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collected, how do you distinguish the
good data from the bad? How are you able
to distinguish patterns and trends in the
data sets when there is a lack of stand-
ardisation? This is where big data has
evolved into its latest form: analytics.
“Several years ago, the focus was first
on data volumes and complexity,” says
Pierre Titeux, chief digital officer, Societe
Generale Securities Services. “In order to
manage these high volumes, major com-
panies and institutions have created data
lakes designed to store the different data
of the company instead of using multiple
data warehouses.”
“Then the focus recently moved to use
cases and added value from the data,
where algorithms, machine learning and
data science are required in order to ben-
efit from enterprise data.”
New tools, new conclusions
According to David Pagliaro, head of
EMEA, State Street Global Exchange, the
concept of data analytics, i.e. the process
of examining data sets in order to draw
conclusions, is not new. However, what is
new is how firms are using new tools and
technologies to draw new conclusions.
“Most think the Holy Grail for data
analytics is being able to drive new reve-
nues from it, but in reality they are using
it to enhance product development and
research,” says Pagliaro.
Firms are aiming to evolve their data an-
alytics capabilities that can provide them
with a golden data source, which can be
then be used for multiple functions across
the business. Even for those fund manag-
ers that operate their own data lakes, they
cannot always ensure the data they have
is the golden copy.
According to SS&C’s Meghew, fund ad-
ministrators that process data across all of
the middle- and back-office are in the best
position to provide this golden source.
“This golden source is coming from the
administrators, and they are then mar-
rying it with other data sets that allows
them to build that level of intelligence,”
says Megaw.
In the global custody world where banks
handle huge volumes of transaction data
across multiple markets with a variety
of client segments, it is down to them to
come up with new tools that allow firms
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to do more with the data.
Asset managers that hold long-term investment plans will need
constant data feeds and regular updates, far removed from the
reactive data storage function of traditional transfer agents or
fund administrators.
Handling vast amounts
According to Mike Clarke, director of product management for
European custody at Deutsche Bank, custodians and other finan-
cial institutions have the potential to make the data landscape
even more challenging if they struggle to deal with the vast
amounts of data in their systems.
“A proper handle on these vast amounts of data requires these
institutions to focus on their business objectives and on building
out their data capabilities step-by-step,” says Clarke.
“In doing so, they should employ a top-down approach to data,
ensuring they learn as they go and design controls and data
quality checks to make sure they have outputs that are depend-
able. Starting bottom-up with the full suite of data can be slow,
painful and unlikely to show any early business value.”
Deutsche Bank has carried out changes to its big data platform,
Hadoop, with a focus on being to handle large volumes of data
and then apply tools to drive data science and analytics.
Furthermore, as seen in our other features on cloud technolo-
gies (page 40) and data scientists (page 32), many custody banks
are hiring data scientists to build these private data lakes in
which all of their own data as well as client and third-party data
“This golden source is coming from the
administrators, and they are then marrying it
with other data sets that allows them to build
that level of intelligence.”
MICHAEL MEGAW,
MANAGING DIRECTOR, SS&C GLOBEOP
can be stored in.
Developing the tools and technologies on top of these data lakes
and provide services that enhance decision-making processes is
becoming a larger part of the custodian’s growth strategy.
“The industry is ingesting billions of terabytes of data into
these data lakes, in which service providers process and make
the data accessible through APIs, or hold on to it. The future
is to use these data lakes to win clients and grow the business,”
says Francis Jackson, global head of client coverage, RBC Inves-
tor and Treasury Services (RBC I&TS).
Jackson explains the bank is also leveraging its network with
Canadian universities to harness artificial intelligence (AI) and
Spring 2018
globalcustodian.com
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