Cable/MSO Operators Rise Above the Data Deluge

"In God we trust; all others must bring data."

Although the phrase is widely attributed to W. Edwards Deming, the father of the post-WWII quality movement, the sentiment applies today in that it champions a data-driven approach to business and decision-making. Deming stressed the importance of tangible evidence, measurement, and analysis to improve outcomes and drive understanding.

For forward-thinking cable/MSO operators implementing artificial intelligence (AI)- and machine learning (ML)-based projects, this approach to ensuring quality data represents a crucial ingredient for improving user experience, increasing productivity, and minimizing security risks. And, with the help of AI platform partners, the hope is to leverage available network data for automated analysis for accelerated actions.

Although the intent is clear and technical alignment with vendors may be solid, many providers will still face some major challenges with the data itself. The sheer volume of data collected can be overwhelming to process, analyze, and make actionable for specified use cases.

Methods Matter When Feeding AI/ML Systems

While having all the data is, of course, extremely valuable, the reality of maintaining, making sense of, and extracting the best parts of that data presents an impossible challenge. Data lakes are massive, and data scientist-level expertise and their time to analyze is often constrained.  A popular metrics, events, logs, and traces (MELT) approach to AI/ML source data can lead to massive data lakes that consume valuable resources.

Imagine trying to make a split-second decision today based on information collected more than a week ago because it takes that long to mine the data.  One alternative approach to this puzzle is to use a data-tiering and pipeline process. With this method, information is chunked into logical, manageable, down-sampled pieces and then routed to other processing layers where analysis and enrichment are then applied. However, while AI/ML functions benefit from latency-sensitive insights, the resulting information is often so decoupled from its original context or only representative of sampled network activity that the derived insights lack completeness and obscure causality.

This approach can limit a frontline team’s ability to respond effectively to customer-impacting issues because the necessary information to isolate and resolve root causes is filtered away.

Because incomplete data leads to underperforming AI models, standard data-tiering processes create an environment where garbage-in-garbage-out analysis leads to significant revenue loss, as a recent industry survey illustrates.

Omnis AI Insights Provide Scalable DPI-based, Enriched Observability

NETSCOUT overcomes the current AI data-sourcing limitations to enable maximum efficiency and preserve data granularity. Powered by deep packet inspection (DPI) at scale, NETSCOUT Smart Data helps cable/MSO operators to achieve unmatched network visibility, no matter how large or complex their ecosystem is.

With Omnis AI Insights, powerful information from network data is not only curated and enriched but also packaged for efficient and targeted downstream use with third-party analytics tools.

For the cable/MSO provider, this translates to real-time visibility for a true understanding of performance and customer experience.  This enriched observability enables operational improvements, including the following:

• Service fault isolation
• Subscriber impact analysis
• Video and over-the-top (OTT) analytics
• Service-level agreement (SLA) management
• Distributed-access architecture (DAA) evolution

Architecturally, the NETSCOUT solution supports traditional wireline monitoring points, with AI sensor instrumentation processing relevant data at both the edge and core for end-through-end visibility. Once collected, data is curated into high-fidelity, use case–specific feeds using AI Streamer for immediate and simplified integration with leading AI platform vendors.

With NETSCOUT’s AI Insights approach, cable/MSOs benefit from higher-quality data at lower volumes for incredible value and more attractive economies of scale.

For more information about how Omnis AI Streamer and Omnis AI Sensor are helping cable/MSO providers rise above the data deluge and achieve deeper insights, advanced analytics, and accelerated results with highly scalable AI- and ML-driven solutions and innovative ecosystem partners, check out NETSCOUT’s AIOps solutions for service providers.