Discover how machine learning can help with anomaly detection.
The term “Anomaly Detection” indicates analyzing patterns of high-volume data to search for unusual occurrences. Most businesses today rely on data-driven metrics to track performance and discover new opportunities. Irrespective of domain and industry, monitoring the data patterns in real-time can enable a business to uncover root causes that are otherwise, undetectable with the traditional data analysis methods.
A lot of companies today apply manual methods for detecting anomalies, which involve the following methods:
Monitoring through Dashboards: Dashboards generate reports for various frequencies such as daily, weekly, monthly. Outlier detection in such a scenario will involve a comparison of results to detect any unusual deviation. The major drawback of this method is its lack of scalability where it is humanly impossible to track more than a dozen KPIs. There is also a chance of missing out on smaller outlier while being in quest for major anomalies. This approach also lacks a holistic approach where only the pre-decided KPIs and metrics are analyzed and the other critical areas may get ignored. This may also result in delays in discovering insights, which may lead to loss or reduction of revenues.
Rule-based approach: This approach involves setting low and high threshold values for each metric. In case of any deviation of value, an alert may be generated. The challenge here is determining the thresholds for each KPI, which may be a daunting task in case of a growing business. Also, in case of faulty threshold levels, a lot of inaccurate alerts may be generated. This will compromise the credibility of the alerts which makes this approach less effective.
How can Machine Learning help?
Eugenie was conceptualized keeping the growing requirement of anomaly detection and real-time analytics in the age of hyper data consumption. To avoid losses and missed business opportunities, only a machine learning-powered system can tackle the humongous business data.
Outlier detection in machine learning involves using algorithms that can learn the patterns and trends of data sets as well as constantly learning as per the data-patterns. There are two main methods of machine learning; Supervised and Unsupervised.
Supervised machine learning involves feeding data and corresponding examples to get the classification and categorization. The system is expected to learn and improve gradually based on the examples. Unsupervised machine learning discovers patterns and results from data sets without exclusively comparing them with existing data or a model.
Supervised learning does not fit into the realm of anomaly detection as the data sets do not explicitly indicate the presence of anomalies but, an investigation and learning are required to establish the anomalies. Eugenie’s solution involves several unsupervised algorithms that can be effectively implemented across different domains.
Machine learning-based outlier detection follows two methods, univariate and multivariate. In univariate outlier detection, the system analyzed each metric on its own without considering the effect of other factors. The advantage of this method is its simplicity and scalability. The other method, multivariate outlier detection analyzes signals from all the inputs to establish the root cause of the detected anomalies. The disadvantage of this approach is the difficulty in scaling and complexity.
Although setting an anomaly detection system is fairly complex, it is imperative for businesses today to scale the businesses, growth and discovering emerging opportunities.