The information economy as a rod. The world is teeming with innovation as new
business models reinvent every in every industry. Inside Analysis is your source of information
and insight about how to make the most of this exciting new era. Learn
more and Inside Analysis dot Comside Analysis dot com. And now here's your host,
thro Eric Kavanaugh. Yes, all right, folks, welcome to the
future. Indeed, your host here, Eric Kavanaugh, the only coast to
coast radio show that's all about the information economy. It's time for Inside Analysis,
and folks, I'm pretty excited to be digging into the wayback machine today
and stretching it all the way to modern times and beyond. Quite Frankie,
we're to talk about the mainframe. That's right, they're still out there.
IBM still sells mainframes, the ZO assets still a very active part of our
commercial world today. Lots of business processes run on mainframes all day, every
day and have been doing so for years, sometimes ten twenty thirty years.
Mainframes are alive and well, we've got an expert with us to kind of
walk through what's going on in that space. Doug Johnson is from Rocket Software
actually used to be asg got purchased by Rocket Software. I just learned that
today. So these shows always teach you something, and we're going to talk
about what's happening and why it matters. So mainframes, like I say,
have been a robust part of our economy for forty plus years, probably almost
fifty years now. A lot of companies have been trying to modernize. But
you know, if it ain't broke, don't fix it. There are a
lot of things that work very well on the main frame, but the question
becomes, how do you integrate mainframe technology with the rest of your stack?
And what is this whole thing the cloud about? What about containers and Kubernetes
and all that fun stuff we've talked about before. That's what we're going to
discuss with our friend Doug Johnson. First of all, tell us a bit
about yourself and what you do in your job as you try to educate and
do product roadmap stuff. Thanks Eric, and I really appreciate the time to
share a little bit about what's going on that way back machine. I like
that, so good stuff. Yeah. So I'm Doug Johnson. I run
product management in our data modernization business unit here at Rocket Software, and my
job really entails working with customers, our account teams and also the industry as
large in terms of the future of what's happening with data and how it's being
modernized in many different areas, including the mainframe. And so my team works
on the roadmaps and works with engineering and customers. It's it's a pretty exciting
place. Yeah, that's good stuff. And you know, when you're in
product management, your main job is to really understand the technology, what does
it do, how does it work, where does it apply? And when
you start talking about the roadmap, obviously you're trying to take care of customer
needs. You're trying to augment what has already been built to handle new capabilities,
for example, and of course in the data modernization space, and you
know, there are a thousand reasons for that. I'm sure one big data
we saw fifteen to five years ago just dominate the conversations in our industry.
Now there's more emphasis on AI and machine learning and things of that nature,
but big data is everywhere now. It's not that it went away, it's
just that we all take it for granted now. And that was one of
the main drivers for this whole modernization concept, right, It's the fact that
we went from relatively small amounts of transactional data to very large amounts of really
machine data, like machines talking to machines, exhaust data, some people call
it. But all these new systems that Google and Facebook and LinkedIn and these
other companies have built, well, they spin out tremendous amounts of data.
And if you can capture that data marry it with other data, you can
start understanding what's happening in your environment, why are things going slow, why
are things working, etc. But that whole big data revolution really drove what
it is now called data monetization. What do you think, Well, it's
a great, great summary of allow some of the big trends that have been
happening in the last several years. And with that, when it comes to
topics around the mainframe, it's very interesting because what we really have experienced is
a lot of customers have gone through. Even though the mainframe has remained the
same over the years, the people who have been working with it in those
systems and understanding what's even there and how it impacts the rest of the business
is just not known. And so this idea of being able to see what
is going on in your environment, but also tying it to other business processes
that you have and the data flows throughout the organization is a key problem customers
have, and so that's one of the some of the things that we do
in our roadmaps is look at tools that can look at the code, look
at the data in those systems, and then stitch it together to see the
connections that are hard to see today. And it really helps, you know,
as part of that modern zation journey, go left, go right,
tweak a little bit here or there, and if someone's looking for some key
information, we can help you find it in terms of bringing it into those
analytics stacks and other things. So again, the mainframe plays a very big
role, and that part of it is just really around having that visibility.
Yeah, and coball, right, a lot of mainframe applications were written in
coball and that was a problem for quite a long while, like the last
five or six years or so. There are so many jobs for coball developers
because guess what, the old apps were written in coball. And even though
that's still the case, something called chat GPT came along and may have helped
out a little bit. I'm curious to hear your thoughts on that, because
a lot of people don't know chat GPT cannot can write poems. It can
also write code. You can write in all sorts of different coding languages,
not just human languages. Right, yeah, it can do that sort of
thing. The interesting thing is when you're talking about the mainframe and that some
of the critical systems that are existing in those environments. You don't want to
enter a three letter prompt or just burning through your code converter and go I
think it'll be good enough for my customer critical transactions. So we haven't seen
in this particular environment, we haven't seen the big push for that, although
we're always watching that space in terms of things that we can offer customers.
But the visibility around, for instance, running some tools against your coball program,
understanding what those connections are, and then maybe where you want to put
investments to make a change. So whether it be used some tools to help
you migrate code or data in those environments, which I'm sure we will talk
more about the data challenges around that, but also just kind of connections is
a first step that we've been focusing on recently. Give you an example is
for instance, around someone trying to run a report against some critical information and
maybe the information exists, maybe in the source from somewhere and they don't know
where, but ultimately it ends up in a bi cube or something where they're
trying to run some reports. But when they do that, they find that
the timeliness of that data is a good twenty forty eight hours behind what a
business needs, and they want to see what is the source of this data
and can I get that data faster? And so that's some of the things
that we do. We help you buy back that time by getting visibility into
where information might exist in the organization, including the mainframe baked into that coball
somewhere, and so then you can go and look at some open standards to
get that data in a more timely fashion. Yeah, that's very interesting because
to your point, there is an immediacy to business these days. You want
to know what's happening right now. Knowing what happened forty eight hours ago could
be useful, but it's not very good for customer experience, it's not very
good for putting out fires. So the question that becomes, how do you
do that? And I guess what you're saying is you've got some tools and
technologies and methods to help companies look into the main frame that they have and
kind of understand where the data is going in, how it's going out,
when it's coming out, and you have other ways of accessing and improvision it.
I mean, there are lots of ways that data is moved around.
ETL was the bread and butter still is for many organizations. Extract transform load,
then you're doing ELT, extract transform, etc. There's data virtualization.
A number of vendors have been that space for a while, and what I'm
hearing from you is that you're able to find ways to see into the environment
and help the business understand where the data is coming from and then how they
can expedite that process. Right. That's exactly right, and it's what I
really like about what you said earlier about the ecosystem and the mainframe is really
important because obviously the world isn't just one technology today, it's a whole series
of technologies. And one of the things that we've really focused on is making
the data that's permeating the entire organization being able to see it and manage it
both for those use cases I was mentioning around, for instance, reporting,
but also very critically things like compliance or doing business change as an example,
So if you consider, hey, there's a project we're considering where we want
to change a system, maybe change a user experience, what impact would that
have to other parts of my organization, including those critical information that exists on
the mainframe and has managed in those environments. And that's some of the things
that are technologies and some of the investments we've been making help customers really gain
those insights to make a choice again on where to put those investments and what
kind of impact of analysis it might have in terms of that decisioning. Yeah,
and the impact analysis is huge, right, because when you transform the
business, that means you change something. These days, that means you're either
changing code, or you're adding code, or you're changing an architecture. You're
doing something new. And let's face it, there are lots and lots of
shining new objects these days. There are lots of new ways to get data.
There's streaming with technologies like Kafka or Pulsar. You've got snowflakes of the
world and data bricks and all these other amazing technologies that have come along to
at people that analyze data, but that doesn't change the fact that a lot
of the data and the systems are still running on the mainframe. So coming
up with that information architecture that's the real key to success these days and being
able to see into these environments. Do the impact analysis, as you suggest,
is crucial to be able to understand what you can change and how you
can change it without breaking something right done absolutely and there are millions and millions
of dollars and then lots and lots of armies of people working at this and
so if you get it right or get it wrong, it's huge impact to
a business. It can really make the difference between are you competitive compared to
what others are doing, or you actually behind if you make the wrong choices.
And that's what we really try to help organizations is buy back that time
and reduce that risk of that critical decision making and along the way in terms
of that decision to make what we also find it knowing the strategy that people
have with the mainframe is really important some organization and say hey, we have
these critical systems, we've had them forever, We're going to keep building an
ecosystem around it, or some other customers say, we really want to have
a strategy where we can take some systems as part of that and have a
hybrid approach where we start moving some of that to the cloud. And then
some customers are all in where they in a five or ten year, fifteen
year time frame even in some cases, they want to have that trail map
to perhaps move from what they have today to some future goodness that they have.
And so that's a key area also in terms of that visibility that it's
critical to the overall corporate strategy and investment portfolio that happens. Yeah, right,
well, and so we should appreciate the fact that mainframes work. And
I can tell you in the operations world, if something isn't broken, they
don't want you to fix it. I mean just generally speaking, leave it
alone is their main message, because they're the ones responsible for you know,
that's where this whole business it divide, I think came from was because the
business wanted to change something. It is like, guys, what are you
talking about? We're ubbing trouble keeping this running as it is. Don't complicate
things, right, But then the cloud comes along and machine learning comes along,
and AI matures and all these things develop, so there's increased pressure on
the business to change something. The key is what do you change? And
it sounds to me like you're offering a very positive surface for being able to
assess what is the current state, how are these applications running, if we
change this, if we change that, how is that going to impact us
down the line? And you know, no doubt about it, Rushing to
the cloud is not something anyone should do. You really want to understand what
are are these business processes, what can we modernize? Where can we modernize
them? And you wind up usually having a sort of layer of abstraction right
above the main frame, but below the cloud or below the on prem data
center director of the data center. You want these layers of abstraction where you
can kind of understand what's moving where and then make your changes in those environments
that affect either upstream or downstream. Right absolutely, and as part of those
that layered approach that you were talking about, trying to really deal with a
lot of the unstructured data that also exists elsewhere in the organization, including the
mainframe, is really important. Things like customer statements or building information. You
know things that aren't very important to customers, right, and so you as
you go to look and modernize how you're doing that, you just really want
to be very thoughtful and you want to you want to make investments that as
you, for instance, build out a new customer portal or build out a
mobile app experience as part of that. When you have that API, that
abstraction that you were talking about, you want to make sure it can get
information wherever it may exist in the enterprise, and of course that also includes
the mainframe. But you don't want to put the burden on, for instance,
an individual app application developer to have to know about arcane formats like AFP
or whatnot. And so that's also what some of our technologies do, is
we help you access that unstructured information and we put it in a modern format
on the fly so that your application developers don't have to know anything. Matter
of fact, what's interesting is people using our technology in many cases, they
don't even know what's on the back end from an end user perspective or an
API perspective. And that fits very much into that kind of middle layer that
you were talking about to really help with that abstraction where you don't have to
force on a developer or an end user person to really have to know about
that endpoint of where it comes from that data right. And you know,
I'll throughout an interesting point here, just kind of an analogy. I just
came back from Clickworld last week and they were talking about how they're moving to
the cloud click Cloud. But they also were very careful to say time and
again, we are not going to forget about all the on prem installations that
we have. So we're still going to be taking care of these technologies.
We're still going to make sure everything works fine in that world as we encourage
people to go to the cloud. And one of the key considerations is that,
but with any sort of applications you've been using and gotten used to,
if you port that to the cloud, usually what happens is you only port
the bare bone stuff first, and then over time you start adding features that
were on prem. And that's for a whole variety of reasons. One dev
time just that takes time to do stuff right. But the point being,
if you were to go straight to a cloud instantiation of some data warehouse,
for example, you'll lose a whole lot of context. You lose functionality,
you lose things, and that's why you want to be careful about what you
do here, because number one, to be able to replatform or sort of
re architect refactor a whole existing environment and the cloud is going to be really
expensive. It's going to be painful to do it. Even cloud natively for
example, it sounds good, but it's very difficult to pull that off.
So I like that you use this term thoughtful to describe how your team should
be as you look at your environment and figure out ways to go forward.
Right, you want to take time to do that, really document everything and
test all along the way, right, Yeah, absolutely, And what's interesting
is organizations definitely going to take a very measured approach, but some organizations also
say we want to take a measured approach, but there's some footprint that we
want to move faster, perhaps even away from the mainframe in some cases.
And so the technologies that we've been working on really help you be able to
move the data without reprocessing it to popular endpoints like for instance S three,
And you can even do hybrid models where where your processing still has done on
the mainframe, but your storage is actually done in a modern fashion. Like
for instance, like with the S three or something like this. And so
what we find is it's really about the the applications before and after the data
that are super important. If you disrupt those thousands millions of lines of code
in many cases that are sitting before and after that data, that's where the
real disruption that happen. And so we've worked on a lot of tools that
can help you mix and match a lot of these hybrid use cases with clouds.
So it doesn't have to be an all or nothing. If your organization
is saying we must move faster, we must do some digital change to change
and experience or bring some data to a customer in a fresh way. Yeah,
that makes a lot of sense because there are pressures to move to the
cloud. There are lots of good reasons to move to the cloud. It's
a fantastic marshaling area for functionality. It's a good marshaling area even for data,
at least data that's born in the cloud. Now taking a whole mountain
of data from on prem and pushing it up into the cloud. There are
companies doing that. You have to be careful about how you do that because
you really want to understand your access patterns. You want to understand what people
are doing with that data, how does it interact with your applications. All
that stuff can be mapped out, and really should be mapped out before you
ever try to pull something like that, because if you go too quickly,
you're gonna have a very unpleasant route awakening when it arrives. But both don't
touch up, that will be right back. You're listening to inside analysis.
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to Inside Analysis. Here's your host, Eric Cavanaugh. All right, folks,
tickets to the future. Indeed, I want to go to the future
where the systems are always running quickly. I never I'm on the phone with
some customer service represolve. I'm sorry, systems are running kind of slow today.
I don't want that. Nobody wants that. You want stuff to work,
right, that's what you want. You want to go to Amazon buy
stuff, go to some other side buy stuff. And have it work and
have it be seamless and quick and fast and secure. That's what we want.
That's the good stuff. You got to modernize to get that. You're
not going to get that with some of these older architectures. And so I'm
talking today with Doug Johnson, who is an expert in this space with Rocket
software these days, a little bit name by the way, how can you
go wrong with Rocket software? But we were talking about containers in the break
there, and I'll just explain quickly to the audience for those of you who
haven't heard before. Google came up with this project called kubernets. If you
ever see kh s K eight, that's a sort of abbreviation for it.
It's a container orchestration system. But basically it's sort of like a de facto
operating system for the cloud. Now, Kubernets does run on Linux, so
that's the operating system that it uses, but it kind of operates like a
de facto OS. And the best way I heard it describe was by a
girl in the open source world who said, look at containers as little boxes
for system processes, so little things that your computer has to do, like
open and application or a lot of times. I'll use a spell check as
an example, Right, you could use a spell check in Word, or
you could use a spell check in Excel in other applications, that's a service.
The spell check is like a service. And way back in the days
of service oriented architecture, we had course grain services and fine grain services.
It was the same general vision. But then Kuberneti came along and really kind
of revolutionize how that stuff gets done. A lot of the new apps that
you see out there, a lot of the cool new stuff that's happening in
the world of technology, is running on Kuberneties. It's everywhere now. They've
basically won this whole shebang, and it's an open source technology, so you
don't have to pay to use Kuberneties, but you do have to get people
who know how to do it. It's not the easiest thing in the world
to figure out, so there is obviously a cost to it in terms of
deployment and management, etc. But it really has one to day and containerization
is a new way to do things. And the last thing I'll say about
it is that what's nice is that because you've broken it into lots of little,
teeny tiny pieces, the big stuff doesn't break as much. Right,
used to get sites that just crash and didn't work. You don't get that
as much in these new environments. What you'll get is one little piece of
it that doesn't work. And then what they do is they kill that pod.
Basically the pod just say up, kill that guy will start again.
But with that, I'll throw it over to Doug Johnson. Kubernatis is changing
the world, but it's not the main frame, and we have to find
a way to get these two systems to talk together and to work together.
And that's kind of what you guys are working on right Yes, absolutely,
And with that, there's a number of areas that our customers and we've seen
in the industry in terms of leveraging the mainframe with kubernets. So you know,
at the base level is we were talking before the break, we were
talking about this kind of abstraction layer where you can access data. And so
what we do is we create some of these pods that you were talking about
that run in that Kubernets layer and put a front end essentially in front of
that mainframe and then provide all sorts of data services to that. So everything
from just raw access like we were talking about before, where it can convert
formats. For a developer or an end user, we can get access to
information. But also if you need greater scalability, it can just create another
pod and have that kind of level of scalability. But then there's also other
things that happen along that same vein. Is within that layer is also where
we put some protective services. So it's the mainframe or really any critical enterprise
information, you really want to make sure it's thoroughly protected, and so we
do everything from having a very strong access control layer that can even look at
where you are in the world and determine whether you can access something, or
even do things like redact information on the fly for you, so if you
go to access some sensitive information, it can make sure that you can see
certain information and then other persons see other information. And so those are some
of the things we've been working on and customers are really enjoying. Yeah,
that's a very interesting comment you just made. There are new technologies that are
coming out, technologies like go Kira, and there's one called Immuta, and
there's one called Previous Era, and there's a few others that have come along
that are all about trying to protect PII personally identifiable information as it gets accessed
by analytic applications right and or even AI. Right. So you've got all
these powerful technologies now, analytics technologies, but what data am I supposed to
have access to? Well, I mean you in the old days, you
could control that a couple different ways. You could control it at access at
the database level, which is something you can do, but it's hard to
manage at scale. That's the problem. Where you could govern it at the
consumption layer in the app somewhere, but there was nothing in the middle,
or nothing meaningful in the middle. And that's all changing now, which is
good because what you want and we're seeing more of this or these sort of
dynamic technologies for in the moment determining well should this person really get access to
this information or not? And so you can see this with sort of ephemeral
access, right, like you get access for thirty minutes or for an hour
or fifteen minutes or something like that. These are all designed to protect the
systems and protect the information because we've seen i mean, look at the Colonial
pipeline hack and how that took down the East Coast of people not being able
to drive because they shut down the pipeline that delivers the gas. Right,
So these hacks are very very serious ransomwhere as serious there are all these different
threads, and you guys are taking a pretty clever approach, it seems,
to protecting that critical information which is on the mainframe right absolutely, from crypting
it at rest to making sure and transit it's encrypted, but also going beyond
and making sure, as we were saying, that dynamic way in which you
access information, this particular user can see that this particular user see this other
thing, including things that protect information and pi on the fly. And what's
also interesting is we also do that with unstructured data as well. So it's
when you kind of look at a system that already knows your data dictionary and
you can see everything, Oh, you know this is a so security number.
You know this is that. That's one problem to solve, but it's
another when you have unstructured information in a reponsitory. You need to kind of
call through that, make some policies and then dynamically apply them. Those are
some of the things that we already have in market, and those are continued
investments we're making. So it's a pretty exciting area and it really helps make
sure that an individual application development team or an individual user or department doesn't have
to really take then the burden of figuring this all out that can actually be
done at a corporate policy And that's why customers really use our technologies is as
it really helps them really I use data more as a service internally and not
be afraid to use it some of the times. What we've also seen is
customers have lots of useful information, but they know it has some I'm not
sure what it has. This is the problem, So there's this kind of
trepidation. So it kind of comes back to those themes earlier about knowing what
you can, you know where the information is, what those flows are,
and then ultimately be able to put it into action even with unstructured information,
which is again an area where we've been investing for years on. Pretty exciting.
Well, that is really cool, and you know, one thing that
gets me very excited about the modern information landscape is what you just mentioned that
you can do this policy management now fifteen years ago, even ten years ago,
and someone saving five years ago policy was just something written in a document
that set on a shelf and you had to hope that the employee you read
it and knew it and adhered to it because there was very little bit you
could do in terms of dynamic policy enforcement. But now we can do that
stuff. There are machine readable policies that can be implemented and protect dynamically against
all sorts of things, even accidents, even someone accidentally accessing information they weren't
supposed to. It protects us in all these different ways. That is a
much different world than we grew up with. Like in the early days of
the mainframe, you didn't have these sort of dynamic policies that you could define,
manage, execute and in monitor over time. Now we do and that
is a very very big deal, right don Yeah, absolutely, And it
ties back to both protecting that critical information you have on the mainframe, but
also it extends beyond. So the way we've we've implemented this technology is it
works with your critical systems, with your mainframe, and that's the bread and
butter of what we focused on. But we also made adapters that top into
other systems, including other unstructured systems. So for instance, whether it be
maybe a SharePoint where pository or where information might exist in other repositories in your
enterprise, and we even have an ability for you to be able to customize
it. Because what we've also run into is we have large enterprises that they
have some custom content system that was created years ago. And speaking of afraid
of change, is someone's like, Bob built the system, Bob isn't hereing
anymore, and you know what do we do now? Right, So what
we've been able to do is kind of put abstractions in front that allow you
to get access to that information. But that policy, unified policy layer applies
to whether it to be the mainframe or beyond, and so that can be
really powerful because again what can happen is end users or developers just need one
API or one user experience to access this information and so they don't have to
worry about, you know, where it's coming from or what format it's in.
It's automatically protected from them on the fly. And so that's some of
the things that our customers have really enjoyed. Yeah, that's a really good
point. You mentioned this a couple of times, so I want to explain
to the audience why why you're doing that. The ability to make change in
your organization requires lots of different stuff, right. You need people who develop,
you need people who understand the systems, You need to know what you
want to change, get that from the business, etc. But the speed
at which you can try things is really really important for reasons of morale,
for reasons of business acumen, and so on and so forth, for reasons
of survival in some cases. And so what had to happen in the old
days is there was a very long, what they call waterfall process that had
to be followed to change anything. And we want to wasting lots and lots
of time because people weren't communicating properly and it's just not a very efficient way
to do. But now we can have this much more agile approach. And
what Doug is describing here is empowering your application development team to really focus on
business problems and not worry about the security, the compliance, all these other
characteristics that need to be handled that can be handled through the platform or through
some of these policies, and that liberates the developers to try new things.
It liberates the business to talk to the developers to try to things. That
is absolutely crucial for digital transformation, right absolutely, And what we've also seen
is this becomes even more important when the stakes are very high. Consider for
instance, emergent acquisition type scenario. Sometimes whether or not to do the deal
is all dependent on how fast can we integrate our organizations. And you know,
many organizations have these critical systems where they have years and years of data
in the mainframe and they need to integrate it into the rest of their ecosystem
again to use that word. And so if you're trying to bring developers together
to bring a unified experience to customers, the ability to do this very quickly
can be the difference of Wow, that acquisition was really worth it. Our
customers really see us as one company. Now we're able to integrate business processes
and be fluid or sorry, boss, it's going to be another six or
twelve months until that's finished, which well, isn't a really a fun conversation
right now. You have to be able to move quickly, and that's really
what we're talking about, is empowering your team to be able to make changes.
First of all, understand what's there. I mean, that's one of
the biggest challenges I think, And we want to talk to about AI and
how AI can come into this equation here, and the bottom line is you
better be careful. I mean, one of the downsides, at least in
the early days of chat GPT, is that if you type in some of
your proprietary code or some of your proprietary recipes or whatever that's going into the
machine and it could become out some other side, to some other place and
you won't even know. There's gonna be some really interesting conversations that be had
around that in the near term. But the point is you have to know
what's out there, know the steady state, and then be able to do
that impact analysis you've talked about to try new things. You try it and
fail, fail fast forward is what they say, right, That's what you've
got to do to be able to solve these real mission critical problems is have
the agility and have the confidence to be able to make some change and know
that you can either undo it or at least fix what you've created right a
lutely. And you started off this segment by kind of talking about the cloud
and Kubernetes and different technologies around that. And what we've also seen is a
lot of customers have really seen this kind of rise to microservice style architectures where
it's very works very well with kubernets because it allows you to manage these different
pods that you were saying, and you might have dozens, hundreds, even
thousands of them depending on what happens, and what we've seen with a lot
of customers is they really like these style architectures where you have these little piece
parts like you were saying. Part of it is because it allows you architectural
purity of scalability and such, and that's a good thing. But the real
reason is it allows the business to scale at a people level. And so
it allows different developers to work independently of each other and build new business logic,
create new business functions, and ultimately let the business go faster. But
then you have two problems or now you've introduced a new problem by doing that
isn't any cases. What happens is you proliferate the access of data. So
in many cases a development team will create their own database or their own store
of where they're putting even unstructured data. And now what can happen is in
from critical information can get out of sync with other systems through these kind of
architectures, and so to remediate this kind of problem, we've looked at technologies
that allow you to look at data quality across those landscapes, and so particularly
considering, for instance, the mainframe is maybe a little bit the source of
truth for many applications. It allows you to look into what's on the mainframe
and then what's in other systems and does everything match up? And this is
very important in like, for instance, reconciliation or financial use cases. And
what we have is customers have used our technologies to make sure everything matches in
real time as they're creating these micro services, and so it really allows this
teams to kind of move very quickly. But at the same time, the
quality teams and the infrastructure teams that want to make sure both quality is intact
but also compliance is intact, really can enforce these rules. So it kind
of gives you the best of both worlds through that approach. M Yeah,
and what Doug is talking about here is the fact that these larger organizations,
you're going to have whole separate teams that are all important. They have to
work together, but you can't have everyone in the same room all the time,
so you have to come up with policies procedures, technology stacks to enable
this kind of collaboration. And then to his point, you want to make
sure that you're getting the latest data, the freshest data, the data that
is accurate because things change, and so that requires What really amounts to an
information architecture is what it boils down to and kind of understanding where and when
you can build in your checks and balances, where and when you can kind
of bring in that last security check, if you will, to make sure
it is secure, a last little bit, but don't touch out. That
will be right back. You are listening to inside analysis. Do you own
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Welcome back to Inside Analysis. Here's your host, Eric Kabanat All right,
folks, back here on Inside Analysis, talking with Doug Johnson of Rocket Software,
all about modernizing the mainframe and really being able to weave it into this
new, very dynamic world of cloud computing. We've talked about the analytics that
you can do. We've talked about data provisioning, which is really important.
There's some other risk management aspects to this as well. I spent a past
life moderating webinars for the Global Association of Risk Professionals or GARP dot org.
I joke that they are the de facto Illuminati, because they're chief risk officers
at central banks all around the world. Of course, there are some banking
issues these days. Silicon Valley Bank collapsed. First Republic just collapse over the
weekend. These are very strange times. A lot of people don't understand that
all it takes is a psychological event for there to be some run on the
bank, and that's what happened with SBB, that's apparently what's happening with First
Republic. People just pulling out massive amounts of money and that creates systemic risk.
So being able to kind of understand that, and especially in this global
world where you've got to all these different countries involved, you have all these
different central banks. Of course it's all electronic now, so you can access
and move money around. A lot of stuff has changed. I mean,
the fintech innovation has been absolutely stupendous, and a lot of these banks all
have mainframes. They've had mainframes for years doing all this stuff. So I'm
curious to know your approach to the risk management side. I can kind of
guess just to being able to look into unstructured data, identify entities, you
know, basically provide a risk score of some contin You tell us a bit
about what you do in the risk management space. Yeah, well, there's
no shortage of opportunities, but also there's with all opportunities, there's also risks
that go along with it. And a lot of the organizations that we serve
are very highly regulated too, and so they have certain regulation regulatory obligations that
they have around how long they have to keep information when it's notifications, if
a customer asks for certain information that it can be expunged in a timely manner,
and many other things that happen from a process and procedure perspective, and
so what happens in that domain is being able to make sure you keep information
just as long as you need it, but no longer. And that sounds
like it's such an easy thing to say, but it's remarkably hard to do,
especially when different classes of information require kind of different retention policies. In
with that, and then in addition to that, though certainly retention management is
a key thing that our customers think about, but also as they look to
change business processes use information in different parts of the organization, they need to
make sure they have custom over that information wherever it might go. And so
what we're seeing more and more as customers are saying, hey, can you
make this data available to maybe some business partner or perhaps some internal process,
but we need to make sure we have the controls that can see the flow
of that information throughout a cycle, who touched it, when, if it
integrated with another system, Do we have an actual audit trail of the data
and time and the proofpoint that some interaction actually happened. And so that closed
loop reporting on that auditing piece is just super important to making sure that organizations
are compliant so that when a regulator comes to you and says, you know,
hey, can you show me that paperwork that shows X or Y.
The answer is, of course, it's right here. Yeah, that's a
big deal. And you know, when regulators come calling, you want to
have several things in place. It seems to me you want to have your
plan, you want to have your architecture documents, and then you want to
be able to show their receipts. Right. You want to say, well,
here's our for how we detect breaches, here's our plan for when we've
detected a breach, what we do, Here's what happened in this particular instance,
X y Z process is fired off. And then you want to be
able to show them in the production system. And here's where rubber meets road.
And if you could do all that, you're going to get a lot
of leeway. I mean, and you know there's always going to be pressure
to improve security, to improve governance, to improve auditibility, etc. But
as long as you have your plan and you're acting on your plan, and
you're listening to the regulators and what they ask you for nine times at a
tenant that you're going to be just fine. What do you think well,
absolutely, and what's really important is the full life cycle managed around this.
So everything from like we were talking about the very beginning, we were saying,
hey, this idea of can you actually go look at the code and
see where data is moving throughout your organization, that's actually proving that the systems
literally are doing what they say they do all the way to what happens at
runtime, where hey, this transaction happened on this date, and here's the
proofpoint that happened, and it's logged in our mutable storage mechanism. To be
able to have that proofpoint across the data but also the processes, the people,
and the systems that it interacted with, that's what's really required by many
of these highly regulated organizations, and so they look at technologies like ours to
help. Yeah, that's very interesting that you mentioned this a couple of times.
So with your software, you're able to look at the code that is
instanting it in a particular environment and basically process it. And do you actually
run the code or is it that you just know what it's going to do
based upon the functions that you see. And therefore you can look to the
log file and kind of triangulate things. Can you explain that a bit more?
Yeah, absolutely, And so what we have or we have these scanners
that look at the code that developers have actually in their system, you know,
in the version control systems, and the actual code itself, and from
that it determined what kind of data is interacted with that one system. And
that can be on the mainframe for instance, as we said with like Cobalt.
But what's also interesting is you use then you throughout your organization, you
go to other systems and run that same procedure. It might be a C
plus plus or Java or something other system. And then the interesting thing is
it looks at that parses that code itself and those entities that are involved,
and it sees the connection where one system calls another and it stitches it together.
So you can see not just the data and one system and the data
and other systems, but you can follow that lineage, that flow of information
from one place to another. And again it becomes an auditor's dream because when
you go when that compliance team says, hey, we want to prove that
something happened, you can actually from a system's perspective prove or where data is
touching another system and as we said, if you want to make business change.
Again, the compliance officer loves it as well, because you can confidently
make changes and because you can see the entire data landscape, so it becomes
a very powerful visualization around the entire organization. Yeah, that's really cool stuff.
And obviously you can understand why banks want that level of certainty because you're
moving money around, right, the classic two face commit Right, it's okay,
I'm sending money over here. Yes, I've sent it. Did you
get the money back? Yes, we got the money back. Okay,
good, Like, now we know we can move on and do something else.
And wow, you think about the innovation and fintech and how much easier
it is to move money around in just seconds and moments. And that took
a tremendous amount of back end work to be able to engage these different systems
to move the money, to categorize, to categorize it essentially do all that
stuff. That's a big deal, right, and so Rocket Software, I'm
guessing, is a significant player in that whole evolution, right, Yes,
And with that, what we've also seen is the ability to scale in terms
of looking at these systems automatically and really scaling, compliance and other things is
super important to be able to get that visibility. But also we found that
scaling from a people perspective is super important. And what do we mean by
that, Well, if you have that data and it's available to two or
three magicians at your organization that know how to run the magic incantations to tell
you what those connections are, that that isn't as effective as what if the
user experience could be more of a consumer type experience where anyone could go in
see those connections. Obviously anyone who's authorized obviously right, but you know at
the departmental level, or if you're looking to make a business change as a
as an enterprise architect at an organization, you don't have to be that magician
to know all of the details of the tool. It really allows you to
see those connections that otherwise you would just spend mountains and mountains of time in
meetings and manual work to try to get achieve the same thing. Yeah,
and not even get to the answer. Perhaps, I mean it sounds a
lot like process mining. Maybe we can talk about that in the in the
podcast bonus segment. But that ability to get down to the code level as
you suggest to run time and be able to do the playback say okay,
well here's what happened. This system connected to that system. This data came
across the trans and went over here. We can verify that it landed,
etc. That is tremendously useful information for any kind of trouble shooting, for
any kind of improvements, you know, because then you can look at that
a certain skill and say, okay, we need to change x, Y
and z to be able to supercharge this one process. You know, you
went into the classic impedance mismatch with some of these systems right where it's like
this one is spending at a certain rate, that one is spending at a
slower rate. How do you how do you get those two to jibe?
Basically, that's not a small problem, right. You want to be able
to see all that, And your point is that you're able to see into
the mainframe and see into these other systems and give a very clear cut view
of which code is enacting at one point in time, which data goes in
there. That is the audit trail, right, that is and really allows
you to make change with confidence. Many organizations they have the meetings, they
have the budget, they have the desire to change, but they really don't
know in the end, is the thing I've created my VISEO diagram that we
had meetings about, is that really how my organization operates? And you just
don't have that confidence, and so we help you kind of buyback that confidence
in those change both from a regulation perspective, but then also just making sure
you're effective your project's going to be successful. Yeah, this is wonderful stuff,
folks. Look these these folks up online Rocket Software at Doug Johnson has
been our guest today. We are booking the second half of the year right
now, So if you want to be on the show, send me an
email. Info at inside Analysis dot com that comes straight to yours truly or
info at dm radio dot biz. We're trying to understand what's happening in the
information economy. We'll talk to you next time. Folks, you've been listening
to Inside Analysis Express one o six point five FEV talk radio station leur KCAA,
the station that leaves no listener behind Express one oh six point five FM.
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