The AI Journey For CSPs: From Big Data To Big Revenue

The AI Journey For CSPs: From Big Data To Big Revenue

The AI Journey For CSPs: From Big Data To Big Revenue

Many of the communications service providers (CSPs) I talk to say they’re struggling to use artificial intelligence (AI) to overhaul their competitive game. They feel that if they could just access the big data locked away in their networks, the floodgates would open, and they’d be on their way to automating operations and enjoying new revenue streams.

But many remain plagued by the age-old question: How do we get there from here?

To help answer that question, let me walk you through what we’ve learned having worked with CSPs that have successfully applied AI and analytics to big data in order to meet their business goals. Their journey and key learnings could be very valuable for your business as well.

1. Decide What You Want To Accomplish And Which Data Will Get You There

It might seem obvious, but it’s important to put this stake in the ground in terms of what you want to achieve.

For example, one Asian provider we work with, with extensive fiber and mobile networks throughout its home country, has aimed high. First, it had its sights set on entering all-new markets. Its long-term objective is to become a one-stop-shop for subscribers in its country along the lines of the Amazon business model. Shorter term, though, it was looking for insight into real-time quality-of-experience (QoE) levels for each customer and to learn what over-the-top (OTT) services they were using.

If you’re a CSP struggling to define the data that’s required to achieve your goals, then it’s important to establish a centralized top-down strategic analytics research team. Such an internal consulting team — composed of data engineers, data scientists, domain experts and analytics engineers, led by a CMO, CTO or CDO — will know what data is needed and where it resides. With help from Software Development and Operations, that team can, through modeling, prototyping and testing, validate the data-goal fit.

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Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here