[MUSIC PLAYING]
JAYANTH MYSORE: It
gives me great pleasure
to see all of you here.
My name is Jayanth Mysore.
I am the group product manager
responsible for enterprise
search products
within the G Suite.
Today is an exciting day for
me, the product development,
the sales, and marketing teams
that are behind this product.
We are pleased to announce the
availability of Google Cloud
Search with third party
connectivity moving forward.
Without further
ado, let me just get
started with what this product
is about, why they built it,
and the benefits we hope
to bring to the market
through the same product.
[APPLAUSE]
Motivating this product is
actually somewhat simple,
but it's also somewhat--
it tells us about the
times we live in, I guess.
Enterprise search at this
point is a market space
that has been occupied by
so many companies, including
Google AdWords, Google
Search Appliance for more
than a decade, right?
But still, even today, when we
talk to leading analysts who
study how people spend time
at work, what we notice
is people waste upwards of 20%
of their time still looking
for information.
There are many reasons for it,
we won't go into that today.
But I think most of us
intrinsically get it.
Finding the content
that they're looking for
continues to be a
major challenge.
It has been compounded
by the types of data,
by large amounts of log
data that people are trying
to analyze, things like that.
But nevertheless,
the problem remains.
On the other hand,
the importance
of information in the lives
of every single employee
has become
increasingly important.
In fact, we come from
the school of thought
that splitting employees into
knowledge workers and everybody
else is fundamentally flawed.
There is a knowledge
worker strand
in every single employee.
That's sort of how companies
these days should be thinking
about the workforce, anyway.
Now, in this context, our
talk is pretty simple.
What if we made finding
information as easy
as a metaphor that a lot of
us are very familiar with?
Just go to Google, right?
The convenience, the
intuitiveness, the scale,
the speed of finding
at Google we felt
should be made available
to every business.
And we spent a fair bit of
time working with our system
integrator partners and
with a lot of customers
who were kind enough
to let us come in
and observe how people
look for information,
and that has informed the
design of the product.
So introducing
Google Cloud Search
with third party connectivity.
With this version of
Google Cloud Search,
businesses can now bring in
content from any location
on prem, on the Cloud, or
enterprise SAAS applications
through a rich set of
connectors and an indexing API,
all securely into an index.
Let me just get
one level deeper,
this is a schematic
that walks you
through the parts of the
product, if you will.
It would be rather
immature of me
to call this an
architecture, it's
just a schematic at this point.
Going left to right, you
have the different types
of data sources as
I've been calling out,
on prem, public data
cloud services, and SAAS
applications.
We are putting a lot of effort
to provide a scalable indexing
API and to bootstrap
the ecosystem provided
an SDK to make it easy
to write connectors
to all of these
different data sources,
and a handful of connectors
to get it all started.
On the other end--
and this is a crucial point--
what we learned
through our users
studies and our
own market research
and observing how
businesses employ search is,
the moment you ask an
enterprise worker to leave
the application that defines
their work day, for a software
engineer it could be the
IDE, for a customer support
representative it could be--
I don't know, it
could be Salesforce
or it could be Service Now
or something like that--
the moment you tell them
to leave that surface,
you have actually created
enough of a distraction
for them to not want to do it.
So the key was to allow
search to happen in context.
Hence, we provided both what we
call a query API and JavaScript
that allows you to create that
Google Ask search experience
exactly where you are.
We call that the
embeddable search widget.
And realizing the importance
of security to enterprises,
a lot of effort has gone into
the design of this product
in preserving the access
control models of every source
throughout the pipeline.
Right?
So we have had very
gratifying stories told
from some of our early
customers wherein somebody
went an opened a
document and closed it
and [INAUDIBLE] changes, right?
To us, it's not
a surprise, given
that's how Google
Search should work,
but we are very glad that
it makes people delighted.
Right?
The other part of this
is that we have also
put in a lot of effort into
understanding the importance
of people search.
So we are creating
dedicated efforts
to make it easy to find people.
So one feature that we're
working on from day one
is to give users of Cloud
Search immersive people
search experiences, as well.
Right?
I'm not stating that
two words that I
think I should state in
every conference these days,
yes, this whole system is
based on machine learning
and artificial intelligence.
OK.
So-- and because it's
search, we realize
that search is an infrastructure
application that's
used across business
critical applications
and lines of business, we
put in a lot of thought
and made sure that this runs
on the same infrastructure
that powers Web Search.
So concerns around availability,
reliability, things like that
should be a non-issue, right?
And along the same lines,
we have studied the market
very closely and we have
come to conclude that Search
is a sufficiently sophisticated
application, that deploying
it would require nothing
more than thinking
about the number of
documents you want to index,
the number of queries
you expect to hit,
and make it easy for you, even
if the estimates went wrong.
We don't want you thinking
about how many VMs to spin up,
how many nodes to buy, how
many Linux boxes to run,
and things like that.
That's really not what setting
up a search application
should be about.
So a lot of thought has gone
into abstracting the service
to a model at which a buyer
can easily make a decision
and use without thinking
about it too much.
Right?
So all of that is
aboard the product.
The part that I'm really
excited about is, from day one,
we have made sure that we
think about this product
as an ecosystem-lead offering.
What I mean by that
is, we within Google
are going to be focused
on what I would like
to think of as difficult
computer science problem
and exposing the
solutions and the ability
to access those solutions in
the form of APIs and SDKs.
But the actual
folks who are going
to bring this power to
businesses all over the world
are going to be our valued
ecosystem partners, both system
integrators and independent
software vendors.
Right?
So the long list of connectors
creating custom solutions
for every business based on a
deep analysis of your business
workflows is all going to be
done by our esteemed ecosystem
partners.
And I feel extremely
fortunate to say
that even as we are
building the product,
we were lucky to get pretty much
the who's who in the ecosystem
when it comes to
such applications.
We have SADA, Onix, Accenture,
we have Perficient, TWT.
We have businesses all around
the world partnering with us
even before we
launched the product.
We have also had--
we've been very lucky working
with ISP vendors, as well.
And I'm very proud to say that
even today, as we announce
the product, we have
the product integrated
with Lumapps, which is an
enterprise-facing portal
provider servicing several
Fortune 1000 companies.
And without further ado, it
would be my pleasure and pride
to say that the first demo
of Cloud Search that most
of the world will
see will in fact be
presented by these partners,
they won't be done by Google.
And to me, that's an awesome
situation to be in, right?
So before I leave
the stage and start
inviting my partners to
kind of come here on stage
and we'll have a panel
discussion with them,
I wanted to leave you all
with one question that's
probably on all of your minds.
It has to do with the
availability of the product.
And there's the deal.
Right?
So the product will be
available in two editions.
First, if you are a domain with
enterprise licenses of G Suite.
And we are starting with
enterprise-grade customers,
because we do want to have
homogeneity in a user base
as we start rolling
this product out.
We are starting with customers
that at least 5,000 enterprise
licenses.
You will get a
preconfigured girl version
of Cloud Search with a pretty
liberal document and query
quota.
We invite you to reach out
to us or talk to your account
managers.
We would be happy to
provide you more details
and help you get started with
one of our system integrator
partners.
Second, and what we
are very excited about,
is Cloud Search will
also be available
as a standalone product.
So if you are a
business that's not yet
ready to migrate
to all of G Suite
and what you're looking for
is a really good solution
to the Enterprise
Search problem,
we welcome you to try
Google Cloud Search.
We have set up a
customer onboarding form.
So anyone who's interested,
please go ahead and sign up
with the customer intake form
that I will be sharing next.
And spread the
word around if you
know other customers who are
looking for a good search
product.
Our sales team and
our system integrator
partner team will be in touch
with you pretty promptly.
And this is the URL
that you should go to.
It's cloud.google.com
/products/search.
We welcome all of you to sign up
and we'll be in touch with you
to make sure that as
many of you as we can
are accommodated in the
first wave of the launch
the rest of this year.
With that, I'm
done talking here.
It's my privilege to invite my
esteemed colleague from Lumapps
to come on stage and walk you
all through the first demo
of Cloud Search.
So, welcome Elie,
CTO of Lumapps.
[APPLAUSE]
ELIE MELOIS: Hi, everyone.
So I'm Ellie.
I'm the CTO and co-founder
of Lumapps and I'm very,
very excited to be--
thank you-- to be
here today to present
what we've been working
on for the past six months
with Google.
So to begin with, just a
few words about Lumapps,
for those who don't
know us already.
So as you may hear,
it's a French company
but with a global presence.
It's a social and
collaborative intranet.
It's trusted by
leading organizations,
like Colgate, Palmolive,
for instance, Finish Line,
Electronic Arts, Logitech, even
Google with the Cloud Connect
community portal, and much more.
So Lumapps is an employer
with a unique positioning
on the market
because in one place
you have all your
internal communications
to inform employee, engage
them, and spread the culture.
You also have
social interactions
between people and
communities, and collaboration,
meaning that all your
business applications
are connected on the platform.
The division that we
have with the product
is to be the starting
point of the cloud
journey for the employees.
So from one place, they
can access communications.
So top-down content, highly
personalized for employees.
Communities where they can share
ideas and files, plan events,
it's highly integrated
with G Suite,
so you have all integrations
with Drive, Calendar,
and much more, and, of course,
other business applications.
The branding of the
platform is really flexible.
So you can make it
look like your company.
And its available on
the web, and on mobile
on Android and iOS.
Just a few words about the
vision we have for the search.
So we share the
vision with Google
that people would
want to find answers
rather than searching
for keywords.
And so we want to provide
an experience that
assists the employee.
And so that's why we
focus on understanding
the natural language
of the user in order
to interpret his request.
We analyze the
behaviors of the users
with machine learning to
deliver a personalized search
experience, and we also assist
employees by suggesting stuff
that they may not even know
that they are looking for.
So now I will do a demo.
So the use case I'm
going to present today
is about a picture database.
So you know we all have lots
of cooperate and marketing
pictures, for
instance, from events.
And wouldn't it be great
to be able to find pictures
based on their resolution,
colors, number of people,
or even emotions?
Let's see if we can do
something with Cloud Search.
So, if we can switch to--
OK.
So this is a portal that
we published on Lumapps,
so you get all the
information for employees,
so big announcements,
corporate content,
personalized
information, so it's very
personalized for the employee.
And what we will be looking
at today during the demo
is the search engine that's been
implemented with Cloud Search.
So let's try to
find something fun.
So I will switch to the
marketing images data
set that's been queried
live from Cloud Search.
And as you can see,
we can find here
results with a lot of metadata.
This metadata has been retrieved
using the Cloud Vision API,
and we'll explain it after.
And so is there
something funnier?
OK.
Ta-da.
You might know this guy, the
one with the orange glasses.
The thing is, he is
not really funny,
so I don't know why he
was picked up by GCS,
but, you know--
that's OK.
And as you can see,
on the right side
you have some facets that are
being provided by Cloud Search.
And so we can refine
the results dynamically.
So let's say you want
to print something,
so I will pick up
the resolution.
I can see how many
numbers of results
have been reached
by Cloud Search.
And if I want to say I want
only two people, for instance,
on the picture.
And that's it.
You get another picture.
So I will explain
just after we assemble
all the pieces of the Google
products to do this demo.
So just to finish
the demo, we also
have a cross data
sources list of results.
So it's based on the
relevancy of the results.
And then you have
some custom data sets
that have been created
here [INAUDIBLE]..
So for all the posts
from our communities
are indexed and searched in GCS.
So here are the facets, are
the ones that we define.
So for instance the list of
communities that we have.
And so you can refine the
results using those facets.
And, of course, you
have all the G suit data
sets out of the box, so
Email, Calendar, and Drive.
OK.
So I will switch back
to the slide deck now.
[APPLAUSE]
So how we made that possible?
So we've uploaded
thousands of pictures
on Google Cloud Storage,
and then we pipe them
to the Cloud Vision API.
Using the Cloud Vision
API, we automatically
add information
about the pictures,
and then we uploaded
them on our system,
so we have categories,
colors, resolution,
entities in the pictures,
all the metadata
that you've seen
during the demo.
Then, we ingest all
this information
in Google Cloud Search using a
custom connector and the Index
API.
And then, finally, we use the
Query API from Cloud Search
to expose the results in our UI.
And it wasn't it was not
even hard to implement.
I've been working with Google
technologies for the past eight
years and I really
like these moments
when all the pieces
come together
and we can present a
use case like this.
Very, very exciting.
That's all for today for me.
If you need more
information, you
can come by our booth
on the first floor.
You can't miss it.
And I really want to
thank Jayanth and his team
for their trust, their
openness, and the support
that you gave us
over the last months.
And we were very proud
to be the first partner
to demo this exciting product.
And, finally, I
just want to also
to congratulate my engineering
team for the hard work they
put into this, and especially
Ivo, who is here today.
So congratulations to all
of you, you are the best.
Thank you.
[APPLAUSE]
JAYANTH MYSORE:
Thank you so much.
Thank you very much, Elie.
I want to emphasize one
crucial point there.
You can Lego-break
pretty much solutions
across GCP APIs and
Cloud Search to create
a lot of magical experiences
like the one that
was just demoed right now.
And you'll see that pattern
repeat over and over
again across lots of demos
that you will see now,
and the ones that we
publish in the future.
So now it's my pleasure to
start the core of the panel
discussion.
And I would like to invite
the team panelists on stage,
starting with Chad
Johnson from SADA systems,
Tommy from Colgate, Andrew from
Whirlpool, and Bryan from Onix.
[APPLAUSE]
Well, thank you all
for taking the time
to be with us here today.
The focus of today's
panel discussion
will be to walk the audience
through the experiences
that our Fortune 1000
scale customers have
had with Google Cloud
Search, and the experience
that developers and system
integrators have had
with the API building
solutions for them,
because we believe this will
be of interest to a lot of you,
as you consider Cloud Search
and you can understand
the power of the
platform itself.
With that, let me get started.
So first, I would like to
understand from our partners
here from the businesses,
from Andrew and from Tommy,
why don't you give the audience
a sense for the business
problems that you're trying
to solve with Cloud Search?
Andrew first.
ANDREW LEWIS: Sure.
So, I'll go first.
So, ours, we had a couple
of different use cases
that we wanted to solve.
And frankly, we had a
Google Search Appliance
that we installed
back around 2015, OK?
We used that for a
couple of different use
cases, one of which was very
engineering-centric that we
called Search Pro.
And what it does
is, it takes a look
at all of the engineering
data that's in a bunch
of different
systems and funds it
so that the end user
has one place to go
to get all that information.
OK?
The second was actually
our true enterprise search,
which we had put on top
of our enterprise portal.
And so we had a lot
of things on there,
such as things that were
actually in the portal itself,
but also we would bring
information in from Drive,
from People Search,
things like that.
So our use case,
actually, the reason
why we were so
excited about this
was because we knew that
the GSA was actually
going to end-of-life.
And when that
happened, we had a lot
of folks coming to
us saying, well, you
need to replace it with this
product or this product.
And we actually had lots
of great conversations
with Jayanth and team saying, we
don't want an on-premise search
appliance.
Whirlpool is on a
Cloud-first journey
and, gosh, 80% of our
transactional information
is in the cloud now.
So it didn't make
any sense for us
to go back to an
on-prem solution.
And so, luckily, we
worked everything out
and it's exciting times now.
JAYANTH MYSORE: Thank
you so much, Andrew.
Tommy, do you want to voice
some context of Colgate?
TOMMATHEW THOMAS: Yeah.
So Colgate is on a journey
to relaunch a new internet
portal partnering with Lumapps.
And in that journey, we
found there's an opportunity
to look at enterprise search,
a true enterprise search,
I think everyone here
has a vision for.
And Google was
uniquely positioned
to help us in two ways.
One was being able
to bring content
sources, any type of content
source, in a very fast manner.
We wanted to bring that
to life really quickly.
The second thing that we
thought was really important
that they were uniquely
positioned for was relevancy.
And we wanted to
make sure, if you're
going to expose all
of these data sources
we need to make sure that the
search results were relevant.
So that's why we
made that choice.
JAYANTH MYSORE:
Thank you so much.
What do you say, Chad?
Any thoughts on the APIs?
The platform itself, as
you built the solutions
for Colgate-Palmolive?
CHAD JOHNSON: You know, Jayanth?
I actually think it was all the
machine learning and AI that--
just kidding.
Three quick things.
The first, the quickness.
The APIs are so fast.
Min, the other product manager,
was standing at our booth
and looking at my
demo, and he actually
blinked and asked
me to redo something
because it happened too fast.
[LAUGHS]
He saw me change something
and he's like, wait, go back
and do that again.
That already loaded?
It was-- you know,
it's indexing content
and being able to search
for it in one second.
With the GSA, that
was 10 or 15 minutes.
Number two, we write
a lot of connectors.
We have to integrate with
a lot of content sources.
Google has made this
very easy for us.
They did not just throw us into
the ocean with an indexing API,
they created a framework
that makes it easy
for us to build connectors.
The software
development kit takes
care of a lot of the
indexing methodology.
And I don't have to do all
the low-level plumbing.
Third, the security.
State-of-the-art.
We have not run
across any systems
that we have not been able
to emulate the security
model in-cloud searches,
access control model.
Lots of sophistication,
hierarchical inheritance,
containerisation for modeling
things like sites and folders,
complex identity mapping so I
can describe access permissions
exactly as they are in
other identity providers,
like Active Directory.
Very impressive stuff.
JAYANTH MYSORE: Great.
Thank you.
We feel very lucky to hear that.
Yeah.
On to you, Bryan, your
experience working with Onix.
BRYAN MCKAY: So I would
echo many of the things
that Chad just described
and with a little context
around Andy and the
situation at Whirlpool.
Like Andy had said, this
was a GSA replacement.
And we also had a short timeline
because there's actually
three separate
applications at Whirlpool
that are running on the GSA.
And we have to get all
three of them off the GSA.
So the first one, which was
really an important proof
point, was Search Pro.
We had to get that built,
tested, and deployed
to QA and our timeline
was about three months.
So the goal was to have that
into QA in the March time
frame.
So we started in
January, we needed
to get it into QA around
the March time frame.
So, obviously, speed of
everything is really important.
And when we did the original
implementation with the GSA,
it took us six weeks to ingest
and index all the content.
So that's 12 million documents,
21 different content sources.
So we actually have 21
connectors that are running.
We're now able to do that,
12 million documents,
and we can do that
in a week, which
I think is an extraordinary
accomplishment.
The APIs are highly performing,
the connectors are reliable,
they're easy to work with.
And even more importantly
in terms of development,
the database connector, which
is a reference connector,
it was for us configuration,
it wasn't development.
If we would have
to develop that,
that would have been an army
of developers trying to get
this thing done in three weeks.
It was a super
reliable connector.
We just had to get it set up,
get one working, proof it,
and then it was
just configuration
for the remainder.
And that was really
a key thing that
allowed us to meet the
timelines and the objectives.
JAYANTH MYSORE: Thank
you so much, Bryan.
So because kind of the crux
of the core parts of the value
proposition here is the ability
to bring in third party data
sources.
It would be great if on of you
can also provide some clarity
on the actual data
sources that were used
in each of the deployments.
BRYAN MCKAY: All right.
I guess I'll keep going.
So we've got four for search.
We've got four major
content sources.
One is a commercial application,
that's PTC Windshield.
So there is a connector
that was originally
developed for the
GSA that was adapted
to support Cloud Search, that
was adapted by a third party.
One of the partners that you
saw up on the board, Fishbowl,
they developed that
connector using the SDK.
There is another application,
which is the Global Engineering
System.
That is a massive database,
it is 12 or 14 applications.
We're indexing 11 of those.
And they're each really running
as a separate application,
so that's where the 11
connectors come from.
Windchill is eight different
modules or objects,
so that's another
eight connectors.
And then we have Google Sites,
so each of the engineering
groups has their own Google
Site that they have set up
and maintained.
And then, on top of that,
we have the Whirlpool people
directory that we're
indexing as well.
Also that gives us the
12 million documents.
So those are the
content sources.
And, again, because of
the performance, because
of the SDK, and because
of the connectors
we were able to meet the goal.
JAYANTH MYSORE: Great.
What about Colgate?
TOMMATHEW THOMAS: Yes.
So I mean, we have a lot
of different data sources,
content sources.
But we started with
some specific ones
that we wanted to target
to prove this out.
And the key ones
that we started with
was, obviously, G Suite was
available out of the box,
so that was great.
But we also wanted to prove out
that we could do an SAP content
source.
So we targeted an SAP RND
system that we were using.
And then, we also wanted
to provide a different data
source.
So we looked at internal IT
support, helpdesk support
content management system
that we have and we used that
as a starting point, as well.
JAYANTH MYSORE: Great.
Thank you so much.
Now, what we'll do via the
system integrated partners,
as well.
In terms of your experience
working with the Google team
in terms of the
feature set itself,
anything that you want to
specifically elaborate on that
would be great
for the community.
CHAD JOHNSON: I don't
know if anyone in the room
has worked with Google
before, but you don't ever
get to talk to an
engineer, you don't ever
get to talk to a
product manager,
it's like the Wizard of
Oz behind the curtain.
This has been a really
amazing experience.
We've been working with the
team for about a year and a half
and they've been holding
monthly office hours with us.
They've been letting us
email them and ask questions.
It's amazing.
Some of the engineers
on the team,
like Mark and Howell and
Tanmay, these guys are not just
answering my
questions, but they're
talking through
why we might want
to do something a
certain way, they're
sending us code samples.
The information flow
from the engineers
has been what allowed us to
implement Colgate's solution so
quickly.
Particularly since
it was the first one,
it was walking into a cave that
we had never been into before.
The other thing that's
been really interesting
is how receptive and interested
they have been in hearing
about what we're doing with the
product, what our clients want
to do with the product.
It's a very humble approach.
They are genuinely
interested in hearing
what they should do
with the product next
and what the roadmap
should include,
and they're basing
that-- they actually
flew out to our client site
with us and listened to Colgate.
It's really amazing.
JAYANTH MYSORE:
Thank you so much.
Anything from your end, Bryan?
BRYAN MCKAY: Absolutely.
Some of the same key
points, the opportunity
to meet the engineers, work with
the engineers, have open forums
monthly offices.
I actually see some of
the wizards over here
and I see a few more of
the wizards are over there.
So when we started the
project at Whirlpool,
just to give you a sense,
there's the usual cast
of characters, you know?
There's a few of us and a
few of the account management
SE types.
And then, 12 the product
management and engineering team
arrived on site.
So that's how we started
the whole project.
It was a massive room, it
was quite a large audience.
And ever since then, we
maintained those relationships
and those kind of contacts.
And for each problem,
we had an opportunity
to reach out to somebody and
actually have a conversation.
And even more importantly,
since this is all--
it was brand new to us, right?
And it was our first
implementation.
And we had an opportunity
to engage one-on-one,
but also started to have
dialogue around best practices.
Hey, we tried this,
we built this schema,
we discovered this.
Didn't quite work
the way we thought,
is there a best practice?
Well, it wasn't a
document of best practice,
but there was a best practice.
So we had a conversation,
product management, engineering
on the phone, to talk about
and learn what is the best
way to structure your data.
So when you get it in the
index, it does the magic
that it's supposed to do.
Those are the kinds
of opportunities
you seldom get in.
And it was like Chad
was saying, it's
a wizard behind the curtain.
Well, folks, the wizards
are all right here.
So you'll have a chance
to meet and talk to them
and ask more questions
later, I'm sure.
JAYANTH MYSORE:
Thank you so much.
So I guess the crux of
all of this discussion
is that would be awesome
to now see a few demos.
So, Tommy, could you
kind of walk the audience
through what was
built at Colgate?
TOMMATHEW THOMAS: Sure.
Ready?
OK.
So this is the site, the
search site, that we built,
or actually SADA
and the team built
to search Colgate content.
And what you see here
is actually a dropdown
for multiple content sources.
I mentioned already our recipe,
our RND, SAP content, G Suite
content, and our internet
content is all here.
So let's take an example.
So I'm sure after
this trip is done,
I'm going have to do
an expense report.
So I'm going to go ahead and
search for expense reporting
because I--
let's just take that I
don't know how to do one.
And I see a bunch
of results here.
And if you notice on the sources
page what SADA and the team
were able to do is, they
color-coded and made
the UI rich so
that I can actually
identify where the content
source is coming from.
So I can easily distinguish
the different content
sources and what they are.
So I can contact the
expense reporting group
or I could go to a
specific document
and learn how to do
expense reporting.
So now I'm done and I need
to grab some of my receipts.
So I think we did a
Uber trip two days ago.
So let me go ahead and
find out a little bit
about my Uber trip.
I see a bunch of
different data sources,
like I said, some
from Drive, and here's
a bunch of my Uber receipts.
But I'm not seeing the
one I'm looking for.
I remember the driver's
name was Melvin,
he was a great
conversationalist.
So let me try Ubering Melvin
by googling Uber Melvin.
No results.
I'm going to call out
Jason Russo for my team.
Jason, I think you
had the receipt,
do you mind sharing it
with me pretty quickly?
Yeah, the Uber trip.
[LAUGHS]
TOMMATHEW THOMAS:
Hopefully, the Wi-Fi works.
[LAUGHS]
OK.
Let me search again.
Look at that.
[APPLAUSE]
So I actually took a tip from
Chad's demo on his booth.
So if you haven't
gone to SADA's booth,
they have some pretty cool demo
material around this search.
But--
CHAD JOHNSON: This surprised us.
We just noticed this was
happening that quickly.
It was amazing.
TOMMATHEW THOMAS: Yeah.
The indexing speed
is super quick.
Now I have my Uber trip
that we took yesterday
and it was just around the
block that we took that trip.
That's it.
Pretty terrible.
[AUDIENCE LAUGHS]
[LAUGHS]
So that was it.
[APPLAUSE]
JAYANTH MYSORE:
Thank you so much.
They say a picture is
equal to a thousand words,
and a demo is worth me
not being here, so--
thank you so much
for all of that.
Actually, in terms
of actual content,
I wanted to cover
with all of you.
We are more or less done.
If there are any remarks
that you want to make,
this is the time to do that.
Otherwise, we can open
the floor for Q&A.
BRYAN MCKAY: There
really is one more
that I want to add because,
Jayanth, as you know,
after the application was built,
tested we sat down with some
of the engineers and we had some
really interesting discussions.
So I would say, first
of all, everything
you heard about the
core capabilities is how
extremely important.
I mean, it's what
allowed us to get
the projects to where it is.
But then, when we sat
down with the engineers,
we had an opportunity to
sit side by side with them.
And I would recommend
this is the best
practice for all of you who
take on a project like this.
Sit with your users, have
them do search tasks,
because you will learn some
really interesting things.
But don't tell
them how to do it.
Ask them just to sit
down and find something.
And what we learned
in the process was--
and, actually, when the
GIF plays in a few minutes,
you'll see this.
And so we would
have a user sit down
they say, I need to find
a lab report, right?
So the first thing
that they do is,
they start thinking about it
as though they are searching
in their legacy system.
They think about it
as a database query.
And so they start to
structure their query,
lab report, and then blender
noise test, for example,
a model shop request,
blender noise,
whatever that search was.
And what we discovered
in that was,
they're framing a
question, they don't really
know how to use the
search bar, and then
how to use the search
bar, we all know Google.
They don't really know in the
context of work and that task
how do you use Google.
And so we said to them,
what if you could just
type in "Show me
all lab requests"
for blender noise test?
They're like, yes, that's it.
That's exactly
what we want to do.
And so what we learned is, they
all want to ask a question,
they don't want to
learn how to search,
they don't want to learn another
structured UI advanced search,
they just want to
ask the question.
And so the key there is,
that's the next thing
that we'll be working on.
We are going to start
introducing that ability
so that those users
can just ask questions.
But that leads to
another thing, and that
is you really have
to know your data,
you really have to
know your users,
you have to know something about
the questions they want to ask.
And therefore, make sure
you have all the metadata
and the right bits and
pieces in the index,
so they will get the
answers to their questions.
ANDREW LEWIS: I'd say to chime
in on that, one of the things--
one of the challenges that
we had with the old system,
not the GSA, but
the pre-GSA days,
is a lot of these databases
all grew up organically, right?
So you have a lab
reports database,
and you have another
Oracle database here,
and then you'd have Windchill.
And all of those search
experiences for the end user
were all different.
So you couldn't just
go in and search,
you had to go,
oh, wait a minute,
this one uses keywords and
this one uses asterisks.
And so folks
actually had to think
about how to find the data
and not just go find the data.
We had a really good
story of somebody
that started at
Whirlpool and her job
was to design lab reports or
lab tests, sorry, for laundry.
And she estimated it took her
about a year to get good at it
just to find all
the information,
find all of the systems,
understand what other folks had
done in this space.
And so from an employee
experience and the productivity
standpoint, just putting this
on top of all that information
is just insanely powerful.
JAYANTH MYSORE: Thank
you so much, yeah.
And for some reason,
it took a few seconds
for the Whirlpool demo to
start up, now that's been done.
It's a big filing system.
CHAD JOHNSON:
Jayanth, one thing I
wanted to add was, a lot of
people have come up to me
and asked me, why
do I need this?
Why do I want this?
And I start asking them about
their own environment at work
and how many different
places content lives.
They-- oh, yeah, I search
Gmail, it works great.
Yeah, but maybe it was
in Drive or-- wait,
was it in SharePoint?
I can't remember.
And providing a single
search interface
for all of the
repositories, that
has really been the mantra
of enterprise search.
But it fails if we
don't index everything.
It fails if we have a room with
the lights off with shadows,
where our search doesn't
see into that part
of the enterprise.
I run a search and I
don't get any results,
I don't know if that's
because I ran the wrong search
or it was because
we aren't indexing
that part of the repository.
So Cloud Search is
one of the first tools
I've seen that has the
scale and capacity to index
not just some of what an
enterprise has, but all of what
an enterprise has.
And that's how we'll be
successful with Enterprise
Search.
JAYANTH MYSORE:
It's great to know.
One thing we have
done is to make sure
that I like to use
terms like limitless
and note be humble about it
because it's truly limitless.
How much [INAUDIBLE] to put
any limits on how much you can
index, how much you
index is up to you.
The whole model has been set
up such that one does not
have to think about
number of VMs to buy,
number of nodes to spin
up and things like that.
It should simply be,
hey, I want to make
that new repository also
available, write the connector,
and you're done.
It starts coming in.
And that's also why we
put in a lot of effort
into the complex accurate
reflection effort,
so that, again,
that's an area that
is so sophisticated
wherein we felt
if we put the owners on
anything but the technology,
nobody would actually do it.
So that's the-- those
are all [INAUDIBLE]
gone to enabling that sort of
an incentive for most customers.
TOMMATHEW THOMAS: You know?
I would echo Chad's comments
on just getting all the content
sources up.
We're really excited
that Lumapps is taking up
partnership with
you because we're
making that journey with them.
But I think when we presented
this in front of our end users,
one of the comments
that they made to Jason
and the team was that
they were impressed
by the speed of the
search and how fast they
could get to their information.
So to me, I think
what's exciting is
that we will provide a
front end to the users that
has the performance that they're
looking for to find insights.
I think that's
going to be the key.
JAYANTH MYSORE: Thank
you so much all of you.
[MUSIC PLAYING]
