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Dear Readers,

What does it really mean to manage data in the age of artificial intelligence?

Across financial institutions and energy trading firms, data has become one of the most critical strategic assets. Artificial intelligence (AI), advanced analytics, and increasingly data-driven trading strategies are reshaping the competitive landscape and redefining how organizations position themselves in the market. Yet as firms accelerate their AI initiatives, many are discovering that the real challenge is not simply accessing data, but accessing the right data.

Over the past decade, market data environments have expanded rapidly. New feeds, specialized datasets, and analytical services have been added to support trading, investment, and risk management. In many organizations, this growth has created complex data landscapes where transparency around usage, licensing obligations, and data ownership is limited and not always fully understood.

As a result, questions around data governance, licensing, and cost transparency are moving quickly to the top of management agendas. Organizations are now recognizing that the value of data isn’t determined by its volume, but by how effectively it’s governed, managed, and used.

In this edition of FORRSight Magazine, we bring together perspectives from across the industry to explore how market participants are addressing these challenges. Through articles, interviews, and expert insights, this issue examines how firms are rethinking market data governance, licensing, and the evolving role of data in financial markets.

The organizations that succeed will not simply be those with more data. It will be those that understand how to govern data wisely, manage it efficiently, and use it to create real value. The future belongs to those who turn data into a true strategic advantage.

Dear Readers,

Enterprise decision-making has been based on IT infrastructure for decades. Cloud environments have accelerated this trajectory, making automated, consistent decisions a standard expectation rather than a competitive differentiator. Artificial intelligence is now introducing the next structural shift, operating at a different order of magnitude.

Awareness is already high. AI tools are actively used every day at the individual level across organizations. The opportunity lies at the enterprise level, where data quality, process coherence, and governance frameworks determine how much of the available AI capability an organization can deploy systematically. Most organizations clearly recognize this: capabilities exist from external vendors, but the internal assembly capabilities need to catch up.

The implication is structural. Organizations cannot realize compounding advantage without the ability to operate existing businesses stably while simultaneously adopting new AI capabilities at pace. Each delayed adoption cycle is an opportunity surrendered to competitors who are better prepared. The two-gear operating model is not optional – it is the condition under which enterprise AI value is captured or forfeited.

The analytical demand is precise. Organizations need a platform that can bridge the gap between AI capability and operational readiness. This platform must be able to run machine learning models in production, automate AI-driven processes, and integrate with evolving data and governance architectures without disrupting the enterprise core.

Recognizing precisely this execution gap, FORRS has built GRYT as an enterprise platform product that absorbs the complexity of enterprise technology, so that clients can direct their full attention towards business value creation rather than the construction and maintenance of underlying technology capabilities.