In today’s data-driven world, businesses are drowning in a sea of metrics, making it increasingly difficult to extract meaningful insights efficiently. At AlvaLinks, we’ve tackled this challenge head-on with our Datapath Performance Score (DPS), a breakthrough innovation reshaping how organizations approach observability and machine learning.

The Birth of a Transformational Metric

When we first introduced DPS, our goal was simple: to help users of our precise observability solution focus on critical deviations in their KPIs. By defining “acceptable” levels, we empowered organizations to detect anomalies efficiently. We didn’t anticipate how pivotal this score would become for Machine Learning (ML) and Artificial Intelligence (AI) to detect anomalies in the network behavior.

Traditionally, anomaly detection in vast datasets demands significant computational resources, with systems sifting through petabytes of synchronized time-series data to flag irregularities. This costly, inefficient approach often produces false positives that waste time and energy. But with our DPS, we’ve created a precision spotlight that illuminates high-impact anomalies, enabling our ML models to focus on what truly matters.

Optimizing ML/AI with a Precision Spotlight

Rather than unthinkingly expend valuable computing power to scan massive datasets, our approach targets data caught in the DPS spotlight. Anything within this spotlight likely contributes to a low score, meaning we can identify anomalies instantly and trace them back to their source.

For example, a network route change causes a subtle increase in latency or jitter. The actual change might occur well before the score reflects an impact. However, using our targeted ML models, we can correlate the event with the resulting degradation in performance and preemptively mitigate future risks. This level of precision revolutionizes observability, allowing businesses to act before problems escalate.

From Observability to Predictability

The next frontier in AI isn’t just observing anomalies—it’s predicting them. With our small, dedicated ML models trained on observability events, we create an ecosystem where proactive issue resolution becomes the norm, not the exception. The ability to pinpoint root causes and forecast potential disruptions is a competitive advantage that organizations cannot afford to ignore.

At AlvaLinks, we are committed to pushing the boundaries of what’s possible in AI-driven observability. The Datapath Performance Score is just the beginning.