In any Financial Services organisation, data sits at the heart of the operation. For a Chief Operating Officer, maintaining accurate, timely data is essential. Without this foundation, critical decisions, risk assessments, and client reporting could be undermined, leading to inefficiencies, reputational risks, and regulatory concerns. Yet, running a data system that ensures these qualities is no small task. The challenges increase exponentially when scaling such systems across an expanding business, making data management one of the most demanding, yet crucial, areas of focus for any asset manager.

The Importance of Accurate and Timely Data

At the core of any successful asset management firm lies its data. The COO must ensure that data flows efficiently and accurately through all parts of the business. From client portfolios to compliance reports, every piece of information needs to be reliable. This is particularly important given the speed at which markets move and the regulatory scrutiny financial institutions face. Timely data allows firms to respond swiftly to changes, while inaccuracies can lead to costly errors. Maintaining a robust data infrastructure is therefore not just a technical necessity but a fundamental component of high-quality operational performance.

Golden Data Sources: The Cornerstone of Quality

Golden data sources are a key focus in ensuring data quality. These sources serve as a single, verified point of truth for critical data sets across the organisation. However, the creation and maintenance of these sources are fraught with challenges. Cleaning up existing data to match a “golden” standard requires considerable effort and collaboration across teams. In many firms, this is compounded by the difficulty in driving adoption of these golden sources. Achieving widespread buy-in is no easy feat, as teams often resist changes to established processes or prefer to rely on their own datasets.

To address this, financial organisations are increasingly clarifying the roles of data owners, product owners, and data guardians. These roles are being defined more clearly to establish who is responsible for managing, overseeing, and safeguarding the accuracy of data. This clarity is crucial to promoting accountability and ensuring that data remains consistent across all departments.

Challenges in Defining Ownership

One of the most significant challenges faced by COOs in asset management is the lack of clarity around data ownership. While efforts are ongoing to delineate responsibility for data governance, progress is often slow. The presence of multiple business rules and differing interpretations across various systems complicates matters further. This fragmentation can lead to inconsistencies, making it difficult to pinpoint who is ultimately accountable when data issues arise.

Addressing these challenges requires both cultural and structural changes within the organisation. Data management is not merely a technical problem; it involves behavioural shifts that ensure everyone understands their role in the data lifecycle. As organisations seek to scale, a stronger sense of ownership and responsibility must be embedded into the business at all levels, not just within the IT or data teams.

Efficiency and Scaling: The Key to Unlocking Potential

The potential for efficiency gains through improved data management is immense. For a financial organisation, scaling operations effectively hinges on building a well-structured data model that links data producers and consumers seamlessly. By creating stronger connections between the teams that generate data and those that rely on it, firms can reduce duplication of effort, streamline workflows, and improve decision-making.

However, the complexity of managing data at scale should not be underestimated. As businesses grow, the number of systems and data sources increases, adding layers of complexity. To combat this, COOs must focus on identifying the most significant problem areas and prioritise improvements that offer the greatest return on investment. Incentivising teams to share and distribute data more effectively across the organisation is critical in overcoming these hurdles.

Golden Source Adoption and Accountability

Efforts to drive the adoption of golden data sources have already begun, with investment reviews increasingly centred around these verified datasets. When teams take data issues seriously and align themselves with these golden sources, significant improvements can be observed. Yet, there remains an understanding that, in certain contexts, data discrepancies may be acceptable. For example, different interpretations of data might be suitable depending on its use case, and not all variations represent a risk.

Ensuring accountability in this process is paramount. Teams must be encouraged to maintain a high level of data accuracy and to raise issues when they arise, but also be trusted to apply their judgement where appropriate. As a COO, fostering a culture of accountability without stifling flexibility is a delicate balance to strike.

Governance, Strategy, and Regulatory Challenges

One of the most challenging areas where data strategy needs to evolve is in client reporting. With increasing regulatory pressures and ever-changing compliance requirements, governance plays a crucial role in ensuring that data is accurate, timely, and compliant with legal standards. Firms are working to better delineate the roles involved in data consumption, production, and ownership. While not necessarily creating new roles, businesses are establishing more defined accountability structures to ensure that everyone understands their part in the data management process.

Data Ownership: Formal Roles Versus Embedded Responsibility

Some organisations are formalising data ownership as a distinct role, while others choose to embed these responsibilities into existing job functions. There is also a distinction between data owners and data guardians. Data guardians, often drawn from other areas of the business, take on additional responsibilities to oversee data governance and ensure quality, whereas data owners may be appointed specifically to manage certain data assets.

Exception reporting is increasingly becoming part of the data guardian’s responsibilities. This ensures that any issues with data are quickly flagged and addressed. However, organisational change can be slow, and questions remain as to whether data ownership should reside with an individual or a team. This is a critical consideration for COOs seeking to establish a clear, scalable data governance framework.

Tools, Lineage, and the Role of Technology

In the face of stringent regulatory demands, data lineage—the ability to trace the origin and flow of data—is a growing challenge. Firms are turning to tools from companies like Informatica and Microsoft to help manage this complexity. While these tools are useful, they are not perfect, and many organisations are still in the early stages of building centralised data marketplaces for easier access and management. This is a long-term project that requires careful planning and phased implementation.

Accountability and Empowering Data Guardians

As firms move towards a single-accountability model, the role of the data guardian becomes increasingly important. There are ongoing discussions about whether data guardians should oversee all data use cases or focus specifically on downstream data consumers. Client reporting, in particular, is an area where this accountability is being tested. Technology plays a critical role here, but ensuring data quality remains a challenge, particularly for portfolio managers and their clients.

Derived Data and Internal Disputes

Some organisations have established groups of experts to validate derived data before it is used or shared externally. However, this process can sometimes lead to disputes over who has the final say on data-related decisions. When disagreements arise, the decision often escalates to senior leadership, adding complexity to an already challenging environment.

External Pressures and Risk Management

Externally, many financial institutions feel uncertain about how to manage core data issues effectively. Data management is a costly endeavour, and regulators are increasingly scrutinising how firms handle data lineage and governance. The challenge for the COO is to balance the need for centralised governance with the requirement for flexibility to accommodate business-specific data needs.

The Role of AI in Data Management

AI is playing an increasing role in data management, particularly in automating tasks traditionally handled by data stewards. However, its usefulness largely depends on having clean, structured data in place. While AI shows promise in areas like data access—through natural language processing (NLP) to SQL conversion, for example—it is less effective in improving data quality or maintaining data over time. As a COO, it is important to recognise that while AI can offer support, addressing the root causes of data issues often lies in improving underlying business processes.

Conclusion: Simplification and Focus

Ultimately, for the COO of a financial services organisation, rationalising and simplifying data processes is key to achieving long-term success. Resources are finite, and firms must focus on high-impact areas that address the root causes of data governance challenges. By building clearer accountability structures, driving adoption of golden data sources, and investing in the right tools, financial organisations can ensure that their data strategies support both operational efficiency and regulatory compliance.

Although this article has focused on asset management firms, the lessons and strategies outlined here are just as applicable to other financial services organisations. Whether it be retail banking, insurance, or markets, all sectors of the financial industry face similar pressures to manage and govern their data effectively. The importance of accuracy, timeliness, and governance in data management transcends industry segments. Regulatory scrutiny, client demands, and the need for operational efficiency mean that all financial organisations must develop robust data frameworks.

Retail banks, for example, rely on accurate customer data for everything from credit assessments to personalisation of services. Insurance companies need reliable data to assess risk and calculate premiums, while wealth management firms must ensure that portfolio data is consistently accurate to meet client expectations. In all these sectors, poor data governance can lead to regulatory penalties, operational inefficiencies, and loss of client trust. The rise of digital banking, automated underwriting in insurance, and robo-advisors in wealth management further underscore the need for financial services organisations to manage data in a way that is scalable, secure, and adaptable to evolving technological landscapes.

Moreover, as financial services increasingly embrace AI and machine learning, the importance of clean, structured data becomes even more critical. Without a strong foundation in data governance, AI implementations may falter, producing unreliable outcomes. Therefore, the insights discussed—around data ownership, accountability, and technology adoption—are not confined to asset management but resonate across the entire financial services ecosystem. By prioritising high-quality data management, financial institutions of all types can improve their decision-making capabilities, enhance client services, and meet the ever-growing demands of regulators in today’s complex and data-driven world.