Part 1 of 5: Decision velocity for the front office

Investment managers face unprecedented market complexity, where the ability to transform data into actionable insights—decision velocity— has become the defining competitive advantage. Meeting this challenge requires more than incremental improvements; it demands a fundamental reimagining of how data flows through investment organizations.

AI has amplified the data imperative by raising the stakes of data quality. Since AI systems only perform as well as their underlying data, inconsistent or fragmented information doesn't merely slow decisions—it amplifies poor ones at scale. "The challenge isn't just about having more data," observes Alun Cutler, Senior Director at SimCorp. "It's about having the right data, in the right format, at exactly the right moment when decisions matter most."

This reality exposes a critical flaw in traditional technology architectures that artificially separate pre-trade and post-trade environments. Stale positions, outdated cash projections, and incomplete risk assessments can result in a critical lack of common understanding. “Portfolio managers and traders require seamless, trusted data to support both analytical rigor and market instincts; on the other hand, information gaps often negatively influence alpha capture and agility,” adds Ben Keeler, Partner at Citisoft.

The trust advantage

The consequences of fragmented data extend far beyond delayed trades. As Keeler points out, “when data quality falters, the essential ‘trust and verify’ principle degrades into costly skepticism and redundant reconciliation processes that further delay critical decisions, in turn creating material drag on performance.” This transformation turns investment professionals from strategic thinkers into data validators—a fundamental misallocation of talent that compounds each verification cycle.

This challenge intensifies as execution speed and rapid strategy deployment increasingly define leading investment firms. Markets don't wait for systems to catch up. Cutler stresses,

Organizations that can't deliver real-time, trusted data across their entire investment lifecycle will find themselves perpetually behind—not just in execution, but in identifying opportunities altogether.

Consequently, robust operational data platforms have evolved from competitive advantage to survival prerequisite. These platforms enable confidence in shared information while supporting AI-augmented decision-making across business functions.

The client transformation validates what Keeler identifies as the three fundamental characteristics of high-performing investment management businesses:

• a data-centric operating model that transcends traditional functional silos
• unwavering trust in both acquired and derived data
• organizational agility to capitalize on strategic opportunities as they emerge

Keeler notes that,

These characteristics remain elementary yet critical in our always-on, data-driven market environment—and they cannot be automated, outsourced, or solved by AI utilities alone. Investment professionals who consume data to inform action and generate returns drive every asset management firm's success, but their effectiveness depends entirely on data quality and the operational trust that quality engenders.

While Cutler agrees that competitive advantage requires absolute confidence in data quality, he emphasizes a crucial caveat: the entire process from ideation to execution must remain frictionless. After all, only executed opportunities generate returns. The global asset manager's experience reinforces this principle — "true decision velocity demands seamless integration that transforms market insights into portfolio outcomes without institutional barriers, technological delays, or data uncertainties undermining performance,” he concludes.