AI, Machine Learning Will Drive Market Data Consumption
May 13, 2026 · Source: marketsmedia.com · Topic:
mica-regulation · 247-trading · crypto-defi-blockchain
AI/ML Impact on Market Data Delivery
80%
Percentage of asset managers who view AI and ML as key drivers over the next two years.
Real-Time Data Usage
65%
Percentage of respondents using real-time data throughout the trading day.
Cloud Data Connectivity Adoption
63%
Percentage of participants using public cloud for data connectivity, up from 30% in 2023.
⦿ Executive Snapshot
- What: A report highlights AI and machine learning's growing impact on market data consumption among asset managers.
- Who: SIX, Crisil Coalition Greenwich, asset managers, wealth managers, private banks.
- Why it matters: The findings underscore a significant shift in data consumption practices, driven by technological advancements and the need for real-time data in a 24/7 trading environment.
⦿ Key Developments
- 80% of asset managers view AI and ML as key drivers of market data delivery over the next two years.
- 65% of respondents use real-time data throughout the trading day, influenced by the rise of 24/7 trading.
- Almost 70% of participants expect a budget increase of 1% to 5% for market data, particularly in index, risk, regulatory, and crypto data.
- 63% of participants now use public cloud for data connectivity, up from 30% in 2023.
- 53% of respondents believe that cloud will enhance the delivery of streaming data.
⦿ Strategic Context
- The increasing integration of AI and ML in capital markets reflects broader trends toward automation and efficiency in financial operations.
- The shift from direct-from-source data to reliance on market data vendors signifies evolving preferences in data sourcing and delivery models.
⦿ Strategic Implications
- Immediate implications include enhanced data delivery capabilities, enabling firms to make more informed investment decisions and manage risk effectively.
- Long-term, the focus on AI/ML and cloud infrastructure could reshape market data management practices, requiring firms to invest in robust data governance.
⦿ Risks & Constraints
- Potential risks include challenges in data quality and accuracy as firms adapt to new technologies and seek diverse data sources.
- Regulatory complexities may pose obstacles, necessitating firms to develop comprehensive data management practices to navigate compliance effectively.
⦿ Watchlist / Forward Signals
- Future developments in AI/ML applications in market data and cloud infrastructure adoption will be crucial to monitor.
- Increased spending on market data and shifts in data sourcing strategies will signal firms' adaptation to the evolving landscape.
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