Last month Gartner published its first ever “Market Guide for AI Offerings in CSP Network Operations,” and we’re excited to share that Anodot has been identified as a Representative Vendor in the report.

According to the Gartner report, “CSPs are focusing on automation of their network operations to improve efficiency and customer experience, and mitigate security concerns.”

The market guide presents many new and actionable insights. We’re taking a closer look at a couple of them and sharing our perspective on their significance for operators.

The strategic role of data correlation

The first insight we’d like to discuss is the reason behind why it’s so challenging for operators to meet their top objectives for AI implementation, i.e.:

  • Network monitoring
  • Network optimization
  • Root cause analysis

According to Gartner this is due to the fact that:

“Despite CSPs having multiple data sources, most of it is uncorrelated and is thus processed separately. Vendors are working on ways to unify these data silos in order to create a large dataset for their ML algorithms.”

The great news for operators is that they don’t have to wait to overcome the correlation hurdle. This is because collecting and correlating all data types from 100% of the network’s data sources (however siloed) in real time, is exactly what Anodot’s autonomous network monitoring platform does.

Say goodbye to silos 

Anodot’s unique, off-the-shelf data collectors and agents, collects data from every network domain and layer, and service and app, aggregating inputs from sources that include network functions and logic such as fault management KPIs, xDRs, OSS/BSS tools, performance management KPIs, probe feeds, counters, alerts, and more.

So, the days of monitoring the network in silos, with separate tools for each domain and layer, are over.

Kudos to correlations

As for correlations, Anodot correlates anomalies across the entire telco stack (including the physical, network, and data layers), and between KPIs, alerts, and network types (i.e., mobile, fixed/broadband, and wholesale carriers/transmission).

Root cause at the speed of AI

Through this combination of eliminating siloes and correlating data, Anodot also provides early detection of service degradation, outages, and system failures across the entire telco ecosystem, sending alerts in real time with the relevant anomaly and event correlation for the fastest root cause detection and resolution.

Benefits for CSPs include:

  • Time to detect incidents accelerated by up to 80%
  • Time-to-resolve incidents improved by 30% 
  • Root cause analysis improved by 90%

And this is how operators can address the top three objectives for AI in their network operations.

A quick & important look at KPIs

Another important insight presented by Gartner in the report includes the following:

“CSP CIOs who are looking to leverage automation and AI offerings to support their organizations with evolving business requirements, network operations, and continuing digital transformation and innovation should:

  • Prioritize the critical success factors of your network operations through a structured analysis of your network operations center (NOC) and other related operations that can benefit from AI. Identify your service-level objectives and select AI vendors that contribute to your business-driven key performance indicators (KPIs).” 3

Let’s focus on KPIs. Operators have millions to billions of network-centric KPIs that help uncover performance issues.

But without autonomous anomaly detection on 100% of the data, it can be impossible to deal with the volume and velocity of data, identify the anomalies within network big data, and resolve issues before they impact business-driven KPIs.

The impact of impact scoring

Here too, Anodot comes with an innovative approach. Not only does the platform continuously and autonomously monitor and analyze millions (and billions) of performance and customer experience KPIs by leveraging patented algorithms. It takes this even further. Once it detects an anomaly, it assigns a score based on its impact on what’s important to the operator.

In fact, every single KPI that comes in goes through a classification phase and is matched with the optimal model from a library of model types. Then, Anodot’s statistical models group and correlate different KPIs in order to analyze them based on the use case. 

Moreover, it automatically groups related anomalies and events across the network into one alert, reducing alert noise by 90%.

Indeed, at Anodot, we believe in the importance of aggregating and correlating data from multiple data sources to address the key AI objectives of CSPs. We are also committed to assuring that AI brings a strategic contribution to business-driven KPIs.

And, being identified as a Representative Vendor in Gartner’s report serves as a great validation of this approach. 

The  Market Guide for AI Offerings in CSP Network Operations is available to Gartner subscribers.

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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