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.
Small and slow leaks sink ships – by analogy, slow and small leaks can also cause significant losses for any business if not detected and fixed early. Are small leaks interesting? Suppose an eCommerce business sees a decline of 50% of purchases in the last day – the entire company would be called in – from the CEO all the way to R&D, Support, to figure why it happened as quickly as possible.
In our previous article (How to Scale and Manage Millions of Metrics), we looked at correlations in terms of name similarity, but there are other types of similarities that occur between metrics.
What’s surprising to see today is how business operations struggle to get an integrated view of all business metrics. With greater volumes of data being collected, data analysts just can’t keep up with the pace. This state of affairs alone doesn’t hit as hard as the fact that many in data analytics have just come to accept this situation as a norm and simply bear with this daily struggle.
With businesses collecting millions of metrics, let’s look at how they can efficiently scale and deal with these amounts. As covered in the previous article (A Spike in Sales Is Not Always Good News), analyzing millions of metrics for changes may result in alert storms, notifying users about EVERY change, not just the most significant ones. To bring order to this situation, Anodot groups correlated anomalies together, in a unified alert.
