When boarding a plane, most passengers instinctively glance into the cockpit. Even though modern aircraft largely operate on autopilot, the presence of a pilot remains essential, providing oversight, judgment, and control when it matters most. The analogy translates well to today’s data-driven investment landscape. Data powers automation, analytics, and increasingly AI-led decision-making, but its true value lies not in scale or speed alone, but in accuracy, structure, and trust. This is where providers like S&P Global Market Intelligence position themselves: not merely as data vendors, but as foundational infrastructure enabling investment processes to function reliably and efficiently.
Four Pillars of Competitive Advantage
According to Michael Patton, Global Head of Business Development for Investment Management at S&P Global Market Intelligence, hedge fund managers consistently pursue four sources of competitive edge: access to unique data, speed of acquisition, superior interpretation, and behavioral discipline.
“First, it’s about getting data that no one else has,” Patton explains, emphasizing that proprietary or exclusive datasets can offer a clear informational advantage. The second dimension is speed, ensuring data is ingested, processed, and made usable faster than competitors. While ultra-low latency remains the domain of high-frequency players, most managers still require robust infrastructure and data pipelines capable of transforming raw inputs into structured, actionable datasets in near real time.
“Our job is to enable clients across these areas through the products and services we offer. We think about them across everything we do, from new data products and delivery channels to AI capabilities and partnerships with hyperscalers.”
Michael Patton, Global Head of Business Development for Investment Management at S&P Global Market Intelligence.
The third pillar, however, is where many hedge funds differentiate themselves most effectively: interpretation. “A good deal of hedge funds rest their laurels on being able to interpret data better than everyone else,” considers Patton. In an environment where access to data is increasingly commoditized, the ability to extract signal from noise, apply bespoke frameworks, and translate insights into investment decisions remains a scalable source of differentiation across investment processes. The fourth dimension is behavioral, often overlooked but equally critical. It is about “not succumbing to panic” and maintaining discipline in volatile markets, avoiding the tendency to sell into stress-driven dislocations.
Hedge fund and asset managers are ultimately trying to determine where along these four dimensions they can strengthen their investment process and what combination best aligns with their strategy and investor base. S&P Global’s role, according to Patton, is to support that effort by providing the data, tools, and infrastructure needed to enhance those capabilities. “Our job is to enable clients across these areas through the products and services we offer,” he explains, adding that this principle guides how teams develop solutions and engage with clients. “We think about them across everything we do, from new data products and delivery channels to AI capabilities and partnerships with hyperscalers.”
From Data Input to Core Infrastructure
Within the broader investment management industry, data has evolved from a supporting input into a core component of the investment process, underpinning everything from idea generation and portfolio construction to execution and risk management. Despite this central role, managers continue to face structural challenges, including fragmented data sources, inconsistent quality, and the operational complexity of reconciling multiple identifiers across vendors. S&P Global Market Intelligence, “as a data company at the heart of it,” aims to address these inefficiencies at scale. “We put significant effort into how we source, standardize, and normalize data, and how we deliver it across a global universe,” Patton explains, highlighting that while technologies evolve, the firm’s commitment to data quality and robustness remains constant.
“We put significant effort into how we source, standardize, and normalize data, and how we deliver it across a global universe.”
Michael Patton, Global Head of Business Development for Investment Management at S&P Global Market Intelligence.
Importantly, automation has enhanced, but not replaced, the need for human oversight. While advances in data collection and processing have improved efficiency, maintaining data integrity still requires rigorous validation and control frameworks. “Automation has augmented our processes, but it hasn’t eliminated the need for people ensuring quality,” says Patton. “We’ve made a conscious decision that it will not replace that layer of control.” This hybrid model reflects a broader industry reality: even as systems become more automated and AI-driven, the reliability of outputs ultimately depends on the integrity of underlying data and the checks surrounding it.
Solving the Fragmentation Problem
One of the most persistent challenges in the investment ecosystem is data fragmentation. Managers typically rely on multiple providers, each delivering datasets in different formats, structures, and identifier systems, which complicates integration and limits usability. S&P Global Market Intelligence has addressed this by building Cross Reference Services, a unified data architecture that links datasets across instruments and entities into a common framework.
“We thought, what if we take all these datasets, link them, map them together, and distribute them through a common channel? When all this data is linked together, that’s when people are able to make more informed decisions.”
Michael Patton, Global Head of Business Development for Investment Management at S&P Global Market Intelligence.
“We thought, what if we take all these datasets, link them, map them together, and distribute them through a common channel?” Patton explains. By mapping millions of securities and corporate entities through standardized identifiers, the firm enables clients to construct comprehensive global security and entity masters. “When all this data is linked together, that’s when people are able to make more informed decisions.”
AI Adoption Accelerates
Artificial intelligence is further accelerating this transformation. As the volume and complexity of available data continue to expand, the ability to process, analyze, and extract meaningful insights is becoming a key determinant of the investment process. “Everyone is on an AI journey,” says Patton, noting that adoption varies widely across firms. While larger institutions have been early adopters, smaller and mid-sized managers are increasingly engaging with AI, often driven by rising expectations from their own clients. “There’s been a huge spike over the past 12 to 18 months,” he observes, with managers now actively seeking tools that allow them to demonstrate to investors that they are using “the best technology, the best data, and the best tools” to support their investment process.
“Everyone is on an AI journey. We’ve leaned into this heavily, making our data sets AI-ready so they can feed easily into these platforms.”
Michael Patton, Global Head of Business Development for Investment Management at S&P Global Market Intelligence.
In response, S&P Global has focused on making its datasets AI-ready, ensuring seamless integration with large language models and modern analytics platforms. “We’ve leaned into this heavily,” says Patton, “making our data sets AI-ready so they can feed easily into these platforms.” This includes not only structuring data for machine consumption but also embedding it within broader cloud ecosystems and partnerships. The objective is not simply to provide data, but to ensure that it can be effectively utilized within increasingly sophisticated analytical workflows.
Expanding the Data Ecosystem
At the same time, S&P Global continues to expand its data ecosystem through both organic development and strategic acquisitions, combining decades of historical datasets with newer capabilities. Its fundamental data offering includes deep financial datasets such as Compustat, alongside Capital IQ financials and estimates data, delivered through APIs, cloud platforms, and traditional feeds.
The acquisition of Visible Alpha further enhanced this ecosystem by aggregating detailed financial models from a global network of sell-side analysts. “They provide a deep look under the hood of what’s driving revenue,” Patton explains, enabling investors to compare assumptions, identify divergences, and better understand where mispricings or risks may emerge.
Another example is Business Relationship Analytics, which maps economic relationships between companies, including customer-supplier dependencies. While some of this information is disclosed, much of it is incomplete. By applying advanced modeling techniques, S&P Global estimates the economic significance of these relationships even when not explicitly reported. “We created a model to estimate those relationships,” Patton explains, giving investors a more comprehensive view of corporate ecosystems and hidden exposures.
As financial markets become more competitive and efficient, the source of investment edge is increasingly shifting toward those who can best harness data, combining high-quality inputs with robust infrastructure and advanced analytical capabilities. In this context, data providers are evolving beyond their traditional role as suppliers, becoming integral partners in the investment process. Much like the pilot in the cockpit, their role is not to replace automation, but to ensure that the systems guiding investment decisions operate with precision, reliability, and control.
