In search of alpha


Presenters:

Industry Lead, Financial Services
Cloudera

Managing Director
Greenwich Associates

Director Financial Services
Arcadia Data
WEBINAR, AIRED: SEPTEMBER 13, 2017
Put Alternative Data to Use in Capital Markets
Alternative data for capital markets, such as satellite imagery, logistics data, and social media feeds, has been getting a lot of attention recently. Like any trending topic, its uses and benefits can be hyped up a bit but if the right plumbing and creativity is in place, those benefits can be realized.
In this webinar, Cloudera, Arcadia Data, and Greenwich Associates will discuss:
- Examples of alternative data use cases, sources, and recent market trends.
- Why a big data platform that facilitates self-service and collaboration is critical in monetizing alternative data.
- How alternative data can be applied to enhance current processes (demo).
Transcript
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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
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Market structure update we
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will be continuing our coverage of
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the alternative data space and
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more specifically we're going to look beyond the definition
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and categorization of alternative
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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
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practice and I'll be the host today
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I'm going to be joined by a
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great panel at panels going to be moderated
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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
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are in the alternative ecosystem
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in in to show some of the names
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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
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in fact that and CR another.
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& Bradstreet on here but you see
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also other than you maybe have
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never heard of and these are all
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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
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ask for the participants
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here we
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do you think you fit in
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that alternative data ecosystem
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thanks
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for getting us started and kind
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of like the same question
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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
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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
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of which focused on the
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value of Trade Credit transactions
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payment behaviors of public and private companies
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information about business
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relationships businesses supply chain
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that information we found
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especially in the The Evolution
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here of alternative data has become
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valuable to other
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by sides ability to
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provide predictive signals within
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their algorithm so that's kind of how the data is
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used today Adam
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from liquid knots perspective where do you sit in this
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ecosystem Dad
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thanks Kevin liquid net
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you know being a broker-dealer and
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having a front-end interface
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that Services
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800 by
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side firms globally
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would sit somewhere between you
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know a beneficiary and a
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in a big data provide
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better we acquired a company
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earlier this year called switch
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and work extensively with london-based
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trading desks
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to be able to transform data
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a combination of Market data fundamental
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data and alternative data into
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actionable insights and
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then integrate those inside seamlessly
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into the end of the year.
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Acquisition you know we've
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acquired the ability of ingest
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this all
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kinds of data including alternative data
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and then process
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in such a way to make it relevant for
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for our customers true
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beneficiary of that data to
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but yeah I
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really
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have to be hard with
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everybody Arcadia
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data we are on the technology
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side but we do the date of visualization
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so what we do is we enable
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the end-users the beneficiaries
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the subject matter expert
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tell the visual eyes to be able to see
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the day that I put together and
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to really get the the value
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out of it and all its granular Lori
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are
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the results of the Paul,
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glad to see our audience is a pretty good mix
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of the ecosystem as well but
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a good number of a resonator on
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big data analytics platform the 1st
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which is all
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going to take us through a good
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visual youth kudelka to
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feel what we talk about these things they
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always feel a bit fluffy
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and in the clouds no pun
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intended really understand
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how this can be used in practice
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we think we pick would be helpful
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for the audience and
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get
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everybody to think about what
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alternative date is what they're using it for and
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really to get the conversation going later
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on and I'll
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look at an example of an unconventional
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with alternative data
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so it's really about look
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at your conventional portfolio analysis in
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this example if you're looking
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at retail stores or or something like
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that and if no remote
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standard performance chart you
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looking for for you
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know what's the trade in value and that's all conventional
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apple bloom again here now
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is say okay how can you enrich
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that and you enrich that
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with alternative data sets that's the
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story here so I'm looking again at
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a retail stores in a lot
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of it could be looking at different
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types of data points
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or beta acids and
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tying them together to
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location and then
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you're able to from that really
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be able to see Apples to Apples when you're going from location
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location location that's pretty important I
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think I'm looking at another one
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is going past the
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show in this
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case scenario
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I want to buy a little bit looking
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at this sort of data that that Dun
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& Bradstreet has an account
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with the scenario right so I'm picking up
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a portfolio manager they have fun I'll
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call it a bucket of opportunities to read your
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water companies that they may want to invested
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and from that of
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course you have your fundamental due diligence
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is performed that's right
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here that's your candy bar charts
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and then line charts and things like that but
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the question after that is what
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else can inform the decision-making
[00:13:28.000]
so one of it is
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you know how healthy is the company I'm
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looking at commercial credit scores Financial
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stress what's the health of the
[00:13:36.700]
suppliers are there are
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there any dependencies of the suppliers are
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able to deliver and then the products
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that this company is very good at you
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know starts to fall behind looking
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at other investors are you
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first of the table or
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a lot of people looking at it and so I just
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came up with this network's chart that shows
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that what is the type
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of stuff that. Dun & Bradstreet has and
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it'll take another look it almost gives
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few more examples of how you might
[00:14:14.799]
be able to visualize this again these
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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
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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
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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
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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
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interesting that once you put
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it in the hands of the real
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smart quance
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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
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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
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of use cases that we found and
[00:16:39.399]
the applications for alternative
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data the way that
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I would kind of split it out as
[00:16:45.899]
between the single stock
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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
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more as it's
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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