Capital-markets firms are modernizing the trade lifecycle amid growing data volumes, higher expectations for real-time insight, AI initiatives moving toward production, and shorter settlement cycles, according to a Databricks blog post featuring Andrea DeSosa, Global Head of Capital Markets GTM at Databricks. The post says research, trading, risk, operations, and surveillance often rely on separate systems and inconsistent data views, slowing decision-making, complicating execution analysis, limiting risk visibility, and making it harder to reconstruct events for regulators, clients, or internal control functions. In pre-trade, teams can spend substantial time preparing and reconciling market, fundamental, ESG, and alternative data before testing an investment hypothesis or running a backtest. During execution, desks need timely views of execution quality, liquidity, order flow, and slippage by venue, strategy, and client, but a trader’s desktop view may not reconcile with data available to risk, compliance, or operations. Post-trade, many firms still depend on overnight risk processes, spreadsheet-based reconciliations, and reporting designed for a longer settlement cycle. The post says firms seeing repeatable value from AI are those that can securely connect and govern proprietary context such as order flow, internal research, positions, risk outputs, and client information.