Rewriting the Rules of Market Data Management

This whitepaper explores the key challenges in market data operations, the limitations of existing solutions, and how AI and predictive analytics can drive transformative efficiencies, transforming market data management from a reactive, cost-heavy function into a proactive, strategic, and value-generating operation. By leveraging automation, AI-driven benchmarking, and intelligent spend optimization, financial institutions can achieve cost reduction, improved compliance, and enhanced decision-making.

Market data is the lifeblood of financial institutions, fuelling trading strategies, risk management, and regulatory reporting. Despite its critical role, managing market data is a complex and costly discipline. The industry spends $42 billion today, and we project it will reach $67 billion by 2030. Firms face rising costs, opaque pricing structures, procurement inefficiencies, and aggressive vendor audits, making strategic data management more essential than ever.

We estimate firms overspend by 30% on market data due to poor negotiating tools, inefficient contract structures, and no benchmarking insights. Additionally, demand management inefficiencies account for $6 billion in wasted costs annually.

The growing complexity of market data contracts, licensing agreements, and vendor-dominated relationships further exacerbate these challenges, locking firms into rigid pricing structures with limited flexibility.

Beyond cost inefficiencies, compliance risks add another layer of complexity. Market data contracts impose strict usage restrictions, and firms risk fines of up to 30% of total spend if data is shared or used beyond contractual terms. With increasing vendor scrutiny, firms must maintain detailed audit trails, enforceable access policies, and must align with evolving commercial vendor terms.

The traditional approach to market data management is no longer sustainable. Manual workflows, fragmented procurement processes, and opaque contracts create inefficiencies and financial strain. To overcome these challenges, financial firms must embrace automation, AI-driven analytics, and smarter procurement strategies.

This whitepaper outlines the key challenges in market data management and how AI-driven solutions can help financial firms optimize procurement, reduce costs, enhance governance, and turn market data into a strategic asset.

One of the most pressing challenges in market data management is limited usage and spend transparency. Financial firms collectively spend $42 billion annually, and this figure is expected to reach $67 billion2 by the end of the decade. However, 40% of this spend is unnecessary, largely due to inefficient tracking, opaque pricing, and missed optimization opportunities. Without a centralized contract repository, firms struggle to analyze vendor agreements, track renewal deadlines, and optimize pricing structures, leading to excess expenditures and missed cost-saving opportunities.

Another major issue is ineffective contract alignment. Based on Sia’s expertise, firms overspend compared to peers by 30% because of poor negotiation leverage and a lack of benchmarking insights. Many financial institutions remain locked into vendor-dominated contracts, which lack flexibility and prevent them from optimizing spend based on actual data consumption. Without industry-aligned pricing structures and stronger negotiation strategies, firms are unable to capitalize on potential cost savings, leading to inflated market data expenses.

Demand management inefficiencies further exacerbate the problem. Approximately 15% of total market data spend—equivalent to $6 billion annually—is wasted due to redundant subscriptions, poor demand forecasting, and a lack of insight into market data usage. Vendor fragmentation results in duplicative contracts and underutilized services, increasing costs on institutions. Additionally, the asymmetry of information between data buyers and sellers makes it difficult for firms to align their spending with actual business needs, often leading to unnecessary expenditures on services that provide minimal value.

Beyond cost management, compliance and governance risks present significant exposures. Market data contracts impose strict restrictions on data sharing and redistribution, and violating these terms can result in penalties ranging from 10% to 30% of total market data spend. On average, firms lose between $2 million and $3 million annually in fines and restitution due to noncompliance. As regulatory scrutiny intensifies, financial institutions must adopt more robust controls and disciplines to ensure that data access, usage, and distribution remain within legal and contractual boundaries. Without automated compliance tools and structured governance mechanisms, firms risk severe financial and reputational damage.

Addressing these challenges requires a fundamental shift in how firms procure, manage, and optimize market data. AI-driven solutions, automation, and predictive analytics offer financial institutions a way to reduce inefficiencies, improve governance, and achieve cost transparency.

Efficient market data management starts with a focused approach across contract inventory, governance, and vendor deal-making. Addressing these areas helps reduce spend, improve visibility, and protect the organization from exposure to high penalties from predatory vendor behaviours. The following are the key pillars and principles for market data management.

     1. Centralized Contract Repository

A centralized contract repository is essential for tracking market data expenses, analyzing vendor agreements, and identifying cost-saving opportunities. Without a structured system, firms risk overspending, missing renewal deadlines, and losing visibility over service usage. Our research indicates that organizations lose an average of 10% of annual spend due to poor contract management. By consolidating contracts into a single repository, firms can achieve greater spend transparency, optimize renewals, and ensure alignment with operational needs.

     2. Governance & Risk Control

Strong governance and risk control disciplines are crucial for maintaining compliance with vendors. Financial institutions must implement clear policy frameworks, assign data stewardship responsibilities, and enforce access controls to mitigate legal and financial exposure. Without well-defined governance structures, firms risk data misuse, contract misalignment, and vendor audit penalties. Leveraging AI-powered auditing tools can further enhance compliance by detecting policy violations in real time, reducing risk, and improving operational efficiency.

     3. Data-Driven Deal Management

A data-driven approach to vendor management is critical for maximizing the value derived from market data providers. Financial institutions can leverage analytics to benchmark contract pricing against industry standards, helping to identify cost inefficiencies and negotiate more favorable terms. With AI-driven market intelligence improving negotiation outcomes by up to 20%, firms can assess vendor performance, track service usage, and refine pricing strategies.

Implementing these pillars — centralized contract oversight, strong governance, and data-led vendor strategies — enables firms to control spend, improve compliance, and unlock value from market data. Firms that embrace these strategies, supported by AI and automation, will be positioned to achieve long-term efficiency and cost control in market data management.

Market data management remains fragmented, reactive, and heavily manual characterized by disconnected systems, limited visibility into contract terms, and minimal oversight of usage and vendor performance. To solve this, firms must move toward an integrated model that combines AI, real-time benchmarking, business intelligence, and human expertise.

AI can automate high-effort tasks such as contract parsing, entitlement tracking, invoice validation, and compliance checks. Business intelligence enables dynamic reporting, proactive vendor performance management, and benchmarking against peer firms. However, automation alone is not enough — success depends on domain experts who can interpret insights, apply business context, and drive strategic decisions.

By uniting intelligent automation with expert oversight, this model transforms market data from an administrative burden into a proactive, value-generating function. It enhances cost transparency, reduces risk, and supports smarter, faster decision-making — positioning firms to manage market data with greater agility, accuracy, and impact.

Artificial intelligence is reshaping how financial institutions manage market data. As costs rise and compliance demands grow, AI offers a practical path forward — improving visibility, cutting waste, and supporting smarter, faster decision-making. Firms that adopt this balanced, AI-driven model will transform market data management from a cost burden into a strategic advantage — unlocking long-term value, operational resilience, and competitive strength in an increasingly demanding, fast changing, and complex data economy.