Guavus Blog

Zero-touch networks are the vision for 5G, but the adoption of standard enabling technologies will be key for mobile operators to realize the power of self-driving 5G networks.

While autonomous networks are the ultimate goal in 5G, until we get there, the intelligence generated by NWDAF is critical for human decision making in semi-automated network operations. Let’s take a look at the NWDAF use cases considered by the 3GPP.

A standard approach to 5G network automation will allow Mobile Network Operators (MNOs) to deploy multi-vendor networks with confidence, leveraging the unique capabilities of best-of-breed products.

For CSPs, understanding how their network is utilized by their subscribers and how the subscribers experience the network and services are the significant factors influencing their revenue and business value. Guavus, today announced Guavus-IQ, a comprehensive product portfolio that provides a multi-perspective analytics experience for CSPs.

The majority of communications service provider (CSP) machine learning projects fail. So says David Yates, Vice President of Product Management & Marketing at Guavus. While you catch your breath at that and consider the implications for your business, here he gives five reasons for the failures, and shows how you can beat the odds.

5G promises both tremendous opportunity and challenges. Leveraging AI and analytics, CSPs can take the volume of data generated by the network and turn it into useful, consumable information that helps them grow their business and increase their top line.

Expect to see companies start monetizing big data as technologies emerge to 1) boost the productivity of data scientists, 2) automate a key aspect of machine learning, and 3) spur communications service providers (CSPs) to start riding the digital wave and exit the low-margin, pure-pipe business. These trends will make the job of turning data into more accurate decision-making and actionable business outcomes possible.

Guavus analytics experts explain how applying root issue analysis will drastically reduce the number of network alarms, reveal the hidden structure of issues and clearly identify the strength of the relationships between the issues.