>> Live from Washington DC,
it's theCUBE
covering Inforum DC 2018
brought to you by Inforum.
>> Well we are back here at Inforum 2018 in Washington DC
John Walls with Dave Vellante.
We are in the nation's capital
and joined right now by Massimo Capoccia
who is SVP of Info OS
and Rick Rider, product director at for common at Infor.
Gentlemen, thanks for joining us,
>> Thank you >> Good to see you both.
>> Thank you for having us >> Thank you
>> Let's start first off good job by the way
>> Welcome to keynote..
thanks
stage this morning
we had some time to shine out there.
Your thoughts about the show in general so far?
We've been a couple of days in now,
how is it going for you?
>> Yeah, very very well
the customers have received the Infor OS
and the technology innovation
and what we do with the AI
very very well.
You know lots of people in the hub,
lots of sessions,
so lots of interest on the technology innovation for
Infor OS and for Infor as well.
>> Sure, Rick for you?
>> Yeah, its been great,
it's been interesting.
What we are finding out is
getting a lot of this out in front of customers and partners
is bringing up some interesting opportunities
for us moving forward.
So it is not everyday we get
the opportunity to get in front
of these many people within our network,
so it's been great.
>> So we'll be hearing from folks
Let's talk about AI,
especially for those
who maybe don't know,
haven't embraced it yet.
What are the
Hesitation,
reservations,
I mean what are you hearing
from them as far as
what's going to trigger them
to make a decision?
>> Yeah, to be honest I think
they have been hesitant in the past
just because it hasn't really been clear.
We have talked about AI
in the technology community,
it's been hard to define.
Some people might
in fact define incorrectly,
because we are making assumptions about what technology
can and can't do.
I think what we are uncovering.
I feel we've got
a pretty unique approach
to what we are doing here with Infor OS
and common connected to it.
We are working directly with customers to identify
use cases on how we can apply AI.
Rather than just starting at the top
and saying, "hey we should be
doing all these great things
and let's see how
we can make it work
for our customers."
It's kind of we are flipping the script and starting backwards
and saying, "hey what are the issues?
What are the opportunities
the customers have?
How can we build the technology
using AI to make it meaningful?"
So we have business impact they want.
And by doing that,
I think it's a lot more understandable,
it's a lot more relatable, it's a lot more trust able
from our customers.
>> We from in theCUBE here,
watch and observe the ascendancy of the hype
and so called big data.
And which is sort of moderated now.
But in data is plentiful, insights aren't.
and so we feel we have come to the conclusion
that the innovation recipe,
if you will,
for the next decade or so,
is data, applying machine intelligence,
that data and having a cloud
to be able to scale it.
Having cloud economics to be able to track innovation.
You guys seem to have all three
>> Yeah
>> Of those pieces
But AI without the data is just..
I don't know what it is?
>> Right? Excited.
>> Data without the ability
to extract (laughs)...you know insights...
What good is it?
>> Right >> Yeah. Yeah.
>> Then you got to have cloud to scale it.
Your thoughts on from a platform perspective
with that means?
>> Yeah, Absolutely.
So I was seeing the interview
that you were doing with Charles,
says we build this platform from the beginning.
And one of the big element is that we have
you know, made it possible
to synchronize you know real time all this data
that the applications will generates,
into a single place called the data lake.
So when you have the data and data lake
then you can do many many things
and not only analytics and reporting,
which is the classical use case,
but now it allows you to do AI.
And the difference is we don't have one domain of the data.
So some of the vendors have only CRM data
or ACM data or financial data.
With Infor we have all different domains of data.
So we can go from ACM from financials,
to asset management, to IoT readings for IoT devices,
to ERP and CRM
also.
So when you combine
when you can cross
and combine the relationship with this data,
then your AI is much more smart and intelligent.
When you have only the AI focused on a domain,
is less intelligent.
So that's actually the power that we do.
And our Coleman will
take advantage of that, you know
that you know
rich data lake.
>> Okay and we talked a lot to someone earlier
about the stack. and that the bottom layer,
is the OS >> Yes
>> So everybody is familiar
with what the operating system does
in computer science.
How is your OS similar and different?
What's the function that it does
if we can double-click on that?
>> Yeah, so we..
It's in for operating service and we call it a service.
Because it's not actually in the database
and operating system level, right?
So we believe...
We are more in the application technology.
We are the layer that
takes you know the bare technology and makes it usable
for a business, for an enterprise.
And we build applications on top of it.
So what we believe at Infor,
when you have an architecture with this composite
of a suite of applications.
Or even the new Microsoft architecture
that developers built.
You still have to deliver a uniform user experience,
a uniform business process, uniform security
and data management
and even AI.
So if you look at services
like Facebook or Netflix, they have maybe
the entire Microsoft architecture thousands of that,
but the experience is one.alright?
Thus what we want to bring it to the enterprise.
Infor OS big..
that unify the experience both from the end user
and business process, to the enterprises.
And we do it for all the cloud suites.
Infor OS is all the cloud suites
not just one but all of them the same services.
>> So, I love the Netflix example,
because if you think about digital...
digital transformation, digital business.
My experience with Netflix
is just with Netflix
I don't have a...
There's no marketing department, sales department
service department.
I do have a problem,
I go to Netflix on my app(laughs)
I interact with... >> Absolutely
>> So that's...
I considered that what's called a product.
So Rick,
how does this capability get translated into product?
>> Yeah.
You know
one thing that you brought up a lot earlier is,
with all this interconnectivity
and how we have to package things.
So we've got all these different services that OS offer.
So we've got the data lake, we've got the API gateway.
We've got the integration platform, and...
All those pieces is what bring this together to where,
we can actually deliver something to our customers.
In my case, it's an AI model or it's RPA,
because of all these things are packaged together.
So they don't actually see what's happening,
because it's already packaged for them.
>> Okay, so...
what I was saying the Charles,
you probably you might have seen it,
is when we first discovered Infor was like,
"What do you guys do?"
It wasn't clear exactly what you guys were doing.
But he said, and I believe him,
was always our vision to have a platform.
Now that... the...
it's not opaque anymore,
the platform is pretty clear.
Now you've added the Birst Analytics,
you've added Coleman AI on top of that.
So you know Andy Jassy AWS always talks about
the flywheel effect.
So I suspect that you're entering this flywheel phase.
What is that phase?
What does it kind of mean for you guys, for customers,
in terms of innovation?
>> Yeah, is a very good question.
Actually I worked for years with...
We started with this platform, this journey with Charles
and we start really with...
okay, what's the first first issue.
You know, we want to solve the integration promise.
We want to give an integration platform.
Then we build that.
Then we start to say, okay,
we want to unify the experience.
We build a unified portal with a single sign on.
Then we say, okay,
we want to unify the data, we build a data lake.
So we continue to build out the platform.
We are now at the level we have a platform
and its unique platform because
you can say it fits in one Magic Quadrant.
Because yeah, it does the iPass in the past.
So with all these magic quadrants.
But it doesn't fit in one, it's in all of them, right?
So and in...
The analyst looks at that and say,
Okay, we have a platform doesn't fit in one,
if it's in all of them, right?
>> The Magic Quadrant is now becoming outdated,
because... >> Exactly.
>> Because its as you said... I don't need 15 stove pipes...
>> Exactly.
>> With the stove pipe thinking.
>> Exactly. So..
>> With all due respect to my friends at Gartner
(laughs)
>> But the Fly wheel is... Yeah,
the platform is going to become more and more important,
relevant.
The customers that...
you know are in the cloud,
are not in the cloud,
they will use the platform to get to the cloud.
It's going to be a new enabler
for those customers are still on premises,
to go to the cloud.
We the Infor OS is enabler for hybrid process.
So some some application can be in the
on premises or in the cloud.
With the OS they can take the journey
and get to the cloud and their own place.
>> So architecturally, you don't care.
>> We don't care what the application side,
>> Okay. But you've certainly done a lot of work to
optimize AWS, you know,
we're AWS customer, we know it's, it's not trivial,
you have to,
you know work it.
It's simple, developers love it,
but to really take advantage of it,
integrate it with your processes will take some work.
But architectural, you don't care.
But it's not.
That's not a that's not an offering statement, is it?
I mean, today, can I run that multi cloud,
run their software anywhere?
>> Well
>> Doing that?
>> Well, today, we have a mix off,
we use open source library,
but we do utilize AWS, the data lake is built on S3.
On AI, we use Laks, or Sagemaker
for the training on the models.
So we do a lot of AWS,
Because it gives you our computing power
and any out of the box solution for certain certain pieces.
What we do we build interfaces to our application,
so that our customers doesn't need to
take care of all the plumbing, it's all interconnected
and done.
So that's,
that's one of the power of Infor OS.
It brings that application technology layer,
between the business application and
you know, the basic, you know, technologies
>> And the customer doesn't want to think about
the plumbing these days, right?
>> Right.
>> To most customers, infrastructure is irrelevant,
you know, again, apologies to my hardware, friends,
but they don't care about hardware, right?
I mean,
>> Yeah.
>> It's interesting, Charles said in the keynote yesterday,
when we were an onPrem software company,
we didn't manage servers for our customers.
Customers didn't care really about the server,
and any more than they care about the plumbing today, right?
>> Right. Yeah.
And if I want to relate that to the AI space,
all the training, all the science,
all the highly computational things that we have to do,
customers don't really want to know what that means
or how to use that.
So what we're actually doing is in conjunction
with some of the AI services we're working with,
with AWS is we've built a modeling platform
to where they're operating in one location.
They've got no concept of where this is hosted,
what's going on behind the scenes,
and then we connect it, we expose an API,
and they can do any sort of RPI that they want to.
>> So...I mean you are talking about
when you talk about your customers, and
they don't care about, you know, what's behind the curtain,
they just wanted it to handle, maybe something up front,
but yet,
you have to understand what they can do. Right?
You have to understand their potential.
So how do you do that, when you're dealing with
different companies, different sizes, different priorities,
different challenges, they're different technology stages.
How do you all address them individually
and help them get to that better place?
>> Yeah, I think, you know,
it's never a one size fits, all right.
So we try to give them
what we've called citizen developer tool sets in the past.
And I've even started to try to say,
citizen data science tool set.
So how can we make it more consumable by all types of users?
So yes, we can provide templates, we can create these things
that might work somewhat out of the box.
But each one of these customers their data is,
is just slightly different than need to make tweaks.
So we really want to be able to, you know,
provide all that flexibility.
And it gets back to we start with our use cases.
And then we build from there.
So we get all that feedback,
and make sure we're making we're hitting those key points.
>> So I want to pick up on something you said about citizens,
citizen data scientists.
I've used that term before in front of data scientists
some of them don't like it, right.
That denigrates what they do.
And it's true, a data scientist is a math whiz,
maybe a stats, was there a data hacker they can code,
Okay. And that's not every business person, right? Clearly.
However, when you think about things like our RPA,
I mean, you really want to enable business users.
You don't want to repeat the same problem
that we had for years with things like decision support,
where you had two people in the company that knew how to
build a Cube.
And you had to have line up with an ask,
please can you build my cube, I have a deadline
while everybody else does too. Just there wasn't effective.
So things like our RPA and low code, citizen data scientists
spread that technology throughout.
Now, part of that is having a platform that is
I vision a studio, whereas a user, I can actually create
some kind of process and code that in software, you know,
code it.
It is something that's repetitive
that I don't have to do every day.
I do it every day, I do it the same way.
Somebody gave the example might have been Soma,
I know somebody else,
expense report approval?
>> Yeah, yeah. Yeah.
>> I've never not approved and expense report.
I don't crack them open.
Look, I don't know, maybe every now and then somebody does.
Somebody does, by the way.
(laughs)
>> (shouts) So don't get any idea here.
>> I always press the approval button, right?
Why can't a robot do that and look for anomalies and say,
Oh,
a $300 scotch?
That's...
>> Yeah Yeah. Absolutely...
So is that a capability that you're working on,
that you have today.
That what I'm envisioning a studio
and then I imagine this got some orchestrator...
>> Yeah.
So yeah, so if you look at throughout all Infor OS,
is completely Model Driven.
So either you, you build a new integration, or a workflow,
or a, an AI model,
or a even, we have a platform as a service Mongols,
where you build with low code applications.
So you can take it to end to end where you you train models
in AI, us suppose as an API.
You can build your own app on top of it with low code
and then, you know, give it to your business users.
Very, very simple and in the cloud.
You know, in the browser and
you can do every customer can do it.
So that's very important for us.
We work from the beginning with this model
to give you know, the tools to everybody,
not only an elite of people that can do
and then you know,
there is the rest of the people that cannot do it.
Every new computer science engineer that comes out gets
you know, AI out of the box.
When I did computer science,
Yeah, I got some AI, you know,
but it was not really like today.
So every everybody can program AI now.
And we want to give this tools to every developer
and not just went to an elite.
>> Yeah.
And the workflow prediction model
that you've been talking about.
If you want to come join us down there,
we've actually got a model that we're working on
for that exact use case.
>> Oh, cool.
>> Yeah. So yeah.
Giving the ability for those business users,
as you say to...
it's almost like lowering the barrier to entry
to a lot of this AI technology.
It's not devaluing or anything, data science,
because we've got those advanced tool sets,
to where if you want to do something in our studio,
bring it over into the Coleman AI platform.
You certainly can, we're not devaluing that.
But you know, what,
if we want to start and take little bites off
and you want to give this in the hands
of the business users,
we've got a great solution for that.
>> So this is all the cool stuff.
This is stuff that business users care about?
I mean, do they...
My question is,
do people care about what's under the covers?
I mean, are they asking you or what's the database?
And how does this work? How does that work?
Or they just really want to focus on that functionality
that they're getting in the business impact?
>> Yeah, with the advent of the cloud,
you know, people,
just those questions like we sh...
you know, operating system database,
which technology you use?
it just went away, right?
So people just want to know,
the functionality and the value.
You know, maybe there are companies that I have more,
you know, an IT architects and they want to know, more,
you know, that's what they want to go down into the details,
then you go into the architecture of the OS,
of the application, we integrate with AWS.
So we do that as well.
We, you know,
we talk to customers about it.
But most of them, they just want to know, okay,
"how can I use this platform to make my business better,"
right.
So it runs the cloud suit, but I now
I can connect to other cloud services,
I can connect to the other application,
I can build my own app and bring it in.
So they want that business value immediately.
And that's why we built this Infor OS,
so that they can run the cloud suite and add business value.
>> You guys at last year's analyst meeting,
gave a little glimpse of some of the architecture
and it was very useful, actually,
analysts love that kind of stuff.
I didn't get the invite this year,
maybe something the some smarmy questions I ask.
(laughs)
But I found that actually quite impressive
in terms of the tech behind it
and the RND that you guys are doing there.
But ultimately, it comes down to what products you can build
and what business impact that has, right?
>> Yeah, absolutely.
I think where we're heading with this, we really don't have
many limitations for what we're seeing right now.
We're built in a way to where we can apply to every single
industry, every single cloud suite.
We have the unique, you know,
possibility to where we can go through all these different
industries and create these sort of value.
So we've got a very unique future ahead of us.
>> So. Yeah,
So how much better or can you give us an idea of a road map
a little bit about what you think Coleman can go?
>> Yeah so,
we're starting to play in the image recognition space
a little bit.
Maybe looking at how we can utilize things like
drone technology and do
inspection reports, those sort of things.
It's maybe and at least my opinion, I think others kind of
express the same, it's maybe the least developed area
and we want to make sure we have something that works
for customers the way that
they're going to see value immediately.
But also we're starting look at edge AI.
So how can.... not necessarily just an IoT,
but how can we how can we build something in the cloud?
How can we create a model, then deploy that for our onprem
customers who aren't quite ready,
so that they can get that AI experience as well,
and that predictive insight.
>> It's dvallante@Siliconangle.com
Is that right?
Your email for the invitation
>> David.valante...
>> (laughs) to make sure...
so what will exchange information later.
>> We'll invite you
(laughs)
>> I'm sure this is not your territory.
(laughs)
>> Its on me.
>> Thanks for joining us. >> Thank you.
>> Its been a pleasure.
Thank you for the time we appreciate that.
Back with more here from Washington DC
right after this.
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