WEBVTT

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The information economy as a rod.
The world is teeming with innovation as new

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business models reinvent every in every industry. Inside Analysis is your source of information

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and insight about how to make the
most of this exciting new era. Learn

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more and Inside Analysis dot Comside Analysis
dot com. And now here's your host,

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thro Eric Kavanaugh. Yes, all
right, folks, welcome to the

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future. Indeed, your host here, Eric Kavanaugh, the only coast to

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coast radio show that's all about the
information economy. It's time for Inside Analysis,

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and folks, I'm pretty excited to
be digging into the wayback machine today

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and stretching it all the way to
modern times and beyond. Quite Frankie,

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we're to talk about the mainframe.
That's right, they're still out there.

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IBM still sells mainframes, the ZO
assets still a very active part of our

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commercial world today. Lots of business
processes run on mainframes all day, every

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day and have been doing so for
years, sometimes ten twenty thirty years.

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Mainframes are alive and well, we've
got an expert with us to kind of

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walk through what's going on in that
space. Doug Johnson is from Rocket Software

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actually used to be asg got purchased
by Rocket Software. I just learned that

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today. So these shows always teach
you something, and we're going to talk

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about what's happening and why it matters. So mainframes, like I say,

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have been a robust part of our
economy for forty plus years, probably almost

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fifty years now. A lot of
companies have been trying to modernize. But

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you know, if it ain't broke, don't fix it. There are a

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lot of things that work very well
on the main frame, but the question

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becomes, how do you integrate mainframe
technology with the rest of your stack?

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And what is this whole thing the
cloud about? What about containers and Kubernetes

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and all that fun stuff we've talked
about before. That's what we're going to

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discuss with our friend Doug Johnson.
First of all, tell us a bit

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about yourself and what you do in
your job as you try to educate and

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do product roadmap stuff. Thanks Eric, and I really appreciate the time to

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share a little bit about what's going
on that way back machine. I like

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that, so good stuff. Yeah. So I'm Doug Johnson. I run

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product management in our data modernization business
unit here at Rocket Software, and my

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job really entails working with customers,
our account teams and also the industry as

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large in terms of the future of
what's happening with data and how it's being

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modernized in many different areas, including
the mainframe. And so my team works

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on the roadmaps and works with engineering
and customers. It's it's a pretty exciting

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place. Yeah, that's good stuff. And you know, when you're in

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product management, your main job is
to really understand the technology, what does

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it do, how does it work, where does it apply? And when

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you start talking about the roadmap,
obviously you're trying to take care of customer

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needs. You're trying to augment what
has already been built to handle new capabilities,

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for example, and of course in
the data modernization space, and you

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know, there are a thousand reasons
for that. I'm sure one big data

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we saw fifteen to five years ago
just dominate the conversations in our industry.

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Now there's more emphasis on AI and
machine learning and things of that nature,

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but big data is everywhere now.
It's not that it went away, it's

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just that we all take it for
granted now. And that was one of

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the main drivers for this whole modernization
concept, right, It's the fact that

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we went from relatively small amounts of
transactional data to very large amounts of really

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machine data, like machines talking to
machines, exhaust data, some people call

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it. But all these new systems
that Google and Facebook and LinkedIn and these

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other companies have built, well,
they spin out tremendous amounts of data.

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And if you can capture that data
marry it with other data, you can

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start understanding what's happening in your environment, why are things going slow, why

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are things working, etc. But
that whole big data revolution really drove what

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it is now called data monetization.
What do you think, Well, it's

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a great, great summary of allow
some of the big trends that have been

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happening in the last several years.
And with that, when it comes to

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topics around the mainframe, it's very
interesting because what we really have experienced is

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a lot of customers have gone through. Even though the mainframe has remained the

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same over the years, the people
who have been working with it in those

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systems and understanding what's even there and
how it impacts the rest of the business

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is just not known. And so
this idea of being able to see what

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is going on in your environment,
but also tying it to other business processes

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that you have and the data flows
throughout the organization is a key problem customers

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have, and so that's one of
the some of the things that we do

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in our roadmaps is look at tools
that can look at the code, look

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at the data in those systems,
and then stitch it together to see the

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connections that are hard to see today. And it really helps, you know,

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as part of that modern zation journey, go left, go right,

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tweak a little bit here or there, and if someone's looking for some key

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information, we can help you find
it in terms of bringing it into those

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analytics stacks and other things. So
again, the mainframe plays a very big

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role, and that part of it
is just really around having that visibility.

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Yeah, and coball, right,
a lot of mainframe applications were written in

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coball and that was a problem for
quite a long while, like the last

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five or six years or so.
There are so many jobs for coball developers

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because guess what, the old apps
were written in coball. And even though

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that's still the case, something called
chat GPT came along and may have helped

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out a little bit. I'm curious
to hear your thoughts on that, because

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a lot of people don't know chat
GPT cannot can write poems. It can

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also write code. You can write
in all sorts of different coding languages,

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not just human languages. Right,
yeah, it can do that sort of

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thing. The interesting thing is when
you're talking about the mainframe and that some

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of the critical systems that are existing
in those environments. You don't want to

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enter a three letter prompt or just
burning through your code converter and go I

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think it'll be good enough for my
customer critical transactions. So we haven't seen

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in this particular environment, we haven't
seen the big push for that, although

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we're always watching that space in terms
of things that we can offer customers.

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But the visibility around, for instance, running some tools against your coball program,

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understanding what those connections are, and
then maybe where you want to put

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investments to make a change. So
whether it be used some tools to help

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you migrate code or data in those
environments, which I'm sure we will talk

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more about the data challenges around that, but also just kind of connections is

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a first step that we've been focusing
on recently. Give you an example is

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for instance, around someone trying to
run a report against some critical information and

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maybe the information exists, maybe in
the source from somewhere and they don't know

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where, but ultimately it ends up
in a bi cube or something where they're

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trying to run some reports. But
when they do that, they find that

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the timeliness of that data is a
good twenty forty eight hours behind what a

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business needs, and they want to
see what is the source of this data

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and can I get that data faster? And so that's some of the things

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that we do. We help you
buy back that time by getting visibility into

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where information might exist in the organization, including the mainframe baked into that coball

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somewhere, and so then you can
go and look at some open standards to

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get that data in a more timely
fashion. Yeah, that's very interesting because

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to your point, there is an
immediacy to business these days. You want

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to know what's happening right now.
Knowing what happened forty eight hours ago could

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be useful, but it's not very
good for customer experience, it's not very

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good for putting out fires. So
the question that becomes, how do you

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do that? And I guess what
you're saying is you've got some tools and

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technologies and methods to help companies look
into the main frame that they have and

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kind of understand where the data is
going in, how it's going out,

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when it's coming out, and you
have other ways of accessing and improvision it.

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I mean, there are lots of
ways that data is moved around.

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ETL was the bread and butter still
is for many organizations. Extract transform load,

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then you're doing ELT, extract transform, etc. There's data virtualization.

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A number of vendors have been that
space for a while, and what I'm

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hearing from you is that you're able
to find ways to see into the environment

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and help the business understand where the
data is coming from and then how they

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can expedite that process. Right.
That's exactly right, and it's what I

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really like about what you said earlier
about the ecosystem and the mainframe is really

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important because obviously the world isn't just
one technology today, it's a whole series

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of technologies. And one of the
things that we've really focused on is making

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the data that's permeating the entire organization
being able to see it and manage it

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both for those use cases I was
mentioning around, for instance, reporting,

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but also very critically things like compliance
or doing business change as an example,

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So if you consider, hey,
there's a project we're considering where we want

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to change a system, maybe change
a user experience, what impact would that

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have to other parts of my organization, including those critical information that exists on

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the mainframe and has managed in those
environments. And that's some of the things

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that are technologies and some of the
investments we've been making help customers really gain

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those insights to make a choice again
on where to put those investments and what

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kind of impact of analysis it might
have in terms of that decisioning. Yeah,

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and the impact analysis is huge,
right, because when you transform the

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business, that means you change something. These days, that means you're either

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changing code, or you're adding code, or you're changing an architecture. You're

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doing something new. And let's face
it, there are lots and lots of

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shining new objects these days. There
are lots of new ways to get data.

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There's streaming with technologies like Kafka or
Pulsar. You've got snowflakes of the

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world and data bricks and all these
other amazing technologies that have come along to

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at people that analyze data, but
that doesn't change the fact that a lot

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of the data and the systems are
still running on the mainframe. So coming

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up with that information architecture that's the
real key to success these days and being

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able to see into these environments.
Do the impact analysis, as you suggest,

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is crucial to be able to understand
what you can change and how you

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can change it without breaking something right
done absolutely and there are millions and millions

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of dollars and then lots and lots
of armies of people working at this and

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so if you get it right or
get it wrong, it's huge impact to

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a business. It can really make
the difference between are you competitive compared to

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what others are doing, or you
actually behind if you make the wrong choices.

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And that's what we really try to
help organizations is buy back that time

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and reduce that risk of that critical
decision making and along the way in terms

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of that decision to make what we
also find it knowing the strategy that people

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have with the mainframe is really important
some organization and say hey, we have

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these critical systems, we've had them
forever, We're going to keep building an

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ecosystem around it, or some other
customers say, we really want to have

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a strategy where we can take some
systems as part of that and have a

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hybrid approach where we start moving some
of that to the cloud. And then

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some customers are all in where they
in a five or ten year, fifteen

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year time frame even in some cases, they want to have that trail map

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to perhaps move from what they have
today to some future goodness that they have.

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And so that's a key area also
in terms of that visibility that it's

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critical to the overall corporate strategy and
investment portfolio that happens. Yeah, right,

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well, and so we should appreciate
the fact that mainframes work. And

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I can tell you in the operations
world, if something isn't broken, they

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don't want you to fix it.
I mean just generally speaking, leave it

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alone is their main message, because
they're the ones responsible for you know,

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that's where this whole business it divide, I think came from was because the

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business wanted to change something. It
is like, guys, what are you

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talking about? We're ubbing trouble keeping
this running as it is. Don't complicate

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things, right, But then the
cloud comes along and machine learning comes along,

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and AI matures and all these things
develop, so there's increased pressure on

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the business to change something. The
key is what do you change? And

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it sounds to me like you're offering
a very positive surface for being able to

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assess what is the current state,
how are these applications running, if we

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change this, if we change that, how is that going to impact us

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down the line? And you know, no doubt about it, Rushing to

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the cloud is not something anyone should
do. You really want to understand what

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are are these business processes, what
can we modernize? Where can we modernize

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them? And you wind up usually
having a sort of layer of abstraction right

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above the main frame, but below
the cloud or below the on prem data

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center director of the data center.
You want these layers of abstraction where you

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can kind of understand what's moving where
and then make your changes in those environments

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that affect either upstream or downstream.
Right absolutely, and as part of those

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that layered approach that you were talking
about, trying to really deal with a

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lot of the unstructured data that also
exists elsewhere in the organization, including the

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mainframe, is really important. Things
like customer statements or building information. You

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know things that aren't very important to
customers, right, and so you as

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you go to look and modernize how
you're doing that, you just really want

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to be very thoughtful and you want
to you want to make investments that as

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you, for instance, build out
a new customer portal or build out a

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mobile app experience as part of that. When you have that API, that

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abstraction that you were talking about,
you want to make sure it can get

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information wherever it may exist in the
enterprise, and of course that also includes

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the mainframe. But you don't want
to put the burden on, for instance,

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an individual app application developer to have
to know about arcane formats like AFP

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or whatnot. And so that's also
what some of our technologies do, is

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we help you access that unstructured information
and we put it in a modern format

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on the fly so that your application
developers don't have to know anything. Matter

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of fact, what's interesting is people
using our technology in many cases, they

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don't even know what's on the back
end from an end user perspective or an

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API perspective. And that fits very
much into that kind of middle layer that

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you were talking about to really help
with that abstraction where you don't have to

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force on a developer or an end
user person to really have to know about

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that endpoint of where it comes from
that data right. And you know,

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I'll throughout an interesting point here,
just kind of an analogy. I just

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came back from Clickworld last week and
they were talking about how they're moving to

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the cloud click Cloud. But they
also were very careful to say time and

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again, we are not going to
forget about all the on prem installations that

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we have. So we're still going
to be taking care of these technologies.

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We're still going to make sure everything
works fine in that world as we encourage

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people to go to the cloud.
And one of the key considerations is that,

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but with any sort of applications you've
been using and gotten used to,

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if you port that to the cloud, usually what happens is you only port

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the bare bone stuff first, and
then over time you start adding features that

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were on prem. And that's for
a whole variety of reasons. One dev

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time just that takes time to do
stuff right. But the point being,

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if you were to go straight to
a cloud instantiation of some data warehouse,

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for example, you'll lose a whole
lot of context. You lose functionality,

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you lose things, and that's why
you want to be careful about what you

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do here, because number one,
to be able to replatform or sort of

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re architect refactor a whole existing environment
and the cloud is going to be really

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expensive. It's going to be painful
to do it. Even cloud natively for

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example, it sounds good, but
it's very difficult to pull that off.

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So I like that you use this
term thoughtful to describe how your team should

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be as you look at your environment
and figure out ways to go forward.

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Right, you want to take time
to do that, really document everything and

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test all along the way, right, Yeah, absolutely, And what's interesting

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is organizations definitely going to take a
very measured approach, but some organizations also

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say we want to take a measured
approach, but there's some footprint that we

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want to move faster, perhaps even
away from the mainframe in some cases.

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And so the technologies that we've been
working on really help you be able to

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move the data without reprocessing it to
popular endpoints like for instance S three,

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And you can even do hybrid models
where where your processing still has done on

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the mainframe, but your storage is
actually done in a modern fashion. Like

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for instance, like with the S
three or something like this. And so

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what we find is it's really about
the the applications before and after the data

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that are super important. If you
disrupt those thousands millions of lines of code

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in many cases that are sitting before
and after that data, that's where the

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real disruption that happen. And so
we've worked on a lot of tools that

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can help you mix and match a
lot of these hybrid use cases with clouds.

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So it doesn't have to be an
all or nothing. If your organization

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is saying we must move faster,
we must do some digital change to change

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and experience or bring some data to
a customer in a fresh way. Yeah,

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that makes a lot of sense because
there are pressures to move to the

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cloud. There are lots of good
reasons to move to the cloud. It's

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a fantastic marshaling area for functionality.
It's a good marshaling area even for data,

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at least data that's born in the
cloud. Now taking a whole mountain

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of data from on prem and pushing
it up into the cloud. There are

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companies doing that. You have to
be careful about how you do that because

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you really want to understand your access
patterns. You want to understand what people

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are doing with that data, how
does it interact with your applications. All

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that stuff can be mapped out,
and really should be mapped out before you

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ever try to pull something like that, because if you go too quickly,

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you're gonna have a very unpleasant route
awakening when it arrives. But both don't

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00:17:57.960 --> 00:18:00.599
touch up, that will be right
back. You're listening to inside analysis.

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That's eight hundred two four five one three

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seven five, eight hundred two four
five one three seven five. Welcome back

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00:22:07.400 --> 00:22:15.440
to Inside Analysis. Here's your host, Eric Cavanaugh. All right, folks,

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00:22:15.519 --> 00:22:18.400
tickets to the future. Indeed,
I want to go to the future

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00:22:18.440 --> 00:22:22.279
where the systems are always running quickly. I never I'm on the phone with

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00:22:22.400 --> 00:22:26.720
some customer service represolve. I'm sorry, systems are running kind of slow today.

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00:22:26.920 --> 00:22:30.440
I don't want that. Nobody wants
that. You want stuff to work,

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00:22:30.799 --> 00:22:32.839
right, that's what you want.
You want to go to Amazon buy

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00:22:32.880 --> 00:22:34.279
stuff, go to some other side
buy stuff. And have it work and

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00:22:34.279 --> 00:22:38.440
have it be seamless and quick and
fast and secure. That's what we want.

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00:22:38.759 --> 00:22:41.519
That's the good stuff. You got
to modernize to get that. You're

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not going to get that with some
of these older architectures. And so I'm

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00:22:45.400 --> 00:22:48.559
talking today with Doug Johnson, who
is an expert in this space with Rocket

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00:22:48.640 --> 00:22:51.599
software these days, a little bit
name by the way, how can you

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00:22:51.640 --> 00:22:55.440
go wrong with Rocket software? But
we were talking about containers in the break

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00:22:55.440 --> 00:22:57.599
there, and I'll just explain quickly
to the audience for those of you who

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haven't heard before. Google came up
with this project called kubernets. If you

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ever see kh s K eight,
that's a sort of abbreviation for it.

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It's a container orchestration system. But
basically it's sort of like a de facto

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operating system for the cloud. Now, Kubernets does run on Linux, so

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that's the operating system that it uses, but it kind of operates like a

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de facto OS. And the best
way I heard it describe was by a

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girl in the open source world who
said, look at containers as little boxes

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for system processes, so little things
that your computer has to do, like

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open and application or a lot of
times. I'll use a spell check as

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an example, Right, you could
use a spell check in Word, or

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you could use a spell check in
Excel in other applications, that's a service.

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The spell check is like a service. And way back in the days

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of service oriented architecture, we had
course grain services and fine grain services.

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It was the same general vision.
But then Kuberneti came along and really kind

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of revolutionize how that stuff gets done. A lot of the new apps that

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you see out there, a lot
of the cool new stuff that's happening in

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the world of technology, is running
on Kuberneties. It's everywhere now. They've

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00:24:07.680 --> 00:24:11.799
basically won this whole shebang, and
it's an open source technology, so you

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00:24:11.799 --> 00:24:14.319
don't have to pay to use Kuberneties, but you do have to get people

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who know how to do it.
It's not the easiest thing in the world

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00:24:17.359 --> 00:24:19.920
to figure out, so there is
obviously a cost to it in terms of

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00:24:19.960 --> 00:24:23.799
deployment and management, etc. But
it really has one to day and containerization

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00:24:23.880 --> 00:24:26.799
is a new way to do things. And the last thing I'll say about

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it is that what's nice is that
because you've broken it into lots of little,

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teeny tiny pieces, the big stuff
doesn't break as much. Right,

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used to get sites that just crash
and didn't work. You don't get that

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as much in these new environments.
What you'll get is one little piece of

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it that doesn't work. And then
what they do is they kill that pod.

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Basically the pod just say up,
kill that guy will start again.

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But with that, I'll throw it
over to Doug Johnson. Kubernatis is changing

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00:24:49.799 --> 00:24:53.519
the world, but it's not the
main frame, and we have to find

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a way to get these two systems
to talk together and to work together.

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And that's kind of what you guys
are working on right Yes, absolutely,

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And with that, there's a number
of areas that our customers and we've seen

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in the industry in terms of leveraging
the mainframe with kubernets. So you know,

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at the base level is we were
talking before the break, we were

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talking about this kind of abstraction layer
where you can access data. And so

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what we do is we create some
of these pods that you were talking about

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that run in that Kubernets layer and
put a front end essentially in front of

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that mainframe and then provide all sorts
of data services to that. So everything

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from just raw access like we were
talking about before, where it can convert

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formats. For a developer or an
end user, we can get access to

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information. But also if you need
greater scalability, it can just create another

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pod and have that kind of level
of scalability. But then there's also other

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things that happen along that same vein. Is within that layer is also where

365
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we put some protective services. So
it's the mainframe or really any critical enterprise

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information, you really want to make
sure it's thoroughly protected, and so we

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do everything from having a very strong
access control layer that can even look at

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where you are in the world and
determine whether you can access something, or

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even do things like redact information on
the fly for you, so if you

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go to access some sensitive information,
it can make sure that you can see

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certain information and then other persons see
other information. And so those are some

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of the things we've been working on
and customers are really enjoying. Yeah,

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that's a very interesting comment you just
made. There are new technologies that are

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coming out, technologies like go Kira, and there's one called Immuta, and

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there's one called Previous Era, and
there's a few others that have come along

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that are all about trying to protect
PII personally identifiable information as it gets accessed

377
00:26:45.160 --> 00:26:48.519
by analytic applications right and or even
AI. Right. So you've got all

378
00:26:48.519 --> 00:26:52.519
these powerful technologies now, analytics technologies, but what data am I supposed to

379
00:26:52.559 --> 00:26:56.680
have access to? Well, I
mean you in the old days, you

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00:26:56.680 --> 00:27:00.799
could control that a couple different ways. You could control it at access at

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00:27:00.799 --> 00:27:03.720
the database level, which is something
you can do, but it's hard to

382
00:27:03.759 --> 00:27:07.759
manage at scale. That's the problem. Where you could govern it at the

383
00:27:07.000 --> 00:27:11.279
consumption layer in the app somewhere,
but there was nothing in the middle,

384
00:27:11.359 --> 00:27:14.640
or nothing meaningful in the middle.
And that's all changing now, which is

385
00:27:14.680 --> 00:27:17.720
good because what you want and we're
seeing more of this or these sort of

386
00:27:17.799 --> 00:27:22.720
dynamic technologies for in the moment determining
well should this person really get access to

387
00:27:22.759 --> 00:27:26.480
this information or not? And so
you can see this with sort of ephemeral

388
00:27:26.519 --> 00:27:30.839
access, right, like you get
access for thirty minutes or for an hour

389
00:27:30.000 --> 00:27:33.599
or fifteen minutes or something like that. These are all designed to protect the

390
00:27:33.640 --> 00:27:38.200
systems and protect the information because we've
seen i mean, look at the Colonial

391
00:27:38.240 --> 00:27:42.000
pipeline hack and how that took down
the East Coast of people not being able

392
00:27:42.039 --> 00:27:45.759
to drive because they shut down the
pipeline that delivers the gas. Right,

393
00:27:45.839 --> 00:27:49.720
So these hacks are very very serious
ransomwhere as serious there are all these different

394
00:27:49.720 --> 00:27:53.079
threads, and you guys are taking
a pretty clever approach, it seems,

395
00:27:53.079 --> 00:28:00.359
to protecting that critical information which is
on the mainframe right absolutely, from crypting

396
00:28:00.400 --> 00:28:03.640
it at rest to making sure and
transit it's encrypted, but also going beyond

397
00:28:03.799 --> 00:28:07.440
and making sure, as we were
saying, that dynamic way in which you

398
00:28:07.480 --> 00:28:11.839
access information, this particular user can
see that this particular user see this other

399
00:28:11.880 --> 00:28:15.400
thing, including things that protect information
and pi on the fly. And what's

400
00:28:15.400 --> 00:28:21.640
also interesting is we also do that
with unstructured data as well. So it's

401
00:28:22.519 --> 00:28:26.319
when you kind of look at a
system that already knows your data dictionary and

402
00:28:26.359 --> 00:28:27.880
you can see everything, Oh,
you know this is a so security number.

403
00:28:27.960 --> 00:28:32.319
You know this is that. That's
one problem to solve, but it's

404
00:28:32.359 --> 00:28:36.359
another when you have unstructured information in
a reponsitory. You need to kind of

405
00:28:36.400 --> 00:28:40.599
call through that, make some policies
and then dynamically apply them. Those are

406
00:28:40.640 --> 00:28:42.880
some of the things that we already
have in market, and those are continued

407
00:28:42.920 --> 00:28:48.279
investments we're making. So it's a
pretty exciting area and it really helps make

408
00:28:48.319 --> 00:28:55.119
sure that an individual application development team
or an individual user or department doesn't have

409
00:28:55.160 --> 00:28:57.480
to really take then the burden of
figuring this all out that can actually be

410
00:28:57.519 --> 00:29:03.720
done at a corporate policy And that's
why customers really use our technologies is as

411
00:29:03.759 --> 00:29:08.079
it really helps them really I use
data more as a service internally and not

412
00:29:08.200 --> 00:29:11.599
be afraid to use it some of
the times. What we've also seen is

413
00:29:11.799 --> 00:29:17.200
customers have lots of useful information,
but they know it has some I'm not

414
00:29:17.240 --> 00:29:21.279
sure what it has. This is
the problem, So there's this kind of

415
00:29:21.319 --> 00:29:23.920
trepidation. So it kind of comes
back to those themes earlier about knowing what

416
00:29:25.079 --> 00:29:26.720
you can, you know where the
information is, what those flows are,

417
00:29:26.799 --> 00:29:30.400
and then ultimately be able to put
it into action even with unstructured information,

418
00:29:30.519 --> 00:29:34.359
which is again an area where we've
been investing for years on. Pretty exciting.

419
00:29:34.559 --> 00:29:37.440
Well, that is really cool,
and you know, one thing that

420
00:29:37.480 --> 00:29:41.880
gets me very excited about the modern
information landscape is what you just mentioned that

421
00:29:41.960 --> 00:29:45.880
you can do this policy management now
fifteen years ago, even ten years ago,

422
00:29:47.000 --> 00:29:51.720
and someone saving five years ago policy
was just something written in a document

423
00:29:51.759 --> 00:29:53.359
that set on a shelf and you
had to hope that the employee you read

424
00:29:53.400 --> 00:29:56.400
it and knew it and adhered to
it because there was very little bit you

425
00:29:56.440 --> 00:30:00.240
could do in terms of dynamic policy
enforcement. But now we can do that

426
00:30:00.240 --> 00:30:07.160
stuff. There are machine readable policies
that can be implemented and protect dynamically against

427
00:30:07.240 --> 00:30:11.440
all sorts of things, even accidents, even someone accidentally accessing information they weren't

428
00:30:11.480 --> 00:30:15.799
supposed to. It protects us in
all these different ways. That is a

429
00:30:15.000 --> 00:30:18.880
much different world than we grew up
with. Like in the early days of

430
00:30:18.880 --> 00:30:22.880
the mainframe, you didn't have these
sort of dynamic policies that you could define,

431
00:30:22.440 --> 00:30:26.119
manage, execute and in monitor over
time. Now we do and that

432
00:30:26.240 --> 00:30:30.599
is a very very big deal,
right don Yeah, absolutely, And it

433
00:30:32.200 --> 00:30:36.960
ties back to both protecting that critical
information you have on the mainframe, but

434
00:30:37.039 --> 00:30:41.119
also it extends beyond. So the
way we've we've implemented this technology is it

435
00:30:41.160 --> 00:30:45.480
works with your critical systems, with
your mainframe, and that's the bread and

436
00:30:45.519 --> 00:30:48.559
butter of what we focused on.
But we also made adapters that top into

437
00:30:48.640 --> 00:30:53.079
other systems, including other unstructured systems. So for instance, whether it be

438
00:30:53.279 --> 00:30:59.400
maybe a SharePoint where pository or where
information might exist in other repositories in your

439
00:30:59.480 --> 00:31:03.000
enterprise, and we even have an
ability for you to be able to customize

440
00:31:03.000 --> 00:31:07.480
it. Because what we've also run
into is we have large enterprises that they

441
00:31:07.519 --> 00:31:11.880
have some custom content system that was
created years ago. And speaking of afraid

442
00:31:11.920 --> 00:31:15.599
of change, is someone's like,
Bob built the system, Bob isn't hereing

443
00:31:15.640 --> 00:31:18.599
anymore, and you know what do
we do now? Right, So what

444
00:31:18.599 --> 00:31:22.480
we've been able to do is kind
of put abstractions in front that allow you

445
00:31:22.519 --> 00:31:27.400
to get access to that information.
But that policy, unified policy layer applies

446
00:31:27.440 --> 00:31:30.480
to whether it to be the mainframe
or beyond, and so that can be

447
00:31:30.640 --> 00:31:37.480
really powerful because again what can happen
is end users or developers just need one

448
00:31:37.519 --> 00:31:41.200
API or one user experience to access
this information and so they don't have to

449
00:31:41.200 --> 00:31:45.680
worry about, you know, where
it's coming from or what format it's in.

450
00:31:45.880 --> 00:31:48.960
It's automatically protected from them on the
fly. And so that's some of

451
00:31:48.960 --> 00:31:52.119
the things that our customers have really
enjoyed. Yeah, that's a really good

452
00:31:52.160 --> 00:31:55.240
point. You mentioned this a couple
of times, so I want to explain

453
00:31:55.279 --> 00:32:00.160
to the audience why why you're doing
that. The ability to make change in

454
00:32:00.200 --> 00:32:02.599
your organization requires lots of different stuff, right. You need people who develop,

455
00:32:02.680 --> 00:32:06.920
you need people who understand the systems, You need to know what you

456
00:32:06.920 --> 00:32:09.240
want to change, get that from
the business, etc. But the speed

457
00:32:09.279 --> 00:32:15.079
at which you can try things is
really really important for reasons of morale,

458
00:32:15.160 --> 00:32:17.119
for reasons of business acumen, and
so on and so forth, for reasons

459
00:32:17.119 --> 00:32:21.359
of survival in some cases. And
so what had to happen in the old

460
00:32:21.440 --> 00:32:24.640
days is there was a very long, what they call waterfall process that had

461
00:32:24.680 --> 00:32:28.599
to be followed to change anything.
And we want to wasting lots and lots

462
00:32:28.599 --> 00:32:32.599
of time because people weren't communicating properly
and it's just not a very efficient way

463
00:32:32.599 --> 00:32:37.160
to do. But now we can
have this much more agile approach. And

464
00:32:37.319 --> 00:32:42.880
what Doug is describing here is empowering
your application development team to really focus on

465
00:32:42.960 --> 00:32:46.119
business problems and not worry about the
security, the compliance, all these other

466
00:32:46.200 --> 00:32:52.319
characteristics that need to be handled that
can be handled through the platform or through

467
00:32:52.359 --> 00:32:55.680
some of these policies, and that
liberates the developers to try new things.

468
00:32:55.680 --> 00:33:00.599
It liberates the business to talk to
the developers to try to things. That

469
00:33:00.759 --> 00:33:06.680
is absolutely crucial for digital transformation,
right absolutely, And what we've also seen

470
00:33:06.960 --> 00:33:12.640
is this becomes even more important when
the stakes are very high. Consider for

471
00:33:12.680 --> 00:33:17.079
instance, emergent acquisition type scenario.
Sometimes whether or not to do the deal

472
00:33:17.359 --> 00:33:22.319
is all dependent on how fast can
we integrate our organizations. And you know,

473
00:33:22.920 --> 00:33:29.519
many organizations have these critical systems where
they have years and years of data

474
00:33:29.519 --> 00:33:31.720
in the mainframe and they need to
integrate it into the rest of their ecosystem

475
00:33:31.799 --> 00:33:37.599
again to use that word. And
so if you're trying to bring developers together

476
00:33:37.640 --> 00:33:42.440
to bring a unified experience to customers, the ability to do this very quickly

477
00:33:43.079 --> 00:33:46.400
can be the difference of Wow,
that acquisition was really worth it. Our

478
00:33:46.440 --> 00:33:51.920
customers really see us as one company. Now we're able to integrate business processes

479
00:33:51.920 --> 00:33:55.000
and be fluid or sorry, boss, it's going to be another six or

480
00:33:55.039 --> 00:33:59.799
twelve months until that's finished, which
well, isn't a really a fun conversation

481
00:34:00.160 --> 00:34:02.519
right now. You have to be
able to move quickly, and that's really

482
00:34:02.559 --> 00:34:07.720
what we're talking about, is empowering
your team to be able to make changes.

483
00:34:07.759 --> 00:34:09.239
First of all, understand what's there. I mean, that's one of

484
00:34:09.239 --> 00:34:13.519
the biggest challenges I think, And
we want to talk to about AI and

485
00:34:13.559 --> 00:34:16.039
how AI can come into this equation
here, and the bottom line is you

486
00:34:16.159 --> 00:34:20.159
better be careful. I mean,
one of the downsides, at least in

487
00:34:20.199 --> 00:34:23.840
the early days of chat GPT,
is that if you type in some of

488
00:34:23.880 --> 00:34:28.679
your proprietary code or some of your
proprietary recipes or whatever that's going into the

489
00:34:28.719 --> 00:34:31.039
machine and it could become out some
other side, to some other place and

490
00:34:31.079 --> 00:34:35.320
you won't even know. There's gonna
be some really interesting conversations that be had

491
00:34:35.360 --> 00:34:37.599
around that in the near term.
But the point is you have to know

492
00:34:37.679 --> 00:34:40.960
what's out there, know the steady
state, and then be able to do

493
00:34:42.000 --> 00:34:45.280
that impact analysis you've talked about to
try new things. You try it and

494
00:34:45.320 --> 00:34:47.199
fail, fail fast forward is what
they say, right, That's what you've

495
00:34:47.199 --> 00:34:51.320
got to do to be able to
solve these real mission critical problems is have

496
00:34:51.480 --> 00:34:54.440
the agility and have the confidence to
be able to make some change and know

497
00:34:54.840 --> 00:34:59.559
that you can either undo it or
at least fix what you've created right a

498
00:35:00.079 --> 00:35:04.039
lutely. And you started off this
segment by kind of talking about the cloud

499
00:35:04.119 --> 00:35:07.679
and Kubernetes and different technologies around that. And what we've also seen is a

500
00:35:07.719 --> 00:35:15.519
lot of customers have really seen this
kind of rise to microservice style architectures where

501
00:35:15.840 --> 00:35:19.679
it's very works very well with kubernets
because it allows you to manage these different

502
00:35:19.719 --> 00:35:22.400
pods that you were saying, and
you might have dozens, hundreds, even

503
00:35:22.480 --> 00:35:27.599
thousands of them depending on what happens, and what we've seen with a lot

504
00:35:27.599 --> 00:35:31.000
of customers is they really like these
style architectures where you have these little piece

505
00:35:31.039 --> 00:35:35.880
parts like you were saying. Part
of it is because it allows you architectural

506
00:35:35.920 --> 00:35:38.679
purity of scalability and such, and
that's a good thing. But the real

507
00:35:38.760 --> 00:35:43.880
reason is it allows the business to
scale at a people level. And so

508
00:35:43.920 --> 00:35:51.320
it allows different developers to work independently
of each other and build new business logic,

509
00:35:51.360 --> 00:35:54.280
create new business functions, and ultimately
let the business go faster. But

510
00:35:54.360 --> 00:35:59.119
then you have two problems or now
you've introduced a new problem by doing that

511
00:35:59.599 --> 00:36:05.079
isn't any cases. What happens is
you proliferate the access of data. So

512
00:36:05.119 --> 00:36:08.239
in many cases a development team will
create their own database or their own store

513
00:36:08.280 --> 00:36:13.760
of where they're putting even unstructured data. And now what can happen is in

514
00:36:13.840 --> 00:36:17.400
from critical information can get out of
sync with other systems through these kind of

515
00:36:17.480 --> 00:36:23.079
architectures, and so to remediate this
kind of problem, we've looked at technologies

516
00:36:23.119 --> 00:36:28.320
that allow you to look at data
quality across those landscapes, and so particularly

517
00:36:28.719 --> 00:36:31.400
considering, for instance, the mainframe
is maybe a little bit the source of

518
00:36:31.480 --> 00:36:36.519
truth for many applications. It allows
you to look into what's on the mainframe

519
00:36:36.719 --> 00:36:39.760
and then what's in other systems and
does everything match up? And this is

520
00:36:39.920 --> 00:36:45.320
very important in like, for instance, reconciliation or financial use cases. And

521
00:36:45.480 --> 00:36:51.800
what we have is customers have used
our technologies to make sure everything matches in

522
00:36:51.920 --> 00:36:57.440
real time as they're creating these micro
services, and so it really allows this

523
00:36:57.559 --> 00:37:00.000
teams to kind of move very quickly. But at the same time, the

524
00:37:00.159 --> 00:37:07.559
quality teams and the infrastructure teams that
want to make sure both quality is intact

525
00:37:07.639 --> 00:37:14.039
but also compliance is intact, really
can enforce these rules. So it kind

526
00:37:14.039 --> 00:37:16.519
of gives you the best of both
worlds through that approach. M Yeah,

527
00:37:16.559 --> 00:37:20.880
and what Doug is talking about here
is the fact that these larger organizations,

528
00:37:20.880 --> 00:37:23.079
you're going to have whole separate teams
that are all important. They have to

529
00:37:23.119 --> 00:37:27.440
work together, but you can't have
everyone in the same room all the time,

530
00:37:27.519 --> 00:37:30.840
so you have to come up with
policies procedures, technology stacks to enable

531
00:37:30.960 --> 00:37:35.960
this kind of collaboration. And then
to his point, you want to make

532
00:37:36.000 --> 00:37:38.039
sure that you're getting the latest data, the freshest data, the data that

533
00:37:38.119 --> 00:37:44.400
is accurate because things change, and
so that requires What really amounts to an

534
00:37:44.440 --> 00:37:47.639
information architecture is what it boils down
to and kind of understanding where and when

535
00:37:47.719 --> 00:37:51.880
you can build in your checks and
balances, where and when you can kind

536
00:37:51.920 --> 00:37:54.519
of bring in that last security check, if you will, to make sure

537
00:37:54.559 --> 00:37:57.880
it is secure, a last little
bit, but don't touch out. That

538
00:37:57.920 --> 00:38:02.639
will be right back. You are
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Welcome back to Inside Analysis. Here's
your host, Eric Kabanat All right,

591
00:42:17.239 --> 00:42:22.119
folks, back here on Inside Analysis, talking with Doug Johnson of Rocket Software,

592
00:42:22.480 --> 00:42:28.639
all about modernizing the mainframe and really
being able to weave it into this

593
00:42:28.719 --> 00:42:32.159
new, very dynamic world of cloud
computing. We've talked about the analytics that

594
00:42:32.199 --> 00:42:36.280
you can do. We've talked about
data provisioning, which is really important.

595
00:42:36.800 --> 00:42:40.440
There's some other risk management aspects to
this as well. I spent a past

596
00:42:40.519 --> 00:42:46.559
life moderating webinars for the Global Association
of Risk Professionals or GARP dot org.

597
00:42:46.599 --> 00:42:52.079
I joke that they are the de
facto Illuminati, because they're chief risk officers

598
00:42:52.079 --> 00:42:54.119
at central banks all around the world. Of course, there are some banking

599
00:42:54.119 --> 00:42:59.880
issues these days. Silicon Valley Bank
collapsed. First Republic just collapse over the

600
00:43:00.039 --> 00:43:04.800
weekend. These are very strange times. A lot of people don't understand that

601
00:43:05.320 --> 00:43:08.239
all it takes is a psychological event
for there to be some run on the

602
00:43:08.239 --> 00:43:13.000
bank, and that's what happened with
SBB, that's apparently what's happening with First

603
00:43:13.000 --> 00:43:16.320
Republic. People just pulling out massive
amounts of money and that creates systemic risk.

604
00:43:16.679 --> 00:43:21.599
So being able to kind of understand
that, and especially in this global

605
00:43:21.840 --> 00:43:24.440
world where you've got to all these
different countries involved, you have all these

606
00:43:24.440 --> 00:43:29.239
different central banks. Of course it's
all electronic now, so you can access

607
00:43:29.239 --> 00:43:30.280
and move money around. A lot
of stuff has changed. I mean,

608
00:43:30.320 --> 00:43:35.559
the fintech innovation has been absolutely stupendous, and a lot of these banks all

609
00:43:35.599 --> 00:43:37.960
have mainframes. They've had mainframes for
years doing all this stuff. So I'm

610
00:43:38.000 --> 00:43:44.000
curious to know your approach to the
risk management side. I can kind of

611
00:43:44.039 --> 00:43:49.360
guess just to being able to look
into unstructured data, identify entities, you

612
00:43:49.400 --> 00:43:52.199
know, basically provide a risk score
of some contin You tell us a bit

613
00:43:52.199 --> 00:43:55.760
about what you do in the risk
management space. Yeah, well, there's

614
00:43:55.800 --> 00:44:00.480
no shortage of opportunities, but also
there's with all opportunities, there's also risks

615
00:44:00.480 --> 00:44:04.440
that go along with it. And
a lot of the organizations that we serve

616
00:44:04.920 --> 00:44:10.840
are very highly regulated too, and
so they have certain regulation regulatory obligations that

617
00:44:10.880 --> 00:44:16.280
they have around how long they have
to keep information when it's notifications, if

618
00:44:16.280 --> 00:44:21.679
a customer asks for certain information that
it can be expunged in a timely manner,

619
00:44:21.960 --> 00:44:25.960
and many other things that happen from
a process and procedure perspective, and

620
00:44:27.000 --> 00:44:30.960
so what happens in that domain is
being able to make sure you keep information

621
00:44:30.239 --> 00:44:34.599
just as long as you need it, but no longer. And that sounds

622
00:44:34.599 --> 00:44:37.480
like it's such an easy thing to
say, but it's remarkably hard to do,

623
00:44:37.639 --> 00:44:44.159
especially when different classes of information require
kind of different retention policies. In

624
00:44:44.199 --> 00:44:49.599
with that, and then in addition
to that, though certainly retention management is

625
00:44:49.679 --> 00:44:53.199
a key thing that our customers think
about, but also as they look to

626
00:44:53.360 --> 00:44:58.920
change business processes use information in different
parts of the organization, they need to

627
00:44:58.920 --> 00:45:02.039
make sure they have custom over that
information wherever it might go. And so

628
00:45:02.039 --> 00:45:06.480
what we're seeing more and more as
customers are saying, hey, can you

629
00:45:06.519 --> 00:45:12.440
make this data available to maybe some
business partner or perhaps some internal process,

630
00:45:12.840 --> 00:45:15.000
but we need to make sure we
have the controls that can see the flow

631
00:45:15.039 --> 00:45:19.719
of that information throughout a cycle,
who touched it, when, if it

632
00:45:19.760 --> 00:45:22.639
integrated with another system, Do we
have an actual audit trail of the data

633
00:45:22.639 --> 00:45:28.840
and time and the proofpoint that some
interaction actually happened. And so that closed

634
00:45:28.880 --> 00:45:35.960
loop reporting on that auditing piece is
just super important to making sure that organizations

635
00:45:35.960 --> 00:45:39.400
are compliant so that when a regulator
comes to you and says, you know,

636
00:45:39.519 --> 00:45:44.039
hey, can you show me that
paperwork that shows X or Y.

637
00:45:44.480 --> 00:45:47.239
The answer is, of course,
it's right here. Yeah, that's a

638
00:45:47.280 --> 00:45:51.679
big deal. And you know,
when regulators come calling, you want to

639
00:45:51.719 --> 00:45:53.760
have several things in place. It
seems to me you want to have your

640
00:45:53.760 --> 00:45:57.280
plan, you want to have your
architecture documents, and then you want to

641
00:45:57.280 --> 00:45:59.440
be able to show their receipts.
Right. You want to say, well,

642
00:45:59.440 --> 00:46:04.199
here's our for how we detect breaches, here's our plan for when we've

643
00:46:04.199 --> 00:46:07.000
detected a breach, what we do, Here's what happened in this particular instance,

644
00:46:07.199 --> 00:46:09.719
X y Z process is fired off. And then you want to be

645
00:46:09.760 --> 00:46:15.079
able to show them in the production
system. And here's where rubber meets road.

646
00:46:15.400 --> 00:46:16.639
And if you could do all that, you're going to get a lot

647
00:46:16.679 --> 00:46:20.360
of leeway. I mean, and
you know there's always going to be pressure

648
00:46:20.400 --> 00:46:24.440
to improve security, to improve governance, to improve auditibility, etc. But

649
00:46:24.480 --> 00:46:29.000
as long as you have your plan
and you're acting on your plan, and

650
00:46:29.039 --> 00:46:31.039
you're listening to the regulators and what
they ask you for nine times at a

651
00:46:31.079 --> 00:46:34.880
tenant that you're going to be just
fine. What do you think well,

652
00:46:34.920 --> 00:46:38.119
absolutely, and what's really important is
the full life cycle managed around this.

653
00:46:38.280 --> 00:46:43.239
So everything from like we were talking
about the very beginning, we were saying,

654
00:46:43.280 --> 00:46:45.599
hey, this idea of can you
actually go look at the code and

655
00:46:45.639 --> 00:46:51.440
see where data is moving throughout your
organization, that's actually proving that the systems

656
00:46:51.760 --> 00:46:54.000
literally are doing what they say they
do all the way to what happens at

657
00:46:54.079 --> 00:46:59.239
runtime, where hey, this transaction
happened on this date, and here's the

658
00:46:59.280 --> 00:47:04.599
proofpoint that happened, and it's logged
in our mutable storage mechanism. To be

659
00:47:04.639 --> 00:47:08.079
able to have that proofpoint across the
data but also the processes, the people,

660
00:47:08.119 --> 00:47:13.280
and the systems that it interacted with, that's what's really required by many

661
00:47:13.320 --> 00:47:16.840
of these highly regulated organizations, and
so they look at technologies like ours to

662
00:47:16.920 --> 00:47:21.159
help. Yeah, that's very interesting
that you mentioned this a couple of times.

663
00:47:21.159 --> 00:47:25.360
So with your software, you're able
to look at the code that is

664
00:47:25.440 --> 00:47:30.880
instanting it in a particular environment and
basically process it. And do you actually

665
00:47:31.039 --> 00:47:35.159
run the code or is it that
you just know what it's going to do

666
00:47:35.519 --> 00:47:37.920
based upon the functions that you see. And therefore you can look to the

667
00:47:38.000 --> 00:47:40.840
log file and kind of triangulate things. Can you explain that a bit more?

668
00:47:42.199 --> 00:47:45.920
Yeah, absolutely, And so what
we have or we have these scanners

669
00:47:45.960 --> 00:47:50.840
that look at the code that developers
have actually in their system, you know,

670
00:47:50.840 --> 00:47:54.800
in the version control systems, and
the actual code itself, and from

671
00:47:54.840 --> 00:48:00.840
that it determined what kind of data
is interacted with that one system. And

672
00:48:00.920 --> 00:48:02.960
that can be on the mainframe for
instance, as we said with like Cobalt.

673
00:48:04.440 --> 00:48:07.000
But what's also interesting is you use
then you throughout your organization, you

674
00:48:07.039 --> 00:48:10.519
go to other systems and run that
same procedure. It might be a C

675
00:48:10.679 --> 00:48:15.760
plus plus or Java or something other
system. And then the interesting thing is

676
00:48:15.760 --> 00:48:20.119
it looks at that parses that code
itself and those entities that are involved,

677
00:48:20.400 --> 00:48:24.280
and it sees the connection where one
system calls another and it stitches it together.

678
00:48:24.360 --> 00:48:28.199
So you can see not just the
data and one system and the data

679
00:48:28.199 --> 00:48:31.480
and other systems, but you can
follow that lineage, that flow of information

680
00:48:31.599 --> 00:48:37.079
from one place to another. And
again it becomes an auditor's dream because when

681
00:48:37.079 --> 00:48:40.559
you go when that compliance team says, hey, we want to prove that

682
00:48:40.639 --> 00:48:46.039
something happened, you can actually from
a system's perspective prove or where data is

683
00:48:46.079 --> 00:48:50.519
touching another system and as we said, if you want to make business change.

684
00:48:50.880 --> 00:48:53.760
Again, the compliance officer loves it
as well, because you can confidently

685
00:48:53.800 --> 00:48:59.400
make changes and because you can see
the entire data landscape, so it becomes

686
00:48:59.440 --> 00:49:05.320
a very powerful visualization around the entire
organization. Yeah, that's really cool stuff.

687
00:49:05.360 --> 00:49:09.239
And obviously you can understand why banks
want that level of certainty because you're

688
00:49:09.280 --> 00:49:14.400
moving money around, right, the
classic two face commit Right, it's okay,

689
00:49:14.480 --> 00:49:15.400
I'm sending money over here. Yes, I've sent it. Did you

690
00:49:15.440 --> 00:49:17.320
get the money back? Yes,
we got the money back. Okay,

691
00:49:17.320 --> 00:49:21.039
good, Like, now we know
we can move on and do something else.

692
00:49:21.119 --> 00:49:23.760
And wow, you think about the
innovation and fintech and how much easier

693
00:49:23.800 --> 00:49:28.239
it is to move money around in
just seconds and moments. And that took

694
00:49:28.280 --> 00:49:32.239
a tremendous amount of back end work
to be able to engage these different systems

695
00:49:32.280 --> 00:49:37.360
to move the money, to categorize, to categorize it essentially do all that

696
00:49:37.400 --> 00:49:39.360
stuff. That's a big deal,
right, and so Rocket Software, I'm

697
00:49:39.400 --> 00:49:45.159
guessing, is a significant player in
that whole evolution, right, Yes,

698
00:49:45.239 --> 00:49:49.760
And with that, what we've also
seen is the ability to scale in terms

699
00:49:49.800 --> 00:49:54.760
of looking at these systems automatically and
really scaling, compliance and other things is

700
00:49:54.800 --> 00:49:59.800
super important to be able to get
that visibility. But also we found that

701
00:50:00.280 --> 00:50:04.679
scaling from a people perspective is super
important. And what do we mean by

702
00:50:04.679 --> 00:50:07.440
that, Well, if you have
that data and it's available to two or

703
00:50:07.480 --> 00:50:13.119
three magicians at your organization that know
how to run the magic incantations to tell

704
00:50:13.159 --> 00:50:16.400
you what those connections are, that
that isn't as effective as what if the

705
00:50:16.519 --> 00:50:21.320
user experience could be more of a
consumer type experience where anyone could go in

706
00:50:21.679 --> 00:50:25.360
see those connections. Obviously anyone who's
authorized obviously right, but you know at

707
00:50:25.360 --> 00:50:30.000
the departmental level, or if you're
looking to make a business change as a

708
00:50:30.000 --> 00:50:34.280
as an enterprise architect at an organization, you don't have to be that magician

709
00:50:34.400 --> 00:50:37.199
to know all of the details of
the tool. It really allows you to

710
00:50:37.239 --> 00:50:43.760
see those connections that otherwise you would
just spend mountains and mountains of time in

711
00:50:43.920 --> 00:50:49.320
meetings and manual work to try to
get achieve the same thing. Yeah,

712
00:50:49.360 --> 00:50:52.079
and not even get to the answer. Perhaps, I mean it sounds a

713
00:50:52.079 --> 00:50:54.079
lot like process mining. Maybe we
can talk about that in the in the

714
00:50:54.119 --> 00:50:59.400
podcast bonus segment. But that ability
to get down to the code level as

715
00:50:59.400 --> 00:51:02.000
you suggest to run time and be
able to do the playback say okay,

716
00:51:02.000 --> 00:51:06.480
well here's what happened. This system
connected to that system. This data came

717
00:51:06.519 --> 00:51:08.559
across the trans and went over here. We can verify that it landed,

718
00:51:08.599 --> 00:51:15.000
etc. That is tremendously useful information
for any kind of trouble shooting, for

719
00:51:15.079 --> 00:51:17.239
any kind of improvements, you know, because then you can look at that

720
00:51:17.360 --> 00:51:21.000
a certain skill and say, okay, we need to change x, Y

721
00:51:21.079 --> 00:51:23.719
and z to be able to supercharge
this one process. You know, you

722
00:51:23.760 --> 00:51:29.079
went into the classic impedance mismatch with
some of these systems right where it's like

723
00:51:29.639 --> 00:51:30.800
this one is spending at a certain
rate, that one is spending at a

724
00:51:30.840 --> 00:51:34.840
slower rate. How do you how
do you get those two to jibe?

725
00:51:35.119 --> 00:51:37.920
Basically, that's not a small problem, right. You want to be able

726
00:51:37.960 --> 00:51:40.480
to see all that, And your
point is that you're able to see into

727
00:51:40.480 --> 00:51:45.239
the mainframe and see into these other
systems and give a very clear cut view

728
00:51:45.679 --> 00:51:49.360
of which code is enacting at one
point in time, which data goes in

729
00:51:49.400 --> 00:51:52.920
there. That is the audit trail, right, that is and really allows

730
00:51:52.960 --> 00:52:00.480
you to make change with confidence.
Many organizations they have the meetings, they

731
00:52:00.519 --> 00:52:05.239
have the budget, they have the
desire to change, but they really don't

732
00:52:05.280 --> 00:52:09.199
know in the end, is the
thing I've created my VISEO diagram that we

733
00:52:09.239 --> 00:52:14.119
had meetings about, is that really
how my organization operates? And you just

734
00:52:14.159 --> 00:52:16.800
don't have that confidence, and so
we help you kind of buyback that confidence

735
00:52:16.800 --> 00:52:22.159
in those change both from a regulation
perspective, but then also just making sure

736
00:52:22.159 --> 00:52:25.440
you're effective your project's going to be
successful. Yeah, this is wonderful stuff,

737
00:52:25.639 --> 00:52:30.199
folks. Look these these folks up
online Rocket Software at Doug Johnson has

738
00:52:30.239 --> 00:52:34.400
been our guest today. We are
booking the second half of the year right

739
00:52:34.400 --> 00:52:36.039
now, So if you want to
be on the show, send me an

740
00:52:36.079 --> 00:52:39.199
email. Info at inside Analysis dot
com that comes straight to yours truly or

741
00:52:39.239 --> 00:52:44.039
info at dm radio dot biz.
We're trying to understand what's happening in the

742
00:52:44.039 --> 00:52:46.360
information economy. We'll talk to you
next time. Folks, you've been listening

743
00:52:46.440 --> 00:53:01.719
to Inside Analysis Express one o six
point five FEV talk radio station leur KCAA,

744
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the station that leaves no listener behind
Express one oh six point five FM.

745
00:53:13.559 --> 00:53:16.679
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769
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eight zero eight eight Monday through Saturday
nine am to five pm California time.

770
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That's eight one eight six one zero
eight zero eight eight to ebot club dot

771
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com. With sixty years of fascinating
facts. This is the man from yesterday

772
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and back in time we go to
this time in nineteen seventy. NBCTV is

773
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giving Don Knots his own variety show, so it'll begin this fall on NBC.

774
00:55:35.920 --> 00:55:39.719
Don Knots is best known as Deputy
Barney Fife on Andy Griffith, Well,

775
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boy, just once, I'd like
to have them sideburns come out even

776
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one inch from the lobe. What's
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777
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is short? Well, maybe that's
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he. And back in time we
go to this time. In nineteen ninety

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co host of ab CTVS Good Morning
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with ABCTV to work on some primetime
projects. Now you will continue to see

782
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Joan on television. But I want
to make the argument here that in this

783
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she has been one of the most significant

784
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figures in women's changing role on television
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Harriet Show scores his first hit record
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Rickey Nelson imitated Elvis on a recent
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gonna do when well run straight,
You're gonna run away and time I'm gonna

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run right by your son be burn
to Bland. I'm walk with Moore at

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00:58:45.639 --> 00:58:51.800
help you pick the plan that's right
for you. Hi, I'm food critic

816
00:58:51.800 --> 00:58:55.440
Gallenborgan, and I'm excited to tell
you about Rage Shanghai Bistro, located next

817
00:58:55.480 --> 00:59:01.199
to Redlands DMB on Laconia in Redlands, Ray Shanghai Bistro offers the largest,

818
00:59:01.280 --> 00:59:07.199
him most delicious array of traditional and
original Chinese dishes available in Ulum Empire.

819
00:59:08.079 --> 00:59:13.159
Some of my favorite dishes are the
house spain hot stickers, the crisp pork

820
00:59:13.239 --> 00:59:17.360
sparrabs with garlic, their unique spicy
lamb with bamboo, the sweet and tangy

821
00:59:17.559 --> 00:59:22.960
deep fried orange peel beef, and
the savory basil spicy shrimp, plus lots

822
00:59:22.960 --> 00:59:27.960
of vegetarian dishes. Whether you dine
in, pick up the food, or

823
00:59:28.000 --> 00:59:30.920
have them cater your next party or
special occasions, you will see why ray

824
00:59:31.119 --> 00:59:37.000
spelled r Ui of postrophes. Shanghai
Bistro is truly the best Chinese restaurant in

825
00:59:37.079 --> 00:59:43.280
the Ulum Empire. Their website is
raised Shanghai Bistro dot net. That's raised

826
00:59:43.480 --> 00:59:50.239
Shanghai Bistro dot net. Ruis Shanghai
Bistro dot net. Happy eating, you

827
00:59:50.360 --> 00:59:58.519
won't be disappointed. Kilistina KCAA Lomolinda
at one six point five FM, K

828
00:59:58.679 --> 01:00:04.840
two ninety three CF You know Valley, NBC News Radio. I'm Chris Garagio.

829
01:00:04.880 --> 01:00:07.440
Authorities in Texas are still searching for
a motive in Saturday's mass shooting and

830
01:00:07.480 --> 01:00:08.559
a shopping mall near down

