workload on a particular platform
if you so desire.
If you want to plug-in a you know AI, ML model that happens
to be developed on a specific,
let's say an Agile or AWS,
you'll be able to bring that in because the architecture
is open completely API driven as you mentioned.
So we're able to you know,
our customers have the flexibility to plug-in.
And we try to make that a little easier for them also
you know as you might have seen some of the demos yesterday.
We are providing recommendations and saying,
you know for this particular segment of your workload,
here are the choices that we recommend to you
and that's where CLAIRE,
John,
you know comes in.
Because it's very hard for users to keep up
with all of the different possibilities,
you know or options that they might be having
in that particular data landscape.
And we can provide those recommendations to them.
>> I want to ask about something you were saying earlier,
and this is that companies are using data
to realize that they can charge a premium
for a better customer experience.
And that really requires a change in mindset
from a gut-driven decision making
to a data-driven decision making method and approach.
How are you seeing this mindset shift?
Is it...
Are companies still having a hard time sort of giving up?
But my gut's telling me to do this.
In particular with relationship to the new...
The acquisition you made in February of AllSight.
>> Yes.
Yeah you know I think the good news
is you know across the board,
line of business leaders,
CEOs,
even boards are now recognizing customer experience.
Customer engagement happens to be top of mind.
But there's also equally a you know a recognition
that data is what is going to help you know make
this a reality.
But, so that was one of the reasons why we went out
and you know did this acquisition of AllSight.
Because if you think about it,
customer data is no longer just a handful of slowly
changing attributes that can name an address
and a telephone number.
Or a social media handles that you know could be used
to contact us.
But it's really about now the thousands of interactions
we might have on the websites,
clickstream data,
web chats,
you know even calls into call centers.
All of this and even more PR Tweeting about a product
or a service online is all the interactions
and touch points that need to be pulled in and the dots
have to be connected in order to build
that customer profile.
So we have to do that at scale,
and that's something that AllSight has been doing very well.
But it's now become more about disconnecting the dots.
So we can say that here is this customer and this is all
the different touch points this customer's had,
all the different products and approaches from us over
the last few months,
few years.
But now can we derive some insight,
some intelligence to find connecting four pieces
of information?
Can I infer a life event?
Can I detect that an insurance customer is ready to retire?
Can I detect that this family is actually shopping
for a vacation to Hawaii?
That's the first level of derived intelligence insight
that we can now offer with AllSight.
The next level is also about saying,
can (mumbles) understanding you know some of these,
you know intent?
Can you also understand how happy is this customer?
Have they been mentioning competitor products
which can allow us to infer that this person's
probably going to go off and buy a competitor's product,
if this problem they're having with this device
or product is not resolved.
So churn scoring,
sentiment scoring,
and now the third level on top of that which I think
is really the game changer,
is now can we infer what the next best action
or interaction should be?
Based upon all these things.
Can we even do things such as,
as I...
let's say I'm not too happy customer with a particular
maybe laptop that you know I purchased,
I call the call center.
Can...
As the call is coming through,
can we infer what I'm calling about based upon all
of the interactions I've had over the recent past,
and direct that call to a level two
or level three technician
who specialize in the laptop model that I have
in order to make me you know,
continue to be a customer for life.
>> One of the biggest challenges happening
in the technology industry is the skills gap.
I want to hear your thoughts on it
and also how concerned are you about finding
qualified candidates for your roles?
>> So you know,
I think being a globally you know global organization,
with R&D centers distributed around the world,
I think one of the luxuries we have is we're able
to look across not just you know,
we're from Silicon Valley,
you know and there is a definitely a you know
a huge competition for skills over there.
I think one of the things that we have been able
to do is locations like Toronto.
We were just talking about that's where AllSight is based.
Extremely cool technology that's come out
that's really transforming organizations,
and their approach to customers.
Stuttgart,
you know Dublin,
you know Bangalore,
Chennai,
Hyderabad.
So you know we are tapping into centers
that have lots of skill you know folks.
Call it hedging you know our approach,
and looking at this globally.
Yes there's definitely going to be even more of a demand
as a lot of technology changes go for these skills.
But I think you know by spreading you know
that skills and having complete developed R&D centers
in each of those locations helps us mitigate that problem.
>> What about kids in school,
elementary school,
high school,
college or even people retraining.
Is there a certain discipline,
stats,
philosophy,
ethics,
where you see data opportunities for folks
that may or may not have been obvious or even in place.
I mean Berkeley just had their first graduating class
of data science this year.
That's so early.
People want to hone in.
What do you see as successful people
attaining certain skills?
What do you recommend?
>> So I think there is definitely a combination
of technical skills,
whether it is the newer AI and ML applications.
But I think there is also you know,
in the past we would have said,
let's go and hire then someone who's done computer science
you know and is very deep into that topic.
But look at the problems we're trying to solve
with data and the application of AI and ML.
They're all in service of a business outcome.
Some kind of a business outcome.
And the more we find people who are able to bridge
that gap between the strong application
of the newer technologies AI and ML,
and also an understanding of the broader world
and the business.
I think is really that combination
of skills is really what's going to be required to succeed.
>> Excellent.
Great note to end on.
Thank you so much Suresh for coming on the show.
>> Thank you.
>> Great insight.
>> Thanks.
>> I'm Rebecca Knight for John Furrier.
You are watching theCUBE.
(techno music)