In search of alpha

[00:00:00.299]
everybody I think we're ready to get started

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and pay personal

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thanks for joining and welcome to Credit Associates

[00:00:07.099]
Market structure update we

[00:00:09.699]
will be continuing our coverage of

[00:00:11.800]
the alternative data space and

[00:00:13.800]
more specifically we're going to look beyond the definition

[00:00:16.300]
and categorization of alternative

[00:00:18.500]
data and get more effectively putting that

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data into use managing

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director hair Greenwich Associates I'm

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responsible for the market structure and Technology

[00:00:28.600]
practice and I'll be the host today

[00:00:30.600]
I'm going to be joined by a

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great panel at panels going to be moderated

[00:00:35.299]
by Kevin who's our head of research

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within the market structure and Technology

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practice is going to be joined by

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Paul last-minute who is the practice

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lead an advisor for financial

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services are at Arcadia data

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Adam Sussman head

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of Market structure and liquidity Partnerships

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at liquid man and biotic

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senior director Global alliances

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and great

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paddle I'm sure it's going

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to be very interesting in

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just a couple of housekeeping

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items if you're hearing an

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echo in your ears are ringing please

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check the audio settings make sure the telephone

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is selected so

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you're not getting duplicate audio

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feed coming through the

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presentation questions please

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feel free to type them in the dialog box will

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try to answer as many as we can during the webinar

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or certainly at the end when

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when time allows the Q&A if

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we don't get to them absolutely

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come back to you post

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the webinar in Iraq

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ban if you have additional questions

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after the webinar we're going to give you

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the contact details for the participants

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please feel free to reach out directly

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I think we also have a few polling questions in

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the webinar today so please feel

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free to provide input when

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when when they come up on Friday and

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then finally I'm as always encourage

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you to fill out the survey following the event

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always looking to improve and we certainly

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want to make sure we cover those

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areas and our future webinar that are

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most interest to you

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our clients and friends in the industry so

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before I turn it over to Kevin

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in the panel let me pick

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up a minute just to plug Granite Associates

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in the market structure practice in particular in

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the practice we here

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are focused on everything the changes

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in the ecosystem of institutional

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Financial Services whether that be changes

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and that how they

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may impact the market

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participants and those folks

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that glue the buy and sell side together differentiator

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in here tremendous amount of

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research with

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the market participant in general and

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this year I'm going to ask about three

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and a half million questions to 65,000

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Market participants on the

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phone is the basis of the

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data that then informed our analysis

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and opinion so we think we've

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got a great perspective and

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it's based on data and

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the market with

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that with that marketing blurb out of the way

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let's let's push forward into the end

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of the presentation today

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first

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day of talking about alternative

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data we are we

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are Awash in in Dade everyday

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see some of the components on

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the slide everyday with creating

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2 1/2 quintillion

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bytes of data so both posted

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on how many which quintillion and

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1802 that 2.5

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and you'll get through a quintillion start

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stacking up those Blu-ray disc and get the

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Eiffel Towers fall think

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of how much data that is that we create

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and then some specific examples from

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their Bank of Walmart

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near Walmart processing a

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million customer transactions every

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hour right literally

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every piece of media from

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around the world whether that be in

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print in radio

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was that be social and make that

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available in America

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I'm in does that for free amended

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amount of information V in there and

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look at the Facebook they're

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uploading over 400 million photos every

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day I have

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to have Twitter versus the New York Times Twitter

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today is going to post more

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words in their New York Times has printed

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in the last 50 years think

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about that for a fact and

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what that means is a tremendous

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business out there and you'll see that there

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are projections that this is going to gross I fold

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over the coming

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year it's a big business today it's

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going to be a much as we

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go forward and we think it's going to be

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a very

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important role in the investment on

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the investment decision making

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oh wait we try to make a very basic

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do you hear of the alternative alternative

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data ecosystem and

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who are the market participants there

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the least amount

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of data Originators and

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these are everyone from you may

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you may have seen traditional data

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originated those

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type of things it really has

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turned into a morph into anybody

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that has an exhaust from

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their day-to-day operations and

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another component

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of that ecosystem who are

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the providers of

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the Big Data platform those folks

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that can take the data in

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normalize it and make it applicable

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in useful for Market participants

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and I want that happens you need the subject

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matter experts those folks that not

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only can identify the the alternative

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data but

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understand what it means and how

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it impacts an investment decision

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and then it gets all the way around to the beneficiaries

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whether that be the asset manager is all

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the way down to individual

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investors like all of us here

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today and

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I'm trying to put together a bit of a an

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ecosystem view the

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intense Harrison to be exhausted

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because quite honestly I

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shut this down by probably

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80% to get the name on

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this flight the

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attention was really just a show where

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we think some of those categories

[00:06:50.899]
are in the alternative ecosystem

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in in to show some of the names

[00:06:56.100]
that are out here something you know

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right so

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you see Arcadia is in there but they're Bloomberg

[00:07:02.899]
in fact that and CR another.

[00:07:06.000]
& Bradstreet on here but you see

[00:07:08.000]
also other than you maybe have

[00:07:10.000]
never heard of and these are all

[00:07:12.100]
very active names in the alternative date

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of space let's

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let's turn it over to our first question

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I think I first pull question is going to

[00:07:23.500]
ask for the participants

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here we

[00:07:27.699]
do you think you fit in

[00:07:29.699]
that alternative data ecosystem

[00:07:33.500]
thanks

[00:07:37.000]
for getting us started and kind

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of like the same question

[00:07:56.399]
Akai Kevin an audience thanks

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we're clearly

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a source of the

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state of it but you'd call hear a date originator

[00:08:06.100]
Aaron we provide data

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and business intelligence on both public

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and private companies and have

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unique transaction data

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we also have proprietary scores

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are developed by around 11:16 most

[00:08:19.800]
of which focused on the

[00:08:21.800]
value of Trade Credit transactions

[00:08:24.000]
payment behaviors of public and private companies

[00:08:26.399]
information about business

[00:08:28.399]
relationships businesses supply chain

[00:08:30.699]
that information we found

[00:08:32.799]
especially in the The Evolution

[00:08:35.100]
here of alternative data has become

[00:08:37.100]
valuable to other

[00:08:39.700]
by sides ability to

[00:08:41.700]
provide predictive signals within

[00:08:43.700]
their algorithm so that's kind of how the data is

[00:08:45.700]
used today Adam

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from liquid knots perspective where do you sit in this

[00:08:51.399]
ecosystem Dad

[00:08:54.799]
thanks Kevin liquid net

[00:08:56.799]
you know being a broker-dealer and

[00:08:58.799]
having a front-end interface

[00:09:00.899]
that Services

[00:09:02.899]
800 by

[00:09:05.200]
side firms globally

[00:09:07.200]
would sit somewhere between you

[00:09:09.500]
know a beneficiary and a

[00:09:11.899]
in a big data provide

[00:09:15.899]
better we acquired a company

[00:09:18.100]
earlier this year called switch

[00:09:20.200]
and work extensively with london-based

[00:09:23.299]
trading desks

[00:09:25.299]
to be able to transform data

[00:09:28.500]
a combination of Market data fundamental

[00:09:31.000]
data and alternative data into

[00:09:33.100]
actionable insights and

[00:09:35.200]
then integrate those inside seamlessly

[00:09:37.600]
into the end of the year.

[00:09:40.899]
Acquisition you know we've

[00:09:43.000]
acquired the ability of ingest

[00:09:45.399]
this all

[00:09:48.000]
kinds of data including alternative data

[00:09:50.100]
and then process

[00:09:52.500]
in such a way to make it relevant for

[00:09:54.600]
for our customers true

[00:09:59.399]
beneficiary of that data to

[00:10:04.799]
but yeah I

[00:10:08.600]
really

[00:10:13.299]
have to be hard with

[00:10:15.299]
everybody Arcadia

[00:10:18.000]
data we are on the technology

[00:10:20.399]
side but we do the date of visualization

[00:10:22.600]
so what we do is we enable

[00:10:24.799]
the end-users the beneficiaries

[00:10:27.299]
the subject matter expert

[00:10:28.799]
tell the visual eyes to be able to see

[00:10:31.000]
the day that I put together and

[00:10:33.700]
to really get the the value

[00:10:35.899]
out of it and all its granular Lori

[00:10:38.200]
are

[00:10:40.899]
the results of the Paul,

[00:10:43.700]
glad to see our audience is a pretty good mix

[00:10:45.799]
of the ecosystem as well but

[00:10:52.899]
a good number of a resonator on

[00:10:55.399]
big data analytics platform the 1st

[00:10:58.799]
which is all

[00:11:05.000]
going to take us through a good

[00:11:07.000]
visual youth kudelka to

[00:11:09.000]
feel what we talk about these things they

[00:11:11.200]
always feel a bit fluffy

[00:11:13.399]
and in the clouds no pun

[00:11:15.399]
intended really understand

[00:11:17.700]
how this can be used in practice

[00:11:20.000]
we think we pick would be helpful

[00:11:22.200]
for the audience and

[00:11:32.200]
get

[00:11:34.299]
everybody to think about what

[00:11:36.799]
alternative date is what they're using it for and

[00:11:39.700]
really to get the conversation going later

[00:11:42.200]
on and I'll

[00:11:45.299]
look at an example of an unconventional

[00:11:47.399]
with alternative data

[00:11:49.399]
so it's really about look

[00:11:52.000]
at your conventional portfolio analysis in

[00:11:54.299]
this example if you're looking

[00:11:56.299]
at retail stores or or something like

[00:11:58.299]
that and if no remote

[00:12:00.399]
standard performance chart you

[00:12:02.899]
looking for for you

[00:12:05.000]
know what's the trade in value and that's all conventional

[00:12:07.799]
apple bloom again here now

[00:12:09.899]
is say okay how can you enrich

[00:12:11.899]
that and you enrich that

[00:12:14.000]
with alternative data sets that's the

[00:12:16.100]
story here so I'm looking again at

[00:12:18.100]
a retail stores in a lot

[00:12:23.299]
of it could be looking at different

[00:12:29.000]
types of data points

[00:12:31.600]
or beta acids and

[00:12:33.799]
tying them together to

[00:12:36.799]
location and then

[00:12:38.799]
you're able to from that really

[00:12:41.500]
be able to see Apples to Apples when you're going from location

[00:12:43.799]
location location that's pretty important I

[00:12:46.299]
think I'm looking at another one

[00:12:48.700]
is going past the

[00:12:50.700]
show in this

[00:12:52.700]
case scenario

[00:12:55.700]
I want to buy a little bit looking

[00:12:57.700]
at this sort of data that that Dun

[00:12:59.700]
& Bradstreet has an account

[00:13:01.799]
with the scenario right so I'm picking up

[00:13:03.899]
a portfolio manager they have fun I'll

[00:13:06.100]
call it a bucket of opportunities to read your

[00:13:08.600]
water companies that they may want to invested

[00:13:11.200]
and from that of

[00:13:13.899]
course you have your fundamental due diligence

[00:13:15.899]
is performed that's right

[00:13:18.600]
here that's your candy bar charts

[00:13:20.700]
and then line charts and things like that but

[00:13:23.600]
the question after that is what

[00:13:25.799]
else can inform the decision-making

[00:13:28.000]
so one of it is

[00:13:30.100]
you know how healthy is the company I'm

[00:13:32.200]
looking at commercial credit scores Financial

[00:13:34.700]
stress what's the health of the

[00:13:36.700]
suppliers are there are

[00:13:38.700]
there any dependencies of the suppliers are

[00:13:41.899]
able to deliver and then the products

[00:13:44.299]
that this company is very good at you

[00:13:46.700]
know starts to fall behind looking

[00:13:51.100]
at other investors are you

[00:13:57.600]
first of the table or

[00:13:59.899]
a lot of people looking at it and so I just

[00:14:01.899]
came up with this network's chart that shows

[00:14:05.299]
that what is the type

[00:14:07.500]
of stuff that. Dun & Bradstreet has and

[00:14:10.500]
it'll take another look it almost gives

[00:14:12.799]
few more examples of how you might

[00:14:14.799]
be able to visualize this again these

[00:14:16.799]
are just ideas just get the ideas

[00:14:19.299]
going so you don't look at the idea

[00:14:21.299]
of a company help that we

[00:14:23.299]
talked about a little bit earlier

[00:14:25.700]
looking at the company

[00:14:27.899]
relationships substitute

[00:14:30.399]
Sherry I'll make it to the show The Beneficial

[00:14:32.899]
owners what's the percent of it.

[00:14:35.200]
So the thing and going

[00:14:37.200]
back to the other idea where

[00:14:44.700]
are they located I

[00:14:48.600]
was just looking at my

[00:14:54.500]
own I looked at in touch this would be

[00:14:56.500]
interesting this might provide

[00:14:58.500]
additional Insight so it just

[00:15:00.600]
shows the idea of being able to explore

[00:15:03.200]
and come up with your own insides based

[00:15:05.200]
on the data.

[00:15:08.500]
I was

[00:15:15.100]
going to pick up on you as

[00:15:19.700]
one of the more interesting factors that we

[00:15:22.000]
found that the data

[00:15:24.299]
that we provide both alive

[00:15:28.000]
and active and in Trials what we found this

[00:15:30.000]
interesting that once you put

[00:15:32.100]
it in the hands of the real

[00:15:34.100]
smart quance

[00:15:36.200]
that works with the hedge funds and some of the other portfolio

[00:15:38.600]
managers it's exciting

[00:15:42.100]
to see how they come up with with their

[00:15:44.100]
own ideas

[00:15:46.200]
as you just a articulated that

[00:15:48.799]
maybe we are nobody else is ever saw that but I

[00:15:50.799]
think that's the value here when you look at what

[00:15:53.000]
the meaning of alternative data is regardless

[00:15:55.200]
of what kind of data every

[00:15:58.200]
every end user

[00:16:00.200]
has their own value Ford

[00:16:02.299]
based on their strategy based on

[00:16:04.299]
on the kind of algorithms day they

[00:16:06.500]
deploy based on how smart they are not

[00:16:08.799]
whatever their experience might be and

[00:16:11.200]
I think that is a opener

[00:16:14.500]
for us here and

[00:16:16.500]
you probably

[00:16:19.200]
see it too I might be interested

[00:16:21.399]
in hearing what from Adams

[00:16:23.500]
point of view with you see the same kind of think

[00:16:25.600]
that I'm from your end uses about

[00:16:27.700]
to a creative aspect of this

[00:16:31.299]
yeah I know that's it that's a great question to

[00:16:33.299]
any other way this is the kind

[00:16:35.399]
of use cases that we found and

[00:16:39.399]
the applications for alternative

[00:16:41.899]
data the way that

[00:16:43.899]
I would kind of split it out as

[00:16:45.899]
between the single stock

[00:16:48.399]
folks out

[00:16:50.500]
there and and inverses programmatic

[00:16:52.799]
trading and then

[00:16:54.799]
also thinking about it along the lines

[00:16:56.899]
of you know discretionary meeting

[00:16:59.799]
that there's a human deeply involved in the investment

[00:17:02.200]
process vs. systemic

[00:17:04.799]
in the type of data as

[00:17:06.799]
well you know how the

[00:17:08.900]
data gets fed into that those processes

[00:17:11.299]
varies widely between

[00:17:13.799]
you know those kind of four different

[00:17:15.799]
those four

[00:17:17.900]
different in

[00:17:20.000]
a ways of waste

[00:17:29.000]
and now it is the Traders

[00:17:31.000]
are acting more and

[00:17:33.000]
more as it's

[00:17:35.799]
kind of like substitutes for what

[00:17:37.900]
the traditional sales Trader used

[00:17:39.900]
to do I told you know as we

[00:17:42.000]
all know that you know that the compact

[00:17:44.299]
among the poles brackets in the decline

[00:17:46.299]
of November or folks

[00:17:48.500]
Manning and so has

[00:17:52.500]
become the person that the portfolio

[00:17:54.900]
manager looks to for

[00:17:57.200]
what's happening in the marketplace so you

[00:17:59.900]
know that's the kind of data and

[00:18:02.000]
the way that they use that day that you know is

[00:18:04.299]
probably about the

[00:18:06.299]
investment process because you

[00:18:09.000]
know the p.m. is already gone through that right

[00:18:11.099]
now what's

[00:18:14.000]
happening in the market did I

[00:18:16.099]
might need to know in

[00:18:18.099]
order to kind of sides

[00:18:20.299]
that I want to get done or the

[00:18:22.299]
urgency of

[00:18:24.400]
completing the order and

[00:18:26.599]
that's where it gets back to you know

[00:18:29.000]
give it back to

[00:18:31.099]
you know that I had a smorgasbord

[00:18:33.500]
of data providers out there would

[00:18:35.500]
be more along the lines of in

[00:18:37.500]
a real time real

[00:18:40.299]
time alternate day too maybe that's

[00:18:42.299]
better that you know sentiment analysis or

[00:18:46.200]
you know the near time media

[00:18:48.700]
content from gdl things like that

[00:18:50.700]
and the process that uses

[00:18:54.700]
his to say we're going to

[00:18:56.700]
we're going to look for abnormal

[00:18:58.700]
changes in the actual behavior of the stock

[00:19:01.099]
that's the first thing to look at right and

[00:19:04.299]
then you know that that alternate

[00:19:06.700]
data is is explanatory right

[00:19:09.700]
so then we try to say okay there's some

[00:19:11.799]
there's something happening and you need to pay

[00:19:13.799]
attention now let you

[00:19:15.799]
know help you find any explanation

[00:19:17.900]
for why that happened

[00:19:20.000]
does a slightly different use case for what

[00:19:22.099]
we're at what we're going through here but I think it makes sense that

[00:19:24.400]
you know on the trading desk you're going

[00:19:26.400]
to be looking for more real-time data when

[00:19:28.500]
you're going up to the portfolio management

[00:19:30.599]
level as you're all you're probably looking at

[00:19:32.700]
in a longer time Horizons

[00:19:34.700]
when it comes to the kind of data

[00:19:36.799]
that you're looking at

[00:19:43.700]
no it's okay. I was just going to say anything about

[00:19:45.700]
this could be to you to you to you as well

[00:19:47.700]
but you know you finding out the

[00:19:49.700]
Traders are quickly getting getting

[00:19:51.700]
the joke or it's in the valley or

[00:19:53.700]
is it feel like it's been a steep learning curve group

[00:19:57.200]
about that maybe has been doing what they do

[00:19:59.400]
to the family of her for a long time you

[00:20:02.700]
know what I mean by

[00:20:19.799]
side is a very heterogeneous

[00:20:22.299]
population right you got up all

[00:20:24.400]
sorts of different kinds of personalities and

[00:20:26.500]
work clothes so it's hard to

[00:20:28.500]
paint them with the broad brush-stroke

[00:20:30.500]
but I would say that everybody

[00:20:33.900]
is has looked at

[00:20:35.900]
things like in our social at the social

[00:20:38.099]
velocity index in for some people

[00:20:40.200]
it's been relevant in for other people

[00:20:42.700]
you know annoyed so

[00:20:45.000]
I don't know if it's still like about me know getting

[00:20:47.000]
the Joker not as to whether he knows more

[00:20:49.200]
about you know how much

[00:20:51.299]
does it really help than

[00:20:53.299]
an Executor on the objectives of

[00:20:55.299]
their portfolio manager if you're buying

[00:20:57.400]
a hold you know shop

[00:20:59.500]
and you're holding names for 5 10

[00:21:01.500]
years and then it's just not it's just not

[00:21:03.700]
going to be alright in

[00:21:06.099]
the portfolio manager may not care that Trump

[00:21:08.599]
has tweeted something just

[00:21:11.400]
be annoyed by knowing that so

[00:21:14.000]
that other shops where

[00:21:16.200]
they might they might still be lonely

[00:21:18.700]
but they're training around position

[00:21:20.900]
right so they're going to hold

[00:21:23.000]
a stock for a long period of time but they

[00:21:25.000]
might trade around it and thighs up inside

[00:21:27.099]
down depending on market conditions that's

[00:21:29.700]
where you start to get into work

[00:21:32.299]
clothes where this kind of data becomes

[00:21:34.900]
irrelevant and you know really helps

[00:21:37.500]
them add performance to the to

[00:21:39.599]
the portfolio move

[00:21:41.900]
to an even more you know higher

[00:21:44.299]
turnover shop like a hedge fund Trader

[00:21:46.400]
actually had some discretion over

[00:21:48.599]
the size of the of the

[00:21:50.599]
position you know then now

[00:21:52.700]
your get you know to the real Cutting

[00:21:55.299]
Edge Xbox and

[00:22:00.200]
from from Donna Bradford perspective I

[00:22:02.200]
think we

[00:22:04.599]
indicated

[00:22:07.099]
more from the with the the

[00:22:09.799]
duck wanting to date at

[00:22:11.900]
the big data analyst

[00:22:14.599]
at the shop so they undoubtedly

[00:22:17.700]
will get the joke they

[00:22:20.299]
are so immersed in this

[00:22:22.500]
that once you get them to

[00:22:24.799]
understand that

[00:22:26.700]
all big data is not equal alternative

[00:22:29.299]
day does not equal and social media date

[00:22:31.500]
is different from geolocation

[00:22:33.700]
data the data

[00:22:36.000]
that we provide which is on a business

[00:22:38.000]
performance payment Behavior things like that

[00:22:40.099]
very quickly the eyes

[00:22:42.200]
open oh yeah that could be something

[00:22:44.299]
I never considered because honestly when you look

[00:22:46.400]
at the the Twitter than the square

[00:22:48.900]
spaces that's almost old knows

[00:22:51.099]
that the dead has had some Muse

[00:22:53.099]
some people don't actually think that there's

[00:22:55.400]
other financial data business performance

[00:22:57.400]
data once you get you a

[00:22:59.500]
present that it's very quickly absorb

[00:23:01.700]
and then the quads would want to dig deep

[00:23:03.900]
into it and try to understand where

[00:23:06.000]
some of the correlations are and we've

[00:23:08.099]
done a lot of that ourselves but they come up with their

[00:23:10.099]
own often and then or ask us

[00:23:12.099]
and then interative process to make

[00:23:14.099]
some but hey can you do this hey what

[00:23:16.200]
if I do this can you do that so yeah

[00:23:18.500]
because I think we're dealing with the quants

[00:23:20.599]
they're all over it and

[00:23:23.299]
I think that I think the

[00:23:25.299]
two you up again Paul I think the

[00:23:27.299]
kind of tools in Arcadia provides really

[00:23:29.500]
the way that that they want to look at things that

[00:23:31.700]
the charts

[00:23:34.500]
and graphs that you show here just on these couple

[00:23:37.299]
of slides is it.

[00:23:39.400]
That's the way that they used to before

[00:23:49.799]
okay yeah thanks X Factor that

[00:23:51.799]
we

[00:23:54.099]
see some questions talking about you

[00:23:56.500]
know providing access natural language

[00:23:58.599]
processing and so

[00:24:00.599]
forth with your questions

[00:24:02.599]
play question by next

[00:24:04.900]
talking about you know how this all right

[00:24:07.099]
and so we have Twitter

[00:24:15.299]
feeds things like that and a

[00:24:17.700]
lot of it looked at it putting into

[00:24:19.700]
it they don't like you what the data repository

[00:24:22.799]
where you put in

[00:24:25.500]
a native format rifle pre-processing

[00:24:28.099]
so glad they liked it a lot more

[00:24:30.099]
to it but

[00:24:32.099]
I'll just leave it at that

[00:24:33.799]
traditionally what you

[00:24:35.799]
have is you have these layers on top of it so yeah if

[00:24:37.799]
you extract the data warehouse putting

[00:24:40.299]
into these cubes putting it into

[00:24:42.400]
a data visualization server so

[00:24:44.500]
that people can get

[00:24:46.599]
access to it to make all

[00:24:48.700]
this work you know you're

[00:24:50.799]
going from this layered

[00:24:52.799]
architecture and

[00:24:56.500]
going directly to you where the data

[00:24:58.500]
results so I'm taking that same story

[00:25:00.500]
got all these data sources and they're

[00:25:02.500]
going into that they don't like the difference

[00:25:04.900]
fear is that you're

[00:25:07.000]
able to do your visual analytics.

[00:25:09.299]
But if I

[00:25:11.299]
was just referring to is you're actually going

[00:25:13.799]
directly to that date or you're inside

[00:25:15.799]
it and you're getting direct

[00:25:18.000]
access to it to do the official analytics

[00:25:20.400]
on and it's from that they

[00:25:22.599]
are able to do have the web-based interface

[00:25:25.299]
and build out these customized

[00:25:27.299]
application directly

[00:25:30.500]
on that data or just give me a little background

[00:25:33.799]
download that you see or things that I did

[00:25:35.900]
on my own just by exploring

[00:25:38.299]
looking at the data trying to figure out what the story

[00:25:40.400]
would be and if I

[00:25:42.400]
didn't have a data scientist working

[00:25:44.799]
on it was really you

[00:25:47.099]
Billy just to be able to explore it and I'm

[00:25:49.200]
not a I'm not a programmer myself

[00:25:52.099]
that's what he

[00:25:56.799]
was talking about he's asking how

[00:25:58.799]
does Arcadia data provide natural language

[00:26:01.099]
processing capabilities before

[00:26:03.599]
the visualization and also about

[00:26:05.799]
looking at strange so

[00:26:08.000]
going back to this the natural

[00:26:10.000]
language processing and so it was all that is being

[00:26:12.099]
done at the lake level or you could see

[00:26:14.200]
it as a source of data so you

[00:26:16.400]
have some machine learning algorithms things

[00:26:19.000]
like that that are going through and trying to figure out okay

[00:26:21.400]
what is the sentiment what is what are people

[00:26:23.700]
thinking about certain certain

[00:26:25.700]
brand that is to me a

[00:26:27.900]
data source and that were able

[00:26:29.900]
to access it and then you're able to make some sense

[00:26:31.900]
out of it and join it with other things

[00:26:33.900]
so we are not processing

[00:26:36.599]
in that sense we are

[00:26:38.900]
able to take that data unstructured

[00:26:41.400]
data real-time data historical

[00:26:43.599]
data and be able to allow

[00:26:45.799]
the end-user the subject matter expert

[00:26:48.099]
to really be able to see it and look at it

[00:26:52.900]
that's quite all the great day to hear

[00:26:58.500]
from the results of the study that we not

[00:27:01.099]
too long ago trying to understand what people

[00:27:03.700]
are actively using

[00:27:06.099]
today one of the findings

[00:27:08.299]
from here with that social media date of the

[00:27:12.400]
first thing that they think it's

[00:27:14.700]
pretty common at this point.

[00:27:16.700]
The manager can Edge find

[00:27:18.799]
so much though it's feeling

[00:27:20.900]
a little less alternative and more of

[00:27:23.000]
a normal normal course of business rate

[00:27:29.299]
other examples of where I'm

[00:27:31.400]
alternative data what type of alternative geolocation

[00:27:43.500]
satellite imagery

[00:27:52.900]
Spice in your life and

[00:27:55.000]
many of them probably more than you realize right

[00:27:57.500]
everything from your phone to quite possibly

[00:27:59.799]
your car I know my oven has a

[00:28:01.900]
Wi-Fi connection right so it's just it's really

[00:28:04.099]
going on and on and on and Illustrated

[00:28:09.500]
very well through Paul's demo

[00:28:11.799]
is finding that

[00:28:13.900]
data or realizing it existed non-trivial

[00:28:17.400]
on however the real tricky

[00:28:19.500]
part of taking a data and knowing exactly what

[00:28:22.500]
to do with it which

[00:28:24.700]
was the

[00:28:29.000]
date of birth strategy do

[00:28:31.099]
you have a portfolio manager

[00:28:33.099]
with a strategy who looks

[00:28:35.299]
to find data to support that idea or

[00:28:38.700]
do you have or call your master out

[00:28:40.700]
looking for data that

[00:28:42.700]
will potentially create a

[00:28:44.700]
new investment strategy under

[00:28:46.900]
no single answer that question

[00:28:48.900]
if we were to look at a high level tends

[00:28:51.500]
to be asset manager

[00:28:53.200]
surreal

[00:28:55.799]
money and bastard to have a strategy and

[00:28:58.000]
look for data to help enhance

[00:29:00.500]
a strategy of support that strategy

[00:29:02.700]
were at the hedge fund anyone

[00:29:05.500]
are more likely to just

[00:29:07.900]
start digging around through data I'm

[00:29:10.099]
looking for something interesting looking for a new correlation

[00:29:12.700]
to God you got for the visualization play

[00:29:15.799]
some

[00:29:17.900]
play that's what I was referring

[00:29:20.099]
to earlier right I feel like I could take

[00:29:22.099]
these categories and they

[00:29:24.200]
would pretty much line up with

[00:29:26.200]
you know whether the asset

[00:29:28.500]
manager or hedge fund was

[00:29:30.799]
single stock or

[00:29:32.799]
no program base

[00:29:35.000]
and or discretionary

[00:29:38.200]
vs. quantitative right

[00:29:40.700]
so you

[00:29:42.700]
know you like I don't think that you're going to find

[00:29:44.900]
a single stock

[00:29:47.599]
from that's going to say new types

[00:29:49.599]
of data Inspire new strategy idea

[00:29:51.900]
right

[00:29:53.099]
you know that they're already going

[00:29:55.099]
through a process to try to you know pick

[00:29:57.200]
a stock that's not going to change the

[00:29:59.799]
but the data

[00:30:02.000]
that they use could better inform

[00:30:04.500]
you know the preamble

[00:30:07.400]
are you finding

[00:30:09.400]
Adam yet that you're having customer

[00:30:11.599]
call and and point out hey you

[00:30:13.799]
know I did I did I did something that I wouldn't

[00:30:16.000]
have done without this or do you think it's a little still

[00:30:18.099]
too early days for that

[00:30:20.900]
anecdotally

[00:30:24.299]
yes we we do hear

[00:30:26.299]
that and it's usually

[00:30:28.500]
so far and this is probably as

[00:30:31.599]
indicative of where we are

[00:30:33.599]
in our life cycle of integrating

[00:30:35.900]
hotas into our front

[00:30:38.200]
end system is anything else is

[00:30:41.299]
that it

[00:30:43.700]
has more to do with

[00:30:46.099]
where went in a chilling

[00:30:48.099]
at any data in the right context

[00:30:50.400]
as opposed to you know

[00:30:52.500]
the data itself

[00:30:55.000]
right so it could be that someone told

[00:30:57.000]
us that you know hey I found

[00:30:59.099]
something really interesting via

[00:31:01.700]
you know the feed

[00:31:03.700]
from inside or score or from estimize

[00:31:05.900]
or Wall Street Horizons

[00:31:08.500]
you know a few of the data

[00:31:10.799]
providers that we have feeding into the system

[00:31:13.000]
but

[00:31:15.000]
it has more to do with where they were

[00:31:17.000]
in the process and how we

[00:31:19.099]
alerted them to the

[00:31:21.299]
change in the data then it was

[00:31:23.700]
like the data itself if that makes

[00:31:25.799]
any sense so that's all you know how I think

[00:31:27.799]
this is how this data gets integrated

[00:31:30.200]
into the process and how if

[00:31:32.299]
there's a human involved you know what

[00:31:34.599]
kind of contacts are shown around the

[00:31:38.000]
change in the data is as

[00:31:40.000]
important as you

[00:31:42.000]
know the other issues

[00:31:44.299]
that we've discussed

[00:31:45.599]
just

[00:31:48.700]
a reminder to the audience if you do have questions

[00:31:50.900]
please feel free to submit them through the through

[00:31:53.599]
the gotowebinar interface and thought

[00:31:55.599]
I was going to take it over to you and are

[00:31:57.599]
you off tonight I to stop one client

[00:31:59.700]
find ideas based on your data they

[00:32:02.500]
want to keep those ideas to themselves so are

[00:32:04.700]
you having clients to explain to you how

[00:32:06.799]
they're using it and if not it's

[00:32:08.799]
that up

[00:32:18.599]
they can you probably hit on the

[00:32:20.900]
the one of the most challenging things we

[00:32:22.900]
have to do so certainly

[00:32:25.900]
it as we would expect there are some

[00:32:28.400]
that share very

[00:32:30.400]
little to nothing there are others that

[00:32:32.599]
will outline the purposes

[00:32:35.799]
of of working with us and trying to get

[00:32:37.900]
us to understand what else

[00:32:39.900]
we might be able to do with the data

[00:32:42.099]
that help they have to share

[00:32:44.400]
some of the how they're using it or

[00:32:46.400]
at least frame

[00:32:48.400]
of a particular use case or scenario

[00:32:51.599]
what did Nick would help them it doesn't necessarily tell

[00:32:54.000]
us how they use it but they but they will

[00:32:56.200]
frame some scenarios for us and

[00:32:58.400]
help us understand what they're

[00:33:00.400]
looking for and if we could manipulate

[00:33:03.000]
data to provide

[00:33:05.099]
what they're looking for but it

[00:33:07.099]
does make it that at the nature of the

[00:33:09.099]
business and we do this going in it

[00:33:11.299]
does make it very different

[00:33:13.400]
from other businesses applying

[00:33:15.500]
data because we know we're not going

[00:33:17.500]
to know a whole lot about how

[00:33:19.799]
I think about it

[00:33:22.000]
the best clients we have the one that again the most

[00:33:24.099]
value of the ones are going to provide the least amount of information

[00:33:26.200]
back to us by definition

[00:33:28.200]
so that

[00:33:30.299]
combined with the the element of of

[00:33:32.400]
the the heterogeneity of

[00:33:34.500]
that no

[00:33:37.099]
two are alike so it's going to depend on how

[00:33:39.200]
many days that they have what their

[00:33:41.200]
technology is like what different

[00:33:43.299]
strategies they deploy where they are on their

[00:33:45.400]
cycle so just because had

[00:33:48.200]
fun decks using the data very

[00:33:50.900]
effectively Identify some opportunities

[00:33:53.200]
that doesn't mean that other hedge funds will so

[00:33:55.200]
not only do we not know precisely

[00:33:57.400]
how everyone is using the data but

[00:33:59.500]
even if we did that sometimes would

[00:34:02.099]
challenges find

[00:34:04.900]
the same guy to the next one everything is unique

[00:34:07.099]
and it makes it somewhat challenging but

[00:34:09.300]
it also was interesting when you begin to recognize

[00:34:11.900]
the value in many many

[00:34:14.500]
different had fun too many scenarios for

[00:34:20.000]
you as a true

[00:34:28.000]
value of the app that right I suspect

[00:34:30.000]
that I provided

[00:34:32.599]
in the value it

[00:34:36.000]
yeah yeah it is I mean obviously we

[00:34:38.000]
have our are a framework

[00:34:40.099]
for pricing and how we charge

[00:34:42.099]
for our data

[00:34:44.099]
sets but to some extent you hit

[00:34:46.099]
on a point in that the value of data

[00:34:48.300]
is a really

[00:34:50.800]
shell could easily be measured

[00:34:53.000]
by the value to that particular and

[00:34:56.199]
user and since we don't know the specific

[00:34:58.500]
value how much they might be making on it or how

[00:35:00.500]
they're using it we have really have no

[00:35:02.599]
other way to the frame it other than pricing

[00:35:06.900]
and packaging it by quality

[00:35:09.300]
of data type of data we have and

[00:35:12.199]
I think that works certainly

[00:35:14.300]
for now hopefully over time will

[00:35:16.800]
grow more intelligent about the uses

[00:35:18.900]
and and the market will become more

[00:35:20.900]
understanding and accepting of

[00:35:22.900]
that and we can have more variable

[00:35:25.000]
pricing strategy for for now it's

[00:35:27.699]
pretty easy on our side although we are aware

[00:35:29.800]
that there are unknowns there

[00:35:31.800]
that could change things over time

[00:35:33.599]
Kershaw rental in and finding

[00:35:35.699]
that value right it's it's at the corner

[00:35:37.800]
earlier it's one of the hardest part

[00:35:39.800]
even for the end users you

[00:35:42.400]
can see here when we add to her

[00:35:44.699]
folks looking at alternative date

[00:35:46.699]
of the use of data visualization

[00:35:49.000]
tools are number one growing

[00:35:51.300]
number

[00:35:53.500]
of the providers are you can't

[00:36:00.900]
help but notice you know Excel

[00:36:02.900]
just won't go away not

[00:36:05.800]
necessarily that we wanted to go away but the

[00:36:07.800]
power of excelling interesting

[00:36:10.300]
although I may befall put the deal

[00:36:12.300]
you have to imagine that is as

[00:36:14.900]
the customizable as

[00:36:17.099]
XL is your point earlier ride

[00:36:19.099]
you did what you did without without being

[00:36:21.300]
a programmer that you're still have

[00:36:23.500]
a hard time especially a data set guide

[00:36:25.500]
non-standard and very very big

[00:36:28.000]
I'm exhausted yeah

[00:36:30.400]
I know shows

[00:36:34.099]
that the use of alternative

[00:36:36.500]
data to temperature your

[00:36:38.800]
strategies or how you out you're going

[00:36:40.900]
to look at it it's very very much

[00:36:43.800]
a self-serve sort

[00:36:46.000]
of process right now you need

[00:36:48.099]
to be able to be

[00:36:50.300]
able to use your mic XLR but like Kevin

[00:36:52.500]
said XL everybody knows how to use it and

[00:36:54.900]
they're

[00:36:59.099]
really doing the expiration on the Rhone

[00:37:01.099]
earlier

[00:37:04.000]
that it's really it's a service

[00:37:06.800]
they are driving process and

[00:37:10.199]
they're also working you know it is in

[00:37:12.300]
group shout it's also

[00:37:17.000]
yeah by the tool that allows for

[00:37:28.300]
the conversation about value clearly

[00:37:30.599]
investment Community is being

[00:37:32.599]
value of the

[00:37:34.900]
headlines out of our research that we put out

[00:37:37.000]
last month with

[00:37:39.000]
90% of the end up with

[00:37:41.699]
that are currently using alternative data

[00:37:43.800]
they're getting their return in

[00:37:46.199]
the bathroom already planning to spend more money

[00:37:48.900]
on this in the future

[00:37:52.099]
so what

[00:37:54.900]
if we know what is the next to

[00:37:57.900]
we in some ways

[00:37:59.900]
it's so obvious that this year

[00:38:02.000]
but not everybody's doing

[00:38:04.099]
it as

[00:38:07.500]
you better think about what else you can do with the data

[00:38:09.599]
how do you get it out there more you

[00:38:12.800]
know what are some of the major parties and

[00:38:14.900]
think that you're focused

[00:38:18.500]
I

[00:38:21.199]
think we're looking at not

[00:38:24.000]
just that the data applications

[00:38:27.400]
in this way but also where where else

[00:38:29.400]
does it fit in this industry

[00:38:31.400]
idea how can I date a cyst

[00:38:33.599]
for instance m&a

[00:38:35.599]
in a private Equity so

[00:38:38.000]
there are other factors that

[00:38:40.000]
we're considering and how to bring out some

[00:38:42.000]
of the date that we have to

[00:38:44.599]
make it and apply more effectively

[00:38:47.000]
in this business more broadly specifically

[00:38:50.099]
when you're looking at evaluation it's

[00:38:52.699]
weird talking in

[00:38:55.599]
this discussion here is primarily in the equity

[00:38:57.800]
space but in the same way that we

[00:38:59.900]
may be able to look at our private

[00:39:02.199]
company data and how it applies

[00:39:04.199]
to say private investor private Equity the

[00:39:06.400]
same holds true for fixed-income right by

[00:39:10.099]
our data and that way to to learn

[00:39:12.199]
more about a particular entity and

[00:39:14.800]
how it's how

[00:39:17.400]
it's linked in

[00:39:18.500]
I work in a business elements have

[00:39:21.500]
more information we can apply hot what

[00:39:23.599]
what kind of scores can we create for instance

[00:39:25.699]
to predict business

[00:39:27.699]
performance in a different way so we're always looking at

[00:39:29.800]
that and we learn from those

[00:39:32.199]
are trialling and those that are actively using

[00:39:34.199]
we also learn from people

[00:39:36.500]
like you and people like bike

[00:39:38.800]
Arcadia friends instead have ways of presenting

[00:39:41.300]
and displaying data that

[00:39:43.500]
bring out some of these opportunities so

[00:39:46.599]
it is always tricky and we know that probably

[00:39:48.699]
the most interesting part of this

[00:39:50.699]
is and you said I think

[00:39:52.800]
of money reports Kevin I said he won't turn the date

[00:39:55.000]
is only alternative for short time it's

[00:39:57.199]
consistently evolving so how we going to do

[00:39:59.400]
this next year and Beyond is something

[00:40:01.599]
that challenges I think all of us here

[00:40:03.699]
in this small company

[00:40:07.099]
right here trying to tell your daddy to as many people as

[00:40:09.199]
you can but on the other hand if you sell it to

[00:40:11.199]
everybody does it says it started and lose

[00:40:13.300]
value if I did not.

[00:40:16.800]
yeah it's at the challenge and not

[00:40:19.000]
just selling it but in marketing it because

[00:40:21.199]
if you know if we want to

[00:40:23.199]
when you want a phone call or if you're talking to you

[00:40:25.800]
want to say while everybody uses this so you should

[00:40:28.000]
too there's a an

[00:40:30.300]
aspect that you mention that because well you know if everybody's

[00:40:32.500]
using it I don't necessarily want to use it but

[00:40:35.300]
at the same time without that kind

[00:40:37.400]
of validation that others are getting value

[00:40:39.500]
from it you lose some of the potential

[00:40:42.500]
value in the selling point so they everybody

[00:40:44.599]
in this business has a similar challenge but

[00:40:47.099]
I think we know and I think everybody were

[00:40:49.300]
talking with nose so

[00:40:51.500]
it's not as if they think

[00:40:54.599]
nobody's using it they know that others use

[00:40:56.699]
it maybe you're not telling them but it is

[00:41:01.500]
check in and out of

[00:41:03.500]
your customers because alternative

[00:41:05.599]
date of the world is so big they probably

[00:41:07.800]
don't even know what to ask for it

[00:41:09.800]
so how do you guys figure out what

[00:41:12.400]
day to could be used to your

[00:41:14.599]
customer base us

[00:41:17.800]
one of the subject matter you

[00:41:20.199]
know experts or areas of expertise

[00:41:22.699]
that we gain through the acquisition

[00:41:24.800]
with oat I have a very in

[00:41:27.199]
a robust process through which they can

[00:41:29.300]
take in a inject data and

[00:41:31.800]
then c e you know what the

[00:41:34.099]
see if there's any outfit in there see

[00:41:37.199]
what type of outfit is see

[00:41:40.099]
if it's one

[00:41:42.199]
way to measure a data set is

[00:41:44.199]
over in Ohio over a long

[00:41:46.500]
or abroad list of stocks

[00:41:48.900]
if it is raining

[00:41:51.500]
Alpha signals then you

[00:41:53.699]
know what you know how much golf is

[00:41:55.699]
there when it's looking across do you know

[00:41:57.800]
a large set of fire buy and sell signals

[00:42:01.500]
Enterprise customer doesn't

[00:42:04.199]
really matter right we're

[00:42:06.500]
going to show a piece

[00:42:08.599]
of data to someone that only

[00:42:10.599]
has 10 names on

[00:42:12.599]
their blotter you know being

[00:42:14.800]
right 51% of the time isn't

[00:42:16.800]
good enough so you

[00:42:18.800]
know again depending on who

[00:42:20.800]
were looking to the service

[00:42:22.900]
and who's consuming the data you

[00:42:25.300]
know the kind

[00:42:27.300]
of outcomes that we're looking for

[00:42:29.300]
Change and so but

[00:42:31.400]
those are the ones that

[00:42:33.699]
manage that process

[00:42:35.800]
in there you know evaluating different

[00:42:38.699]
data sets and thinking

[00:42:40.699]
about how we might incorporate it into

[00:42:42.800]
our front end

[00:42:44.900]
trading system but then also for

[00:42:46.900]
you know I owe

[00:42:49.900]
you know the kind of original

[00:42:52.500]
otash consumers that might

[00:42:55.000]
not be using it only for training

[00:42:57.500]
purposes but might also be using

[00:42:59.599]
it for the investment

[00:43:01.400]
as well

[00:43:04.500]
yeah it's

[00:43:06.500]
going back to you know it's only alternative for

[00:43:08.800]
certain amount of time it's really

[00:43:10.800]
about how well if used right cuz

[00:43:13.900]
everybody can. It's not really the dataset that's

[00:43:15.900]
going to get you how you how the

[00:43:18.199]
the portfolio manager

[00:43:20.199]
has been able to tie together Analyze

[00:43:22.900]
That looked at it and you

[00:43:25.000]
know how their team is able to put it together and it's

[00:43:29.400]
not actually two data sets by myself just

[00:43:34.699]
to get really a specific on our

[00:43:36.699]
use cases you know the way we think

[00:43:38.800]
about it is that you

[00:43:41.199]
would quit

[00:43:43.300]
eating without data and lower liquidity

[00:43:45.500]
without intelligent kind

[00:43:48.000]
of Reckless right because you could be

[00:43:50.000]
making a big trade at the wrong

[00:43:52.000]
time if you don't have the right date

[00:43:54.000]
in front of you but conversely

[00:43:56.099]
data without liquidity

[00:43:58.900]
is useless right so what

[00:44:01.000]
we're trying to do is bring together you

[00:44:03.400]
know all these days that

[00:44:05.900]
have some support but then also

[00:44:07.900]
be no show that out

[00:44:09.900]
a time where someone knows they

[00:44:12.000]
can take action there is a inability

[00:44:14.300]
to execute on that idea in

[00:44:16.699]
a right now I

[00:44:20.599]
think you bring up an interesting point that

[00:44:22.900]
there's such a

[00:44:24.900]
rush and focus on

[00:44:27.000]
big data and and the outset

[00:44:29.800]
of this call laid out

[00:44:31.800]
how much data cable kind of everybody

[00:44:38.699]
wants more data the more

[00:44:40.900]
there is only one and

[00:44:43.000]
there is a risk involved then you just hit it that what

[00:44:46.699]
day does not better necessarily unless

[00:44:48.699]
it's smart and actually it could be

[00:44:50.699]
risky or if you're not do you have

[00:44:53.000]
when you're not smart

[00:44:55.000]
enough and more

[00:44:57.000]
if you have of making that mistake and

[00:44:59.300]
that they can be compounded may wait over because

[00:45:02.300]
of the bed today that you have on

[00:45:04.300]
the bed was you using it so I

[00:45:07.000]
think that's something you'll have to bring out on that

[00:45:09.000]
nor lot of our trials are

[00:45:12.500]
our customers and Prospects are asking

[00:45:14.699]
for long histories of the date I want

[00:45:16.800]
more than they want more and more testing and

[00:45:20.199]
history so they can try to prove it

[00:45:22.199]
out but there is a risk factor

[00:45:24.199]
involved whatever

[00:45:30.400]
the price tag is $10,000

[00:45:33.900]
on a data set that you know because

[00:45:35.900]
you found a correlation and nobody's ever found

[00:45:37.900]
before that you need to make sure that

[00:45:39.900]
it's not like the price

[00:45:41.900]
of butter in Bangladesh compared to the S&P

[00:45:43.900]
500 right back. It's actually called

[00:45:46.199]
H3

[00:45:57.099]
audience that's really

[00:45:59.199]
active in the space and thank you everybody

[00:46:01.199]
for filling out those questions when you

[00:46:03.300]
register the vast majority of a book

[00:46:05.599]
about on the webinar today are I'm

[00:46:07.800]
in some way shape or form actively involved in

[00:46:09.900]
the states which which

[00:46:11.900]
is great and when we asked about him

[00:46:14.099]
what what comes to mind

[00:46:16.199]
when you think alternative date of the list of

[00:46:18.500]
responses is

[00:46:20.500]
Ali dawah how much

[00:46:22.599]
really just friend called how big

[00:46:24.800]
this Market is and how it's only going

[00:46:26.900]
to get bigger yeah

[00:46:29.000]
so I mean really just can be any

[00:46:31.000]
type of data I'm

[00:46:33.099]
applying at the financial services really only goes

[00:46:35.199]
back to you know how creative

[00:46:37.500]
you know the end users of

[00:46:39.500]
matter expert quad can be with

[00:46:41.500]
it the

[00:46:43.599]
understanding for the how do you put some workout

[00:46:46.000]
interact with talked about a lot of the dynamic

[00:46:48.099]
the ready today you want to sell

[00:46:50.099]
as much as you can to make money but if you felt too

[00:46:52.099]
much you're not going to make much money understanding

[00:46:54.599]
of complexities are you are

[00:46:56.599]
pretty important

[00:46:58.400]
does it doesn't new you

[00:47:00.500]
know when new world and in many many

[00:47:02.599]
ways to write the the data didn't

[00:47:04.599]
exist before the tools to use to

[00:47:06.599]
utilize it didn't exist before I

[00:47:10.500]
need to know what to do with the situation today

[00:47:12.900]
over that is no small thing of

[00:47:15.500]
course yeah this is definitely a case of Pinot

[00:47:18.599]
first-mover advantages huge

[00:47:20.599]
decide

[00:47:23.599]
if you have information that nobody else does which

[00:47:26.400]
is something obviously that's as old as Wall

[00:47:28.400]
Street I'm in most certainly applies here

[00:47:30.400]
and will be quite a bit I'm more more

[00:47:32.400]
value there so so before

[00:47:34.699]
I before we wrap it up and

[00:47:36.699]
call it may be any search Final

[00:47:39.000]
the big sauce from any

[00:47:41.099]
of our panelist what the audience to think about

[00:47:43.099]
and and and

[00:47:45.400]
yet it will end up all

[00:47:47.800]
bobetta I just what

[00:47:50.000]
I said this is Paul again and we'll exchange from

[00:47:52.099]
the the research report that we that

[00:47:54.099]
we were going to try and everything

[00:47:57.300]
else the other alternative

[00:47:59.500]
data as a process and

[00:48:01.500]
it's an iterative process and it's an exploratory

[00:48:03.599]
process and I

[00:48:05.599]
think it's just going to get a lot more interesting

[00:48:07.699]
as we as we go forward

[00:48:14.599]
final thoughts well

[00:48:17.900]
I think your first of all one

[00:48:20.500]
thing to recognize this when at

[00:48:22.699]
a time now when Capital

[00:48:25.400]
markets investing you become

[00:48:27.400]
so much more efficient and

[00:48:29.599]
your passive investing is growing I

[00:48:31.599]
think you have to look at it is it's

[00:48:33.699]
becoming more and more difficult that little sliver

[00:48:35.699]
of valve is more of a challenge to

[00:48:37.699]
reach in sometime Hill alternative data

[00:48:39.699]
can be the difference between Alpha and beta for

[00:48:42.800]
any professional investor so looking

[00:48:45.800]
at turning over every Rock and trying to

[00:48:47.800]
understand where the value is and

[00:48:50.400]
trying to stay ahead of the curve is

[00:48:52.900]
really a challenge as no

[00:48:55.199]
more more money comes in from the fiduciary

[00:48:57.500]
there is so much more of its

[00:48:59.599]
going past is that if you're if you're active

[00:49:02.000]
and your manager going to head that

[00:49:04.099]
way you've got to identify the

[00:49:06.500]
data and most importantly find the best

[00:49:08.599]
ways to use it and apply it to fit

[00:49:10.800]
the strategies that that you have and

[00:49:12.800]
whether it's finding

[00:49:14.699]
no data from a provider

[00:49:17.000]
such a stunning bride Street or or or

[00:49:19.300]
getting a platform like Arcadia that

[00:49:21.300]
to be able to bring out

[00:49:23.400]
some of those opportunities or

[00:49:25.599]
apply it to trade broker

[00:49:29.800]
like liquid everybody's

[00:49:33.099]
radar now it was looking to

[00:49:35.199]
try to identify that that

[00:49:37.400]
particular flavor that's going to take them all

[00:49:39.500]
over the top and then she supposed

[00:49:42.000]
to be I think

[00:49:46.400]
that no matter where you fall in

[00:49:48.500]
the ecosystem no

[00:49:50.500]
becoming more familiar with

[00:49:52.699]
alternative data and the processes

[00:49:55.199]
that are you convert

[00:49:57.599]
that into you know whether it sent

[00:49:59.599]
to men or in the living room kitchen whatever

[00:50:01.900]
it is no to me that's just like

[00:50:04.199]
a new form of literacy that everyone

[00:50:06.699]
in this industry is going to have to have some

[00:50:08.900]
level of comfort with you

[00:50:11.199]
know it had like it reminds me of being

[00:50:13.199]
a going back to the early

[00:50:15.699]
nineties and electronic trading and if you didn't

[00:50:17.900]
know the basics of how

[00:50:20.000]
computers work Torino

[00:50:22.599]
simple jargon around

[00:50:24.599]
programming in a year were going

[00:50:27.000]
to be a programmer

[00:50:29.199]
I'm not a date but I think

[00:50:31.400]
it's incumbent upon anyone in this industry

[00:50:33.400]
that they become more familiar

[00:50:35.400]
with data science and it's just

[00:50:37.400]
basic constructs so that when

[00:50:39.599]
they are looking at or reading

[00:50:41.900]
about different types of products

[00:50:43.800]
different types of data they

[00:50:45.900]
can kind of quickly filter

[00:50:48.099]
out what might be worth investigating versus

[00:50:51.199]
what may not be as relevant for their own

[00:50:56.099]
let

[00:50:58.800]
me talk to quick questions out of the panel first

[00:51:01.000]
one is alternative

[00:51:06.500]
income

[00:51:08.800]
portfolio managers are

[00:51:10.900]
given at the traffic insecurities that I

[00:51:13.000]
left liquid with lower price transparency me

[00:51:15.500]
about

[00:51:20.400]
credit score and spend office

[00:51:22.900]
in the market that impact

[00:51:25.199]
set it up a company or

[00:51:27.300]
not all that much different than two factors that can impact

[00:51:29.599]
of the equity in

[00:51:31.599]
a company understanding understanding

[00:51:35.199]
profactor

[00:51:37.900]
the reason it all the way

[00:51:39.900]
back up some of that would

[00:51:42.000]
have now become the obvious answers of taking

[00:51:44.199]
pictures of parking lot time to stand

[00:51:46.199]
up for down

[00:51:48.300]
for a retail

[00:51:50.300]
chain and in some ways the opacity

[00:51:53.099]
of pricing and even

[00:51:56.500]
more right for alternative data because

[00:51:59.199]
everybody.

[00:52:02.199]
Trying to figure out how they can better

[00:52:04.400]
understand valuation door or

[00:52:06.400]
predict the direction of the market because the market

[00:52:08.500]
is I

[00:52:13.699]
think I can I think

[00:52:15.800]
I can be even more useful

[00:52:25.199]
you know the early used to make

[00:52:27.800]
sure that's all work before applying to more complex

[00:52:29.800]
probably doesn't

[00:52:32.000]
go back to Adams associated

[00:52:47.400]
with the use of any

[00:52:49.400]
Edge funds from

[00:52:52.000]
embracing them to the fullest extent you

[00:53:01.699]
collect as a farm and what you do with it but

[00:53:03.900]
it's some Jazzy unknown hurting

[00:53:06.199]
its adoption

[00:53:09.300]
from the point

[00:53:11.800]
of view of a data provider

[00:53:13.800]
or day or a source yeah

[00:53:16.699]
they're there are things that as

[00:53:19.199]
you noted that the

[00:53:21.400]
limited

[00:53:24.099]
to disclose based on the way

[00:53:26.199]
we collect the data that

[00:53:28.199]
we or were fully aware

[00:53:30.199]
that if we were able to disclose it make

[00:53:32.400]
that more transparent it would become more

[00:53:34.400]
valuable to the fund

[00:53:36.699]
there and there's a number of times where we

[00:53:38.800]
have to find ways to aggregate

[00:53:41.599]
disguise and otherwise

[00:53:43.599]
obfuscate the

[00:53:45.900]
ability of some of the funds

[00:53:48.900]
not only to see the

[00:53:51.400]
details but to

[00:53:53.400]
be able to reverse engineer it so

[00:53:55.500]
we got to make sure that they can't

[00:53:57.599]
recently reverse-engineer the data because

[00:53:59.800]
of the agreement we have the collecting it so

[00:54:02.000]
yeah that's certainly inhibits

[00:54:04.400]
awesome as well as many other data

[00:54:06.500]
provided it

[00:54:08.599]
doesn't ugly shoes that are legal

[00:54:10.699]
agreement issues all that fall into place when

[00:54:13.300]
you look in alternative date I think that that's

[00:54:15.599]
at the corner of adult alternative for a reason

[00:54:17.599]
because sometimes we don't really know what

[00:54:20.500]
were able to reasonably

[00:54:22.900]
disclose and end and that you said it before

[00:54:24.900]
Kevin what's your oven disclosing

[00:54:27.699]
about the way you live

[00:54:29.900]
your life and how much of that is if

[00:54:31.900]
worth

[00:54:36.699]
no no ahead and interesting

[00:54:38.900]
concept but if you go back to sleep

[00:54:40.900]
early around

[00:54:44.099]
the the primary

[00:54:47.599]
networks what would happen

[00:54:49.599]
Aaron and how

[00:54:55.099]
and when do something like that come into the all

[00:54:57.099]
data space.

[00:54:59.300]
Time will tell me a space to

[00:55:01.300]
watch up and then

[00:55:08.599]
we actually asked for from

[00:55:10.699]
everybody on the line so I think we're here

[00:55:12.900]
we'll get back to those folks individually

[00:55:15.900]
and certainly if we have not addressed the particular

[00:55:18.300]
question feel free to reach out to us would

[00:55:21.000]
love to make that connection let

[00:55:23.300]
me think all Adam

[00:55:25.599]
Bob and everybody on the line for the

[00:55:27.599]
participation the fact that we did go over

[00:55:29.599]
certainly talks to the interesting

[00:55:33.300]
nature of the subject it continues to evolve

[00:55:35.400]
into on our radar

[00:55:37.500]
and happy to share

[00:55:39.500]
that with you as we as

[00:55:41.599]
we progressed if there's anything

[00:55:43.699]
further that we can do once

[00:55:46.199]
again please get back to you got our contact

[00:55:48.199]
at the

[00:55:50.199]
vet thanks again for your time and

[00:55:52.300]
it will be back again later

[00:55:54.300]
thanks everybody preciate it thank

[00:55:56.400]
you thank you