Today, managing and monitoring the distributed system’s performance is a struggle, though necessary. With hundreds of thousands of items to watch, anomaly detection can help point out where an error is occurring, enhancing root cause analysis and quickly getting tech support.
Anomaly detection (outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset’s normal behaviour. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance, a change in consumer behaviour. Increasingly, machine learning is used to automate anomaly detection.
With all the analytics programs and management software available, companies can now measure every aspect of business activity more effectively than ever before. It involves measuring the operational performance of applications and infrastructure components and evaluating key performance indicators (KPIs) that indicate the organisation’s success. With millions of metrics to measure, companies often have a substantial dataset to analyse their performance.
Within this dataset are data patterns that represent business as usual. Data anomalies refer to changes in data patterns or an event that deviates from the expected data pattern. An anomaly is a deviation from the norm.
A successful anomaly detection relies on analysing time-series data, which consists of a sequence of values over time in real-time with accuracy. Since time series data contains information that can be used to make educated guesses about the future, anomaly detection systems use those to uncover anomalies and alert.
Time-series data anomaly detection can also be used for metrics such as: web page views, daily active users, mobile app installs, cost per lead, cost per click, customer acquisition costs, bounce rate and more.
An organisation needs to establish a baseline for normal behaviour for its primary KPIs to detect time series anomalies. With that baseline understood, time series anomaly detection systems can identify cyclical patterns within crucial datasets. To track thousands or millions of metrics and deliver meaningful business insight, spotting anomalies must be automated.
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Types of time series anomalies
Understanding the types of outliers that an anomaly detection system can identify is critical to getting the most value from the generated insights. As soon as your anomaly detection system alerts you to an issue or opportunity, you risk making the wrong decision without knowing what you’re up against. In general, anomalies in your business data fall into three main categories: global outliers, contextual outliers, and collective outliers.
- Global outliers: These outliers exist far outside the entirety of a dataset.
- Contextual outliers: Data points deviate significantly from the other data points in the same context. Anomalies in one dataset may not be anomalies in another. In time-series data, outliers are common because those datasets are records of particular quantities over a given period.
- Collective outliers: An anomalous subset of data points within a dataset is a collective outlier. When you combine different time series, you start to see these types of outliers. Individual behaviour might not deviate from the normal range in a specific time series dataset. Anomalies become more apparent when combined with another time series dataset.
With all the analytics programs and various management software available, an organisation can measure every aspect of its business activity more effectively than ever before. This includes both the operational performance of applications and infrastructure components and key performance indicators (KPIs) that evaluate the business’s success.
Having millions of metrics to measure can leave an organisation with a massive dataset to explore, and it gets more complex when data patterns unexpectedly change. Generally, these anomalies result from real-world business incidents, whether it’s a new marketing campaign that generated leads, a promotional discount that drove sales, a price glitch that adversely impacts revenue, or anything in between.
Since there are millions of metrics to track across your business, there are as many ways to gain insights from anomaly detection. Here are three primary business use cases for anomaly detection — application performance, product quality, and user experience.
Anomaly detection for application performance
Application performance can make or break workforce productivity and revenue.
Waze, which boasts more than 100 million monthly active users worldwide, uses machine learning algorithms to allow its anomaly detection solution to seamlessly correlate data with relevant application performance metrics to provide a complete picture of business incidents that the IT can act upon team. It’s not just software and app companies like Waze that benefit from anomaly detection. Other industries can also benefit:
Telco: Since telco operators produce large amounts of time-series data, they need advanced solutions to mitigate anomalies that could cause system-wide degradation in their complex networks. To monitor performance in real-time, telco needs to detect anomalies such as jitter, latency, call quality, etc., across its networks.
Adtech: A daily processing of trillions of transactions within 40 milliseconds with live auctions leaves little time for monitoring KPIs manually. In addition to technically challenging data centre issues, more complex application performance trends are less visible. Rubin Project, one of the largest ad exchanges globally, monitors all transactions in real-time to maintain the health of its marketplace.
Anomaly detection for product quality
Any product-based business can benefit from anomaly detection, and the following are two key examples:
eCommerce: Even though developers can handle the technical aspects of monitoring an eCommerce platform, the business funnel, conversion rates, and other key performance indicators need to be monitored. When you rely on static thresholds to monitor dynamic funnel ratios, you will miss important alerts in the context of seasonality and other time series elements. If you don’t spot pricing glitches, it will lead to a site crash, angry customers, and a significant loss of revenue. Product quality issues like price glitches are detected faster through anomaly detection before a site crashes and customers are affected.
Fintech: Security is critical for any digital business but is even more so for fintechs. You need to stay ahead of advanced attacks, so your customers and financial partners have confidence that transactions are processed securely. When anomaly detection is in place, data sources are integrated into a centralised platform, giving you total visibility into performance and operations and revealing critical security vulnerabilities.
Anomaly detection for user experience
A faulty release, an attack caused by DDoS, or a change to your customer support process that backfires can result in usage lapses across customer experiences. To avoid frustrations resulting in churn and revenue loss, it is critical to respond to these lapses before impacting the user experience. A proactive streamlining and improvement of the user experience will benefit a variety of industries, including:
Gaming: The permutational complexities of gaming experiences cannot be monitored with manual thresholds. With artificial intelligence (AI), anomaly detection solutions monitor operating systems, levels, user segments, different devices, and more to ensure that glitches and errors that could harm user experience are quickly addressed.
Also Read: How AI is Improving Predictive Analytics
Identifying different anomaly detection methods
In the past, manual anomaly detection was a viable option. There were only a few metrics an organisation needed to track across its business, and the datasets were manageable for an analytics team. However, now here is more data than ever, and traditional, manual anomaly detection doesn’t scale.
Modern businesses need automated anomaly detection to provide accurate, real-time insights regardless of how many metrics they track. An automated anomaly detection system would include detection, ranking, and grouping of data, thereby eliminating the need for teams of analysts to analyse large amounts of data.
Automating anomaly detection requires finding the right combination of supervised and unsupervised machine learning. In most cases, an organisation wants data classification to take place unsupervised (without human intervention). In any case, it should still be able to have analysts feed algorithms with valuable datasets for creating benchmarks. Using a hybrid approach ensures an organisation can scale anomaly detection while maintaining the flexibility to make manual rules for specific anomalies.
Anomaly detection is not an option anymore but a must-have for any organisation.
