Today we are discussing data at Goldman Sachs.
We're speaking with Nemo, Raphael the firm's head of data
engineering and their Chief data officer.
I've been at Bowman about 20 years, always as a software
engineer data, engineer rat, or want as we call volunteer
stress. And now, as you mentioned, I run
our data team or data, engineering team and chief data
officer. For Goldman data engineering,
Goldman is probably a little little bit different than other
places. I, I, sort of organize my brain
and my aim and sort of three buckets.
We have our platform team, which hopefully, I'll have a little
more time to talk about and our curation, our content and
curation team. And also as a chief data
officer, with my chief data officer hat on sort of our
governance and quality teams. So that's that's sort of the
background of the work we do. Data is so obviously important
to financial services but it would be really interesting to
hear your perspective on the role of data how data fits into
the world of Goldman Sachs information is the lifeblood of
financial services and people say well what is that?
And and I think you know, in financial services at Goldman,
of course you know information is is actually our currency So,
a lot of a lot of decision making a lot of helping our
clients, a lot of innovation is all based on what information we
see out in the world and what information we see internally.
And and so organizing that making sure it's readily
accessible, making sure people could get to that data quickly
to, to do to do their work or to help their clients.
Starts becoming sort of like a core, a core component of doing
business, that Goldman Sachs, you mentioned, the various
aspects of your role, how you break things up.
Do you want to dive into that? And tell us a little bit more
about that. The first thing I think about
data is that it has to be a first class asset.
Right at Goldman. People have to believe that it
could help solve problems. Help, our clients help us
innovate and financial services. So, so the first thing we sort
of did is okay, how do we want to work with data Goldman and
again, as an engineer, that always push me to sort of think
about data as as an engineer would think about their code.
So I the analogy, I always say, or maybe it's not even analogy,
but the way I always talk about data is like, you got to think
about data, just like you were thinking about your code and
that's sort of has pushed us to build a platform team.
That really thinks about that. Things about data engineering as
an engineering function. He thinks about the workflows of
data Engineers, how we could help get, leverage and scale to
our businesses so that they could start making decisions
better faster, cheaper easier. And so the platform team and my
team is really laser focused on that, right?
How do you make data engineering workflows just seamless?
Beautiful, and all the things that, you know, Developers are
used to right? I ve s code completion.
Making sure that we have like reproducibility of our data.
Like we have code think that so all of that sort of becomes
their first class like role is that won't is to only think
about how do we make data awesome working with data also
and then we have a Content team. So again like I said, A lot of a
lot of our workflows at Goldman run on data.
So you can imagine, you know, part of our team is a real-time
Market data team. So all the all the data
streaming from exchanges or different, venues things like
that in real time, super low latency.
You know, we're talking about like millisecond latency here
right to get to Trader screens or to get into the Algos.
And so there's a, there's a whole team of mine which is in
the curation business. The content business, whether
it's real-time Market data or things.
Like our reference data that we use a, you know, like who our
clients or what products, can we trade things like that?
And so that team is really, I'd say, sort of a data engineering
bent team that uses our platforms to sort of do their
work, make sure the data is amazing, quality wise accessible
and ready to go. And then, the third bit is Is
really about the framework that we do data governance and data
quality around like so a little bit around policy and Frameworks
but then how do we push our platform team to then build in
those mechanisms or controls on the platform.
So when you do your work at Goldman, you sort of get those
for free, I say or complimentary.
So that's sort of the, a little more about the three buckets, we
talked about here. What I find particularly
Interesting is unlike most businesses which are of course
have their their business data. You're dealing with huge volumes
of real-time information of real-time data at which there is
enormous amounts of money and risk at stake.
I have to imagine that that places and additional kind of
intense bird. Ian on you, as you're thinking
about this whole technology landscape, I would sort of flip
it in a positive. I don't think it's actually, we
don't think of it as a burden. We think of it as a real
opportunity, right? That I think, is the first sort
of mindset shift. You have to think about when
you're in these sort of high-stakes games.
Yeah, of course, it's high stakes.
Of course, things could go wrong, but really, it's more
about the opportunities to make sure that again, we're helping
our clients, do the right thing, we're helping the economy, we're
hoping the Financial system do the right thing.
So we sort of take pride in that instead of a sort of Burden
thing. And the other one thing I'd say
is, I actually like to say you said, huge amounts of data, I
actually like to say Goldman Sachs were in the sort of medium
sized data but very complex data World.
Our data super complex, the types of data are complex, the
speed is complex, there's everything from super real.
Time low latency all the way to, you know, end of day batch
processing the relationships between our data as complex,
right. The product catalog is complex,
right? It's not just use of whatever
its actual financial instruments that people have have made up.
So the complexity is really the really interesting and cool
challenge here versus I'd say the volume what makes The data
complex, or just describing a little bit.
Can you drill into that? The complexity again comes from
various angles, like you mentioned the complexity comes
from the, from the speed is one angle one dimension, right?
But also the relationships of the data.
So you could imagine, you know, there's data about the stocks
and bonds that peep that our clients want to trade for one
example, but then there's a whole infrastructure.
Around those like I don't know how many people here are
familiar with financial services, but there's
derivatives on those products, right?
So now you're not just talking about stocks and bonds, but
you're talking about really complex.
Now, algorithms plus complex data elements about now that
have a relationship that have been now made up in the world on
those stocks and bonds so layering of this data and the
Alexa tea. Now that people have come up
with such creative ideas to help our clients and things like that
starts pushing the boundaries of like how this data is related
and interconnected and curated. Hopefully, that gives a little
bit of a sense. You are managing these very
large volumes of real-time data. Do you want to tell us at all
about your, your technology infrastructure?
It's not a question. I usually ask, but it seems that
in this case given the the equities, the derivatives,
everything else that you had just mentioned it.
Seems the technology infrastructure has to play a
very important role in the structure.
Absolutely plays a huge role. Here, we have an incredible
infrastructure team and you could imagine again that this
goes all the way down to sort of the hardware Hardware layers,
like the networking stack, the computers you use the network
cards in the computers use. All this stuff sort of actually
matters at At this sort of latency in scale, but I got it.
I don't want a super over index on the Real Time stuff.
The Real Time stuff is seen as a very important, very critical
and important part of of of our of our world, for sure.
And the team does an amazing job all the way from the hardware
layer up. But we also have, you know, data
challenges just after, you know, as the real-time data.
As in, you know, when you do a trade right, that store also
starts being complex and so we have built sort of a data
platform here, we call Legend and we've actually open source
that recently in the last couple of years.
We've built this Tech stack over the last 10 years.
Internally, two years about two years ago, maybe three.
Now we open sourced it it's fully on GitHub, you can check
it out. We gave the code to a non-profit
and open source nonprofit called Fitness, Around GitHub happy to
talk more about about that as well.
Why did you open source at first glance?
It seems like an odd decision to me.
Because one would think that a firm like Goldman Sachs would
want to keep all of that infrastructure to yourself.
There's a couple of reasons. One is, as we were building
this, we talked to a lot of clients who are having exactly
the same data challenges. We Were and, you know, we're
very obviously, a very client-centric firm.
So we thought look, this has helped us so much internally.
This platform has helped us so much internally, we'd love to
give it a chance. Help our clients to help push
the industry forward as well. And and so you also have to
separate a little bit about the platform itself versus the
content that we curate and work in the platform.
So, we haven't yet. Or maybe never will open source
the actual content, but we could talk about that as well.
But the platform and the work we've done to sort of
standardize, how we work with data.
We thought that was so powerful. That that actually giving it out
to the community and building a community around, that could
actually help the whole industry and our clients and and
ourselves you are wanting to share best practices.
Is approaches to working with data things like that with the
community and then of course, everybody's going to have their
own data content within within those constructs.
That's exactly right. The interesting part is even the
so even if we don't give out the content, right?
We are working with industry standard bodies, to even
describe the data and structure the data, and the linkages to
the data. So, it's a really cool and
Powerful technique. And, you know, working with
these standardization bodies and you know like what does a
derivative look like and even if even if our data looks slightly
different than our clients. At least we could standardize on
how we talk about those those things.
And that's what the legend platform, really excels at.
So it's really a contribution to the broader data science data
and analytics Community essentially.
Ultimately absolutely absolutely.
I think the platform is Is a general purpose platform for
working with data and then you know we specialize on some
standards and things like that working which is I think another
contribution to the into the financial industry in general.
Is just how do you get to that interop and standardization of
at least data contracts or how we talk about different terms
and relationships of different pieces of information.
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We have Have a number of questions on Twitter so why
don't we jump over to some of those?
And we have an interesting question from our Salon con are
salons, a regular listener and so thank you our salon for, for
your, for listening, and for this great question.
He says, given that data is such an important asset.
How do you assign the financial value to that data?
Who is it that? Decides that what data?
As Financial value and how much it's worth and, and I'll just
add to that. I'm assuming that, that helps
that attachment of value is one of the things that guides your
priorities in terms of where you focus your team focuses, what we
do is we work with our business line, so I run our core data T.
We work with our business lines about what their, what they need
for their client to serve their clients better what they need
for their business to scale what they need from us, to help them,
sort of get an edge or innovate in the data realm.
And so, a lot of it is about the value of the overall outcome
versus just like ascribing value to some piece of data.
And so, the way I, the way I think about from a core team,
helping people is really what is the outcome?
We're trying. To drive for our clients and for
our businesses and for Innovation and really, look at
that holistically. I think, again, I think it's a
little bit of a, little bit of a misnomer, a mistake to sort of,
try to say, okay, this dataset creates this much value, this
creates that much value is like, what did we do for that business
and for our clients and really we take those wins.
As platform and data, winds collectively with, with those
teams that makes perfect sense because the point is not some
piece of data, the point or body of data.
The point is, what are we doing with that data?
And as you said, what are the resulting business outcomes and
then you have a framework for for valuing, the the data
because we're trying to get to To the outcome.
Sorry, I didn't mean to answer for you.
No, no, that's exactly right. That's it.
That's a great. That's a great.
Exactly. This, the right summary.
And that's exactly how we do. Our oh, I like our return on
investment. On the work we do, is we
actually work hand in hand with those teens and say, okay, like
it, we were we able to reduce Risk by X or were we able to
help our clients do? Why better or faster or get them
into better position for y or did we?
Would enable the business to do some new thing that they were
not able to do. And that's really like the ROI
calc and at that level we have another question from Twitter.
And another great question, this is from Natalie Bean who says,
how do you balance data quality and data quantity?
And we haven't even spoken about data quality yet, so this is a
great question. When you think about data as a
first-class, Class concept, when you think about data is the,
when you do the same things, you do with your code, that you do
with data, like, think about your data architecture up front,
think about how you structure your data, how it relates to
other pieces of data, right? And you do that, work up front,
we have seen the yeah, The Upfront work takes a little bit
longer but the, but the huge benefits of that become apparent
as you're trying to scale these things.
So we deal with it. We deal with the scale and the
volume and the complexity, like any engineering organ.
Is we build tools. We build platforms, and then we
make sure that those scale to those problems.
And right and so now when we do data Goldman Sachs it's like we
do it on the platform and everyone knows like okay now the
quants are going to get their data you know and days instead
of months because we have set up the right platform constructs
and Engineering constructs for that.
So again it's not a perfect Silver Bullet, answer of like as
things grow like, you know, there's some there's some
equation but we have Seen that the investment in the platform
and the tools and the workflows. That's that's the thing that
helps us scale again. I find it really fascinating
that the platform and as you said, the, the workflows play
such a crucial role, but I suppose it's entirely logical
that when you need that data when that data is so important
and it needs to be right? And it needs to be consistently,
write that Need that infrastructure and the
automation to make it happen at that, at that level of quality.
That's exactly right. This is my personal view coming
from an engineering background. I solve pry eyes off, scale,
problems of all your problems and complexity Problems by
breaking it down into engineering steps and
Engineering platforms. And so that's that's sort of
been our ethos of this whole legend platform is really about
that. How to attack that problem from
an engineering lens. We have another Another
excellent question from Twitter. This is from Lizabeth Shaw, who
is now alluding to governance which you mentioned earlier, but
we definitely should talk about that.
And she says, how do you build in the mechanisms that support
data policies? The point of the platform isn't
that? It just magically makes these
things go away. It's that it makes you think of
them upfront, it makes you think of these Concepts.
As you're doing your data design and your data work and so really
when you when you're on the legend platform, thinking about
your data, the first thing you think is what is your data
contract, right? That's actually the first bit of
the workflow is describe your data, describe how you want to
publish it, describe how you want other people to see it or
consume it how you want the track things like lineage.
All of that is sort of built in baked in, as a first class
concept. So again, I don't want to
oversell the thing. You still have to think about
it, you still have to do it, but the point is, bringing it up
front, instead of hiding it as some secondary thing.
And so, have to think about the entitlements, the security, the
encryption, all of that. You think of, as sort of the
first class assets, as you're just a first-class Concepts as
you're designing, your data flow or your data workflow, or your
data, Production or data consumption patterns, and all
that sort of comes together in in the platform.
It's fascinating again to me because it seems that your
emphasis is definitely placed higher on that infrastructure
and platforms. Then other Chief data officer,
others that I've spoken with, but at the same time, you were
Dealing with a level of data complexity combined with the
speed and the and the financial consequences associated with it,
that I think few other companies would have that set of
combination of circumstances. Exactly like the quality the
data has to be right. It has to be consistently,
right, as you said, and the ramifications of that are pretty
or pretty big. We have another question
relating to to governance. And this is from again, from
Arsenal and con. And he says, how do you decide
what data is good to use? What is not?
How do you address bias? He's in the data you're
collecting and using. And this is interesting, he
says, do the business lines, agree with your conclusions.
So how do you also get everybody on the same page around this
stuff? If we are not some like isolated
team in the corner with like pointy hats, right?
Like doing this in isolation. I think that's the first most
important thing to get. Like, we are hands on keyboard
together with the businesses, making sure that the data were
using first is right, right? But the right for them and write
for the use case, And really is solving their problem.
So, the first point I would make is, you know, aligning ourselves
with business outcomes and with the businesses is the first
thing. So like not being in some
isolated back room, like oh, we know best about everything,
every piece of data. And again, the the financial
domain is so complex that we would never even pretend to do
that, right? You know, we have people who are
highly skilled and finance, Financial areas.
So that we could make those decisions with our our
businesses, like, in sort of a joint venture fashion, but we
would never say we're the experts in everything.
So I think that's the first piece is like, you have to be
connected to the business and the business outcomes.
So that's that's the first piece then, then again the quality and
the governance, right is then becomes an aligning incentives.
Of a joint sort of venture, right?
It's becomes there's a healthy tension, right?
Of course we want to do things at scale and they want to sort
of solve the problem immediately.
But the point is again that, you know, bringing these two teams
together really helps accelerate accelerate that and then get to
the right answers and the right data.
So this then leads to the question about the composition
of your team. It's obvious that you have very
three deep technology and data expertise, but as you just
alluded in order to do your job. Again, I have to assume that you
need equivalent Financial depth of expertise especially when you
go into Concepts like derivatives, as you were, as you
were describing earlier, we do have a team of stress or quants
in the maybe more financial world, who Come from being on
the desk. Understanding how data is used
on the desks and have sort of a stem background, whether its
technology math, physics, whatever who are equivalent,
counterparties in Mighty counterparts, in my team that
work with these groups and actually understand the
financial domain, but cross over to the technical domain.
So that's that's what we call our data design and curation
team. I'm pretty pretty cool,
high-powered, high-powered, crew, that that really is bound
to those business. Was those folks which team are
they part of? Are they part of your
organization or part of the finance or trading organization?
Where do they actually fit in our team?
We have a subset of that team, right?
But those, those sort of people are also all embedded in the
business as well. So there's Bedded what we call
embedded, death strats or embedded quants in various
businesses. But then I have a sort of small
selection of that team that works specifically on data with
those other business teams, if that makes sense.
What are the elements that comprise a successful data team
such as you have? It really follows sort of the
structure of the team like the platform team is high-powered
soft. For engineers right there.
They come from a software background.
Their goal is to make the software bulletproof and build
the right workflows for data engineers.
And the second bucket is basically data, Engineers,
people who are content experts but can use the platform
configure. The platform build data
pipelines, build curation pipelines.
Build data models that then get shared out then the third
bucket. Is really this sort of hybrid
hybrid. Again, I'll use the word strap
because that's what we use internally.
This hybrid team, that's really straddles sort of deep finance
and deep Tech together. And then the fourth is sort of
our governance, you know, framework team.
They're the people who sort of set the policies set the
framework on and and and work with the divisions to sort of,
make sure there. They're working in the bounds of
our framework. One of the topics that we have,
not really touched upon is the notion of data interoperability
and I know that's important to you.
So can you tell us about that? We touched a little bit of pain.
It, that's sort of why a big big reason we open sourced.
Our Legend platform was exactly for that was because we felt
that like if we could bring some Platform standardization in the
industry for us or clients are counterparties that then at
least the discussions about how data works or how it should be
connected or how it should be described in the industry that
we could help push that forward and we can help push that
forward with standards with other bodies, but they could all
do that work in one sort of saying way in in our platform.
Well, not it's not even our For me, in the open source Community
platform and and that will benefit everybody.
So it's a bit, it's absolutely a big, a big, big play, I'll
mention, you know, one project we're doing in the in US
Community which is the open source nonprofit that we gave
gave Legend to. We brought is though, which is
this the derivative standards d'oeuvres body?
They have built a The model called CDM, common domain model
for derivatives and now that sort of that data model is now
available in legend for people to collaborate on in a
collaborative environment out in the wild it's has nothing to do
with Goldman Sachs they're just using the platform.
So these are like pretty cool things that you know we're
trying to do to push the interop and and standardization out how
has the uptake bin in the broader community?
Of of this platform and data. Interoperability the cool part
is that our code is out there we have clients, actually deploying
it using it. We have other counterparties
also looking at it, doing pocs, in their own environment, but
the flip side of that is, you know, building an open source
Community is very difficult. I mean, we vastly underestimated
how much time and effort and energy goes into, you know,
making Making sure people know how to use the thing, the
documentation. Do they understand the value?
Can they contribute code back? Like, all of these things, we
knew it would take time, but I think we sort of grossly
underestimated how much time to build sort of that, at that big
Community around these. These projects, what are the
benefits that you have presented to the community as to why they
should engage in this, it has helped us Date of better at
Goldman, is helped us organize our data.
It has helped break down silos. It has helped the data quality
massively. It has helped data governance
aspects, massively internally. And so we point to sort of those
success stories about how we do it internally that we could also
help clients do the same thing. And you know if there's appetite
of course that we could also help the broader industry break
down those silos as well. We have Another couple of
questions that are coming in from Twitter.
Do you think of your data or your platform as a product that
you can sell, given that you've built all of this
infrastructure? I'll say that's not the
intention of doing it. I think, though, again, we have
seen that as clients use it as other Banks, use it, that people
sort of come back to us and ask, okay, well, what could we What
could the support model B, or, hey, can you host this as a SAS
for us? So that we don't have to deal
with setting up the infrastructure, running the
infrastructure or things like that.
So I think we're still again, just not to oversell anything.
I think we're still in a very crawl stages of even thinking
about that community and how we could help do better and better
but Our intention isn't like Like make money off of this
thing. It's really about the
standardization parts now if people see a large adoption and
see a value, I think it could be an exciting potential
opportunity. We have another question from
Twitter. Can you give any examples where
using the platform has resulted in innovative ways of for users
to use and or combined? the data we sort of took again,
a data-driven approach to helping our firm and our Risk
Managers and our sales. People in our Traders,
understand that risk from a data-driven perspective, right?
We have combined all that data and information into our
platform and have basically given I hate to use this word
but democratized the access to that our team has linked a bunch
of that data together done. Some interesting analysis but
more importantly, we have In that those based sets of data,
those relationships, the relationships between those data
that may not be obvious. To our users hands.
That now, they have the access to that information and now they
could come up with creative things or risk, mitigating
things to on top of that data. So I think that's been, you
know, maybe one that resonates pretty recently here, it's kind
of the reverse of Shadow it that cios didn't like years ago.
Some going back, maybe, five years, you're Ali, taking the
data and I assume the tools and placing it in the hands of
users, so that they can be creative.
They can innovate with with that data.
Exactly. But in a fully governed quality
way, right, where that shadow, it problem isn't really a
problem. We have put all the right guard
rails and all the right governance around that.
So now people can actually innovate in a safe space.
In a safe way but it's not just lock to our team.
Can you give us some insight into what kind of governance or
how do you balance the need for security for privacy against
making that data, available accessible and easy to use for
folks before we do anything with data.
Right? We have first of all very strict
policies and Frameworks about, you know, understanding who's
allowed to see what. Data.
And even if you're allowed to see it, should you see it?
You know, we call it need to know, right?
Like just because you're allowed to see it.
Maybe you don't actually need to know that client information.
So first of all, as an overarching theme, I'd say like
one of the one of the most critical pieces is is you know
writing those policies down and actually then sort of enforcing
them in the platform. But before we do anything like
that is a clear thing and then and we have very clear rules
About who can get access to what?
How, and then again, would be a little broken record encode,
those rules in the platform and make sure that that is like a
Slayer. Like if we get that wrong,
nothing matters, right? So the first thing is to make
sure that that Baseline Works before before giving access to
him. So in other words, as long as
the foundation of governance, A risk control, compliance with
regulations, whatever is necessary.
As long as those elements are in place, then you're able to share
the data. And let people, I was going to
say have free range, obviously that's not the case, but have
enough rain, where they can use it.
Use that data creatively in the service of whatever their
business goals happen to be much better said, then that then
white man eyes that it but Exactly, exactly.
The right mental model. What advice do you have for
folks who are building a data strategy, based on what you've
learned at and done at Goldman Sachs?
Make sure you attach yourself to business outcomes.
The things that people care about at your company, right?
That I think, As a technologist. You know sometimes as
technologists in general maybe I'll make a broad generalization
sometimes. You know, we care a lot more
about the tech than the outcome but specifically for data
strategy, I think it's even more important to over-index on the
outcomes because data sort of becomes this nebulous thing
where now you know like well what does it mean to have a data
strategy? What is that like what is data
even mean? Why do I need that?
Like an abstract? I get why I want information,
but like what are you talking about?
So the first thing, I the first, first thing I talked about
always is attached yourself, the business outcomes and show how
the data and the data strategy actually makes those cheaper
faster, better easier makes money for your clients.
Helps reduce risk, help. Save money, do so.
That's that's the first sort of Baseline advice.
I give to everybody. And then then I think, you know,
but then below that then become sort of okay set out.
What is the platform strategy going to be?
How are you going to actually make the engineering work?
Think about the business workflows and the Pro workflows.
And then, you know, we talked a little bit about the org
structure and the framework. So I think those are also key
pieces, right? Make sure that, you know, if
you're going to do it in an engineering strategy, you have a
strong platform team, if you're going to, you know, have a
Content T. Make sure they actually
understand the domain that you're working with.
And so bringing those like or pieces together.
But, but the number one key is like, make sure you're driving
outcomes. What's the relationship between
your data strategy, and Cloud, where does cloud fit into all of
this? I can't believe we haven't
spoken about that. I think of the cloud again, as a
tool in the toolbox, right? It's not a means to an end it's
in my Arsenal how I want to build my platform.
Yeah. I want infinite scalability, I
wanted it, I want, you know, other people have done, the hard
work on the infrastructure front, I want great databases
that people have built to not rebuild that on my own.
And so to me, the cloud is a great enabler, it's a great
tool, my toolbox, it's a great way to get the scale.
So it's definitely a big part of the overall data strategy.
But again, it just to be clear. I just make sure like, It's not
a thing for the sake of a thing, it's like, okay.
These are great components, great pieces of infrastructure
that other people that I can now stand on the shoulder of giants
execute on. And that's the way I think about
it. It was super important part of
the strategy just to be clear. I can see that you are you're
very purpose-driven, you are always coming back to the
reference point. Why are we doing it?
What are we doing at? What are we getting out of it
from them from the business? Outcome standpoint.
That's Very, very, very clear. And finally, one last question,
what is the relationship between data and building business
models economic models Financial models?
How does how to those pieces fit together in technology?
It comes down to algorithms and data and so like those are the
two big inputs, right? Like, you need the data to be
great. You need it to be clean and
needed to be organized. You need to make sure that like
all those pieces are set, it's easy.
Accessible. It's findable to govern and then
that becomes a major input into the Algos, whether you're doing
forecasting or algorithmic trading or helping your client
with something that those pieces just have to like work together
as a as a team to get the job done.
Again, I'm putting words in your mouth, but this is just
summarizing what you've just been talking about is the
linkage to the out. Outcome and being clear about,
what's the data going in? What's the expected result at
the other and and ensuring that the to match up?
Yep, yep, exactly. And with that, unfortunately,
we're out of time Nema. I just want to say a huge.
Thank you for spending time with us.
I really, really appreciate it. Thank you, thanks for the great
questions. Thanks audience, amazing
questions. I really had a really fun time
talking to talking to you Michael.
So thanks for having me, everybody.
Thank you. For watching, I just want to say
a huge thank you to Nema Rafael. He is the head of global data
engineering and the chief data officer of Goldman Sachs.
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