>> Announcer: From Seattle, Washington.
It's theCUBE
covering AWS IMAGINE.
Brought to you by Amazon Web Services.
>> Hey, welcome back, everybody.
Jeff Frick here with theCUBE.
We're in downtown Seattle at the AWS IMAGINE EDU conference
it's the second year of the conference.
Part of the public sector,
kind of a carve out with Andrew Coast group,
really all about education and education from K through 12
to higher education, community college education
retraining of people coming out of the military
it's a huge segment
and we're really excited to have our next guest.
He's going to give a keynote later this afternoon
on a new paper that he just published.
So we welcome Richard Palmer,
the practice leader for public sector for Ovum.
Richard great see you.
>> Good to be here
>> So tell us, it's called reaching for the cloud.
>> Yes, look what we found is that
for many universities moving into the cloud
has proved to be difficult.
That there are lots of barriers in the way
and they get a part of the way along,
and all of a sudden they hit a wall and it takes time.
the big number that we keep looking at
is only 30 percent of application
workloads are in the cloud at this point
and after six, seven years
of the public cloud being available,
it really suggests that there are barriers,
significant barriers.
>> Right.
So what are some of those?
Again, as you said,
we're kind of well down this path,
so, whether it's just legacy stuff that's not worth moving
but I would imagine most of the new workloads are coming in,
I mean they've got to be getting with this program.
>> Purchasing SSAS is an obvious ploy,
it gets you right out of all of the problems
that you had before.
Look, the first thing
buried that people find is that cloud's different,
so, the skills that you've got in your team,
the way that you work finances, your project methodologies
everything is different to engage with cloud properly.
>> Jeff Frick: Right.
>> And the way that you design and build applications
is different in the cloud,
so taking a traditional organization trying to go cloud
has everybody involved from the CFO with funding cycles,
to governance boards,
which are the most wonderful thing ever in higher education.
>> Right.
>> All the way through to staff skills
and the way that staff think about applications.
And if there's one thing in my time in higher ed
that I saw time and time again,
it's the instant legacy problem.
So, somebody creates something and does it a special way
because they know better than the vendor,
and we had this infrastructure anyway.
>> Right.
>> So why not reuse it, and they create an orphan
that is neither manageable by the vendor
nor manageable by the organization,
requires that individual to remain with the organization
well past their expiry date.
>> Right.
>> Let's put it that way, because they've put things in
that are just unique to this single installation,
and that's the transformation you see in the cloud.
It's software as a service.
It's a native thing you don't look at how it's hosted,
you don't care about anything but using it.
But the danger point is in moving to
infrastructure of service or platform as a service
that you carry over,
that customization thinking.
>> Right.
>> Which creates, if you like, instant legacy
>> Right.
>> So that's one of the barriers that we see.
I'm just curious because you brought up
two really big things, you know,
just the whole financing and the way you buy it,
the way you budget it is completely different
than a big capital expenditure
that you're depreciating over time,
and then as you said the skill set.
So in the enterprise space,
everyone's got big piles of money
and they hire the big SIs to come help them.
They have instant skills they can bust them in,
buy the minis, the dozens, and you know,
help them with some of that financial engineering.
How is the system integrator or the services
kind of industry evolving in education
to help them make this transition?
>> So there are two ways.
and education being a public sector enterprise
has that awful problem that if you provide advice,
often you can't provide services.
We tend to be getting over that a little bit now,
but the obvious way is
most higher education institutions
who've moved through that process
have engaged a strategic partner to help them
to plan.
>> Jeff Frick: Right.
>> That's the first piece,
and there are lots of them around,
and often they're very good,
but moderately expensive.
But the thing that they don't tend to do as well
is to find the right partners for the actual transition.
So, often engineers are trying to learn cloud technologies
and apply them,
and get it right the first time around,
and of course we all know that in the experimentation
you want to have
learn fast and then relearn when you've come to something
that you shouldn't have done.
>> Jeff Frick: Right
>> But if a engineer is thrown
into a live production style project,
there's no time for that
relearn re-platform learn as you go.
>> Right.
>> So having it, not so much an SI,
but an implementation partner is really important.
And luckily
many of the vendors
or their networks are really quite good
at doing those mid-level implementation projects now,
so it's a matter of finding the right one.
>> Right.
>> But certainly in my home Australian context,
for almost all
moves to the cloud,
there's somebody who's done it several times before
in education
and has a good reputation,
so I suspect in the u.s. that's multiplied,
it's a ten times larger economy,
it is probably ten times as many people
who have done it well before.
>> Right, so the other piece that I'm curious,
it just came out, right?
So, there's the cloud as a more efficient way
to run to your infrastructure
and all that that means and cost savings,
but much more importantly
in some of the things we're hearing today
is really to enable innovation.
To enable you to develop stuff faster, whether it's Alexa
or some of these other things we're hearing about.
How does that play in people
kind of getting through the pain
of getting through this process,
because if you don't innovate,
and we just had somebody on before,
he said that they're worrying about competing with online,
and really having a good experience
for the students on campus.
Is that the driver?
Is it the cost savings?
How do you see that kind of slicing?
We've seen several drivers.
the one that's most common is student success and retention.
That is ubiquitous in higher education
to bring the cost down
and to make sure that every intervention
that the college or school does
is meaningful and produces a positive outcome.
So, that's kind of the core business,
and so things like analytics play into that,
and now machine learning more and more.
But the motivator, yes this competitive motivator,
but it actually works the same,
for they're on campuses, they're online,
that if you can help every student to be successful,
you'll gain reputation.
If you can do it efficiently, you'll drive down costs,
so that's beneficial.
>> Jeff Frick: Right.
But then you're asking about innovation.
That's a step after you've put all the pieces together
to do core business well.
>> Jeff Frick: Right.
>> And the key elements in doing core business well
is shifting from traditional to agile,
because agile projects have benefits on the business side
as well as the technical side.
One of the most important things in the agile space
is to be able to iterate quickly,
and that is just as important on the academic side
as it is on the technical side,
because usually the academic or the administrative folks
don't know what they need
until they've actually experienced it.
Most times when you're replacing the system,
you ask the people on the front line what they want
and they answer,
"Exactly what the last system did, but better".
>> Jeff Frick: Right.
>> So that innovation cycle,
do then measure,
and then cycle through
is part of the agile piece,
and the second part of it is being able to differentiate
between what is actually going to make a difference
for your students and what is just pure win.
What we think might be better
but is actually going to cost money, create legacy,
move us away from standard practice,
and actually is going to bring no benefit.
So, really important to attach real KPIs
to differentiating practices.
>> Right.
>> And get away from customization
where produces that benefit.
The third element's platforms.
Once upon a time we used to build
our special systems from the code up,
we shouldn't do that anymore.
We shouldn't be caring about what database is underneath.
Application platforms are faster, more effective,
and require less in-house skills
to maintain over lifetime.
So that's the third element.
>> So what are the things
that the enterprise has been able to benefit from,
is you know we just leave the RP alone, right?
There's a lot of stuff
that's just not worth lifting and shifting.
But, you know it's kind of customer interaction applications
and there's a whole kind of class of applications
that open up the opportunity to leverage
all these kind of platforms and fast development etc.
How's that playing out on the public sector side?
Because before turning the cameras,
you talked about just the pain of lift and shift
and you run into all kinds of issues,
you don't get that that good easy win,
that good fast win.
Are they thinking in terms of setting aside
kind of an innovation development team
that's working on some of these kind of new-age things
that aren't kind of the core systems
that maybe you don't necessarily
want to lift and shift anytime soon.
>> I'm a big fan of innovation teams
when you're working directly with research.
Not sure that that's the best model
for mainstream innovation.
It's much more useful to leverage the folks
who are actually working directly with the business,
people like business analysts,
and to shift those into thinking beyond the mundane,
because the business analysts usually
has a very intimate relationship, in the nice way,
with their business partner,
and they can engage with what would make life better?
What would make things more productive?
And then to quickly bring resources in
behind that idea and do a quick proof of concept,
but you've hit on another whole issue there
that the idea of a ubiquitous engagement layer
that both delivers a really high-quality
online digital student experience,
but also provides a whole lot of information
that can be then analyzed to work out
what was the best thing to do with the student,
is really transformative,
and we're seeing the best vendors move into that space,
even with traditional systems.
And what they're doing,
and I'll use a couple student management system vendors
as an example without naming them,
but their traditional systems,
they will either host them for you
or you can do it on premises,
but their new analysis engagement systems are cloud-based.
So, doesn't matter where your implementation is,
you can buy a new software as a service
that gives you really good analytics
and a new communications collaboration engagement layer
often with CRM and collaboration tools mixed in
in a brand new platform.
And that's really transformative,
so it allows you to keep your transactional system in place,
but re-skin it with a new engagement layer.
And if you can clip your university services
into that engagement layer,
then you get that 360-degree view of the student
without actually having to shift major systems.
>> Right.
And as you said, that's just money spent
to lift and shift a system
because there's no strategic benefit,
except if it allowed you to upgrade.
Because so many universities are stuck
with an old version of a system,
either because they customized it
or they haven't got the infrastructure
to host the new version or whatever it happens to be.
So there is a strategic benefit
to be able to stay with the latest version,
particularly as most good vendors are providing new features
pretty regularly on their most up-to-date
and are only doing maintenance releases
for previous versions.
>> Right.
It's pretty interesting
that Andrew pulled out of the three themes for the show
tomorrow's workforce role of ML
and innovation transformation.
That ML got its own bullet point
because it is such a kind of an underlying infrastructure
that drives so much value across lots of applications.
And I just found it interesting that kind of to retrain
academic institutions in the ways of big data
because it's very different than maybe the way that they
grew up thinking about data,
the quantity and and the way that you deal with it
and how much you have,
and, you know, sampling of old data
versus all the real-time flow.
>> So, yes but the next generation is autonomous.
And whether it's self-driving cars or student advisement
we're seeing the leading edge providers,
provide, in the education space,
pretty much autonomous student advising now,
except to the point where you go out of the mainstream.
but that rapid good advice from bots, basically,
but when you get to the real world and autonomous systems,
we're going to see a real shift
in even the university sector
of that interact with people and the environment.
If you're doing self-driving cars,
you're talking sub-millisecond responses,
so that whole world of IOT plus sensing technology
plus smart campus all coming together in the next iteration.
And being paralleled in the services
and maybe even the academic world
that'll probably be a bit slower,
but taking the same autonomous kind of thinking
and moving beyond just supplementing human transactions.
>> Alright, well Richard thank you for taking a few minutes,
good luck on your keynote this afternoon
and we'll look forward to digging into the paper.
>> It's been a pleasure, thank you.
>> Alright, he's Richard, I'm Jeff,
you're watching theCUBE.
We're at AWS IMAGINE edu in downtown Seattle.
Thanks for watching, see you next time
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