A recent report by Gartner casts light into the world of AIOps, and the need for deploying it in organizations today. AIOps is a modern approach to DevOps which is based on recent AI technology. Gartner’s vision of the AIOps platform is one that enables continuous insights across IT operations management.
If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.
There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.
To keep you up-to-date with what’s going on in anomaly detection, we keep an ongoing list of the biggest glitches happening in the business world. Here is what made waves in June. June 25, 2019 When Dutch telco KPN suffered a major outage on the evening of Tuesday, June 25, the 112 emergency number was also knocked out across the country. “We have no reason to think it was (a hack) and we monitor our systems 24/7,” the company spokesperson told Reuters.
Companies invest in anomaly detection in order to proactively identify risks, such as revenue loss, customer churn and operational performance issues. Anomaly detection essentially enhances traditional BI and visualization tools, venturing beyond a summary view of your data. It constantly scans every metric, at a granular level, to find abnormalities. But in order for this technology to have an impact, you must be able to trust it.
Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.
Many data-driven companies struggle with tracking anomalous behavior in their KPIs, let alone their more granular metrics, because traditional BI tools just can’t keep pace with big data. Manual processes, whether monitoring dashboards or setting static thresholds, often leads to missed incidents or prolongs time to resolution.
Getting the right metrics at the right time could be the difference between running a smooth operation and making costly mistakes. And yet, data delays are still very much a part of data analysis today.
The global data supply reached 4.4 zettabytes (ZB) in 2013 – or 4.4 trillion GB – but less than five percent of that data was actually analyzed, according to the seventh Digital Universe report by the IDC. Approximately 22 percent of all existing data would actually be useful if tagged and analyzed.
Success in business hinges on making the right decisions at the right time. You can only make smart decisions, however, if you also have the insights you need at the right time. When the right time is right now, outlier detection can help you chart a better course for your company as storms approach – or as the currents of business shift in your favor. In either case, quick detection and analysis can enable you to adjust your course in time to generate more revenue or avoid losses.
