Anomaly detection: Machine learning for real-time decision making

Anomaly detection is bridging the gap between metrics and business processes to provide more efficiency

Ever since the rise of big data enterprises of all sizes have been in a state of uncertainty. Today we have more data available than ever before, but few have been able to implement the procedures to turn this data into insights. To the human eye, there is just too much data to process. Tim Keary looks at anomaly detection in this first of a series of articles.

Unmanageable datasets have become a problem as organizations are needing to make faster decision in real-time. Machine learning has emerged as one of the critical technologies confronting this challenge. In fact, according to Adobe around 15% of enterprises are using AI solutions with 31% expected to incorporate AI solutions over the next 12 months.

While people are no match for large datasets organizations are leveraging machine learning and anomaly detection to process data faster than ever before. Anomaly detection is bridging the gap between metrics and business processes to provide more efficiency.

What is anomaly detection? A responsive replacement for traditional data monitoring

In the simplest terms, anomaly detection is a form of technology that uses artificial intelligence to identify abnormal behavior within a dataset. Datch systems defines anomaly detection as ‘ a method used to identify irregular or unusual patterns in a complex environment ‘. In other words, anomaly detection spots patterns in a way that a human user is unable to.

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AI solutions with anomaly detection automatically analyze datasets and determine the parameters of normal behavior and identify breaches in the patterns that signal an anomaly In network monitoring systems with anomaly detection, the AI can monitor the performance of a computer and spot malware based on specific data patterns.

The widespread applicability of these solutions has led them to be developed by many leading technology providers. For example, Microsoft Azure makes use of Time Series Anomaly Detection in Machine Learning Studio to flag up inconsistencies in time series data. In real terms, this helps the user to monitor their service and see unusual resource usage.

Network and performance monitoring and how anomaly detection is keeping enterprises secure:

Network and performance monitoring platforms using machine learning and anomaly detection have the potential to respond to threats in real-time

Anomaly detection is leading the charge to take organizations away from the limitations of manually monitoring datasets. In its place is a wave of solutions that can not only make use of large data stories but also become more intelligent over time. Anomaly detection solutions build up experience each time they run. With each further use the responses of the platform become more accurate.

Next generation of machine learning platforms

Though many vendors are launching anomaly detection platforms as a whole, these tools remain in their infancy. The youth of these tools hasn’t stopped data experts from embracing this technology with an ambitious mindset. Sheryl Zhang and Rupali Saboo of Data Services are in the process of building an anomaly detection platform of their own. They aim to create a platform that can locate anomalies within text data.

The most efficient way to find these anomalies has been to use machine learning. Rupali outlines that “rule-based checks to detect anomalies would create an unmanageable solution, given the variety of errors possible in text data.” Instead, the two have a different direction in mind.

“Our goal was to create a low-cost and easy to use platform which would work on different text-based datasets. If we could pull this off, the system could be used effectively to detect anomalies in any text datasets” said Rupali. The rationale is to create a platform that can monitor text-based datasets more efficiently than is currently possible.

Why do enterprises need anomaly detection platforms?

The rising popularity of anomaly detection platforms all comes down to efficiency. It is impossible to manage data that you can’t interpret or understand on demand. Organizations need to be able to respond to fast-moving changes to data at the drop of a hat. Immediate responses are particularly crucial for cybersecurity threats where employees need to respond immediately to prevent damage and downtime.

Unfortunately, there is no manual way to stay on top of these datasets. No matter how experienced your team is, they won’t be able to analyze and interpret thousands of statistics a second. However, a machine learning solution can. An anomaly detection solution provides you with a real-time interpretation of data activity. The moment a pattern isn’t recognized by the system you’ll know about it.

Anomaly detection platforms can delve down into the minutiae of data to pinpoint smaller anomalies that wouldn’t be noticed by a human user monitoring datasets on a dashboard. As a result, the only way to get real-time responsiveness to new data patterns is to use a machine learning platform.

The CTOs guide to anomaly detection

Information Age’s guide to anomaly detection for CTOs and tech leaders

Machine learning: Real-time and evolving

Given the quantity of data that modern organizations are managing, anomaly detection systems have become a necessity. These platforms are a must for reacting to changes in data immediately. To attempt to do so through manual analysis alone would lead to sluggish decisions based on outdated information.

In many ways anomaly detection systems have become a prerequisite for making up to the minute decisions. Whether this is within the context of optimizing the user experience or merely maintaining a network, these systems provide you with a much more straightforward procedure to process data.

At this stage, there is still a long way to go. Machine learning platforms haven’t reached their limit. The long-term learning potential of these systems puts them in a constant state of evolution. The more experience these tools develop, the more potent they will become. In the future this will not just result in quicker response times but better insights as well.

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