Financial institutions are facing greater complexity in managing market and referential data due to rapid growth in regulatory demands. This has led to escalating costs, making market data one of their top expenses. Better strategic approaches to data sourcing, integration and governance are needed to balance cost and performance. 

Balancing costs and performance in market data strategy

Many institutions have developed costly and non-transparent ‘spaghetti’ architectures, involving multiple data sources and providers serving overlapping user needs. This raises concerns about data integrity, accuracy, and redundancy. 

As a result, business users and procurement departments struggle to justify costs against the benefits of the current data management model. According to the Burton-Taylor survey of the global market-data industry players conducted in early 2024, global spend was forecast to exceed historic growth rates last year.¹ 

As financial institutions increasingly rely on market data to drive trading, risk management, and investment strategies, the cost of acquiring high-quality data has become a significant challenge. Market data providers often offer premium access to real-time, high-frequency, and historical data, but these costs can quickly add up, particularly when firms are subscribing to multiple data feeds across different asset classes. The balance between obtaining the data necessary for accurate analysis and controlling expenditure is critical. Without a strategic approach, firms risk overspending on data they may not fully utilize, or worse, compromising the quality of their analytics by opting for cheaper, less comprehensive data.

Categorizing into mission-critical and non-essential

To effectively balance cost and performance, financial firms must assess their specific data needs. Not all data streams are equal in terms of value—some may be crucial for real-time trading decisions, while others may only be necessary for periodic reporting or compliance. By categorizing data into mission-critical and non-essential categories, firms can streamline their data subscriptions, eliminate redundancies, and focus on the feeds that deliver the highest value. Moreover, leveraging advanced data management systems or consolidating data sources from multiple vendors can further help reduce costs while maintaining performance. This strategic approach enables firms to optimize their market data usage while ensuring they aren't overspending on unnecessary or redundant information.

Emerging pricing models and technologies 

Looking ahead, firms can take advantage of emerging pricing models and technologies to enhance their market data strategies. New models, such as pay-per-use or tiered pricing, allow organizations to pay only for the data they actually consume, providing greater flexibility and reducing waste. Additionally, technologies like data lakes and cloud-based solutions can provide more scalable and cost-effective storage and access options. By integrating artificial intelligence to analyze data more efficiently and automate decision-making, institutions can further enhance the performance of their market data strategies, ensuring that they can continue to make data-driven decisions without the burden of escalating costs.

Tackling fragmented market data in today's financial landscape

Market data in financial services often comes from a wide array of sources, including exchanges, vendors, and proprietary systems, leading to fragmented data that’s difficult to integrate. This fragmentation hampers accurate analysis and decision-making, as firms struggle to reconcile different data formats and access methods. The key challenge is ensuring that data is both consistent and accessible.

To address this, firms should prioritize data integration strategies, leveraging centralized platforms or data lakes to consolidate data from multiple sources. APIs and standardized data formats can simplify integration, allowing seamless access and improving data accuracy.

Adopting advanced data governance practices is also crucial. By establishing clear processes for data validation and quality assurance, financial firms can eliminate inconsistencies and enhance the reliability of their market data, supporting more informed decision-making.

Ensuring compliance in market data management: Strategies and tools

In today’s regulatory environment, financial institutions must ensure that their market data is managed in compliance with laws like MiFID II, GDPR, and Basel III. Failure to meet these regulations can result in fines, reputational damage, and operational disruptions. Additionally, each data vendor or exchange often has its own subscription model with varying terms, such as usage restrictions, redistribution clauses, and access limitations that must be adhered to.

To ensure compliance, firms should implement strong data governance frameworks, including robust audit trails, clear data lineage, and access controls. This helps ensure that data is accurate, transparent, and accessible only to authorized individuals. Firms must also track their subscription licenses and maintain a comprehensive catalogue of contracts with each vendor to ensure that data usage aligns with each vendor's terms, preventing legal issues from overuse or redistribution of proprietary data.

Utilizing automated compliance tools can streamline the process of monitoring data usage, managing vendor contracts, and identifying potential violations. These tools integrate with existing systems to simplify reporting, improve auditability, and reduce the risk of non-compliance.

How AI is reshaping market data strategy in financial services

The integration of AI with market data is transforming the way financial institutions analyze, trade, and make decisions. By enabling the automation of processes, improving predictive capabilities, and driving more personalized services, AI is taking financial analytics into the future. 

For a tech-savvy audience, this promises greater efficiency, smarter decision-making, and an edge in highly competitive markets. AI-powered algorithms can now analyze massive amounts of real-time and historical data, uncovering trends and insights that would be too complex for traditional models. This allows financial institutions to move from reactive to predictive decision-making, offering a competitive advantage in market forecasting, risk management, and trading.

AI’s ability to process and integrate diverse data sources in real-time has been a game changer in improving market data strategy. Financial services are increasingly leveraging AI-driven tools to process unstructured data—such as news articles, social media sentiment, and market commentary—alongside structured market data. 

This integration allows firms to gain a more comprehensive understanding of market movements, enabling them to act on insights instantly. For example, AI-powered trading algorithms can now execute trades based on predicted price movements with minimal latency, improving profitability while minimizing risk. With AI continuously adapting to new data, financial institutions can stay ahead of market shifts, enhancing the overall quality of their trading and investment strategies.

Looking ahead, AI is set to revolutionize how financial institutions personalize services and optimize client offerings. By analyzing individual client behaviors and preferences, AI can recommend personalized financial products, tailor risk assessments, and even provide customized investment strategies. This would allow firms to cater to a broader range of client needs, offering more accurate and relevant financial advice. 

As AI-driven predictive models continue to evolve, they will help financial institutions automate decision-making processes, streamline operations, and mitigate risks more effectively. The future of financial services will undoubtedly see deeper AI integration, making data-driven strategies more precise, efficient, and adaptable to market dynamics.

Conclusion 

The financial services industry is navigating an era of rapid technological change and rising data demands. To succeed in this evolving landscape, financial institutions must adopt strategic approaches to managing costs, integrating diverse data sources, and complying with regulations. By leveraging advanced technologies such as AI, firms can enhance predictive capabilities, streamline operations, and make more accurate data-driven decisions faster. 

Moving forward, financial institutions should prioritize robust data governance, adopt scalable data solutions like cloud technologies, and stay agile in response to emerging trends. These steps will help ensure that their data strategies remain competitive and compliant, positioning them for success in an increasingly complex market environment.

How Capco can help

At Capco, we specialize in helping financial institutions optimize their market data strategies. Our deep industry expertise in data management, regulatory compliance, and AI-driven analytics enables us to deliver tailored solutions that address the unique challenges of modern market data. Whether it's integrating cutting-edge technologies, streamlining data governance practices, or ensuring compliance with complex regulations, Capco consultants work closely with clients to provide actionable insights and implement innovative solutions. With our extensive experience in financial services, we help firms unlock the full potential of their data, reduce operational costs, and gain a competitive edge in the market. Partner with Capco to transform your market data strategy and achieve long-term success.



References

¹ Financial Market Data 2024 Kickoff Survey | Burton Taylor (burton-taylor.com)