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>> Hi and welcome to another CUBE Conversation.
Today we're at BMC Helix's Immersion Days
in the Santa Clara Marriott in Santa Clara, California
and we're having a great series of conversations
about the convergence of digital services
and operations management.
Now, one of the most important features of that
is how to utilize analytics.
Analytics is on the
tip of everybody's tongues these days.
But it's being applied, marketing and sales,
kind of the,
the cobbler's children that
aren't getting the same treatment
are in fact the IT organizations.
So what we're going to do in this
next CUBE Conversation is
learn more about how IT analytics
is beginning to transform IT
and facilitating this convergence with
digital services and operations management.
And to do that, we've got Guarav Rewari,
who is the president--
Or co-founder and CEO of Numerify.
Guarav, welcome to the CUBE.
>> Thank you, Peter. Pleasure to be here.
>> So, Guarav, tell us a little bit about
Numerify. Let's start there.
>> Sure, yeah. I know, I liked you know in your
opening statement you talked about IT,
in terms of it's own use of analytics,
being a little like, you know, a situation
where the cobbler's children don't have any shoes.
Because IT stood up pretty powerful
analytical applications for the CMO,
the CFO, VP of Sales, etc.
But, not for IT itself.
And so, we think, yes it's ironic.
But it's also untenable.
And it's untenable because in the age
of digital transformation
where you're opening up digital channels
for revenue generation, and the like,
and customer engagement.
IT is really moving up
from the basement to the boardroom, right?
So you have CEOs who worry now
about things like availability,
about sort of a speed of innovation with quality,
and things like that.
And so, to be able to have a decision support system,
a system of intelligence, if you will,
that, across rank and file of IT,
across the plan-build-run life cycle,
across the entire IT estate, from
infrastructure, to apps, to business services,
gives you recommendations and
intelligent insights on how to,
you know, improve the quality of your work,
the health of your systems,
to reduce the risk of your systems.
That, we felt, was an idea whose time had come.
And so, that's why we got started with Numerify,
and we rolled out a bunch of targeted
analytical applications.
Across areas like project analytics,
DevOps analytics, service analytics,
asset analytics, and the like.
And so, it's sort of a string of pearls
that you can deploy across your
IT organization, and it's interconnected,
so you can ask cross-cutting questions as well.
So that's in a nutshell
the story of Numerify and it's vision.
>> So, Guarav, I've been within,
or approximate to IT, or in IT for a long time now.
And it's not that we didn't have reporting,
because IT was always doing reporting.
We report on, you know, stop lights,
projects, whatever they were.
But, I think what you're saying is
something a little bit more fundamental.
It's really, can we do a better job
of really capturing the resources
that are creating value for the business,
understand how to deploy them more successfully.
And we're not just talking about the infrastructure.
We're talking a lot about the people,
have I got that right?
>> You hit that, the nail on the head there.
Ultimately, you know, IT is a business.
And you have to, if you want to face
sort of the epic challenges,
and opportunities of tomorrow that IT alone
can really
take on,
you have to understand
the people, process, project,
and product dimensions of the IT business.
And so, what the means is, if you want to
drive down your INO costs from,
you know, roughly 72% of IT budget,
which is what is the average today,
to 50%, which is the gold standard.
That's half a trillion dollars for the G2K, right?
And you want to take that savings
and reinvest it in agility, in faster
app dev with higher quality, right?
How do you do that without tapping into
things like automation and the use of analytics
to drive down your INO cost
rationally and increase your dev,
your development velocity intelligently, right?
So, that's why analytics has a huge role to play.
>> But also, it's got to be,
if I could interrupt you-- >> Yeah, yeah.
>> It's got to be that you have to have,
you have to start with visibility into
what resources are generating the greatest return,
why are they generating that return,
why are other resources not generating return,
and seeing how all that gets connected
across the range of activities
that an IT organization is performing
on behalf of the business. Right?
>> Yeah I know, exactly.
I think the how is really about
getting that visibility across sources.
And it's a nontrivial problem to do that
when you have a plethora of sources
that were never built to talk to one another, right?
You may want to, for example,
within IT understand, you know,
the total open work on each persons plate, right?
So they may have a bunch of
incident resolution work that they're doing
and the data, and the signal from that
comes from a BMC or a ServiceNow.
And yet they may be pulled into app dev work,
which the signal may be coming from a Jira or a CA.
How do you pull that together into
a single dashboard that gives you that
view of what everyone's working on.
And then you can make decisions like,
"Goodness! With so much unplanned work that's
"gone Fred's way, there is no way that
"the epic that he's involved with
"is going to, you know, be completed on time,
"so I have project risk, I have release risk as a result.
"I may even have attrition risk."
And so, the ability to pull together data
into a single model, answer the visibility
question to your, to the point you make,
and then go the next step
of predicting likely outcomes.
That's the magic and that's the use of analytics
to sort of transform IT into,
you know, operating in a far more
intelligent paradigm than it has thus far.
>> Other tools have attempted to do this,
but they attempted to basically
be the soup to nuts tool.
So they forced users to,
to install agents everywhere.
That there was a single process model
that was expected to be employed.
To administer all kinds of different resources.
There are very significant limits on how you
consider the application development or
application management, for example.
>> Yeah. >> Why is Numerify different?
>> Yeah, what we've tried to do is really take
a leaf from the page and books of those
who've sort of, succeeded in this endeavor before.
So if you look at, you know, the solutions
that a CMO might have at her fingertips,
or a CFO might have, right?
Fundamentally, it's about pulling data into
existing systems, not requiring a
change of behavior, but pulling data from
existing systems into a canonical model,
into a standard, sort of analytical data model,
that runs on, sort of an, in a dedicated stack.
And then you basically have this layer of
descriptive, prescriptive, and predictive
analytics sort of folded in.
And that's the approach we've taken.
Where we say, "Hey, look.
"We want, there is such a thing as
"a change management system, that doesn't go away.
"We would like to mine the accumulated history
"of all the changes you ever put into production,
"by tapping into your IT service management system.
"And then, your upstream dev and test system,"
because change is often a piece of code,
it began it's life somewhere in a dev cycle.
So how many times was that piece of code
rolled back, tested, how many times did it
fail the testing cycle, who worked on it,
what's been the success rate thus far?
And then, with respect to the change itself,
in the past, how often has a change like this failed.
You know, if changes were done on a weekend through
a combination of an onshore and offshore team,
is that implicated in failed changes in the past?
And then downstream with the change, in the past,
you know, was it a decline in performance
or usage or availability, as gleaned
from your monitoring tools?
So we pulled data from all these sources,
without requiring you to re instrument them
into a standard model, and then for
every upcoming change we tell you,
"Hey, this one is a risky change. Go look at it,
"send it back for further testing."
"Hey, this one is a low risk change,
"push it to production directly."
And so, inherently, the ability to
pull data from multiple existing sources
into a standard data model, and have
best practice reports and insights,
sort of a layer on top,
that's the approach we've taken.
>> Well, look, I really like this.
And, let me see if I can summarize
something that you just said.
So, Numerify is not immediately antagonistic
to anything that anybody has within the shop,
that it starts from a proposition.
Look, what you're doing is working, or not,
but let's start from a premise that
you're doing something now.
Let's learn more about it.
Let's then ask, can we do it better?
Yes, or no? And you have the analytics to do that.
And if it should be replaced,
can you actually get to the point of,
that you can actually indicate or suggest
how and when to replace something.
>> Yeah, that's a fabulous question.
I think, you know, increasingly what were seeing
is that our customers are pulling us in
the direction of making active recommendations
on decisions that they could potentially make.
Such as, you know, you may want to consider
consolidating a certain class of applications
because, you know, given it's revenue and usage,
the amount of support burden associated with it is too high.
"Hey, you might want to take a more refined
"and data driven, insight driven,
"approach to asset retirement."
Because, you know this whole, you know,
"Everything that's Lenovo and five years old must go"
is too blunt an instrument, you know.
Retire those assets that are
the most error prone and keep alive those assets
that still have useful life in--
>> And that process might by itself be
extremely expensive and very limited returns.
>> Precisely, precisely.
So the ability to transcend now
from just visibility
and dashboards,
to providing active recommendations
for every action along the way.
You know, project risk, release risk,
attrition risk, change risk,
service quality risk, etc.
We see that as the vision for us, you know.
So the use of AI not just for automation,
you know, sort of,
which clearly the AIOps field is investing in,
but also the use of AI for decision support.
For providing you with intelligent
recommendations across the full sphere
of activities that IT undertakes.
>> Gaurav Rewari from the, from Numerify.
>> Yeah
>> Thanks very much for being on theCUBE.
>> My pleasure, thank you.
>> Once again, this has been a CUBE Conversation
from BMC Helix's Immersion Days.
And, we look forward to seeing you again.
Thanks for listening.
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