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>> Hello, and welcome back to theCUBE's coverage
of Red Hat Summit '21 virtual conference.
I'm John Furrier, host of theCUBE.
We are here with Kevin Martelli,
Principal Software Engineer at KPMG,
joining the conversation.
Kevin, great to see you.
Thanks for coming on.
>> John, thanks a lot for having me.
>> So obviously Red Hat, a lot of action,
cloud native, part of IBM now.
A lot of talk going on around this growth around cloud.
Massive new opportunities,
new modern applications being shaped in,
super exciting opportunities.
So first, before we get into all that,
tell us about your role at KPMG.
>> Sure John, thanks.
So my role at KPMG,
I'm one of our cloud leaders at KPMG
where I really help both from an internal perspective,
so helping our internal enablement and digitalization,
as well as, more importantly,
helping to deliver solutions and applications
to our clients as they go through these digital journeys.
And really focusing on containerization
and enabling it through the cloud.
>> John: You guys have done a lot of AI work,
I know which is cutting edge,
it's pretty much data-driven.
I mean, AI is,
what everyone talks about,
but underlying AI is automation, data, machine learning,
really dealing with kind of new types of datasets,
not just dealing with existing structures.
You have a new platform called Ignite.
Tell us what that is.
What do you guys solve?
What was the problem statement?
And what's going on with it?
>> Yeah, John, thanks a lot for asking.
So Ignite, it's something
that we developed internally initially,
and it really helped to solve our AI initiatives.
We called it our AI platform,
but it's moreso an ecosystem.
And it solves not only our own internal needs
and internal use cases,
but also choose to help support
and deliver these solutions to the clients.
One of the foundational principles of our platform
is it's built on top of containerization,
which we know is a hot area now,
today in the marketplace really gives you
the ability for scalability,
flexibility, security, et cetera,
but more important, what we're seeing
large scale of adoptions in our clients,
is using this platform to really get value
out of both unstructured and structured data
in a way that they're able to do this in a secured fashion
and then easily get it deployed.
It's a pretty scalable platform,
and something that we've just recently
received the patent for it.
>> So what was the internal conversation
to put this together?
Was it the fact that there was business needs?
Cloud native gave you that scale advantage?
What was some of the drivers behind Ignite?
'Cause this is like, was it IoT?
Was it,
take us through the mindset.
What were some of the first principles around building this?
>> Hey John nice, it's a good question.
And actually to be fair,
this was probably a little bit
before the time of IoT
and some of these newer technologies
were coming up.
At this time, we were really kind of scratching
on the surface of data science and advanced analytics.
And what really generated the need for this is
as you could imagine, working in a consultancy firm
and many of our clients deal with tons of contracts
and the lightboard documents for financial services,
there was much rich information,
these unstructured data documents
and we had no way to get this information out.
So really it was generated out of the need
to get information out
of a lot of these contractual documents
that we had and pinpoint specific information.
So really taking it holistically on ingestion
on transformations, running NLP,
algorithms,
it really evolved into a whole end to end complete platform,
running on top of a containerized ecosystem,
such as OpenShift.
>> John: Yeah, I think just not to go on a tangent here
but I think one of the conversations we've been having
on all these events and certainly with COVID
was the highlight of all these silos.
And you know the old days was about break down the silos.
But now with containers and cloud scale,
you can extract out data,
kind of create that horizontal data plane if you will,
or view observation space,
some call it.
This just seems to be a huge trend
you guys were on it early.
How has that, what's your take on that?
The silos used to be kind of like
an advantage if you had a monolithic application
but now you have a lot of diverse distributed databases.
What's your take?
>> Kevin: Yeah, it's, it's good.
And how we are kind of coining.
It is really through the power of,
some of the toying and OpenShift
that really gives organizations
the ability to defer risk.
In the sense that allows you
to run certain types of workloads
on-prem and a private cloud containerized way.
It allows you to burst certain other types of workloads
into the different CSP provider.
So you can get advantage of their scale,
their capacity without maybe moving some sensitive data
and then another benefited
with some of that vendor lock-in it
sometimes clients are concerned about is
being able to kind of easily deploy your workloads
and applications from one cloud provider to another.
And I think as we look at this distributed processing
no one client will totally be in one cloud provider.
So having the ability to move workloads quickly and fastly
where they make sense,
where the security and risk is aligned
is something that would
what makes a successful use cases deployments.
>> John: Just let me ask you another question.
You guys, KPMG obviously
have your own big data effort going on with analytics.
You've got clients that you serve
and ultimately they have customers as well.
So you have that Red Hat equation.
What are some of the advantages
that you guys see as your firm
and your clients with Red Hat analytics,
'cause this becomes ultimately
the number one conversation.
Like, okay, what's in it for me?
>> Yeah. That's a good point.
I would say we're seeing a few things.
Some of them are highlighted.
One is, as you're well aware,
we chose Red Hat's OpenShift
as one of our strategic options
to deploy our platform.
And whenever you're deploying these platforms
it's very important that you have the flexibility
the agility, and the ability to scale
and Red Hat underneath the hood
really helps take care of a lot of that,
for you in a way that not only can you do it on your own
as mentioned earlier,
your private cloud
but also onto the public CSPs
and multiple CSPs.
In addition, some of the other things
I think that we saw that were very beneficial,
a lot of times as an application user.
So application users of ignite,
the developers,
the data scientists,
the business users, the analysts,
they all need to interact with the platform.
They want to worry about getting the insights
about getting the efficiencies in the platform.
They don't want to worry about
how the infrastructure's being put together,
how the workloads are being moved
how the scalability is occurring, et cetera
and Red Hat really takes a lot of that
away from you having to worry about it.
And one of the other things that's also important is,
is we have a strategic relationship
with Red Hat.
And as we look to help to enhance
and develop these capabilities and experiences
as our clients are doing private cloud,
hybrid cloud and multi-cloud,
we're really going to be able to let them
take the power of open source,
into their own control
and how they want to deploy it in themselves.
>> Well, got you on the topic there.
I got to ask you the question.
What would you say to the people out there
that haven't really kicked the tires
on Red Hat in a while?
What's the modern update?
How would you describe the current situation
at Red Hat for people who are going to re-look
and or bring the Red Hat conversation up a notch?
>> Yeah, it's a good question.
I think we see this in any type of software
in the industry today.
There's so many choices
and there's so many options out there.
And how do you choose the right source
for the right use case?
For the right client,
for the right company?
And how we always like to talk with clients
is that yes, there are a lot of choices in there
and the orchestration for the standardization
but when you're looking for something
that's celebrated in the market
that has the security built into it
that many organizations are looking for
that gives you the flexibility
without having to do a lot of additional
operational overhead of moving from on-prem
into the cloud
and the way that it can scale
and kind of make the overall ecosystem operations
and deployments easier,
it's one of the benefits that we see
have gone with a tool like Red Hat OpenShift.
>> Well, Kevin, I really appreciate the comments there
and on Red Hat, that's awesome.
Red Hat Summit, honestly, a big event around Red Hat
and future cloud and modern applications.
So I got to ask you as a software engineering leader
in the industry,
you got to be pretty excited about artificial intelligence
and machine learning as it relates to,
what it can be doing
for changing the software development paradigm.
Obviously there's also the no code,
low code, serverless.
You've got cloud native,
you've got containers
you got all this new capability.
So how does, how do you see those trends?
What are the big trends around machine learning
and AI as it relates to
someone who's going to be building modern applications
in the cloud.
Because certainly there's a huge ups upside there.
Some are saying that if you don't have AI
that's going to be a table stakes
and we'll lower the valuation of the software
or the application.
What's your take on all these big trends around AI?
>> Yeah, I agree with that.
We've actually done several studies.
And what we're hearing industry leaders saying
is it was quite a few things.
One is, we at KPMG, COVID-19 whiplash.
And really what that means is that
the pace and acceleration of adoption in AI
has been tremendous over the COVID 19 period
of our pandemic period.
And so much so that industry leaders
are a little bit concerned
about how fast this adoption is going.
And is it going too fast?
In addition, we recently published a study
called Thriving In An AI world
where we were able to identify that business leaders
and insiders are really bullish on to your point
of using AI and ML
to make some poor, critical decisions.
How can we make vaccines?
What's the distribution process?
Fraudulent analytics where financial services.
However, what I will say is
we're still seeing a lot, a lot of questions
and challenges around AI.
Its security,
its ethics associated to it.
How you keep managing governing your process
then privacy associated to it.
So there's a lot of points around those areas.
I think that industries are still trying to struggle
and figure out how to solve for.
And one of the things that we are hearing
is that what the new administration
there's different think tanks
and industry leaders that are feeling that
the new administration,
while open to a lot of these advanced techniques
and technologies
are going to put a little bit more rigor around
and regulations around how AI can be used
in the marketplace.
So hopefully that would give some companies guidance
around these security and privacy and ethics concerns.
>> Yeah, it's interesting.
I was talking to a friend the other day
who's a leader at a big company
that's a customer of Red Hat
and a lot of other clouds as well.
And we were joking about the agility speed,
oh, agility and speed.
Of course, yeah, you get that with here
but you got a lot of fast and loose situations going here.
You got to know when to put the pedal to the metal.
When there's a straight narrow,
we can really kind of gas it with AI and machine learning
and then know where the potential curves are.
See, will use that metaphor
because you can go fast
but with speed comes dangerous new things
for breakage.
Is always, and you're seeing that all the time.
You're seeing that,
with software because you can push new update,
but still, when you talk about operational integrity
and security
fast and loose, isn't always the best way to go.
But if you know there's a straight and narrow,
you can really push it.
This was what we were saying,
he's like, "Hey, we know when to go straight
and narrow and go fast.
And then when to slow it down, pull it back."
What's your take on that?
What's your assessment?
>> No, I agree.
I think you hit some valid points there.
And sometimes what we do
is we take some antiquated processes
and we overlay them into these newer technologies
and we try to think them as being the same way
and they may not always hold true.
But it's not only kind of the fast and narrow
and then putting things
in that maybe a little bit more simplistic,
but it's also there's a whole change around
how you productionalized.
How do you get these things into deployment?
How do you monitor these over time?
So some of those biases
or some of those privacy concerns
don't end up creeping up into the algorithm over time.
I still think that will work here
and from industries.
There are still struggles around that.
There's still struggles around.
There's a lot of technologies
that can do a lot of these same things.
Our business processes don't always align.
And then how do we really take something from an innovation
from a POC into production?
Is there a fast track for something
that is straightened narrow
and something that has a little bit more complexity?
But what we're seeing today,
there's a lot sort of spout at the same road,
which makes bringing more complex AI algorithms
into production.
Challenging.
>> Yeah. And there's always that big trend
of day two operations.
Which is, hey, you deploy
it's great.
And then, okay, wait a minute stop,
set in a break.
We need better monitoring.
We need better data analytics.
What's instrumented.
What's not.
What services are being generated and terminated.
These are all big cloud native kind of themes.
With that, I got to ask you from a customer standpoint,
these are new first-generation problems at scale
that with this new cloud native environment,
the pros and cons.
How do you guys talk to customers?
What are some of the things you're seeing around
the challenges that they face
with analytics?
All these analytic activity?
>> Kevin: Yeah. So I think one of the challenges
and we've probably heard this year
in year out is around data literacy.
Like really having our folks understand the data
and empowering them to be successful in the organization.
And to be fair
I would say data literacy was a little bit more narrowly
focused in an organizations who needed it.
I need some analysts to use it.
I needed some data scientists and engineers,
but what we're starting to see now
is there's larger programs across the board
where it's more holistic at an organizational level.
Everyone should be involved in data.
Everyone should be able to do their own reporting.
So really data literacy and getting data
kind of into the arms of the folks is important.
Some of the other ones
that we've also kind of talked to about it,
and they kind of go hand in hand
and maybe a little bit on our prior conversation
was the technologies.
Technology especially in open source is exploding.
And as well as commercial.
So how do you choose the right technologies
the right tools?
You don't have too many tools in your toolbox per se
but use the ones that are really differentiating
and try to standardize on the ones that are more standard.
Finally it's bringing those processes
and that wrapping them back into the technologies.
Again, a little point we hit on earlier
but what we're finding is as technology
is rapidly increasing,
you're able to use it for your analytics.
Your processes are still antiquated
and legacy processes which makes it
a little bit harder for you to really take advantage
of what you're trying to achieve
in your organization from a digital transformation.
And then one final one I would add in there
is around the risk that organizations have.
So there's a lot of concern about reputational risk.
If they're doing these types of activities
that people don't understand,
the data they don't understand the algorithms.
Are there some impacts that can be heard?
And they're figuring out how to control that
and then how not to.
And then I think finally the workforce is,
as we know, it's getting the workforce up to speed,
retooling where need be
and putting their people in the right place
to be successful.
>> Kevin that's great insight.
Thanks so much for coming on theCUBE.
I got to ask you one final question.
>> Go ahead.
One more thing, you mentioned COVID whiplash
means a lot of post COVID activity discussions going on.
If you look at what's happened with COVID
there's been an exposure of all the projects
that need to be doubled down on,
ones that may not be continuing.
People working at home.
Honestly, a change of the environment,
you mentioned workforce is among others.
What do you think the biggest conversation
around your customer base
or within KPMG right now
around some of these growth strategies around post COVID?
What are companies thinking around
how to deploy the people, process and technology
is a big part of this conversation.
What is the post COVID general theme
that you're seeing among large enterprises
and businesses in general?
>> I mean, that's a good question.
So I think in general, we're seeing the acceleration
of digital agendas that may have been pushed out
for five years school moving closer.
But one of the most interesting things I think
that I've gathered out of working with the clients
that we're working with
is that before to get stuff into production,
AI solutions
even in any type of smaller production system
that was taking months
months, several months to get something in production.
And it seemed to be once the COVID pandemic hit,
organizations can accelerate that journey
of the deployment of applications into production
in very, very quick timeframes
without hindering or impacting
any types of control frameworks they have in place,
but just working quicker.
So I think some of the things I see as we move forward
is that these digital channels
are going to be push forward more quicker.
The data list on POC is good
our pilot's good,
is long past.
It's now they want to see the results in the outputs
in the enterprise,
in production.
And I think they realize
that they have the tools to do this
in a period of time that is weeks versus months,
and in some cases, years.
>> So, would you agree then,
just as a quick followup to that
that obviously when we get back to real life,
post COVID that the visibility
and the economics
and the productivity gains
from this new environment
is going to stay around longer
and probably be permanent.
What's your, do you agree with that statement?
>> I hope it is.
but we are creatures of habit.
And sometimes you go back to
back to the way that we had done things,
but I'm hopeful that they were able to see
to be successful in these types of environments
and make these types of decisions
that those processes that are evolving
to take into consideration what we learned.
One is terrible pandemic,
and be able to apply that to the post pandemic.
>> Yeah. Who would have known the word hybrid cloud
actually means something more than just cloud technologies?
Hybrid events, hybrid workforces,
the word hybrid has been kicked around.
Kevin, thanks so much for coming on theCUBE
for Red Hat Summit coverage.
Thanks for coming on.
Great insight.
>> Thank you, have a great day.
>> Thanks.
I'm John Farrow with theCUBE here
for Red Hat Summit coverage, 2021 virtual.
Thanks for watching.
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