Written by Imran Abdul RaufTechnical Content Writer
Anomaly detection, also termed outlier analysis, identifies rare occasions, atypical and unusual patterns, and outliers of a dataset, which are vastly different from the rest of the data. Anomalies often indicate equipment malfunction, technical issues, structural mishaps, bank frauds, intrusion attempts, medical problems, and associated problems.
Anomaly detection methods are helpful in interpreting the context, getting rid of possible causes, and improving data quality and datasets.
Anomaly detection is primarily used to produce valuable business insights and maintain core operations. The techniques can empower IT teams, to limit the above-stated problems and their accumulated consequences on the business.
The following are the problems addressed by anomaly detection processes.
Financial transactions require easy, secure processing. And detecting anomaly trends in transactions for customers, vendors, or partner businesses can locate security gaps and likely prevent potential frauds from occurring.
Time-sensitive decisions are the most critical components in the healthcare sector, and time anomaly detection can prevent some really health issues from escalating. For example, if any patient’s vitals function beyond the normal, healthy range, the warning indicates a problem.
Other than individualized detection, anomaly detection also helps in public health issues, for instance, potential epidemic outbreaks.
Throughout the COVID-19 pandemic, many people around the globe have misused government healthcare facilities, including fraudulent insurance claims, stimulus checks assigned to dead people, etc. Network anomaly detection technology helps recognize and prevent similar suspicious activities by preventing misuse and resource waste.
Throughout the peak COVID-19, we saw plenty of panic buying and online shopping when physical stores closed down due to a lack of goods. The sudden spike in demand saw ecommerce services struggle to meet the demand.
Ecommerce companies can use anomaly detection to identify the upcoming trends proactively and fluctuations in demands to be better prepared the next time chaos buying hits.
IT teams regularly monitor user behavior to understand trends and detect unusual activities in the company’s security systems. Anomaly detection helps identify and prevent such attempts before any attack on the business’s confidential data.
Detecting outliers and data drifts earlier dramatically help in improving data quality which is used to train analytical models. As models need quality data for better functioning, they’ll produce more reliable results and improve their accuracy with time.
Anomaly detection of outliers, drifts, and schema changes and patterns consistently provides top-quality data to enterprise systems. In addition, users can minimize downtime by limiting anomalies before they affect the downstream tools.
Anomaly detection techniques work through the assumption that anomalies are rare occurrences and considerably different from normal behavior. Still, the detection techniques rely on the behavior’s context to identify any abnormal behavior.
Time series data shows a context through a sequence of values over time, and each instance or point in the time-series data has a timestamp and a metric value. This context defines the bottom line for an ideal behavior pattern, helping identify odd outliers or patterns.
Enterprise-level anomaly detection works through the following settings:
Time series contains a sequence of values against time, i.e., each point represents a pair of 2 items. The items indicate the time instance when the metric was measured and the value related to that particular metric in that specific time interval. Any successful anomaly detection is based on precisely analyzing time series data in real-time.
Understand that time series data isn’t a depiction of itself. Instead, it’s a piece of information used to make future predictions. Anomaly detection systems use this data to extract actionable projections within the business’s data, uncovering outliers in vital KPIs and alerting the respective stakeholders to associated events in your company.
Time series anomaly detection depends on the type of use case and your business model. It calculates robust metrics like cost per click, web page views, mobile app installs, churn rate, customer acquisition costs, average order value, etc. First, the system must develop a benchmark that will be considered normal behavior for major KPIs. With that baseline constructed, the detection systems can track the cyclical patterns of behavior within essential datasets.
But when it comes to scaling millions of metrics, tracking time series data, and identifying anomalies need to be automated to provide crucial business insights.
There are various challenges in anomaly detection, including separating noise in identifying real outliers, but modeling normal behavior in providing the proper context is the most complicated activity.
Time series gives the fundamental context for normal behavior for detecting data anomalies. Still, without a suitable context identifying outliers is challenging, especially when the activity involves large, complex systems like environmental trends, traffic fluctuations, etc.
Predictive data quality is used to overcome this obstacle and facilitate unsupervised anomaly detection at the organizational level, producing rough statistical models by stuffing raw data into 100 times smaller chunks to benchmark and baseline datasets with time.
Related content: Achieving Enterprise-Wide Data Reliability
Also, modeling normal behavior through approved variance enables locating anomalies with precision.
This overview should give you a firm idea of data anomaly detection, its use cases, and how systems work at the organizational level. From a holistic standpoint, anomaly detection is only a part of data governance. And building a solid data governance program is all you need to strengthen your anomaly detection algorithms and data quality exercises.