Extending the Competitive Advantage in Telecom

The telecom industry has always seemed to navigate well through tech changes. As the industry has evolved, it’s managed to transform from landline to mobile carriers, then from voice calls to messaging and data-centric networks. In many developed markets telcos are creating ecosystems for the data-driven economy. The next frontier is shaping up to be one driven by machine learning (ML) and artificial intelligence (AI).


Integrating Time Series Correlation to Accelerate Root Cause Analysis

In any platform of sufficient complexity, multiple anomalies are likely to occur. For many organizations, NOC operators triage multiple anomalies based on their severity. There are internal, non-customer-facing issues that might affect only a small part of your workforce and one-time issues that affect only a small number of customers. Both of the issues get ticketed and sent to low-level support.


Real-Time Analytics for Time Series

Let’s start with simple definitions. Time series data is largely what it sounds like – a stream of numerical data representing events that happen in sequence. One can analyze this data for any number of use cases, but here we will be focusing on two: forecasting and anomaly detection. First, you can use time series data to extrapolate the future.

Adtech Leader Natural Intelligence Now Resolving Glitches in Minutes Rather than Days

Natural Intelligence runs comparison websites that generate millions in ad traffic. A glitch could easily cost the company thousands in ad revenue. VP R&D Lior Schachter shares the difference Anodot’s real-time analytics, with machine learning anomaly detection, has made across the company.

Why Monitoring Business Metrics is Completely Different from Traditional Monitoring

Many companies today try to feed business metrics into APM or IT monitoring systems. Splunk, Datadog and others track your business in real time, based on log or application data – something that would seem to make sense. In practice, however, it fails to produce accurate and effective monitoring or reduce time to detection of revenue-impactful issues. Why? Because monitoring machines and monitoring business KPIs are completely different tasks.


Growth Forecasting Use Cases for Anodot's Autonomous Forecast Solution

Every successful company plans for sustainability and growth. Forecasting the growth path helps companies set their short- and long-term business objectives and make important decisions to help them reach their goals. Short-term forecasts are important in quarterly and annual budget planning and for ensuring that daily business operations help achieve long-term goals.


The Top 3 Use Cases for Machine Learning in Analytics and Monitoring

It’s no secret that machine learning (ML) has experienced tremendous growth and adoption over the last few years. And why not? This exciting technology has enabled us to utilize the power of machines for a wide variety of applications and industries. From image processing to predicting to medical diagnosis, ML has begun to reshape the way we live.


The 5 Whys: Why Use Monitoring at All?

Customers today are faced with a wide variety of industry terminology: APM, IOTA, BPM, OI, BAM and AIOps, just to name a few. Using different terminology like this might help large vendors expand their market size with different positioning offerings, but it certainly doesn’t help their customers understand what they’re getting. Companies spend tens of millions of dollars to solve their problems with the wrong solutions and struggle to get value from it.