(FLASH FRIDAY is a weekly content series looking at the past, present and future of capital markets trading and technology. FLASH FRIDAY is sponsored by Instinet, a Nomura company.)
As data becomes central to modern operations, financial institutions are racing to modernize how they manage, interpret, and govern the information that underpins their activities. However, building a more agile, scalable, and intelligent data infrastructure presents significant complexity and obstacles.
One of the most pressing hurdles institutions face today is the fragmentation of data across siloed systems. According to Tim Lind, Managing Director of DTCC Data Services, “the primary challenges institutions are facing stem from siloed systems across functions and asset classes”.
These disconnected platforms—often a mix of on-premise and cloud—hinder firms from realizing the full potential of their data, he said.
Lind emphasized that firms are beginning to modernize by “capturing positions, transactions, and reference data from core systems into cloud-native ecosystems.” These ecosystems empower business users through data discovery and self-service models—eliminating bottlenecks and placing actionable insights directly in the hands of decision-makers, he said.
Cboe’s Senior Vice President of Data and Analytics, Eileen Smith, shared a similar story: “Cboe a few years ago went from siloed databases that required technical skills to a democratized data environment through a company-wide, self-serve platform.”
“Instead of raw data dumps, we created bespoke and usable data streams for each of our business lines that helped consolidate actionable information,” she said.
As cloud infrastructure becomes the foundation of modern data architecture, artificial intelligence and automation are reshaping the way firms approach enterprise data management.
“AI is going to bring scale and precision to capturing data from documents,” said Lind, pointing out that today, much of this work is manual and error-prone. By automating routine tasks—like data entry and validation—AI reduces operational costs while improving data accuracy and timeliness. More importantly, AI’s ability to identify patterns and anomalies in real time enables firms to act faster and with greater confidence.
Julie Gerdeman, Managing Director, Global Head of Data & Analytics, CEO of Eagle Investment Systems, BNY, noted that this transformation is well underway at BNY: “In our data platform AI is driving efficiencies in the onboarding, governance, and migration of high-quality data,” she explained.
This shift allows employees to focus on higher-value work while enabling more adaptive, AI-enabled systems that can manage entire segments of the investment lifecycle, she said.
Smith also underscored the significance of these technologies in strengthening governance frameworks: “We’ve consolidated our data pipelines with increased automation, which not only streamlines operations but significantly enhances our data governance capabilities.”
Balancing Agility with Control
As data volumes and complexity continue to rise, DTCC’s Lind emphasized the importance of rethinking internal data flows and adopting flexible architectures that can adapt to evolving business needs.
For Lind, normalization of data within cloud ecosystems is a critical precursor to unleashing the full capabilities of AI. “Cloud and AI will mask the underlying complexity of data, shifting the focus to data intelligence rather than the mechanics of data engineering,” he explained.
However, none of this progress is possible without a cultural shift. “Fostering a data-driven culture is key to obtaining a balance of agility and control,” Lind said, noting that the business case for data transformation must be driven by leadership, not spreadsheets.
Smith echoed that sentiment, emphasizing that data strategies must be viewed as evolving organisms. “A data strategy should never be finished—we’re constantly evolving as new use cases and capabilities emerge,” she said. Cboe’s AI Center of Excellence, for example, prioritizes education to ensure that teams not only adopt new tools, but understand them deeply and use them responsibly, she said.
Gerdeman believes the next major challenge lies in achieving a “single pane of glass” view—especially as private markets become a larger focus for institutional investors. “Nearly 40% of investment managers are undertaking significant operating model transformations—with data at the center—to get to this next stage of data maturity,” she said.
Despite the technical challenges, there’s a growing sense of optimism among data leaders that the industry is at a turning point.
“We cannot overhaul systems overnight and this transition will take time,” Lind acknowledged. “But we can begin to connect workflows and convey the data more efficiently.” He predicts more progress in the next two to three years than the industry has seen in the past two decades.
Ultimately, the shift toward connected, intelligent data environments is not just about technology—it’s about unlocking creativity, enabling discovery, and transforming how decisions are made. As Smith put it, “I constantly encourage our team to be curious and continuously learn and adapt to the rapidly evolving landscape.”
And in Gerdeman’s view, the building blocks for this new era are already being laid: flexible architectures, responsible AI practices, and the talent to make it all work. These are no longer optional—they’re becoming table stakes for any institution hoping to thrive in the future of data.
From siloed systems to cloud-native platforms, and from manual data entry to AI-driven intelligence, enterprise data management is undergoing a profound transformation. The firms that succeed will be those that embrace change, invest in modernization, and foster a culture of curiosity and continuous learning.
“We are at a pivotal moment—an opportunity to modernize data management and build a more resilient, agile, and interconnected financial ecosystem,” Lind concluded.
