While enterprise leaders are constantly looking to innovate, there’s one area where “business as usual” should be a focus — spotting anomalies in your data. When it comes to time series data, “business as usual” is the baseline or expected behavior of the KPIs you track. Any unexpected deviations in those patterns can be classified as anomalies. However it’s important to keep in mind that anomalies can be either negative or positive.

Digital, network-connected systems are transforming every aspect of business — from your mission-critical workloads to your most rarely used applications. But the increases in scalability and cost efficiency come at a cost. Because every system is so reliant on network connectivity, unplanned downtime is becoming increasingly expensive.

Back in the days of the wild wild web (www) and post JQuery era, one web framework stood above all others: AngularJS. A “ring to rule them all”, AngularJS consolidated quite a few micro-frameworks and provided many extensibility points of expansion if needed. Over time though, many performance and architectural questions began to arise, to the point of no return – when the guys @Google decided to migrate from AngularJS to Angular (a poor naming decision).

Having just passed the 10-year anniversary of Malcolm Gladwell’s bestseller “Outliers: The Story of Success“, we thought to mark the occasion by taking a look at outliers and how they relate to success in the business world. Gladwell describes outliers as “those [people] who have been given opportunities — and who have had the strength and presence of mind to seize them.” At Anodot, we’ve also made it our mission to spot outliers, albeit of the data variety.

Simple enough to be embedded in text as a sparkline, but able to speak volumes about your business, time series data is the basic input of Anodot’s automated anomaly detection system. This article begins our three-part series in which we take a closer look at the specific techniques Anodot uses to extract insights from your data.

A little more than four years ago, Anodot started applying advanced AI/ML and unsupervised learning technologies to simplify monitoring challenges for DevOps teams. Today our company has customers from a variety of verticals and departments harnessing our unique platform to monitor business health, user behavior, product usage, IT ops, machine learning processes and even IoT.

Another holiday season will soon be upon us, and many retailers and eCommerce businesses are already making plans. As you take inventory of what you learned last holiday season, let’s start with some lessons learned by the entire retail industry this time last year. In addition to stocking up on hot items and planning your promotions, the most competitive sites found that using AI/ML to optimize customer experience not only kept customers happy, it dramatically increased their revenues.