AI initiatives depend on the data behind them. In capital markets, that means preparing market data so models and agents can consume it, understand its context, and use it consistently across research and production workflows.
In this episode of KX Pulse, Dan Tovey speaks with Mick Hittesdorf, Product Architect for OneTick products, about what makes market data AI-ready and why the data foundation can determine whether an AI initiative moves successfully from pilot to production.
What Makes Market Data AI-Ready?
AI-ready data needs to work within the workflows that models and agents depend on.
For Mick, that starts with structure. Data needs to be organized and provided at a level of granularity that an agent can consume effectively. It also needs metadata that describes what the data represents and provides the context required to interpret it correctly.
Time is an important part of that context in capital markets. Prices change, identifiers change, corporate actions take effect, and the meaning of a market event depends on when it happened.
The final element is validation. Firms need processes that can test the quality of the data systematically and establish whether it is suitable for the AI workload using it.
Together, structure, context, and validation provide a practical basis for assessing whether market data is ready for AI.
AI Starts With the Data Foundation
AI attracts attention because of what firms hope to build with it: new research capabilities, automated workflows, and agents that can work across increasingly complex information.
Much of the work required to make those applications successful happens earlier in the process.
Market data has to be collected, ingested, cleaned, transformed, cataloged, and validated before an AI system can use it effectively. These are familiar requirements for quantitative research and machine learning, and they remain important as firms introduce large language models and agentic AI.
The quality of that foundation also affects how much confidence teams can place in an AI output. Large language models can produce convincing responses even when the underlying information is incomplete or incorrect. Reliable data gives firms a stronger basis for assessing those responses and tracing them back to the information the system used.
Why Temporal Context Matters
Market data can be accurate at one point in time and inaccurate in another context.
A change in a company’s ticker symbol is a simple example. The historical record may refer to one identifier before a corporate action and another afterwards. An AI system needs enough context to understand that those records relate to the same underlying company.
That requires consistent handling of corporate actions, historical symbology, mappings, and other changes that affect how market data should be interpreted over time.
For AI workloads, preserving this temporal context reduces ambiguity. It helps models and agents understand the state of the market at the time represented by the data rather than interpreting historical information without its original context.
Connecting Structured and Unstructured Data
AI applications increasingly draw on information beyond structured market data.
Research notes, regulatory filings, company reports, news, social media, and other unstructured sources can add information that helps explain what was happening around a market event.
Bringing these sources together introduces another data-readiness challenge. The information still needs a consistent time dimension so an AI system can relate a document, statement, or event to the corresponding market context.
Combining structured and unstructured information in this way can give models and agents a more complete basis for analysis while preserving a clear relationship between the information and the time at which it was relevant.
From Assumed Data Quality to Verified Data Quality
Data quality becomes harder to treat as an assumption when AI moves into production.
Firms need to know whether their data meets defined expectations for factors such as timeliness, accuracy, and consistency. That requires repeatable testing rather than relying on the absence of visible problems.
Mick discusses the work underway with TrueTick, a new product that will apply systematic data quality testing across market data. This includes regular validation against explicit quality criteria and greater transparency into how data has been assessed.
The goal is to make data quality measurable. When teams can see how data has been tested and where it meets defined standards, they have a clearer basis for deciding whether it is fit for research, machine learning, or agentic AI workflows.
Moving AI From Pilot to Production
A successful AI pilot does not automatically establish that the underlying data foundation is ready for production.
Pilots often operate within a controlled scope. Production introduces more data, more users, more dependencies, and greater expectations around consistency and reliability.
Weaknesses in the data foundation can become more visible at that stage. Data defects can affect results, and repeated problems can reduce stakeholder confidence in both the data and the AI initiative using it.
Mick recommends starting with the outcome the organization wants to achieve and working backwards to the data requirements needed to support it.
That means giving data quality the same attention as the application built on top of it and putting systematic validation in place before problems reach stakeholders.
Four Questions for Data Leaders
1. Can Our AI Systems Consume the Data Effectively?
Assess whether data is structured, aggregated, and provided at the right level of granularity for the models and agents that need to use it.
2. Does the Data Include Enough Context?
Check whether metadata, historical mappings, corporate actions, and temporal information give the system enough information to interpret each record correctly.
3. Can We Demonstrate Data Quality?
Establish repeatable tests for the dimensions of quality that matter to the workload, including accuracy, consistency, and timeliness.
4. Will the Data Foundation Hold Up in Production?
Work backwards from the intended AI outcome and identify the data dependencies that will need to operate consistently when the workload moves beyond a controlled pilot.
Building an AI-Ready Market Data Foundation With KX
OneTick Market Data gives analysts and researchers access to market data for research, backtesting, and AI-related workflows.
Preparing that data requires processes around ingestion, normalization, corporate actions, historical mappings, and quality validation. These controls help preserve the market context that models and agents need when working across historical and current information.
KX is also extending its approach to systematic market data quality testing, with a focus on giving teams clearer measures of timeliness, accuracy, consistency, and other dimensions of data quality.
For firms building AI applications in capital markets, the objective is straightforward: establish a data foundation that can support the workload from initial research through production use.
Build the Data Foundation for Capital Markets AI
More information on TrueTick will be shared soon. In the meantime, explore OneTick Market Data to learn how managed market data can support the quality, consistency, and context needed for AI and analytics workflows.
To discuss your AI data readiness challenges and how KX can help, talk to our team.
Explore Related AI Resources
- Ashok Reddy on Temporal Integrity and Trustworthy AI in Capital Markets
- Erin Stanton on how Virtu Financial is redefining data readiness for AI success
- Solving The Longitude Problem in Capital Markets: Why AI Needs Temporal Precision
- How to Query OneTick Cloud Market Data in KDB-X
- Agentic AI in Capital Markets Has a Data Readiness Problem
- Stop the Data Tax With Managed Market Data
