You know the insights are in there

OVUM log

Presenters:

Presenter: Tony Baer
Principal Analyst | OVUM
Presenter: Laurent-Olivier Liote
Research Analyst | OVUM
Presenter: Steve Wooledge
VP Marketing | Arcadia Data

WEBINAR AIRED: AUGUST 16

Mysteries of the Data Lake Revealed: Five Secrets to Unlocking Enterprise Value

As organizations modernize their data and analytics platforms, the data lake concept has gained momentum as a shared enterprise resource for supporting insights across multiple lines of business. The use of Apache Hadoop, Spark, and cloud-based data lakes is maturing and must now support direct business end-user access and analyses. A proliferation of SQL-on-Hadoop engines has helped bridge the gap between business users and big data, but it’s not enough.

Join this complimentary webinar with industry experts from Ovum Research and Arcadia Data who will discuss how leading companies are realizing value and success with analytics from data lakes by:

  • Delivering on key requirements to drive value from data lakes and more fully complement EDWs and data marts.
  • Reaching a broader user base with intuitive, code-free UIs for visual analytics on the data lake.
  • Operationalizing machine learning and advanced analytics to complement and extend traditional BI.
  • Securing and governing data lakes with unified security across the data and analytics platforms.
  • Scaling to support hundreds and thousands of users in multi-tenant data lakes with ultra-fast performance.

Transcript

[00:00:03.200]
welcome everyone this is Steve will it for

[00:00:05.299]
the Arcadia data the top of the hour going to

[00:00:07.299]
get started just in a minute here the way

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the system works as people are going to have

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to log into the browser right

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here at the top of the house or just given one minute and we'll

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get started thanks for joining

[00:00:40.399]
alright looks like we've got a

[00:00:42.899]
quorum here today session

[00:00:45.100]
is how to scale bi and analytics with Sir

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Duke bass platforms I'll be

[00:00:49.200]
your host this is Steve willage I

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work at Arcadia State CPA marketing

[00:00:53.399]
and I'm really excited for this topic

[00:00:55.600]
because I've really seen a shift since

[00:00:58.100]
Kayla architecture is primarily around

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data storage and I think the movement

[00:01:02.500]
now is around how do we get analytics

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in value out of the system to scale

[00:01:06.500]
out behind analytics platform so

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that's what we talking about today and

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I'm really delighted to have to

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write presenters with us today our

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special guest is Boris

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Ellison he's the vice president and

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principal analyst serving application

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development and delivery profession who's

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the leading expert in business intelligence work

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to 434 number of years I worked with him over

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a lot of those years and I really like

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working with Boris cuz he also was a practitioner

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early on in his career the

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poem data warehouses at places

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like Citibank and being a strategic advisor

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at JPMorgan Chase so you really understand

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the technology in addition to the

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application and then we got

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print this out who's the co-founder and

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chief product officer here in Arcadia data

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whose instrumental in the development

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of our products and has

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a rich history and analytic

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data bases at things like Astor

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data in teradata so was that

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I'd like to just get a little

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level set with the audience today

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we're going to start out with Boris talking

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about systems of insight and this

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shift ever seen in Next Generation VI

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platforms that scale natively

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with Hadoop another scale-out architectures

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and Priyanka will get a little bit more into Arcadia

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and how we tackle that problem and we'll

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have some time at the end

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in addition we like to run

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a little Pole now just to level set

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with the audience of where you are in

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your big data Journeys so hopefully

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you can see if voting tab

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that will pop up here and will try and show these

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results you felt I'm so go ahead and I'll

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read the question out loud just as you're looking

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at it but we're asking where are you with

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your big data deployment

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FaZe egg be gathering knowledge just

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thinking about his new brother scale outdated

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platforms or maybe you're developing

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a strategy to find the architectures

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collecting tools of your piloting

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and got a system up and running or

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maybe you're in a disappointed face recipe using

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your Foreman

[00:03:04.000]
using analytics to give and users access

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the system so you can select

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any of those choices that

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would be great I'm just give it a

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couple seconds here for

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people to respond

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I guess by the way while you're doing

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that Russell take question throughout the

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session today there should be some

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tabs on your right hand side that you can see where

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you can answer or answering

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questions as we go

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cancel you should see

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the results at the bottom of the screen is

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there a streaming in real time there so it

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looks like 50% of people

[00:03:44.699]
are gathering knowledge we've got some

[00:03:47.300]
people that are deployed at shifting here

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as I thought we can share

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these results back with everybody that's

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what we'll probably do a Blog wrap up at

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the end thanks for taking the time to fill that out

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alright so we'll move on

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then and I'll turn things over to Boris Yeltsin

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to talk about what's happening in the Market

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Force XR thanks very

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much for the introduction and for the opportunity to

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present yes indeed you are correct

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I've been in this market for over

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30 years I think about 35

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years at this point last 10

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years of forest and I'm really really happy

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to see what's happening in the market

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because finally finally after many

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many years of trying to instill

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the discipline or fronting

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business by the numbers by by

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data decision

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driven company

[00:04:36.600]
this is more of the finally beginning

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to Abby, reality is so

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the great great data points putting

[00:04:43.399]
what I just said this one is from Morningstar

[00:04:45.899]
it is predicting of that talk

[00:04:48.500]
in the next few years in size

[00:04:50.600]
driven so we now like the term

[00:04:52.899]
insides driven as opposed to data-driven because

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all this data is just

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bits and bytes he can't look at bits and

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bites Tim Duncan make decisions so

[00:05:01.800]
bi and systems of inside that

[00:05:03.899]
I will talk about in a second I really all about transforming

[00:05:06.500]
that raw data bits

[00:05:08.500]
and bites at the meaningful insightful

[00:05:10.600]
actionable information for those companies

[00:05:13.300]
out there that are going to be inside driven

[00:05:15.500]
are going to grow eight to

[00:05:18.000]
nine times faster than

[00:05:20.500]
their peers 8th to 9th times faster

[00:05:22.800]
than the speed and older so that's a huge

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chunk chunk

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of change.. Something that's easily

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overlooked some some other very interesting

[00:05:32.300]
numbers from Forester basically

[00:05:34.699]
with what's been happening over the last few years is

[00:05:37.100]
a B I slowly but surely

[00:05:39.100]
has been moving from the back offices

[00:05:41.399]
in to the front office is basically

[00:05:43.500]
the morphine from being

[00:05:45.500]
I just about compliance

[00:05:49.500]
or transparency on nice to have

[00:05:51.600]
a type of application to

[00:05:53.600]
to basically corporate asset that

[00:05:55.600]
that everyone is using to compete

[00:05:57.699]
on so she can see on this data point

[00:05:59.699]
pulling customer satisfaction and

[00:06:01.899]
getting competitive advantage of at

[00:06:04.199]
the top of everyone's agendas and

[00:06:06.800]
those of you on those 59%

[00:06:09.000]
of you who will tell us that bi is

[00:06:11.100]
there a top priority also reaping

[00:06:13.100]
some very tangible benefits

[00:06:15.199]
so more than 45% of you

[00:06:17.300]
tell us that to you you are today getting

[00:06:19.399]
double-digit returns a tangible

[00:06:22.199]
Returns on your behind and

[00:06:24.500]
when we correlate your position

[00:06:26.699]
in the industry as an industry leader

[00:06:29.399]
as opposed to an industrial ladder

[00:06:31.399]
that we basically Define that has

[00:06:34.000]
anyone who is growing faster than the

[00:06:36.500]
industry average the average within

[00:06:38.600]
your air industry segment if

[00:06:40.699]
you're growing faster you also

[00:06:42.899]
happen to be investing more more than

[00:06:46.100]
a third of your b i o u i

[00:06:48.199]
t Budget Inn to be high so not

[00:06:50.600]
only tangible are awhile an individual

[00:06:52.699]
be indispensable but also correlation

[00:06:54.800]
between

[00:06:59.699]
call hog roast but

[00:07:01.800]
that road is not without challenges and

[00:07:04.300]
I'm sure all of you on the phone or where

[00:07:06.600]
would those challenges are if I

[00:07:08.699]
am pressed to talk about one

[00:07:11.000]
single challenge will be through there are

[00:07:13.000]
multiple but one single challenge

[00:07:15.100]
use a disconnect between business

[00:07:17.199]
and I T unfortunately I see that all

[00:07:20.100]
over the place today and we

[00:07:22.500]
only ITC definitely

[00:07:24.899]
unfortunately to blame for

[00:07:27.399]
this because we

[00:07:29.399]
are professional

[00:07:31.699]
that's still really stuck in the technology

[00:07:34.199]
for the sake of Technology a conundrum

[00:07:36.899]
that we are overly emphasized

[00:07:39.800]
a streamlined data

[00:07:41.800]
architecture and centralization of

[00:07:44.000]
your bi organization

[00:07:46.300]
of single bi platform

[00:07:48.600]
and that I'm basically trying to get

[00:07:50.699]
to that Nirvana single version of the

[00:07:52.699]
truth and it's know that these are not that

[00:07:54.899]
important priorities absolutely

[00:07:57.300]
by all means is a very very important

[00:07:59.600]
goals and objectives should be

[00:08:01.699]
at all priorities but not the

[00:08:04.000]
top priority and unfortunately

[00:08:07.199]
often to forget that this is how

[00:08:09.199]
I really just want to give their

[00:08:11.300]
jobs so you taking to get

[00:08:13.300]
the job done with Excel faster and

[00:08:15.600]
better than with your Enterprise

[00:08:18.000]
bi platform then you know what you didn't

[00:08:20.199]
do something right so

[00:08:22.199]
that that's a very important lesson

[00:08:25.000]
learned and I delete more and more

[00:08:27.000]
I team it

[00:08:29.699]
of NBI shops are beginning to Embraces

[00:08:32.200]
so as it as a result of this

[00:08:35.299]
act as a result of a complexity of

[00:08:37.799]
architecture that we bring into a

[00:08:39.899]
cheetah that version of the truth we see

[00:08:41.899]
some pretty pretty bleak

[00:08:44.299]
results that on average you tell

[00:08:46.399]
us that you all leverage less

[00:08:48.500]
than 50% of your structure than less

[00:08:50.600]
than 25% of your unstructured data for

[00:08:53.299]
this year than making these a self-reported

[00:08:56.000]
numbers that we know that you are not aware

[00:08:58.200]
of all of the day of the child

[00:09:00.299]
there is specially external data

[00:09:02.299]
expecially unstructured data so

[00:09:04.500]
a more detailed study

[00:09:06.600]
as whenever we go in and we kind

[00:09:08.799]
of analyze the environment

[00:09:10.899]
adult client to see even Bleaker results

[00:09:13.100]
that less than 20% of your

[00:09:15.100]
structured 8 and 10 less than 10%

[00:09:17.100]
of your unstructured data I being used

[00:09:19.399]
for a inside sound decisions

[00:09:21.600]
and I'm sure this fight is near

[00:09:23.700]
and dear to your heart and I know no matter

[00:09:25.799]
how long it has

[00:09:30.000]
been investing

[00:09:32.000]
in the eye and putting out

[00:09:34.100]
bi Publications old

[00:09:36.299]
XL stool side is it's the

[00:09:38.299]
platform that provides instant

[00:09:40.299]
gratification sell about two-thirds

[00:09:42.299]
of you still tell us at more than 50%

[00:09:44.600]
of your B I contact the sitting

[00:09:47.000]
in spreadsheets and other homegrown

[00:09:50.000]
Shadow ITI application so enough

[00:09:52.100]
about the bad news now let's start talking about

[00:09:54.200]
the good news so so what are some of

[00:09:56.299]
the solutions out there

[00:09:58.200]
that we think I'm going to help

[00:10:00.500]
us get closer to that Nirvana

[00:10:02.899]
or for highly efficient

[00:10:04.899]
and effective of the AI environment and

[00:10:07.200]
Enterprises and we had forced to believe that

[00:10:09.500]
the answer is light in these three

[00:10:11.500]
areas will Aleve the topic

[00:10:14.000]
of artificial intelligence for a letter

[00:10:16.100]
time this is this definition for in

[00:10:18.200]
special separate discussion

[00:10:20.299]
today will concentrate in the first two

[00:10:22.299]
topics extremely important would

[00:10:24.500]
be between a business agility and

[00:10:26.700]
deep datta capabilities I think

[00:10:28.700]
it is about 90% of the

[00:10:30.700]
success on a tie is just going to

[00:10:32.700]
be the icing on the cake so

[00:10:35.100]
let's let's dive into these to talk

[00:10:37.299]
it's business agility and these

[00:10:39.299]
day and talk about why they are so

[00:10:41.299]
important the reason that businesses

[00:10:43.399]
you it is so important because today

[00:10:45.500]
we are at the age of a customer

[00:10:47.700]
Woodforest that means is that we are way past

[00:10:49.700]
the age of information and

[00:10:52.100]
we are in the age of a customer customers

[00:10:54.500]
room so I don't really care how

[00:10:56.600]
your internal organ location

[00:10:58.799]
of how you internal Enterprise

[00:11:00.899]
processes such as Finance

[00:11:03.100]
HR supply chain risk management

[00:11:05.299]
Erp CRM I do not

[00:11:07.399]
really care how well oiled those

[00:11:09.600]
processes are and how well they're on

[00:11:11.700]
either those processes are not allowing

[00:11:14.899]
you to follow and embrace

[00:11:16.899]
your customer behavior and

[00:11:19.000]
address your customer Behavior you going

[00:11:21.000]
to a fall behind should we call that not

[00:11:23.600]
Enterprise under the age of a customer

[00:11:25.799]
and the prices are outside in

[00:11:27.799]
driven as opposed to Inside

[00:11:29.899]
Out driven so basically there's

[00:11:32.100]
nothing more important than following

[00:11:34.399]
your customers in addressing their knees

[00:11:36.399]
regardless of what your internal

[00:11:38.799]
business processes are all about and

[00:11:41.200]
once we realize that a few years ago we

[00:11:43.399]
went out and we conducted some research

[00:11:45.899]
where we decided to quantify

[00:11:48.000]
what businesses you it

[00:11:50.000]
is all about so we came up with the

[00:11:52.000]
stand Dimensions that you see on

[00:11:54.000]
the right of this light end dimensions of business

[00:11:56.000]
agility and Israel common sense

[00:11:58.100]
Common Sense Dimension

[00:12:00.299]
sold to see if your channels are integrated

[00:12:03.000]
you are more of a gel and responsive

[00:12:05.399]
if in the middle of this slide

[00:12:08.700]
you or your infrastructure is elastic

[00:12:11.100]
and can shrink and grow and

[00:12:14.000]
you can provisional deprovision resources

[00:12:16.200]
based on customer demand

[00:12:18.299]
to see you're going to more be more

[00:12:20.399]
responsive Michigan

[00:12:22.500]
CBI is front-and-center Right

[00:12:25.000]
smack in the middle of all these capabilities and

[00:12:27.399]
some other capabilities here

[00:12:29.399]
are directly dependent on VI Sol

[00:12:31.500]
Market responsiveness no knowledge dissemination

[00:12:33.700]
all we did is we

[00:12:35.700]
applauded Ibiza

[00:12:37.799]
situated capabilities of about 350

[00:12:41.500]
public companies putting

[00:12:43.700]
them into Dimension to Dimension so

[00:12:45.899]
they are aware this Apollo

[00:12:48.000]
well aware they are all these business

[00:12:50.399]
agility capabilities and how well

[00:12:52.500]
can they can they execute

[00:12:54.600]
on these capabilities and I'm sure you already

[00:12:56.600]
guessing what I'm going show you

[00:12:58.700]
on the next flight yes you are your

[00:13:00.799]
gift correct with high or higher

[00:13:03.200]
performers those companies that grow

[00:13:05.399]
faster than industry averages

[00:13:07.799]
that grow faster

[00:13:09.799]
than their of their

[00:13:11.799]
peers are all over what

[00:13:14.299]
we call the formidable category right

[00:13:16.399]
in the upper right quadrant meaning these companies

[00:13:18.799]
are aware of the business of Judah

[00:13:21.000]
capabilities and they can execute well

[00:13:23.399]
versus lower performers Industrial

[00:13:25.700]
Average on the left side are all over

[00:13:27.899]
with the call Leah humorous the oldest

[00:13:29.899]
of the coolest like a paddle a

[00:13:31.899]
down with the wear and then with executing

[00:13:34.000]
so definitely again some

[00:13:36.299]
quantitative proof of a correlation between

[00:13:38.299]
overall business success

[00:13:40.500]
and business situated then

[00:13:42.500]
the next question is what is it that we be

[00:13:44.600]
eye professionals they need a management

[00:13:46.799]
information management professionals what is what

[00:13:49.000]
is it that we can do to support this

[00:13:51.000]
business agility capabilities

[00:13:53.100]
I'll

[00:13:56.399]
just wet your appetite

[00:13:58.100]
forced to recommend practicing this four-part

[00:14:00.899]
Hedgehog d i a framework

[00:14:03.299]
it is all about the edge

[00:14:05.399]
of software development where we emphasize

[00:14:07.700]
rapid prototypes

[00:14:09.899]
in Rapid pulse of Concepts

[00:14:12.299]
food into Coors eye production

[00:14:14.600]
environment where business users can

[00:14:16.799]
start using

[00:14:19.000]
the systems within hours

[00:14:21.200]
as opposed to two weeks and months when

[00:14:23.500]
it's already too late are the second

[00:14:25.600]
component or a gel organizational

[00:14:27.899]
structures where we realize that neither

[00:14:31.299]
ands of the extreme in

[00:14:33.500]
organizational structures of work

[00:14:35.700]
so organizational of

[00:14:38.000]
silos are old disley

[00:14:40.000]
not good because we

[00:14:43.100]
done been through sources and there is no single version

[00:14:45.299]
of the truth everybody's getting different

[00:14:47.299]
answers to the same question but would

[00:14:49.500]
forget to give The Silo

[00:14:51.799]
some credit silos are much

[00:14:53.799]
more Edge out much more responsive

[00:14:55.799]
than the centralized organization

[00:14:58.100]
because they are if

[00:15:00.500]
I own my own development shop

[00:15:02.500]
if I own my own dude b i t i don't

[00:15:04.799]
need to share those Resources with

[00:15:06.799]
anyone or Miss Lee I call the shots and

[00:15:08.899]
I'm much more responsive to

[00:15:10.899]
the market and we moved over

[00:15:13.000]
to the other side of the extreme where

[00:15:15.299]
sometimes erroneously

[00:15:17.700]
organizations overly centralized

[00:15:20.100]
they are bi support yes they

[00:15:22.100]
are rational eyes resources but

[00:15:24.299]
they now have become highly bureaucratic

[00:15:27.000]
organizations they spend

[00:15:29.500]
endless hours and steering committees

[00:15:31.799]
and prioritization a meeting

[00:15:34.000]
and as a result they move slower

[00:15:36.200]
and when they move slower yes what is

[00:15:38.600]
this and user start doing they go

[00:15:40.700]
back to Excel and homegrown homegrown

[00:15:44.000]
application so we recommend

[00:15:46.000]
some kind of a middle of the ground we've got a lot of

[00:15:48.000]
deep research behind that they welcome

[00:15:50.299]
everyone taking a look at it

[00:15:52.700]
I obviously we need to practice Edge

[00:15:55.299]
out the eye of processes so

[00:15:57.399]
as I said rapid prototyping

[00:15:59.500]
as opposed to a long waterfall

[00:16:02.000]
development cycle is one of

[00:16:04.000]
the examples and last but not least told

[00:16:06.200]
me see what the Arcadia data will talk

[00:16:08.299]
about later in the presentation that

[00:16:10.299]
you have to have an edge LBI platform

[00:16:12.600]
if you if you have an older generation

[00:16:14.700]
that bi platform still

[00:16:16.700]
use the one that the older

[00:16:18.799]
generation are get that check where it

[00:16:20.899]
takes a long time to change anything

[00:16:23.200]
long time to develop anything

[00:16:25.600]
that that's not going to help

[00:16:27.600]
in the business agility

[00:16:29.600]
way in environment business

[00:16:31.700]
agility use a top priority business

[00:16:37.500]
agility and how Edge LBI

[00:16:39.700]
places that plays an important

[00:16:41.700]
role in that environment let's

[00:16:43.700]
move to the second part of the big data

[00:16:45.700]
and let's talk about what the neural Big

[00:16:48.000]
Data plays in this business

[00:16:50.200]
agility equation

[00:16:52.700]
ridiculous that tend to stay

[00:16:54.799]
away from the multiples

[00:16:57.000]
of the definitions of Big Data it

[00:16:59.000]
it's good to start the discussion about

[00:17:01.100]
big data using the terms

[00:17:03.299]
like the volume. Louis City

[00:17:05.400]
variety and variability but

[00:17:07.700]
it doesn't really it's

[00:17:09.799]
not really actually both so you know once once we

[00:17:11.900]
have this discussion on DVDs with

[00:17:14.200]
the clients they say alright well what do I do

[00:17:16.400]
with without now how does at 8-8

[00:17:18.599]
translate into some kind

[00:17:21.799]
of a follow-up items like

[00:17:27.400]
to train the

[00:17:29.400]
discussion along these four lines

[00:17:31.500]
rather than creating a definition of

[00:17:34.099]
these data and then I'll buy the way between

[00:17:36.500]
two or three analysts

[00:17:38.500]
and the two or three vendors do

[00:17:40.700]
you know when you get 5 or 6 people in the

[00:17:42.700]
room you're going to get 20 different opinions

[00:17:44.700]
that's the one big data is

[00:17:46.700]
all about it's not so rather than defining

[00:17:49.099]
it. Just understand that some

[00:17:51.500]
of the use cases

[00:17:52.599]
so we have this four-part discussion

[00:17:54.700]
with our clients the very first one is

[00:17:56.799]
about people and processes right so all

[00:17:58.900]
that technology implementations have

[00:18:01.000]
three components right technology people

[00:18:03.500]
in process if your challenges

[00:18:05.700]
in the people and process part so

[00:18:07.799]
if you are challenged with business

[00:18:10.700]
and I T alignment does I talked about there early

[00:18:12.799]
if you challenged with the data and

[00:18:14.900]
bi Governor if you challenges

[00:18:17.099]
are data quality has nothing

[00:18:19.400]
to do with technology so please don't don't

[00:18:21.500]
equate these challenges

[00:18:23.599]
with anything that big data can't can

[00:18:25.599]
help you solve an address the

[00:18:27.799]
second part of the discussion of all

[00:18:29.799]
about upgrades so if

[00:18:31.799]
your system is running slowly

[00:18:34.299]
if you do not have the right

[00:18:36.599]
architecture

[00:18:38.599]
maybe you still using the old types

[00:18:40.700]
of databases maybe you

[00:18:42.799]
are still using RIT Centric

[00:18:45.000]
of the eye platform where

[00:18:47.000]
a business users are not truly empowered

[00:18:49.700]
to self author

[00:18:51.700]
a majority their own the

[00:18:53.700]
icon that maybe you just need to scale

[00:18:56.000]
up or scale out you need to add some CPUs

[00:18:58.400]
you need to add some nose in your service

[00:19:00.700]
and maybe it is purely technology

[00:19:03.200]
upgrade situation

[00:19:05.200]
which has nothing to do with me then

[00:19:07.700]
we do indeed dive

[00:19:09.900]
into a different discussion wed Big Data

[00:19:12.200]
indeed I have some used

[00:19:14.299]
you know what you do a

[00:19:17.099]
deep dive on a toilet not into that on

[00:19:19.099]
an ex lied and then I

[00:19:21.099]
once we kind of understand

[00:19:23.200]
that indeed your requirements

[00:19:26.500]
for Big Data address to use

[00:19:29.799]
cases are real a big day at application

[00:19:31.900]
then we talk to you about that sounds

[00:19:34.200]
implications when you upgrade to Big Data

[00:19:36.400]
when you upgrade to the Duke

[00:19:38.400]
based Information Management

[00:19:41.299]
the data governance and the single

[00:19:44.000]
version of the truth and data quality

[00:19:46.200]
become completely different

[00:19:48.299]
issues they they still

[00:19:50.299]
need to be addressed but they are address

[00:19:52.599]
a very different than four reasons

[00:19:54.700]
that we'll talk about it in a second

[00:19:57.000]
so here's what's really

[00:19:59.099]
behind at 3rd points on the

[00:20:01.099]
previous line so these are they really

[00:20:03.200]
real,

[00:20:05.400]
for areas off for areas

[00:20:09.700]
of requirements to necessitate

[00:20:11.799]
investments in Big Data

[00:20:13.799]
Base the RBI technologist number

[00:20:15.900]
one your business requirement score for

[00:20:17.900]
linear scalability right so so

[00:20:19.900]
we all know that even

[00:20:22.099]
class Starbase stem bi

[00:20:24.400]
platforms the north scale

[00:20:26.500]
Ali nearly databases

[00:20:29.500]
that are based on a massively

[00:20:31.700]
parallel distributed

[00:20:33.900]
scale-out technology

[00:20:36.099]
but if you'll be high platform is

[00:20:38.299]
not based on this semester of

[00:20:40.299]
parallel and distributed technology that

[00:20:42.400]
old this way you're going to be a limit

[00:20:44.599]
of yourself limiting yourself

[00:20:46.599]
in a linear scalability this

[00:20:48.900]
second part of this is all about that

[00:20:51.099]
business agility

[00:20:52.599]
very important or people that I spent

[00:20:54.799]
the last 15 minutes talking about is

[00:20:57.299]
that traditional early gmbi platforms

[00:20:59.700]
are based strictly on schema

[00:21:02.000]
on right type of

[00:21:04.000]
an architecture and hopefully you all know

[00:21:06.000]
what that is and that that means we're data

[00:21:08.400]
and metadata are very

[00:21:10.700]
tightly bound meaning

[00:21:12.700]
that you first create your day tomorrow

[00:21:14.799]
and then you populate

[00:21:16.799]
the day tomorrow with Dara and therefore

[00:21:19.099]
you there and they tomorrow at tightly

[00:21:21.099]
bound and the only way to change the

[00:21:23.599]
day tomorrow is to rebuild the whole

[00:21:25.599]
database from scratch dropping

[00:21:28.000]
call I'm changing in this is changing

[00:21:30.400]
primary for and keys etcetera etcetera is

[00:21:32.900]
a very difficult adorable but

[00:21:35.200]
they were Source intensive process

[00:21:37.200]
in skimmer on a right

[00:21:39.599]
type of database schema on

[00:21:41.799]
read database is your data

[00:21:44.299]
and your method data are separate

[00:21:46.299]
and therefore

[00:21:48.200]
you and I can have I

[00:21:50.500]
can have two different views into

[00:21:52.700]
exactly the same day I said that by the way

[00:21:54.700]
is why single version of the truth

[00:21:56.799]
requires different type

[00:21:58.900]
of handling but schema on

[00:22:00.900]
Reed's tactics architecture

[00:22:03.099]
definitely infinitely

[00:22:05.200]
more I add shout and the last

[00:22:07.299]
couple of reasons is about

[00:22:10.099]
bottlenecks and moving

[00:22:12.099]
data between clients

[00:22:14.200]
and servers between your front-end and

[00:22:16.200]
back-end application in traditional

[00:22:18.299]
earlier jndi architecture

[00:22:20.599]
you no matter how scalable

[00:22:23.200]
you make it you still Bound by

[00:22:25.599]
all of the traffic all the sequel queries

[00:22:27.900]
that are generated in your front end and go

[00:22:29.900]
to your back end up with a

[00:22:31.900]
database and database generates

[00:22:34.000]
the answers to your queries

[00:22:36.000]
and chance the results back to

[00:22:38.099]
you are a bi application so

[00:22:40.099]
that that traffic

[00:22:42.400]
sometimes causes bottlenecks

[00:22:45.000]
especially if you are sending the data

[00:22:47.200]
across your fire also either

[00:22:49.200]
way we can keep basically

[00:22:51.299]
data and applications together all

[00:22:53.900]
right inside our classes as opposed

[00:22:56.099]
to having to move the data in and

[00:22:58.099]
out of her classes that that's really what's

[00:23:00.299]
behind that of a three or four high-level

[00:23:03.000]
use cases where a big

[00:23:05.099]
day at specifically for Dupont

[00:23:07.500]
Spartan Beast architectures play

[00:23:10.599]
a key role so with

[00:23:12.599]
that in mind with Forest to do a lot of research

[00:23:14.900]
into open source

[00:23:17.099]
Technologies like Hadoop and Spark and

[00:23:19.900]
as you can see the market is really really

[00:23:21.900]
embracing of this type of scale

[00:23:24.099]
of parallel processing technology

[00:23:27.299]
about 800 million is

[00:23:30.099]
being spammed on Hadoop this year

[00:23:32.400]
and about 48%

[00:23:34.799]
of telling us that you already have built hadoop-based

[00:23:37.799]
a data lakes and I'm looking

[00:23:39.799]
at the

[00:23:41.099]
I'm looking at the results over

[00:23:43.400]
the pole that even you

[00:23:45.400]
just ran it yet I do see indeed just

[00:23:48.299]
about 250 plus percent

[00:23:50.799]
of people are telling us that they are piloting

[00:23:53.400]
will have

[00:23:55.400]
already deployed some kind of

[00:23:57.400]
a day late could do based behind

[00:23:59.900]
warm and so I think the numbers differently

[00:24:02.900]
a line here so

[00:24:05.299]
when you when you look at

[00:24:07.400]
your Jeep and Spark base

[00:24:09.400]
the FBI architecture

[00:24:11.400]
and it carries certain

[00:24:13.400]
implication that it's

[00:24:15.599]
not the same as just looking at

[00:24:17.900]
your database vs.

[00:24:19.900]
FBI technical architecture Hadoop

[00:24:23.099]
and Spark are you know

[00:24:25.099]
very complex a project

[00:24:27.099]
there was a huge complex ecosystem

[00:24:29.200]
around them so as you're looking at

[00:24:31.299]
this platforms you definitely understand

[00:24:33.700]
water this data management layers and

[00:24:35.799]
what kind of a file systems are based on what

[00:24:38.700]
is the app management that cost management

[00:24:41.099]
what's the managers that they run on what

[00:24:43.700]
kind of a data processing is it that

[00:24:45.700]
produces at Sparkle

[00:24:48.000]
some other types of architecture

[00:24:50.700]
and last but not least what kind of

[00:24:52.700]
curious what kind

[00:24:54.900]
of data processing

[00:24:57.400]
does the platform support is

[00:24:59.400]
a basin SQL is a based on

[00:25:01.400]
streaming or some other some

[00:25:04.099]
other technology help with that in mind what

[00:25:07.500]
we did a few months ago is we

[00:25:09.599]
ran and evaluation

[00:25:11.799]
process or for several

[00:25:13.799]
top vendors in the space and

[00:25:15.799]
we looked at them there a comprehensive

[00:25:17.799]
list is it going to see on the right

[00:25:19.799]
side of the slide we

[00:25:22.200]
evaluated as vendors by about

[00:25:24.299]
15 criteria

[00:25:26.299]
so on the technology

[00:25:28.500]
side we looked at the data preparation criteria

[00:25:31.400]
will look that large Enterprise features such

[00:25:33.700]
as a security and collaboration

[00:25:36.200]
Administration obviously and

[00:25:38.299]
use a self-service is a very very important

[00:25:41.099]
item data visualization

[00:25:43.200]
you obviously can't have the i in

[00:25:45.299]
analytics without data visualization Sequel

[00:25:48.299]
and Olaf capabilities are important especially

[00:25:50.500]
when you reporting your

[00:25:52.700]
existing Legacy applications

[00:25:54.700]
to these new native

[00:25:56.700]
Hadoop type of Architecture is a

[00:25:58.799]
nosql or date of discovery and

[00:26:00.900]
exploration type of operations also important

[00:26:03.000]
advancement Predictive Analytics

[00:26:05.299]
capabilities that Hadoop and Spark

[00:26:07.299]
architecture that's what I show them the previous

[00:26:09.599]
find that that's what we evaluated in

[00:26:12.099]
this particular a line item integration

[00:26:14.500]
of multiple components making sure that

[00:26:16.599]
a date of preparation and

[00:26:18.599]
data visualization answer

[00:26:20.599]
cell service capabilities of tightly integrated

[00:26:22.799]
we looked at the deployment

[00:26:24.900]
options on Primus Cloud Etc

[00:26:27.700]
and old asleep I

[00:26:29.799]
took into account customer satisfaction

[00:26:31.900]
the Wii released two versions of

[00:26:33.900]
these evaluation one where as you

[00:26:35.900]
can see in the weights collar

[00:26:39.000]
where we gave more weight today

[00:26:41.400]
a user experience

[00:26:43.500]
the user interface and on

[00:26:45.500]
an axe flight exactly the same model

[00:26:47.500]
exactly the same scores but where we

[00:26:49.599]
gave data preparation I

[00:26:52.700]
always because a lot of you out there

[00:26:54.799]
I don't really have complex

[00:26:57.200]
odata visualisations complex

[00:26:59.299]
analysis but your data of

[00:27:01.500]
Euro date is all over the place and

[00:27:03.500]
they are you spend 80% of your time

[00:27:05.500]
just massaging massaging

[00:27:07.799]
the data before you can analyze it

[00:27:09.900]
so that's that's kind of how we

[00:27:12.900]
see the role of big data and the role

[00:27:15.099]
of a native could do but distributed

[00:27:17.799]
a parallel architecture this

[00:27:19.799]
is how it all comes together so

[00:27:21.799]
I'll die addresses and

[00:27:25.000]
user self-service more

[00:27:27.099]
of the business agility Big

[00:27:29.700]
Data addresses more

[00:27:31.700]
of the data available addresses

[00:27:34.200]
agility with that schema

[00:27:36.500]
onry top of the new

[00:27:40.200]
term because used

[00:27:42.200]
to describe this convergence of

[00:27:44.200]
a job behind the data so we now

[00:27:46.200]
call Data Systems of inside why

[00:27:48.500]
did we choose the term Systems off

[00:27:50.500]
because that that is a very familiar Toronto

[00:27:53.000]
to most of you already use the term

[00:27:55.000]
systems of rappers use the term

[00:27:57.200]
Systems off engagement so systems

[00:27:59.500]
of inside was just the natural way

[00:28:01.700]
to the name of that Next

[00:28:04.099]
Generation the sandblaster Alaska

[00:28:07.500]
the closing remarks on the best

[00:28:09.599]
practices so make sure

[00:28:11.599]
that you'll be IHOP locations on

[00:28:13.700]
North Standalone make sure the day they

[00:28:16.000]
are tightly integrated and embedded

[00:28:18.400]
into your operational application

[00:28:20.700]
and processes that's the only way you

[00:28:23.099]
are bi apps will become contextual

[00:28:25.400]
and they will become actionable and

[00:28:28.400]
definitely and I have

[00:28:30.400]
to admit there are none of us do a good

[00:28:32.400]
job of this continuous learning

[00:28:35.299]
and Improvement because all the sudden no one gets

[00:28:37.599]
it right from the start so foreign

[00:28:40.200]
foreign process processes

[00:28:42.299]
and best practices that whatever results

[00:28:44.799]
you get whatever outcomes you get from

[00:28:47.000]
from the inside so

[00:28:49.099]
make sure you document them you

[00:28:51.400]
categorize them as a positive

[00:28:53.599]
negative Etc and

[00:28:55.700]
then the next Generation you'll

[00:28:57.799]
learn and improve so

[00:29:00.400]
2 to close off here

[00:29:02.599]
is some high-level differences

[00:29:05.099]
between older early

[00:29:07.200]
Generation VI where we really just

[00:29:09.200]
concentrated on the

[00:29:11.400]
technology for the sake of Technology

[00:29:13.599]
on the single version of the truth for

[00:29:15.700]
the sake of the single version of the truth the

[00:29:17.799]
day we clearly realize it's

[00:29:19.900]
not about that that is really all about winning

[00:29:22.000]
serving and retaining your customers

[00:29:24.599]
and we typically see that that is much

[00:29:26.799]
better executed when I'm

[00:29:29.200]
not a line of business

[00:29:31.400]
Executives oci Moses

[00:29:33.700]
and the VP of sales own

[00:29:35.900]
a bi and systems of inside

[00:29:38.099]
environment underwear she owes and their organization

[00:29:40.900]
I really just supporting that

[00:29:43.000]
environment and that's why we have for Salida

[00:29:45.400]
the term business technology enough

[00:29:47.500]
as opposed to Information Technology because

[00:29:50.299]
it's just acknowledging up for the sake of Information

[00:29:52.700]
Technology for the sake of business

[00:29:54.700]
results and as you saw from my

[00:29:56.700]
closing slides and

[00:29:59.000]
I'll take a look at these last best

[00:30:01.200]
practices make sure that your

[00:30:03.299]
bi in Norman even if it's

[00:30:05.400]
well architected an

[00:30:07.599]
well-oiled and well deployed if

[00:30:09.900]
it's I load if it's not embedded

[00:30:12.099]
into your other operational applications

[00:30:14.599]
it's not pervasive it's not confection

[00:30:16.799]
Perfection herbal so

[00:30:18.900]
with that in mind let me turn this over to

[00:30:21.299]
my colleagues

[00:30:23.500]
I think Creon QR next

[00:30:26.299]
yeah I'm just going to Steve's going to jump

[00:30:28.299]
in here and just do a quick another

[00:30:31.000]
pole here thank you Boss I was very insightful

[00:30:33.099]
so viewers

[00:30:35.900]
if you look if you're not in

[00:30:37.900]
full screen mode on the underneath

[00:30:40.099]
of the viewing pane there you'll

[00:30:42.200]
see a another tab pop up for just

[00:30:44.400]
one more pool and this question

[00:30:46.799]
is how do you plan to give access

[00:30:49.000]
I'm sure how you

[00:30:51.099]
plan to give users access analyze the data option

[00:30:54.400]
A is development tools such as parking map

[00:30:56.500]
reduce option b or c pool engine

[00:30:58.500]
such as high as Impala drill Park

[00:31:00.599]
sequel see if I'll be out

[00:31:02.700]
tools do you use to do

[00:31:04.799]
Meadows distributed by platforms which

[00:31:07.000]
Forest has spoke about and Annie

[00:31:09.099]
and I you could specify in

[00:31:11.200]
the comment section if there's some other way you're thinking

[00:31:13.200]
about giving access to users

[00:31:15.400]
of your data Laker Hindu

[00:31:17.400]
based platform

[00:31:19.500]
so just take a minute to tally

[00:31:21.900]
the results from that again it should

[00:31:23.900]
be a tab next to where it

[00:31:25.900]
says ask a question is 1/4 vote

[00:31:27.900]
you should be able to

[00:31:32.599]
but your votes in there

[00:31:34.700]
and I should update in real-time we got quite

[00:31:36.900]
a few people on so I'm looking for

[00:31:38.900]
the results to Tremont here

[00:31:44.700]
looks like it is distributed

[00:31:46.799]
bi platform says the lead but

[00:31:49.200]
out traditional bi tools is coming up

[00:31:51.299]
we got sequel engines in there

[00:31:54.000]
in the race as well

[00:32:00.500]
alright well

[00:32:02.799]
just give it another couple seconds here those

[00:32:04.900]
are still coming in

[00:32:10.200]
guess who I think let's just leave that open

[00:32:12.400]
is that there's still a number of you on here I don't want to

[00:32:14.400]
delay the webcast anymore

[00:32:16.400]
but some

[00:32:18.900]
let's go ahead and turn things over to prions

[00:32:23.400]
Pacific you get up alright

[00:32:28.700]
thank you Steve thank

[00:32:31.200]
everyone for attending and I

[00:32:33.200]
will actually Jump Right In and pick up from

[00:32:35.200]
Premier bars and Adventures

[00:32:37.599]
this notion of system

[00:32:39.599]
of inside Alyssa Hernandez

[00:32:42.000]
push and pull like he that he

[00:32:44.099]
touched upon between being agile

[00:32:46.500]
versus having having

[00:32:49.599]
having some

[00:32:52.299]
sort of a centralized architecture

[00:32:54.599]
which has election in the number

[00:32:56.599]
of silos not complete elimination but

[00:32:58.700]
but but silos especially when

[00:33:00.799]
they go go go to the extreme

[00:33:03.000]
of Excel spreadsheets

[00:33:05.000]
on every business users desktop do

[00:33:07.599]
not do not necessarily

[00:33:09.599]
Health connection

[00:33:18.299]
to how how how how a

[00:33:20.299]
lot of you in the audience would have a good

[00:33:22.299]
have seen their pictures right

[00:33:24.400]
now starts out from debate

[00:33:26.500]
houses are the large-scale databases

[00:33:28.700]
where did I from a whole

[00:33:30.700]
bunch of stuff from

[00:33:33.200]
here they are movies and summarized

[00:33:35.500]
into various

[00:33:37.500]
projects Pacific data marks from

[00:33:39.700]
the moves into SLB

[00:33:42.799]
I servers and cubing agencies

[00:33:44.799]
are in memory engine specs that summarize

[00:33:46.900]
the data from the sequel Baystate of mods

[00:33:49.000]
into into another trr8

[00:33:51.700]
it's cashed in memory for actual

[00:33:54.000]
inside and Fracture usage

[00:33:56.400]
by the end tools eventually

[00:33:59.099]
the end-user your business partner

[00:34:01.099]
your business analyst or the end

[00:34:03.099]
of business user actually start getting getting

[00:34:05.700]
access to the day off and

[00:34:07.700]
you know that

[00:34:11.599]
you know this over here to

[00:34:16.900]
this pipeline is

[00:34:19.199]
loss of fidelity because you lose access

[00:34:21.599]
to the granularity of the data

[00:34:23.599]
in the in the in the in the lower systems

[00:34:26.000]
of course the Actors Studio time

[00:34:28.199]
they die evening are not in the

[00:34:30.199]
realm of an architecture like this but

[00:34:32.400]
an interesting side effect

[00:34:34.599]
of this is a high security clearance now

[00:34:36.599]
you have same copies

[00:34:38.800]
of the same versions of that information across

[00:34:41.199]
multiple systems and multiple

[00:34:43.400]
body systems describe are or

[00:34:45.400]
more controlling how security

[00:34:47.500]
is applied to them as

[00:34:52.900]
well as the height issue here

[00:34:55.900]
as most of you are probably thinking to yourself is

[00:34:58.599]
extremely simplistic even

[00:35:00.699]
from even

[00:35:02.699]
from the most most most early

[00:35:05.000]
users of even

[00:35:08.199]
for the most simple users of the author of these

[00:35:10.800]
are the reality

[00:35:13.000]
is what you find is

[00:35:15.400]
that are hundreds of Texas you will find

[00:35:17.800]
this kind of an architecture replicated

[00:35:20.099]
hundreds of times and if you add

[00:35:22.099]
the fact that that is that

[00:35:24.300]
is very Shadow systems that you are not even

[00:35:26.300]
aware of the damn dude in the purview of

[00:35:28.599]
fall off of Managed IT

[00:35:30.900]
system that could potentially go mm

[00:35:33.199]
having

[00:35:38.400]
having the movie

[00:35:40.699]
The Help of silos is good but not then

[00:35:42.900]
it leads to so many copies of

[00:35:44.900]
the data and and completely unmanageable

[00:35:47.000]
architecture especially

[00:35:51.300]
if you're trying to build a scalable

[00:35:53.699]
system backpack that enables

[00:35:56.099]
and generates actionable inside as

[00:35:58.099]
Morris was referring to in

[00:36:03.800]
the cloud on premise you

[00:36:06.300]
start out with a day late

[00:36:08.400]
and you start moving in

[00:36:10.400]
managing the large amount of data across

[00:36:12.599]
these different sources

[00:36:15.099]
into this

[00:36:16.800]
but I want you to realize as you as

[00:36:18.900]
you start playing this is that the consumption problem

[00:36:21.500]
the problem of consuming data

[00:36:23.800]
out of the Snake Still Remains you

[00:36:26.199]
still take some out of that move

[00:36:28.199]
it into a traditional systems before

[00:36:30.300]
after 4 or 5 years you

[00:36:32.599]
get your analyst getting access

[00:36:34.699]
to it and what that does especially

[00:36:37.000]
for systems that are stuck in pilots

[00:36:39.000]
and you been doing long by 6 photo

[00:36:41.000]
for a while is that good impression

[00:36:43.500]
of the delay it started out with

[00:36:45.900]
each other with an important goal

[00:36:48.000]
of being able to combine data across

[00:36:50.199]
completely different to

[00:36:54.099]
the dump in like that

[00:36:56.099]
doesn't help how you how you how you

[00:36:58.199]
are in a burning inside out of the system

[00:37:00.199]
because you still stuck with the same consumption

[00:37:02.400]
side on the business intelligence and

[00:37:04.500]
analytics pictures

[00:37:09.400]
so how does Arcadia is it it's

[00:37:11.500]
a very good with technology.

[00:37:13.800]
What do we do to Estes

[00:37:16.599]
architecture

[00:37:20.900]
that lives right next to

[00:37:22.900]
where the Beatles on

[00:37:30.400]
individual notes on elastic

[00:37:34.500]
tears with lupus finally done directly

[00:37:36.599]
on the last 2 years not require for

[00:37:38.900]
the copies of the day. We created

[00:37:41.000]
one that enables is

[00:37:43.400]
yours for you to bring your business

[00:37:45.400]
users father's use cases in your

[00:37:47.500]
analyst directly on

[00:37:50.199]
the lake and cut short

[00:37:52.300]
the time it requires to move

[00:37:54.500]
data to the different systems and to summarize

[00:37:56.699]
Data before the end users

[00:37:58.800]
are going to get access to it

[00:38:00.800]
I blew it out a little bit and then

[00:38:02.800]
double-click on the Ion on on how this

[00:38:04.800]
works of

[00:38:06.900]
architecture imitation

[00:38:13.500]
architecture and

[00:38:17.400]
underline a dupe system or

[00:38:20.199]
and underline the execution

[00:38:22.800]
engine in as

[00:38:25.400]
an operating system celebrity distributor

[00:38:27.699]
execution that's available in the cluster

[00:38:29.800]
you leverage the data storage

[00:38:31.900]
that could be stored in the body system

[00:38:34.000]
could be stored in objects towards

[00:38:36.099]
the metadata and the security

[00:38:38.599]
permissions and I'm policies that are to

[00:38:40.599]
find out what their what you

[00:38:42.599]
had on top of the SQL

[00:38:44.599]
engines that are available in

[00:38:46.699]
the systems is a date and

[00:38:48.699]
Dave b i compute engine

[00:38:50.699]
this is this is an in-memory engine

[00:38:52.900]
that is distributed it's MVP

[00:38:55.199]
runs on every node and

[00:38:57.300]
it leverages in memory of each

[00:38:59.300]
of these notes

[00:39:00.800]
if I slicing and dicing that

[00:39:03.199]
is required to enable the

[00:39:05.300]
end users to go from

[00:39:07.300]
exploration analysis to

[00:39:09.400]
editing population these these

[00:39:11.699]
dashboards reports and eventually

[00:39:14.000]
immersive applications are actionable

[00:39:16.099]
applications available to Skype or

[00:39:18.099]
two large numbers of architecture

[00:39:23.099]
does your environment

[00:39:25.300]
then when you're than your thinking

[00:39:27.300]
about enabling your

[00:39:29.500]
business partners or your business users an

[00:39:31.500]
analyst on on on data

[00:39:33.699]
that is as far as as

[00:39:36.000]
high in school as well as

[00:39:38.000]
it starts moving as strongly associated

[00:39:40.599]
with the assistance of inside

[00:39:42.699]
round that why

[00:39:48.599]
doesn't really matter in why does it matter to

[00:39:51.300]
actually have an architecture and you want when

[00:39:53.300]
you want to try to scale what

[00:39:55.599]
what does the date on it if I could actually buy you

[00:39:57.599]
it is about a

[00:40:00.199]
boy stuck it out and and and then

[00:40:02.199]
describe which is you

[00:40:04.300]
have to you have to bring agility

[00:40:06.699]
from the individualized

[00:40:08.800]
systems that end users are using and

[00:40:10.900]
still bring it to do a large-scale

[00:40:13.000]
okra in business agility is the

[00:40:15.000]
key over there you can

[00:40:17.000]
enable users

[00:40:19.000]
to not start out by flying these

[00:40:21.300]
models on the data requiring

[00:40:23.300]
them to essentially do what what

[00:40:25.800]
is the screen on Droid to make it

[00:40:27.800]
in there mods or or tubes

[00:40:30.099]
or extract you can still maintain that

[00:40:32.400]
email and read capability and

[00:40:34.599]
enables users to start with

[00:40:36.599]
a simple exploratory visual interface

[00:40:38.699]
and at the same time have

[00:40:41.099]
an architecture the bottom which which continuously

[00:40:43.400]
models for performance the

[00:40:45.800]
data that is in you say monitors

[00:40:48.099]
Martin models that use H pattern

[00:40:50.199]
off to you or off of the

[00:40:52.199]
system to enable fast

[00:40:54.599]
and high concurrency access on Stadium that's

[00:40:56.800]
the first benefit

[00:40:59.500]
is that you bring identity back to

[00:41:01.599]
this large-scale update

[00:41:03.699]
on the scale and let

[00:41:12.300]
it all the time that I like somebody's was inside

[00:41:14.400]
Sterling when you

[00:41:16.400]
when you drive your inside you drive them into actions

[00:41:18.400]
you want them you want to enable your

[00:41:20.400]
business users this actionable

[00:41:22.599]
applications that are embedded in

[00:41:25.199]
skin something that they are generally can human regular

[00:41:27.500]
work so it starts out absolutely

[00:41:29.900]
from the traditional production quality dashboards

[00:41:32.599]
but it moves very quickly into animals

[00:41:34.800]
have customer application nothing but it

[00:41:37.800]
doesn't make the distinction between when

[00:41:39.900]
the data was generated is the date I was was

[00:41:42.199]
generated in was deleted

[00:41:44.400]
in real-time versus if

[00:41:46.400]
it was available making

[00:41:48.800]
a batch of the intercession it still

[00:41:50.900]
incorporates both of these

[00:41:52.900]
dreams right into the application that

[00:41:55.500]
allows you to move Beyond basic charting

[00:41:57.599]
and basic analysis and allows you to embed

[00:41:59.900]
analysis around micro segmentation

[00:42:01.900]
time CDs and event analytics

[00:42:04.199]
right into the end-user

[00:42:06.400]
who can actually act upon it active

[00:42:08.900]
on the inside and drive

[00:42:10.900]
the business spotlight

[00:42:14.099]
let's focus first on the agility

[00:42:16.099]
side what does agility enabling

[00:42:18.900]
in an environment like this what do you running directly

[00:42:20.900]
on your Hadoop cluster or

[00:42:23.099]
in a cloud Caster the idea

[00:42:25.099]
is that you don't have to come

[00:42:27.300]
in I T perspective depend on

[00:42:29.300]
the creation definition of

[00:42:31.500]
cubes in you don't have glue scheme on the

[00:42:33.500]
right which is essentially the definition

[00:42:35.500]
of cubes are xpax you

[00:42:38.099]
should be able to do semen Reid start

[00:42:40.500]
out exploring the data directly

[00:42:42.599]
at

[00:42:45.000]
the scale

[00:42:47.099]
of 1 hundreds of millions of records

[00:42:49.099]
and to enable died in

[00:42:51.300]
a manner that you're hundreds of business

[00:42:53.599]
users are also going to get access to in this should

[00:42:55.599]
not be locked to the Stu the

[00:42:57.599]
small group of the small set of

[00:43:00.099]
of of advanced data

[00:43:02.199]
scientist who are who are capable

[00:43:04.300]
of using the system

[00:43:06.099]
how do you do that actually wear Arcadia

[00:43:08.500]
architectural on Smart

[00:43:10.500]
activation comes in the picture

[00:43:12.699]
via eliminate dependence on cubes buy

[00:43:15.099]
a three-step process if you look on the left

[00:43:17.099]
the

[00:43:19.199]
system Arcadia sitting

[00:43:21.500]
on the Hadoop cluster monitors

[00:43:24.099]
Aquarius that are being fired

[00:43:26.199]
off from an exploratory perspective

[00:43:28.599]
of a conjunction perspective from

[00:43:30.900]
DUI by monitoring

[00:43:33.000]
these crazies you build

[00:43:35.000]
intelligence you build the right

[00:43:37.400]
amount of information to be able to recommend

[00:43:39.500]
right beside

[00:43:41.500]
the bones of the raw data that

[00:43:43.599]
should say it back in the same

[00:43:45.599]
system and what happens with the recommendation

[00:43:47.900]
engine is that it automatically goes and creates

[00:43:50.400]
these leaves the right

[00:43:52.400]
forms of the data that we call

[00:43:54.400]
Emily reviews that has backed

[00:43:56.400]
by hdfs back by back

[00:43:58.500]
by the SD Storage in

[00:44:02.000]
memory so now when the greatest

[00:44:04.300]
comeback again

[00:44:06.000]
or the application gets consumed

[00:44:08.000]
by a large number of users the

[00:44:10.099]
results are cut short by

[00:44:12.099]
accessing this faster did art

[00:44:14.099]
forms are Disney's in Marianna slicing

[00:44:17.500]
and dicing if you generally associate

[00:44:19.900]
with a copy of the data are outside

[00:44:22.400]
your system into the ice at

[00:44:24.400]
work at the bring directly

[00:44:26.599]
to the to the data sitting next

[00:44:28.599]
to the rodeo this

[00:44:30.599]
is really where you eliminate your dependence

[00:44:32.900]
on on on on multiple copies

[00:44:35.199]
of the data and and enable the

[00:44:37.199]
exact same kind of experience

[00:44:39.900]
on a large dataset and

[00:44:42.400]
Benedict open the kind of use

[00:44:44.500]
cases that you can enable for

[00:44:46.699]
your business analyst sorry for your end business partners

[00:44:49.199]
to go against

[00:44:54.300]
access to all data is an important aspect

[00:44:56.500]
of agility you don't want to limit

[00:44:58.699]
your users to think about is

[00:45:01.099]
the data inside my data

[00:45:03.099]
or a nautical order relational system

[00:45:05.199]
what is the data being accessible

[00:45:07.300]
to an SQL engine that is native

[00:45:09.400]
to the hoop system around drill

[00:45:12.300]
Impala or Borax

[00:45:14.500]
or glue baste

[00:45:17.000]
ingestion or is it

[00:45:19.099]
available to spark but it is being processed

[00:45:21.400]
in real time

[00:45:23.300]
are you at are all the data is available

[00:45:25.300]
to a search Babyface net exam sitting

[00:45:28.199]
in a solar bizna elastic index

[00:45:30.500]
or in an SD or

[00:45:32.599]
no sequel system like mango and hbase

[00:45:34.800]
the idea is that you want

[00:45:36.900]
to cut short the multiple-step

[00:45:39.400]
Saturday. Needs to take before it

[00:45:41.500]
can be Blended and combine for

[00:45:43.500]
business inside and connect directly to

[00:45:45.900]
the source of the date that

[00:45:48.099]
you need what what what drives

[00:45:50.099]
agility in an architecture in

[00:45:53.099]
an architecture like this

[00:45:57.199]
the second part is that on applications

[00:45:59.599]
to drive without a big internal application

[00:46:04.800]
for

[00:46:07.300]
your end users as well

[00:46:10.300]
care you combine

[00:46:12.599]
data from real time as well

[00:46:14.599]
as historical systems into the

[00:46:16.800]
same kind of interphase how

[00:46:18.800]
many times have you used your

[00:46:21.099]
alt Tab Key

[00:46:23.900]
combinations on your machine to go between

[00:46:26.099]
two in one that will do your story

[00:46:28.300]
for charting you go to into

[00:46:30.300]
that gives you a little bit of

[00:46:32.300]
access to be her due date

[00:46:34.300]
and then you go to to number 3 which is giving

[00:46:36.400]
you a personal real-time access

[00:46:39.400]
to the screen moving between these

[00:46:41.400]
tools is something that Sandy

[00:46:46.599]
and inside the end-user is trying to get and

[00:46:49.300]
you don't have to do that because the dirt

[00:46:51.300]
with a deed-in-lieu architecture you get access to

[00:46:53.300]
all the way that I do you spell time or historical

[00:46:55.300]
side-by-side cases

[00:46:58.900]
around connected Vehicles

[00:47:01.000]
where you're trying to fit it to me in Suriname

[00:47:03.300]
real-time 7 security yes I'll talk about later

[00:47:05.400]
as well you know then

[00:47:07.400]
goes and does ghost words

[00:47:10.300]
Cedar Lake it starts out initially

[00:47:12.599]
as a as a management

[00:47:14.800]
tools as a management platform to

[00:47:16.800]
just tore the Rotator is becoming

[00:47:18.900]
more and more like a data stream that flows

[00:47:21.000]
through it in real time and

[00:47:23.500]
some of it needs to be accessed and

[00:47:26.000]
leverage in real time for it to

[00:47:28.000]
be actually action about otherwise it

[00:47:30.000]
doesn't have much value as it becomes older

[00:47:36.400]
the next is reality also

[00:47:38.900]
say that debate league does not

[00:47:40.900]
exist in isolation there is data going to

[00:47:43.000]
be in your traditional daily basis and

[00:47:47.099]
you should be able

[00:47:49.099]
to blank the data you should not be required

[00:47:51.199]
to move all the data out of

[00:47:53.199]
those systems of record or doors

[00:47:55.300]
conditional sequel

[00:47:57.300]
databases before you can actually

[00:47:59.400]
enables users to combine

[00:48:01.400]
that with the new screens

[00:48:03.400]
of data being added into a day late

[00:48:05.500]
and being able to do that cross connection

[00:48:07.500]
data plan is an important aspect of

[00:48:09.500]
an evening and evening an application

[00:48:11.800]
experience for the end users

[00:48:16.000]
all this comes together when you are

[00:48:18.000]
combining these different

[00:48:20.099]
views into the data we have time and

[00:48:22.099]
historical and building what

[00:48:24.099]
we call our work flow

[00:48:26.099]
driven applications applications

[00:48:28.699]
there not a single dashboard

[00:48:30.699]
that the user is using just to get a singles

[00:48:32.800]
you into the data but they give you multiple

[00:48:35.099]
views into the day that one video time

[00:48:37.400]
and historical something else coming from

[00:48:39.599]
the space-knight stoop

[00:48:41.599]
to give you an experience that is much more immersive

[00:48:44.199]
and it fits it ships the

[00:48:46.500]
consumption tear from from

[00:48:49.000]
what can be done nearly with

[00:48:51.099]
the NBA basketball game

[00:48:56.599]
this explains all

[00:48:58.699]
this this is Christ to touch upon just

[00:49:01.000]
a small set of four of what what

[00:49:03.099]
are what are Enterprise customers

[00:49:05.099]
are actually doing with the with

[00:49:07.099]
the lack of space.

[00:49:09.900]
Oz from companies like Procter

[00:49:12.400]
& Gamble that they are enabling hundreds

[00:49:15.400]
of grand managers to understand the

[00:49:18.000]
campaign intelligence that during

[00:49:20.199]
the day and then intelligence using micro

[00:49:22.400]
segmentation and a b testing to understand

[00:49:24.800]
how the bands are performing globally or

[00:49:27.599]
for example is NuStar where they're building

[00:49:29.599]
and embedding this kind of an

[00:49:31.599]
application experience into ass ass

[00:49:33.599]
friend that's available

[00:49:35.699]
to different customers for marketing attribution or

[00:49:38.800]
you look at HP Enterprise reflective

[00:49:41.000]
maintenance and understanding the customer

[00:49:43.000]
used engine failure detection off the servers

[00:49:45.800]
and off the devices out in the field

[00:49:47.800]
is the driving Factor behind

[00:49:49.900]
using I need a platform of

[00:49:51.900]
the new the new architecture

[00:49:54.199]
and that scared of potato

[00:49:56.599]
go to eBay and talks about the

[00:49:59.400]
usage of the same platform to

[00:50:01.800]
drive cyber security analysis United

[00:50:04.599]
States of responding to Insider

[00:50:07.099]
threats or two things you can Alice's

[00:50:09.400]
that happens when you combine date of somebody

[00:50:11.599]
else time is it at the story two sources bring

[00:50:14.199]
over to Royal Bank of Canada you

[00:50:16.199]
talking about using

[00:50:18.599]
60 Detroit

[00:50:20.800]
volume that day that they have to capture

[00:50:22.800]
and the combined electronic communication

[00:50:25.500]
to reduce the Regulatory

[00:50:27.699]
and compliance fines

[00:50:30.000]
that's that organisations their size

[00:50:32.099]
are constantly facelift

[00:50:36.400]
find Healthcare Cypress

[00:50:38.500]
Kaiser decor decor use

[00:50:41.000]
cases happen to be around controlling readmission

[00:50:43.400]
risk for your patience for the for the 10

[00:50:45.400]
+ million members that

[00:50:47.500]
are insured by an organization that Kaiser

[00:50:49.599]
Permanente they are utilizing

[00:50:51.800]
the longitudinal data for this patient to

[00:50:54.000]
understand and control the readmission disc

[00:50:56.199]
Whiting better Healthcare experience

[00:50:58.500]
for the end of

[00:51:00.699]
the day examples go beyond this

[00:51:02.900]
light but these are just some representative

[00:51:05.099]
examples across industries of

[00:51:07.099]
what what what platform

[00:51:09.199]
for the water system of inside of the day

[00:51:11.199]
like for an enable

[00:51:13.500]
if you if you are if you

[00:51:15.599]
apply business-to-business

[00:51:18.500]
so glad you

[00:51:20.500]
took it over to it

[00:51:27.000]
this is an interesting guy summarizes

[00:51:31.500]
are or captured water

[00:51:34.300]
what organ organization &

[00:51:37.000]
Gamble actually think so I think

[00:51:39.099]
of these God is distributed to have

[00:51:42.599]
a non cluster architecture

[00:51:44.599]
is really what the work what

[00:51:46.599]
sets it apart from from

[00:51:48.599]
having something that requires you to work

[00:51:51.900]
the system makes it

[00:51:54.400]
makes a lot of sense from an

[00:51:56.400]
architectural perspective and as we saw your

[00:51:58.599]
ad makes a significant amount of impact

[00:52:00.800]
of the year in waiting at the front end of your

[00:52:02.800]
business or you trying to reduce

[00:52:05.099]
the risk for your business video status for

[00:52:07.199]
your customer service come out

[00:52:13.500]
in summary think I want

[00:52:15.599]
to I want to answer this life but just captures

[00:52:18.099]
the three the tiki bitters if

[00:52:20.099]
you think about it think

[00:52:22.199]
about next gen VI and how you're scared

[00:52:24.199]
you'll be I am at these

[00:52:26.300]
larger it's getting late

[00:52:28.300]
MP3 systems are are are collecting

[00:52:30.699]
more and more I will send you

[00:52:33.099]
want to do you want to not fit

[00:52:36.000]
the same model that has skid loader

[00:52:38.099]
has essentially gone away

[00:52:40.500]
from agility around building this is

[00:52:42.900]
multiple copies and cute

[00:52:44.900]
animals

[00:52:50.000]
experience application that allows you

[00:52:52.199]
to take action know just know

[00:52:54.800]
just to explore data

[00:52:57.400]
for the sake of exploding and do

[00:52:59.400]
that why having as a simplified

[00:53:01.400]
architecture that eliminates a lot of the complexity

[00:53:13.500]
the execution sitting next to work

[00:53:15.599]
today at 6

[00:53:17.300]
with that are the transition over back

[00:53:19.300]
to Steve to attention

[00:53:25.400]
I got thanks for that over to you Priyanka so

[00:53:27.500]
we've got some time here for the

[00:53:29.599]
questions of come in and

[00:53:33.300]
as I'm doing that here we've also got

[00:53:35.300]
some links to some of bourses research

[00:53:37.599]
in addition to the Forester

[00:53:39.800]
way that he spoke about some other research talking

[00:53:42.199]
about scaling business intelligence platform

[00:53:45.900]
so the first question looks

[00:53:47.900]
like Boris is my day going for you in

[00:53:51.000]
your research you talk about and

[00:53:53.000]
you talk a little bit today about the difference between custard

[00:53:55.400]
and highly parallel distributed

[00:53:57.500]
be I can you spend a little

[00:53:59.500]
more time talking about what that difference is and

[00:54:01.500]
how it's different yeah

[00:54:04.000]
I think they have definitely

[00:54:06.000]
a couple

[00:54:09.300]
of years because whenever we talk about with

[00:54:11.300]
the Duke class there's all this today are

[00:54:13.599]
a highly parallel and distributed out

[00:54:16.199]
of class there's but I think in the Old

[00:54:18.300]
Days Inn in a few people still think of a

[00:54:20.900]
class there's is basically load balancing

[00:54:23.400]
conglomeration

[00:54:25.400]
of Underwood's where

[00:54:27.699]
each node may be running a

[00:54:30.099]
full set of instructions

[00:54:32.099]
basically the entire program

[00:54:35.199]
but it is a

[00:54:37.400]
load balancing know

[00:54:40.000]
it just kind of distributes distributes

[00:54:42.400]
talks to at least be

[00:54:45.900]
talked about in carnival

[00:54:48.500]
natively architected massively

[00:54:50.900]
parallel processing

[00:54:52.900]
architecture of the software natively

[00:54:55.300]
is designed to be highly paralyzed

[00:54:58.099]
where each note only

[00:55:00.400]
performs its own task

[00:55:02.500]
I does opposed to running through

[00:55:04.500]
the whole program it's only performs a

[00:55:06.500]
small part of the desk and there are specialized

[00:55:09.000]
knowledge that you are angry gation

[00:55:11.099]
over the top so basically Academy

[00:55:13.599]
download multiple rate variations of map

[00:55:15.900]
and reduce type

[00:55:18.000]
of software so basically

[00:55:20.000]
at the end of the day.

[00:55:22.800]
Kind of a parallel highly distributed

[00:55:25.300]
architecture you

[00:55:27.699]
know has has Mike much higher theoretical

[00:55:30.300]
limit when having your nose

[00:55:32.400]
can you scale out to versus the older

[00:55:35.199]
generation clusters

[00:55:37.800]
got it makes sense thank you

[00:55:40.900]
cool guess you had a questions here in if

[00:55:42.900]
anyone else has them please answer them in if

[00:55:45.000]
we don't get to we can follow up with you directly as well

[00:55:47.199]
as Priyanka

[00:55:49.500]
start on which is what are some of the use

[00:55:51.599]
cases you explain song but talk

[00:55:53.900]
a little bit more about needing to get access

[00:55:56.099]
to all the detailed data are there are other

[00:55:58.099]
examples for that's really important

[00:56:02.000]
yes I can I can I can actually

[00:56:04.099]
go down and do a few few

[00:56:06.300]
details that make it very clear I

[00:56:09.000]
was like let me down let me know where where

[00:56:13.000]
where is detecting

[00:56:15.500]
inside a text responding to incidents

[00:56:17.699]
in real time and to put

[00:56:19.699]
on Forensic analysis on your network

[00:56:21.800]
CR users your endpoint is

[00:56:24.500]
one where Larry

[00:56:26.500]
just summarizing out what

[00:56:28.900]
is the number of of incidence

[00:56:31.300]
of tax that occurred in my network will

[00:56:33.300]
not will not get to the root of the

[00:56:35.400]
problem now you have to get to the individual

[00:56:37.599]
behavior analysis with

[00:56:40.500]
that they are doing good

[00:56:42.599]
and

[00:56:47.000]
having access to the data in

[00:56:49.199]
real-time really McCarthy lose

[00:56:51.300]
his cases another example

[00:56:53.300]
is with with

[00:57:01.900]
the granularity is is

[00:57:03.900]
the fee for starting a

[00:57:06.099]
certain vehicle or truck that

[00:57:08.099]
is very sending iot

[00:57:11.699]
beacons back to the structure

[00:57:14.400]
you have to be able to look at the

[00:57:16.500]
behavior of that individual track to be able to

[00:57:18.500]
say that is on

[00:57:27.300]
that pretty clear lake that's another

[00:57:29.400]
example on the iot side and

[00:57:32.099]
7202

[00:57:39.500]
is that the try to catch violations

[00:57:44.199]
in real time or

[00:57:46.500]
what happens is when you try to combine this

[00:57:48.900]
with granular communication information around

[00:57:51.800]
the time a trade is made what

[00:57:53.900]
other information is that

[00:57:56.099]
trailer having access

[00:57:58.099]
to and then being able to combine the actual

[00:58:00.599]
trade cigarettes

[00:58:01.900]
with that other guy I need the information is

[00:58:04.800]
what enables you to catch much broader

[00:58:07.400]
set of fairness or or or potentially

[00:58:10.199]
fraudulent behavior and actually an

[00:58:12.300]
example that again being scared

[00:58:14.699]
really really is

[00:58:17.099]
required if you have to if

[00:58:19.199]
you did if you are to get any meaningful

[00:58:21.300]
inside out of it

[00:58:24.099]
very good example thank you in

[00:58:27.500]
a minute or two left here a couple other questions and

[00:58:30.199]
maybe these are quick can Arcadia

[00:58:32.300]
run on cloud systems talked

[00:58:34.500]
a lot about to do double click

[00:58:36.500]
on other systems and how the Truck

[00:58:38.599]
Works Chuck

[00:58:41.699]
yes I did

[00:58:43.800]
it does run in the in

[00:58:46.000]
the public clouds across across

[00:58:48.599]
all the available public clouds as well as

[00:58:50.699]
in a hybrid in

[00:58:52.900]
line I meant the the Bainbridge

[00:58:55.099]
Reebok in the in the clouds as we are

[00:58:57.199]
we are using the data native

[00:58:59.400]
aspect of our technology and reading

[00:59:01.800]
letter directly from the object stores

[00:59:04.099]
from the from the roster for the day. See if you

[00:59:06.099]
think of Amazon environment that

[00:59:08.300]
would be being able to keep

[00:59:10.699]
the data in S3 and

[00:59:12.900]
still being able to do if you sing

[00:59:14.900]
and I sing some of the eye and visual analysis

[00:59:16.900]
perspective that's how we enable

[00:59:19.599]
that experience in the cloud technology

[00:59:22.000]
that the MP TBI technology is

[00:59:24.300]
is is Big so that

[00:59:26.300]
it's bad and it's bad at all whether you're running

[00:59:28.500]
it thanks for including

[00:59:30.599]
lines

[00:59:33.099]
very good okay

[00:59:35.400]
try

[00:59:37.500]
one question left anything Boris

[00:59:39.900]
that you like that I know we talked a little

[00:59:42.000]
bit about some of the use cases for detailed

[00:59:44.199]
at any other things you thought about that you like to share

[00:59:46.199]
the audience when do the last question

[00:59:48.400]
on

[00:59:51.900]
cybersecurity I think

[00:59:54.000]
of examples like that

[00:59:56.199]
just and financial services when

[00:59:58.300]
looking at the end

[01:00:00.800]
of the balances or any kind of aggregation

[01:00:03.099]
will definitely know what help

[01:00:05.099]
you we dial to fraud if

[01:00:07.400]
you transfer a million dollars

[01:00:09.400]
in the end of the day

[01:00:11.500]
0 but it's really the detail

[01:00:13.500]
transactions are you need to be

[01:00:15.699]
at analyzing another example

[01:00:18.199]
if we run into all the time is

[01:00:20.300]
when you look at a 360 degree view

[01:00:22.400]
of a customer you

[01:00:24.400]
a customer service interaction

[01:00:26.599]
record may have a button check

[01:00:28.900]
that says that this is a a satisfied

[01:00:31.300]
customer because last time you interacted

[01:00:33.900]
with that customer to call something of the customer

[01:00:36.099]
satisfied but then

[01:00:38.099]
when you bring in social media you

[01:00:40.699]
know over the last 24 hours at customer may

[01:00:42.800]
ever put something on Facebook you

[01:00:45.900]
know express your frustrations with your

[01:00:47.900]
particular product so again

[01:00:50.300]
if you net both out is

[01:00:52.800]
that customer sentiment neutral

[01:00:55.199]
because there was one negative and one doesn't

[01:00:59.199]
really tell you the full picture really

[01:01:01.199]
need to understand the root cause we're behind

[01:01:03.400]
that negative sentiment from

[01:01:05.500]
social media examples

[01:01:08.400]
like I'm

[01:01:11.099]
guessing your time and financial services cuz

[01:01:13.300]
you Oughta thoughts on that

[01:01:15.900]
pool table we're actually just

[01:01:17.900]
a minute at the top of the hour so there's

[01:01:20.099]
a couple of questions will follow up directly with those

[01:01:22.199]
folks thank you very much for

[01:01:24.400]
us for joining us today is our gas we really

[01:01:26.500]
appreciate your time I think you Priyanka

[01:01:28.699]
is wealthy overview and thank you to everyone

[01:01:30.800]
in the audience for joining today hopefully

[01:01:32.800]
you got some value in education from today

[01:01:34.900]
and will follow up with some more of this research

[01:01:36.900]
thank you very much and everyone have a great day