David Anderson - Talking Market Data copertina

David Anderson - Talking Market Data

David Anderson - Talking Market Data

Di: David Anderson
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If you work in financial 'Market Data' be you Data Vendor; Exchange; Technology/Service Provider; and especially if you work in a customer firm - we will be discussing all the burning issues and educating on core elements.

© 2026 David Anderson - Talking Market Data Economia Gestione e leadership Management
Episodi
  • Episode 53 Andrea Young of KingsleyWood

    Episode 53 Andrea Young of KingsleyWood
    Sep 1 2026
    David has a conversation with Andrea Young a lawyer specilising in Market Datahttps://www.linkedin.com/in/andrea-c-e-young/https://www.kingsley-wood.com/This episode explores how market data licensing has become more complex, why a legal perspective matters, and where AI is starting to reshape both contracts and compliance. David Anderson speaks with Andrea Young, a lawyer building a market data practice, about the practical problems facing suppliers and data consumers. We discuss why legacy agreements are so hard to manage, why standardization remains elusive, and why AI is likely to force faster modernization across the industry.Key topics• Andrea Young explains her path into market data over the past five years, moving from corporate compliance into data compliance after working in Bermuda and London.• She shares why lawyers are still rare in market data and why the industry cannot be learned from textbooks alone.• David and Andrea discuss the gap between legal drafting and real market data use cases, especially when in-house legal teams do not fully understand licensing nuance.• Andrea describes why simplified licensing is best practice and why one exchange’s streamlined approach stood out as a model.• They unpack how decades of addendums, schedules, and outdated MSAs have created compliance chaos, especially when contracts still reference CD-ROMs and floppy disks.• The conversation covers why audit pressure makes the market more adversarial and why vendors often prefer ambiguity that strengthens their position.• They examine why industry-wide standards are unlikely, since exchanges and vendors monetize data differently and define terms like non-display use differently.• AI becomes the major focus toward the end, especially how it affects licensing, derived data, storage, distribution, attribution, and purge obligations.• Andrea argues that AI needs its own separate policy rather than being bolted onto existing non-display or derived data terms.• They discuss practical AI issues such as external versus internal models, training versus inference, retrieval augmented generation, and whether data can ever truly be purged from a model.• The episode closes on pricing pressure, with David arguing that AI will make more data valuable and more contentious, while Andrea warns that suppliers and consumers both need to stay proactive. Timestamps00:00 - Welcome and Andrea Young’s market data background01:25 - From corporate compliance to market data licensing03:15 - Why lawyers are rare in market data04:11 - Building a market data practice inside a law firm05:52 - How legal and M&A work intersect through relicensing06:54 - Drafting contracts versus compliance after the fact08:18 - Why clarity is the best licensing practice09:31 - Why market data contracts became so messy11:15 - Old MSAs, addendums, and unreadable contract archives13:25 - When messy licensing may benefit vendors in audits17:01 - Why standard contracts are unlikely to happen18:08 - Why definitions like non-display use still vary20:17 - Digital rights management and why automation is still far off23:31 - Why AI cannot yet read legacy licensing cleanly25:58 - Why advice depends on whether you serve suppliers or consumers26:44 - How audits are making the market more adversarial27:36 - Streamlining licensing for long-term business value28:46 - Advice for newer businesses and FinTechs30:34 - Why even large institutions often lack data governance visibility31:12 - The two AI buckets: business usage and operational usage33:13 - How AI is changing the legal field already34:15 - Why AI touches derived data, redistribution, and storage all at once37:03 - Why AI needs its own licensing policy38:36 - Training versus inference, and where RAG fits in40:12 - Purge clauses and the problem of deleting model-trained data41:32 - Whether contracts will need to name technologies like LLMs and RAG44:50 - How vendors are starting to package AI-specific data offerings46:25 - Attribution, watermarking, and AI-generated content47:27 - Copyright questions around AI-generated text48:25 - Why AI is useful but still needs human judgment49:52 - Using AI for search, productivity, and everyday work50:42 - The risk of losing junior talent if AI replaces entry-level work52:45 - The pricing question: whether AI makes older data more valuable55:13 - Why AI could make the market even more contentious56:44 - Why legal expertise in market data is becoming more important58:16 - Final advice: stay proactive, don’t wait for audits or AI incidents59:26 - Training the next generation of lawyers in market data60:50 - Closing thoughts and thanksDAVID ANDERSON -- https://www.linkedin.com/in/atradia/
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    1 ora e 1 min
  • Episode 52 Mike Salk

    Episode 52 Mike Salk
    Aug 4 2026

    Mike Salk - LinkedIn - https://www.linkedin.com/in/mikesalk

    In this episode, Mike Salk shares his extensive experience in market data, discusses the evolution of AI and its impact on financial markets, and explores the challenges of data quality, standardization, and trust in the industry.
    key topics:
    • The evolution of AI and its application in financial markets
    • Challenges of data quality, metadata, and standardization
    • The role of MCP and data protocols in data delivery
    • Trust and transparency between data vendors and consumers
    • The impact of AI on data management and cost efficiency

    DAVID ANDERSON -- https://www.linkedin.com/in/atradia/
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    58 min
  • Episode 51 Oracle

    Episode 51 Oracle
    Jul 7 2026

    In this episode, James Calise and Jason Murphy from Oracle discuss the evolution of cloud infrastructure, AI integration, and the unique challenges faced by the financial market data industry. They explore how Oracle is differentiating itself with enterprise-grade performance, security, and regional deployment capabilities.

    Oracle Cloud Infrastructure - https://www.oracle.com/cloud/

    Jason Murphy LinkedIn - Jason Murphy | LinkedIn

    James Calise LinkedIn - https://www.linkedin.com/in/jamescalise/

    key topics

    Oracle's journey to cloud and enterprise focus

    Performance and security in cloud infrastructure

    AI integration in financial services

    Regional and sovereign cloud deployment

    Market data distribution and latency challenges

    Regulatory considerations for cloud and AI

    Chapters

    00:00 Introduction to Oracle's Cloud Journey

    02:43 Oracle's Unique Position in the Cloud Market

    05:22 Transitioning from On-Prem to Cloud Solutions

    08:37 Jason's Journey from Exegy to Oracle

    11:34 Differentiating Oracle Cloud Infrastructure

    14:44 Key Challenges in Capital Markets

    17:38 The Importance of Network and Resiliency

    20:22 Market Data and Cloud Integration

    23:35 AI's Role in Market Data and Cloud Infrastructure

    32:12 The Evolution of AI and Market Data

    34:35 Cloud as the Democratizer of AI

    37:34 Navigating AI Models and Cloud Relationships

    42:24 Determinism vs. Probabilistic AI in Financial Markets

    46:59 Regulatory Landscape and Data Sovereignty

    54:25 The Future of AI and Entrepreneurial Opportunities

    DAVID ANDERSON -- https://www.linkedin.com/in/atradia/
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    58 min
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