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For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...
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Data Quality Meaning and Importance Transformed by AI: DMS NYC Preview
The data management space has been transformed by artificial intelligence into one that not only seeks to prise maximum value from institutions’ data but also spends almost as much resource ensuring the quality of that information. Without optimised data, the AI and agentic tools to which it is deployed will churn out sub-optimal results. It’s...
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Now in its 16th year, Data Management Summit (DMS) NYC returns on September 17th 2026, to explore how to use data and AI to drive measurable business outcomes – reliably, repeatedly and at scale.
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Alternative Data in Capital Markets Handbook 2026
Alternative data adoption in capital markets has moved into a more disciplined phase. Simply possessing a dataset is no longer a differentiator – the priority now is how data is sourced, synthesised and deployed, and how quickly granular, disaggregated signals can be turned into validated, decision-ready insight. The 2026 edition of the Alternative Data in...



