The Evolution of Exchange Data: Transforming Big Data into Relevant Insights

This decade marks a groundbreaking period in financial markets. Annual data generation is now exceeding all information created throughout human history. How can this exponential growth in data be harnessed to serve the interests of financial exchanges? 

In today's financial markets, data serves two vital roles. First, it underpins market integrity, without which a platform cannot function. Second, data enables exchanges to differentiate themselves, demonstrate the value of their market models and allow them to serve customers directly rather than relying on traditional aggregators and vendors.  

Data’s Evolution 

The proliferation of data has significantly enhanced trader sophistication. Today, regardless of whether you're trading equities, futures, FX, or fixed income, success requires expertise in engineering, mathematics and data science. Paradoxically, these highly skilled professionals spend up to 80% of their time cleaning poor quality data before extracting any useful insights. 

The challenge lies not just in collecting data, but in making it accessible. Consider this: when harmonising level three data across global exchanges, every trading intention ever made is captured at the nanosecond level. The real skill lies in simplifying this vast amount of information for a larger audience. With the advent of 5G, people essentially walk around with an exchange in their pocket.  Simplifying complex data for a wider audience is essential.  

Making Sense of Data 

More data does not necessarily mean more information. As the volume of data has grown , those on the market's periphery struggle to make sense of what's really happening. The challenge isn't about producing more data; it's about producing the right data for the right audience. 

Different market participants have different needs. While high-frequency traders have their own sophisticated data operations, retail investors and small to mid-sized money managers require data that they can download instantly and analyse readily. The key is rethinking how we distribute data to end users, ensuring it meets their needs without overwhelming them. 

Advertiseable Results, AI and Cloud 

Enter the concept of “advertiseable results. It’s a framework where only useful, consumable data crosses the boundary to end users. This approach challenges the common practice of storing massive amounts of data "just in case". Instead, it advocates for a focused approach: collect and store what provides value. This reduces  costs and complexity. 

Artificial Intelligence (AI) plays a crucial role in this transformation, but with some caveats. Quality input data is essential; it's the fuel that powers AI tools. However, the financial sector lags behind other industries in AI adoption. For example, in retail markets AI models handle complex tasks with minimal human intervention. The challenge lies not just in implementing AI, but in ensuring reproducibility and reliability of results. This is a crucial consideration as models become increasingly sophisticated. 

Cloud technology also presents compelling opportunities for data producers in the exchange space. Many organisations lack the resources to maintain a current product pipeline and development roadmap. Cloud environments address this challenge by enabling product teams to create query-based extracts and analytics using AI techniques, without competing with trading infrastructure for IT resources.  

This democratisation of data tools through cloud-based labs, designed specifically for product developers, is driving the transformation of data into a strategic business function. As a result, exchanges are increasingly viewing data productisation as a revenue generator and a key differentiator . 

Modern Infrastructure in Support of Big Data 

The financial exchange industry stands at a crossroads, where data abundance demands meaningful insights.  Success in this environment demands modern analytics tools and a robust infrastructure that drives innovation in trading technology and matching engines.  

Modern infrastructure brings additional advantages. Exchanges can now use APIs to analyse matching engine datasets in  advanced data labs, gaining key insights into their performance, compared to peers. This capability, combined with advanced data-science tools, helps venues to optimise their market positioning through a deeper understanding of market interactions.  

The future belongs to those who can transform big data into relevant, actionable insights while upholding the integrity and efficiency that markets demand.