Start your engines. The holiday season has officially begun! With the upcoming shopping frenzy surrounding Black Friday and gift buying, it is one of the most important times of the year to avoid glitches like the ones below.

At the advent of the technological era, developers found themselves wasting hours of valuable resources on manual QA. As software was released, teams manually confirmed that it was bug-free and reliable, all the while testing new features and checking for regression of existing features. Unfortunately, this manual approach was prone to mistakes, created long delays in workflows and was tedious and time-consuming.

Augmented analytics is trending. You’ve read about it, you’ve heard about it, you may even be in the process of acquiring systems running it. But what exactly is it, and how can you recognize it? As the guys building augmented analytics, we’re here to dispel some of the hype. On the highest level, augmented analytics is the machine learning processes geared at making data more accessible and actionable for both data scientists and business users.

The digital era has brought vast cultural transformations – the sharing economy, microtransactions, lightning-fast communication and much more. Much of this has also resulted in considerable innovation in revenue-related areas. Companies from various industries today manage a large number of revenue streams from different revenue models.

If you aren’t frightened enough this Halloween, spend some time reading these tales of glitchy horror! Then, save your company from becoming a ghost story with Anodot’s anomaly detection and keep your platforms running smoothly.

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).

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.

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.

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.

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.