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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...
The race to implement artificial intelligence at scale within financial institutions’ technology stacks has made it imperative that they ensure their data foundations are solid. Without trusted data, AI is a potential minefield of erroneous outputs, hallucinated responses and inaccurate analyses. This compromises leaders’ ability to make well-informed decisions and poses the risk they’ll make...
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.
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...