AI • IOT • NETWORK EDGE

In a world that runs on data, ensuring it reaches the right recipient at the right time is key for businesses of all sizes. To gain optimal network performance and business function, enterprises need to observe, identify, and monitor the health of every flow traversing their infrastructure.

“Data observability is about ensuring the health, reliability, and quality of your data systems, including data pipelines, databases, and data lakes. It involves monitoring data quality, lineage, performance, and usage to proactively identify and resolve issues, so that the data your business relies on is accurate and trustworthy.” says Matt Dangerfield, Chief Technical Officer at Telesoft Technologies Ltd., a global provider of fintech, cybersecurity, and government infrastructure solutions.

It is particularly critical in the world of high-speed financial trading, where terabytes of data continuously flow through technology stacks. Even a single missed data packet could jeopardize a deal worth millions.

“In financial institutions, we’re offering complete data observability to improve end-customer experience, identify network issues, and ensure regulatory compliance and governance. Our offerings seamlessly integrate with existing infrastructure to provide a comprehensive, orchestrated solution.” says Jenna Smith, Head of Product Management at Telesoft.

Solving Fintech Observability Challenges

One of the most significant challenges in fintech is ensuring that data keeps pace with the speed of business.

“A lot of hedge funds and high-frequency algorithmic trading rely on making decisions in nanoseconds,” Dangerfield explains. “The immense volume of data generated by market participants only adds to the challenge. With petabytes of data moving within a 24-hour period, it’s crucial to not only process this data quickly but also extract actionable insights using the right technology.”

Telesoft provides that “right technology”—a comprehensive suite for complete data observability.

To gather network metrics, Telesoft deploys flow probes, which ingest, analyze, and timestamp every packet on the wire, extracting network telemetry about the flow data—including sender, receiver, data volume, and any potential issues, such as dropped packets or delays. The technology monitors and alerts on the detection of microbursts, sudden spikes in network traffic that can overwhelm routers, causing bottlenecks. For fintech entities distributing market data, the probe monitors the sequence of critical data packets, identifying gaps that indicate missing packets. “Every client must receive every packet; missing even one could mean missing out on critical trades,” says Smith.

Telesoft offers a downstream packet capture device that performs full, unsampled recording of the network traffic, which enables customers to fulfil regulatory compliance requirements and provide evidence of fairness in price data delivery. Each data packet is timestamped to establish provenance and provide proof of dispatch. Such records are vital for resolving disputes. For instance, if two clients are disconnected, timestamped data can help financial institutions determine whether the fault lies with the broker, the exchange, or the client. The institutions value this automated data capture for evidence and reporting; it significantly reduces the time their analysts spend on investigations.

#AI and #MachineLearning, are key elements of the observability platform—automatically analyzing, predicting, and alerting on potential #network issues before they occur. @Telesoft_Tech via @insightdottech

To provide a comprehensive level of observability, Telesoft provides a data lake that stores data captured from probes deployed across the network, ingests additional network telemetry such as log files from core infrastructure, and enriches the data with additional context. Having such a data lake facilitates the final layer: AI and machine learning are key elements of the observability platform—automatically analyzing, predicting, and alerting on potential network issues before they occur.

The Telesoft platform runs on the latest Intel CPUs and uses the power of Intel FPGA technology to exceedingly fast and dense solutions. The company’s PCIe Interface cards are designed and manufactured in-house, giving it complete control over the core technology that underpins its products.

Sustainable computing is also a key priority for Telesoft. “We’re helping our customers reduce their data centers’ operational costs and power consumption by collapsing five racks’ worth of financial technology into a single rack through engineering,” Dangerfield says. Intel technology helps make this possible.

Use Cases for Data Observability: Capacity Planning and Customer Experience

Capacity planning is an important task for financial institutions, ensuring that network infrastructure can handle current and future trading volumes while maintaining optimal performance and minimizing downtime. The institute must have confidence that trading surges during market events can be accommodated.

“Bandwidth utilization of each network link is monitored and baselined by our solution. Machine Learning and AI technology tracks this utilization over time and can perform predictive forecasting of expected future throughput requirements, alerting network administrators before the event occurs,” Smith explains. “If a link is becoming saturated with traffic, the addition of microbursts within that traffic can cause network infrastructure to become overwhelmed, buffers to overrun, and ultimately packets to be dropped. Dropped packets can equate to missed trade opportunities for the clients.”

Enabling a financial institution to predict, investigate, and remediate potential network issues before they start improves customer satisfaction and retention, attracting new customers, and driving business in a competitive market.

The Future of AI in Financial Services

Beyond enhancing data observability, Dangerfield is enthusiastic about the “raw power of knowledge” that AI and ML can bring to financial markets. Traditionally, hedging and market futures have been based on educated guesses—how factors like heatwaves and supply chain disruptions will impact prices. But AI and ML add a layer of intelligence, identifying patterns in data that lead to more accurate forecasts.

No matter what the future holds for AI in financial services, its foundation will be built on data observability. “Ensuring robust observability keeps the technology infrastructure running smoothly, which is exactly what high-stakes fintech markets demand,” says Smith.

This article was edited by Georganne Benesch, Editorial Director for insight.tech.