I recently had the opportunity to speak at AssetOps Toronto, which brought together operational leaders from across the asset management industry with a shared focus: translating strategic dialogue into meaningful, sustained operational transformation.
What stood out immediately was how aligned the industry has become around a central theme. We are entering a phase where data, not systems, is the true differentiator.
From systems to data
For many years, firms have invested heavily in platforms to drive scale and efficiency. Order management, accounting, and execution systems were seen as the foundation of operational excellence. That dynamic is now shifting.
Execution is increasingly standardized, and the real value is moving toward analytics, modelling, and insight generation in an accelerated environment to reflect that while trades are done in T+1 most of the trade support infrastructure is still in batch mode which presents increased pressures for timely reporting for portfolio construction and management. As a result, the key question is no longer which system(s) a firm uses, but whether they can trust, scale, and fully leverage its data.
This is especially evident as firms try to bring together public and private market data into a unified view. The challenge is not the systems themselves, but the complexity, inconsistency, and fragmentation of the underlying data for holistic, sophisticated views of both public and private assets.
Data as the foundation of trust
Institutional client expectations continue to rise, particularly in increasingly risk-aware environments. Firms are expected to deliver consistent, transparent, and audit-ready data, underpinned by robust governance frameworks and clear accountability.
These are no longer points of differentiation. They represent fundamental requirements for operating effectively in today’s market, regardless of firm size or investment strategy.
One of the biggest challenges remains ensuring that all stakeholders are working from the same data snapshot in time and presenting a consistent narrative/algorithm methodologies. Even small discrepancies on decimals can cause material differences across reports, risk metrics, or performance outputs can quickly undermine confidence with clients and line managers alike.
Scaling data in an AI-driven environment
The rapid adoption of AI is driving an unprecedented increase in demand for data at scale. Firms are expanding their use of deep historical datasets, incorporating predictive and forward-looking inputs, and leveraging multiple sources to validate and benchmark outputs.
This shift reflects a fundamental change in approach. It is no longer sufficient to rely on the traditional golden copy as the only or single trusted dataset; instead, firms must effectively manage, reconcile, and govern multiple data sources simultaneously.
At the same time, AI heightens the importance of data quality. Any deficiencies in underlying data are amplified through AI-driven processes. Moreover, less experienced users may not always identify inaccuracies in outputs, reinforcing the need for strong governance frameworks and controlled data environments to enable responsible and effective adoption.
Governance, integration, and what comes next
As data volumes and complexity continue to increase, governance must evolve in parallel. Firms require clear ownership across the data lifecycle, robust validation frameworks, and greater control over how data is accessed, governed, and applied.
At Rimes, this sits at the core of our approach. We continuously validate and manage data across a global network of more than 800 vendors, ensuring the accuracy, consistency, and reliability that institutional investors depend on and we have for 30 years.
In tandem, firms are moving toward more integrated operating models, anchored by a unified data environment. Breaking down silos across trading, valuation, and reporting functions enables analytics and AI to operate on a consistent and trusted foundation.
Looking ahead, several priorities are becoming non-negotiable for firms seeking to maintain institutional trust and remain competitive:
- Dynamic, adaptive data governance
- Consistent, high-quality reporting
- Integrated, scalable data architectures
- Practical, controlled adoption of AI
- Treating data as a strategic asset at the core of the operating model
AssetOps Toronto reinforced a clear and accelerating shift across the industry. Data has become the foundation for performance, trust, and sustainable growth. Firms that make the necessary investments to strengthen their data capabilities and tech modernization to support data today will be best positioned to compete and lead in the next phase of the global marketplaces competitively.
