Data continues to evolve as an increasingly strategic focus
for institutional investors. A new, more complex investment
climate demands more advanced data tools to support the
multiasset solutions that institutional investors now require.
Their future success largely depends on how effectively these
investors can extract insights and value from large volumes of
While the majority of institutional investors recognize this
need for data dexterity, determining just how to achieve that
can be a daunting task. Insurers, for example which have
often grown through acquisition are continually faced
with having to aggregate data from multiple sources and
platforms. Other, external factors such as increasingly
complicated asset classes and constantly changing technologies
can also create barriers. Couple these hurdles with struggles
legacy infrastructure, and a data crisis looms ahead.
Overcoming the data challenge requires an understanding of
the obstacles at each stage of the data management process.
Here are four steps to consider:
Acquisition: Firms need to get a handle on
a complex set of data sources which are incongruent and
in continual flux and create an interoperable
Data governance: Investors must recruit the
Modeling: There should be a concerted
effort to operate and manipulate inflexible data models, while
bringing evolving technologies on board.
Consumption: The constantly changing uses
for data can exacerbate overall data management challenges.
When it comes to their capabilities to handle big data, not
all organizations are starting from the same point. A 2013
State Street study revealed a digital divide among asset
managers. Nearly one quarter (23 percent) were what we at the
firm call data starters, organizations that are in the early
stages of their journey and still using outdated legacy
systems. Of the respondents, 38 percent could be described as
data movers; these are firms taking active measures to improve
their data capabilities. The remainder of survey respondents
fell into the third and final category, data innovators
firms with the most advanced data capabilities.
These companies treat data as a top strategic priority and thus
are able to adapt faster to new business needs.
To increase data dexterity and, if they are not there yet,
make it to the data innovator stage, institutional investors
should implement key measures, one step at a time.
First off, investors should start small. It helps to
identify a single business challenge to solve using data, then
incrementally drive change from there. Common starting points
include breaking down existing data silos, creating effective
risk analytics and complying with regulatory standards.
Second, a firm needs to figure out how it will handle data
governance. At this stage, investors can implement tools and
processes to normalize and aggregate data as it flows. An
organization should determine if data will be managed in-house
or if it will partner with a third party, such as a service
provider or technology company.
Third, a firm should foster cross-organizational support.
Because such processes often involve large-scale changes,
buy-in from senior management is key to improving how a firm
handles big data. Additionally, while data management is
typically thought to exist in isolation within information
technology, sectors across the organization should be well
versed in data to create a support team with strong
technological as well as business-operational skills. Key
parties for these efforts include technology departments,
business operations and front-office teams.
Lou Maiuri is head of
State Street Global Exchange and Global Markets, and Ivan
Matviak is regional head of State Street Global Exchange for
North America; both are based in Boston.
State Streets disclaimer.
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