In today’s trading landscape, real-time data is no longer a differentiator – it’s table stakes. With every firm plugged into immediate market updates, the real challenge is how to leverage historical data to sharpen strategies, refine execution and generate alpha.
Our latest Hive Mind Guide to Historical Data, created in partnership with BMLL Technologies, uncovers a paradox at the heart of the industry: while historical data is widely recognised as essential, many buy side firms still struggle to integrate it effectively into their day-to-day decision-making.
Why Historical Data Matters More Than Ever
Historical data underpins everything from forecasting liquidity trends to stress-testing models across different market regimes. Done right, it fuels more accurate cost predictions pre-trade and validates strategies post-trade. But messy, incomplete, or inconsistent datasets remain a major stumbling block – often forcing traders and analysts to spend more time cleaning numbers than extracting insight.
As Dr Elliot Banks, Chief Product Officer at BMLL, notes:
Consigning traders and data scientists to spend 80% of their day cleaning and organising poor-quality historical data is a waste of valuable time and resources.”
The Barriers Holding Firms Back
From our community’s perspective, several recurring challenges emerged:
Rising costs and uneven access – Smaller firms find the price of comprehensive data prohibitive, while larger institutions benefit from economies of scale.
Integration struggles – Legacy systems and fragmented APIs slow down workflows and create inefficiencies at the point of execution.
Data quality gaps – Even the best AI models fail when trained on flawed or interpolated inputs, leading to mispricing and costly errors.
What Leaders Are Doing Differently
The firms making progress share a few common traits:
They are consolidating data streams into a “single source of truth” within EMS/OMS environments.
They invest in tools that visualise and democratise access to insights across desks, from trading to compliance.
They adopt hybrid models that blend internal repositories with trusted external feeds to balance depth, reliability and proprietary advantage.
The Way Forward
As volatility rises and markets become increasingly algorithm-driven, pristine historical data is no longer optional – it’s mission critical. The firms who succeed will be those who move beyond patchwork solutions and invest in clean, consolidated and easily accessible datasets that power smarter execution and scalable innovation.