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 investment data.
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 around 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 environment.
Data governance: Investors must recruit the right talent.
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what we at the firm call data starters, organizations that are in the early stages of their journey and still using outdated legacy systems.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.
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