David Tattan
Asset managers today operate across increasingly complex combinations of portfolio management systems, execution platforms and data providers, each selected to serve a specific purpose. Each component can perform well in isolation. The challenge emerges in how they work together and whether a firm’s view of positions, risk and execution is actually consistent across them. In normal conditions, small inconsistencies are tolerable. Under tighter cycles like T+1 they become operational risk.
Static data: The quiet determinant of accuracy
Many firms today operate on increasingly integrated data foundations. When market participants think about data, attention typically gravitates towards real-time feeds, latency and signal quality. However, the stability of an asset managers operating model also rests on how consistently underlying reference data is applied, such as the security master, instrument identifiers, contract specifications, naming conventions and corporate action treatment.
The issue is often not the data itself, but how it is consistently carried and interpreted across PMS, EMS and by counterparties.
If that static layer is inconsistent across systems, the consequences can be material. Positions may be mis-stated. Exposures may be calculated differently in portfolio management systems and execution systems. Reconciliations may require manual intervention. In extreme cases, trading decisions can be made on the basis of incomplete or misaligned information.
In other words, even the most advanced trading tools cannot compensate for misaligned reference data. Accuracy at the foundation determines reliability throughout the workflow.
The best-of-breed challenge
Over the past decade, many asset managers have adopted a best-of-breed approach to technology selection, combining separate portfolio management, execution and data systems to optimise capability in each area. Individually, these decisions are rational. Collectively, they create a fragmented operating model where consistency is assumed but not guaranteed.
When PMS and EMS platforms rely on differently implemented data across systems, even subtle discrepancies in identifiers or corporate action processing can create reconciliation friction. For lean operations teams, resolving those differences consumes time and introduces avoidable risk.
In practice, discrepancies may be rare. What concerns senior management is the possibility that they could occur at all - especially at a time when investors, boards and counterparties are applying closer scrutiny.
The risk is not that systems fail but that they remain technically correct but operationally misaligned. Consistency, in this context, is a workflow question.
The prime broker and custodian perspective
This concern is amplified when viewed through the lens of prime brokers and custodians. These counterparties are not just consuming data, they are reconciling it against their own view of the positions, financing and exposure. When discrepancies appear, the issue is rarely a single data point. It is a breakdown in how the data and execution flows connect across the manager’s stack.
Differences in static data treatment can cascade into valuation discrepancies or exposure mismatches. Even if those differences can be resolved internally, they add unnecessary friction to relationships that depend on clarity and alignment.
In that context, market data consistency is not simply an internal efficiency issue. It underpins external credibility. It supports cleaner reconciliation, smoother reporting and greater confidence across counterparties.
From data quality to workflow integrity
High-quality data on its own is essential. But quality alone is not sufficient. The critical question is how that data flows through their operating environment. Are data, execution and workflows aligned or do they operate as parallel systems.
That alignment requires a shared data foundation across the portfolio management and execution, with workflows aligned operating from a single, consistent view.
Whether a firm operates across portfolio management, execution, or both, the underlying data layer remains consistent. The portfolio manager and the trader are not looking at parallel versions of the same instrument, they are operating from a unified view.
For firms that have historically combined separate systems and data sources, this addresses a common question: where does the data originate, how is it validated, and what happens if there is a discrepancy between systems? A unified data foundation reduces that concern at source.
A scalable model for emerging managers
The case for consistency is particularly strong for emerging and mid-sized funds.
These firms often operate with lean teams. They cannot afford to dedicate resources to manual data cleansing or reconciling inconsistencies between platforms. At the same time, they must demonstrate institutional robustness to allocators and prime brokers from day one.
An operating model built on consistent reference data supports that objective. It reduces operational overhead, limits avoidable risk and enables the firm to scale without re-architecting its infrastructure as assets grow.
Consistency, in this sense, becomes an enabler of growth. It allows managers to focus on investment performance rather than operational remediation.
Aligning data and execution
Market data and trading systems are often evaluated separately: one for insight, the other for action. In practice, they are inseparably linked. Data informs decisions. Execution expresses them. If the two are not aligned, the risk does not sit in isolation - it propagates across the operating model.
The strategic advantage therefore lies not simply in accessing high-quality market data, nor solely in deploying advanced execution tools, but in aligning both within a stable, coherent operating model.
For asset management leaders focused on control, governance and sustainable growth, data consistency sits at the core of that model. It ensures that the portfolio manager’s view of positions, the trader’s execution decisions and the operations team’s reconciliations are aligned, drawing from the same underlying data rather than parallel interpretations of it.
Aligning static data and execution infrastructure may not generate alpha directly, but it strengthens the framework around it. Over time, that structural integrity becomes a competitive advantage in its own right.
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