THETRADETECH DAILY
SPONSORED ARTICLE
Low latency has long been viewed as a race to zero- measured in nanoseconds and microseconds and defined by how quickly market data can be delivered and acted upon. That paradigm is now shifting. In modern electronic markets, while speed remains critical, it is no longer the sole determinant of competitive advantage.
Instead, low latency has become a broader, multi-dimensional capability. Firms are increasingly focused on accessing precise, high-quality, and trusted datasets instantly. Data that is not only fast, but also usable, consistent, and ready for analysis at scale. The ability to convert a market event into actionable insight, with minimal friction, is now the true differentiator.
This shift is being driven by several converging forces. Market structures have become more fragmented and data-intensive, while the evolution of algorithmic and quantitative trading has increased reliance on both real-time and historical datasets. As a result, expectations have evolved.
Firms are no longer asking how quickly they can receive data, but how quickly they can trust it, analyse it, and act on it across increasingly complex workflows.
Data scale meets cloud accessibility The scale of market data has grown exponentially, creating both challenges and opportunities. With more than 100 petabytes of data spanning more than 400 venues, London Stock Exchange Group’ s low latency offering exemplifies the industry’ s shift towards vast, interconnected data ecosystems.
However, scale alone does not create value. Historically, firms have spent significant time and resources acquiring, transporting, and managing data before any meaningful analysis could begin. This operational burden often delayed insight and limited agility.
Cloud-based delivery models are now fundamentally changing this dynamic. By making large datasets readily accessible within cloud environments, firms can bring their analytics directly to the data. This eliminates the need for complex data pipelines and significantly reduces time-to-analysis.
As a result, workflows are becoming more fluid and iterative. Research, trading, and risk teams can access shared datasets, isolate relevant subsets, and test ideas in near real time. This not only accelerates decision-making but also enhances resilience, enabling firms to respond more effectively to volatile market conditions. Increasingly, this data is made available natively within cloud environments, enabling clients to access it where they already operate. Whether through direct feeds, cloud-native analytics, or API-based delivery, this flexibility allows firms to integrate market data seamlessly into their existing workflows – effectively meeting them where they are.
The new low latency equation: Speed, scale, and certainty
Pramod Nayak, director of product management, London Stock Exchange Group, examines how low latency strategies are being redefined by data scale, cloud delivery, and advanced capture technologies- highlighting why speed alone is no longer enough to compete in increasingly complex, data-driven global markets today.
Shared storage and a new operating model A key enabler of this transformation is the adoption of shared storage models. Traditional approaches required firms to replicate large datasets within their own infrastructure, leading to duplication, high storage costs, and fragmented workflows.
Shared storage fundamentally reshapes this model. Data remains centrally managed and maintained by LSEG within the cloud, while clients can access, query, and utilise it as if it were their own. This enables firms to extract only what they need without the burden of replication or infrastructure overhead, dramatically reducing time between data access and analysis.
The impact extends beyond efficiency. By enabling multiple teams to work from a single, consistent dataset, shared storage enhances collaboration and ensures alignment across functions such as trading, quantitative research, and compliance. It also improves auditability, as all users operate from the same underlying source of truth.
Crucially, this model reflects a broader shift in how firms view data- not as a static asset to be moved and stored, but as a dynamic resource to be accessed and analysed in place.
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