By Grace Osemerin, MD Valuations Senior Pricing Representative, Confluence
In today’s investment environment, where speed and precision drive returns, one thing remains constant: the importance of high-quality market data. Whether you’re managing a complex fund, building a risk model, or seeking performance in volatile markets, your outputs are only as good as your inputs.
Poor data quality is a liability. Inaccurate, incomplete, or delayed information leads to mispriced assets, regulatory trouble, flawed analytics, and missed opportunities. According to Gartner, the average annual cost of poor data quality to businesses is $12.9 million. Across financial services, that price is paid in operational drag, reputational risk, and lost returns.
Yet despite years of investment, many firms still struggle to ensure data integrity at scale. The challenge now is not just accessing more data – it’s about ensuring the data that flows into decision-making is clean, contextual, and trusted.
Data as the New Alpha
For investment professionals, high-quality data reduces errors and creates an edge.
Real-time, structured data enables smarter portfolio construction and asset allocation. Managers can dynamically adjust exposures, align strategies with macro and ESG factors, and better match investments to client goals. Factor-based models, predictive analytics, and scenario testing only work if the underlying data is timely and reliable.
Even the best minds and algorithms can’t outperform if they’re fed flawed information. A mispriced bond, a stale NAV, or a duplicated position can skew models and introduce risk across portfolios.
Data quality is also key to automation. When every desk – from trading to compliance – shares a consistent data foundation, organisations can streamline workflows, shorten reconciliation time, and make decisions faster. That agility can be the difference between capitalising on market shifts or missing them entirely.
Regulatory Clarity, Risk Precision
High-quality data has become a regulatory imperative. Firms are under growing pressure to meet expanding requirements under SFDR, MiFID II, the SEC’s new Form PF rules, and more. In this environment, accuracy, traceability, and consistency are essential.
Complete and validated data allows risk and compliance teams to confidently calculate VaR, monitor liquidity profiles, run stress tests, and respond quickly to disclosure requests. It reduces the likelihood of reporting errors – and the reputational or legal damage that follows.
Crucially, high-quality data makes risk models more accurate and useful. It gives firms a clearer view of their exposure in fast-moving markets, allowing them to respond before small misalignments become big problems.
Challenges to Getting it Right
Achieving data excellence is not a simple plug-and-play exercise. Firms often face a complex web of legacy systems, siloed teams, inconsistent taxonomies, and manual workarounds. Without a unified approach to data governance and stewardship, errors multiply and trust erodes.
Leaders are responding. Many of the most forward-thinking firms have appointed chief data officers and embedded data quality into strategic workflows. They’re investing in modern architectures – including centralised data lakes and real-time pipelines – and partnering with specialist providers to handle complex asset classes or difficult-to-value instruments.
Crucially, firms are recognising that data quality is not just a back-office issue. It’s a performance lever. It belongs at the centre of investment strategy.
Better Data, Better Decisions
The link between data maturity and financial outperformance is increasingly clear. McKinsey estimates that data-driven firms are significantly more likely to acquire customers and achieve profitability.
High-quality data supports more than compliance and efficiency. It enables personalised products, sharper client segmentation, better risk-adjusted returns, and next-generation use of AI.
Firms with strong data infrastructure are embracing AI for predictive modelling, anomaly detection, and decision support. As investment in automation accelerates, the need for clean, well-structured data becomes critical. Generative engines are only as effective as their inputs – and poor inputs lead to poor intelligence
The Path Forward
Investment firms that treat data as infrastructure – not just an input – are laying the groundwork for outperformance. That means building a culture of quality, embedding governance, and choosing the right partners to help clean, validate, and manage increasingly complex datasets.
In markets where milliseconds matter and expectations are rising, the firms that can rely on their data will move faster, act smarter, and adapt better.
Good data drives good outcomes. The ones who get it right will lead.
