How has the role of data in trading and investment decisions evolved over the last few years?
A robust data strategy and foundation are essential in today’s landscape, especially given the rapid growth of AI, which has set even higher standards. We’ve seen a major shift where investors have moved away from storing data in isolated silos to adopting interoperable platforms.
Data discoverability, entitlements, quality, and delivery have become critical elements to a firm’s data journey. It empowers teams with reliable analytics, reducing manual work, and enabling smarter, faster investment decisions. Data transformation, normalisation, modelling, and a strong semantic layer enables investors to fully harness their data resources. Ultimately, this unlocks new investment opportunities for clients.
By focusing on governance, adaptability, and scalability, firms can move beyond just experimenting with AI – they can achieve real, enterprise-wide impact, which is really exciting. Those who invest in future-ready data infrastructure are the ones best positioned to capture value and drive innovation for their clients.
As regulation and market structure continually change, what challenges or opportunities does this create for market participants?
Evolving regulation and shifting market structures bring both real challenges and opportunities for market participants, especially in private markets. Private market data is often fragmented, unstructured, and missing standardised identifiers, which makes aggregation and analysis a complex task.
At the same time, the diversity of data sources and irregular valuation cycles make transparency and reporting even more challenging, often resulting in inconsistencies and information gaps.
The industry has made meaningful progress in improving transparency and standardisation, but there’s still a long way to go. To address these challenges, firms should adopt advanced data strategies that leverage AI, for challenges like transforming unstructured data into usable formats, or utilise machine learning for effective entity resolution.
Once data is organised, it can be modelled to support sophisticated analytics and comprehensive reporting, helping firms meet regulatory requirements and make more informed decisions. Modern platforms are essential, offering seamless access and distribution of high-quality data, with governance and compliance built in at every step. By maintaining a strong data discipline, firms can not only overcome operational hurdles and adapt to regulatory changes but build a data foundation for AI solutions.
What role and responsibility do platforms like JP Morgan have in helping traders make faster, more effective decisions?
Investors often face the challenge of extracting meaningful insights from an overwhelming volume of information. While traditional data lakes centralise data, they still require significant manual effort to clean, organise, and prepare it before analysis can begin.
This slows down decision-making and limits the value of data, especially in fast-moving markets and periods of heightened volatility.
Advanced solutions like data mastering and customisation can support investors on their journey toward enterprise AI. Our platform Fusion supports the seamless integration and normalisation of data from multiple sources for consistency, supported by a semantic layer that brings business context to complex financial information, with built-in connectivity to analytics solutions and cloud platforms.
Data quantity versus quality is an ongoing trade-off and discussion across the industry. How do you see this balance shifting over the course of 2026?
The industry’s data journey began with a focus on collecting as much information as possible, centralising it in large lakes, and relying on technology to manage quality and structure. But we’ve learned that simply gathering more data doesn’t automatically lead to better insights or outcomes. AI models are only as good as the data they consume.
Without strong governance and consistency, solutions can fall short, becoming unreliable and hard to repeat.
As AI adoption matures, the focus is shifting toward disciplined data management that enables scalable and well-governed solutions. Looking ahead, firms are putting more emphasis on making their data truly ‘AI-ready’, rather than simply adding more inputs. This means investing in robust data management, workflow management, and semantic models to ensure data is accurate, consistent, and meaningful in context.
The most advanced data platforms are leading the way, orchestrating data pipelines, normalising formats, and embedding governance from the very start. Those who prioritise quality and build future-ready infrastructure will be best positioned to turn their data into a genuine strategic asset, driving innovation and smarter decision-making across their organisations.