Accelerating Data-Driven Investing

Effectively leveraging data to make timelier, better-informed investment decisions is the essence of being a data-driven organization. Forward looking investment firms and asset owners embrace the notion of being data-driven, but achieving that goal requires a major rethink of operating models, technology choices and corporate culture.

Data underpins every aspect of the investment process. Portfolio managers require timely and accurate views of positions and investable cash. Traders need to know current market pricing, depth of book and the status of orders and executions. Risk officers monitor counterparty exposures and liquidity conditions, while middle-office teams need accurate information on available collateral and securities on loan to help the front office drive alpha.

The ability to manage and distribute so many diverse data sets across the enterprise is central to generating returns, retaining clients and meeting regulatory requirements. Without a robust data management process, firms are at a significant informational disadvantage.

While artificial intelligence (AI) and other emerging technologies hold considerable promise for unlocking new insights and improving productivity, making effective use of those tools requires modern and scalable infrastructure capable of capturing, curating and validating massive volumes of investment data in a scalable manner.

In this article, we discuss the obstacles and opportunities investment firms face on this journey and articulate our roadmap for achieving a successful data-driven transformation.

Internal infrastructure and technology debt

Several challenges prevent firms from making effective use of their data. Most legacy systems deployed by investment managers were built to support a specific asset class, creating a complex and costly technology footprint and multiple data silos. This results in significant “technology debt” where each asset class managed by the firm requires its own support team, infrastructure and databases. As the original system and database designers retire or move on, these systems risk becoming obsolete and disrupting operations.

Brisk industry consolidation poses another challenge, with 397 mergers and acquisitions in 2021 and more than 400 in 2022, according to Piper Sandler. This creates even more fragmentation and silos as the merged entity begins the years-long process of system rationalization.

Grappling with disparate data sources

Many internally generated data elements need to be enriched with data acquired from external providers including risk analytics, indexes and benchmarks, market prices and corporate actions. Data silos inhibit distribution and enrichment of externally sourced data resulting in compromised decision-making and potential duplication of costly data feeds.

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Whether data is used throughout the investment day, end of day, or end of quarter, synchronizing disparate data elements is critical. Without a central data repository, firms risk disseminating inaccurate or stale data to front-office systems and decision-makers.

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John Plansky
Global Head of State Street Alpha

Managing private equity and other alternative asset classes is highly reliant on unstructured data contained in documents and spreadsheets. Examples include valuation and cash flow models, term sheets and call transcripts. For hedge funds and active managers, data sources like satellite imagery, social media feeds, supply chain data and geocoded information holds significant potential for generating alpha, launching new products, and creating timelier risk forecasts. But relational databases weren’t designed to support these data sources. Without tools to manage and analyze unstructured data, organizations are forced to use inefficient manual workflows to extract information.

Investment management has lagged other industries in adopting cloud-based infrastructure. Maintaining servers and databases in-house increases operating risk and cost, and diverts attention from core competencies. Even the largest investment managers can’t replicate the cybersecurity, availability and resiliency afforded by cloud providers like Amazon, Microsoft and Google. Cloud computing also accelerates access to new software capabilities and data sources, freeing internal operations teams for higher value activities.

While these are significant challenges, they are surmountable. Our decades of experience as an asset servicer and technology provider forms the foundation of a proven roadmap that helps firms make better use of their data and unlock new insights.

Five priorities for the chief investment officer
Open architecture platforms and interoperability

Shifting from monolithic, proprietary investment management solutions to open architecture platforms enables interoperability with external analytics, data and applications providers. This empowers clients with a best-of-breed environment tailored to the unique demands of their product, asset class and geographic mix. Platforms provide much needed optionality for clients, helping them tailor their operating models and technology stack to constantly changing client and regulatory demands. They also enable firms to reduce their accumulated technology debt by retiring legacy solutions that are no longer fit for purpose.

Leveraging the entire data spectrum

The ability to leverage new and differentiated data sources provides a competitive advantage for agile organizations. Evaluating new data sources, whether generated internally or acquired from a data marketplace, data vendors for ESG data, risk analytics or other providers can create valuable insight into new investment opportunities and potential risk exposures. In turn, these insights can help investment professionals build superior risk-adjusted portfolios and develop targeted solutions for both institutional and retail investors.

Harnessing AI

The ability to successfully uncover and deploy AI-driven insights requires a modern, cloud-native infrastructure that supports data scientists and engineers with in-database feature engineering and support for financial time series. The ability to build, validate and deploy predictive models quickly and at scale enables firms to evaluate investment opportunities ahead of competitors, construct more effective hedging strategies, automate manually intensive workflows and provide a superior user experience for both employees and clients.

Gaining new insights by democratizing data access

Providing investment and operations team members with self-service data analysis and visualization capabilities democratizes data access. This is key to building a data-driven culture. Instead of waiting hours for IT to run queries, staff can analyze data and get answers to questions in near real time using Generative AI applications. Data usage promotes ownership, sparking a virtuous cycle where employees find new applications for their data, and make better-informed decisions throughout their workday.

Outsourcing to trusted partners

By outsourcing non-core activities to trusted technology and service providers, investment firms are better positioned to focus on generating alpha, delivering superior client service and bringing new products to market faster. Firms can benefit from the economies of scale that large asset servicers and cloud and data management providers offer by eliminating server maintenance, performance monitoring, software upgrades and other activities that service providers excel at.

Taken together, these initiatives are helping forward-looking investment firms, asset owners and wealth managers become data-driven organizations. Learn how we can help at

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