AI monitoring technologies have the potential to introduce significant cost savings for CSPs. Based on machine learning and fully autonomous, these monitoring solutions provide high ROI by dramatically reducing Time to Detection (TTR), Time to Resolution (TTR), the total number of alerts, and the number of false positives and negatives. Forward thinking CSPs who rely on AI-based monitoring drive operational efficiency, deliver a better customer experience, and prevent critical performance and quality of service issues across the network.
However, for most CSPs, successful adoption and implementation rates of AI monitoring are still low. The main hurdles faced by CSPs are the complexity of the network, limited resources and internal knowledge, and an overwhelming number of potential use cases. In most cases, AI monitoring solutions require heavy investment in setup, data integration, use case development, operation and maintenance — as well as specialized skills typically provided by pricey professional services firms. This results in significantly higher TCO, longer time to value, and slower use case implementation when compared to out-of-the-box solutions.
That’s why Anodot is built from the ground up to deliver AI-based network monitoring with the shortest time to value. It does so by providing a fast and simple integration process, streamlined on-boarding and ongoing use, and completely autonomous monitoring and correlation that requires no manual intervention.
Anodot is built to deliver value fast, so implementation time — including technical integration, data validation and on-boarding — takes weeks instead of months compared to alternative solutions that rely heavily on outsourced professional services. Data integration is fast and simple using one of Anodot’s many turn-key integrations or agents and open source collectors. The platform also has a robust REST API, so CSPs can stream their measures and dimensions from anywhere. There are no lengthy professional services projects, and no data scientists required. This short integration process enables users to seamlessly send data to the platform, deriving immediate value and new efficiencies.
On-boarding and ongoing use
Anodot offers seamless onboarding and usability, with a simple UI that is accessible to all stakeholders, from IT and DevOps to product managers and business owners. Users can easily investigate anomalies directly from an alert, get all the context, and provide feedback to improve system performance. It easily integrates with any type of data sources, and just as easily applied to new services. New use cases can be added on the fly, and no monitoring maintenance is needed even as the network configuration changes. Anodot works seamlessly with existing monitoring solutions to improve the quality of alerts generated and reduce secondary monitoring costs.
Stakeholders receive Anodot’s alerts in real-time, with the relevant anomaly and event correlation for the fastest root case detection and resolution. Alerts channels are easily integrated to surface issues where employees spend their time — Slack, Microsoft Teams, email, etc.
Autonomous monitoring & correlation
Anodot’s thresholding is 100% autonomous for 100% of data streams and metrics. There’s no need to define what data to look for or when, no manual thresholds or business logic to set up or update. Anodot autonomously detects and correlates anomalies across the network for holistic root cause analysis and the fastest time to resolution, leading to improved network availability and customer experience.
For every metric the platform automatically selects the most appropriate algorithm from over 20 algorithms that have been developed and optimized to work at scale and hold specific patents in this area. The appropriate algorithm initiates a baseline for every metric, and learns the appropriate metric threshold across daily, weekly, monthly and annual seasonality. Anodot adapts to changes in metric behavior, and can switch algorithms in case patterns change.
Correlations don’t need to be predefined using business logic or topology mapping. Fast automated detection of correlation patterns is continuously done via a patented correlation engine that works across all metrics and dimensions to create a complete picture of every incident, including root cause analysis. The engine parses the anomaly’s direction, delta, duration and other factors to generate a score for each anomaly. This scoring mechanism ensures that only — and all — mission critical incidents receive alerts. False positives, false negatives and alert storms are left behind.
Get more value faster with Anodot
AI-based monitoring and anomaly detection is the key to ensuring that CSPs can keep pace with the high level of service required for mission-critical services. With Anodot, CSPs can start reaping the fruit of zero touch network monitoring and anomaly detection almost instantly, relying on a fast integration and implementation, ease of use, and completely autonomous monitoring and correlation.
Early, contextual detection is a basic requirement for speedy resolution. AI-based monitoring creates more visibility and provides the agility needed to mitigate the outages, blackouts, glitches and issues that do and will happen. Anodot enables CSPs to quickly reduce the number of alerts by 90% and shorten their Time to Resolve by 30%. This helps operations and NOC teams enhance their network monitoring capabilities, become proactive in their ability to identify service degradations and outages, and dramatically improve customer experience (and retention) and operational efficiencies across the board.
Download our White Paper to learn more about how Anodot’s out-of-the-box solution enables CSPs to seamlessly transform their operations towards zero touch.