Anomaly detection is a technique used in data analysis and machine learning to identify patterns, events, or observations that deviate significantly from the expected or normal behavior within a dataset. These deviations, known as anomalies or outliers, may indicate errors, outliers, or potential issues in the data.
The primary goal of anomaly detection is to distinguish normal patterns from abnormal ones in order to identify instances that require further investigation. Anomalies can represent interesting and valuable insights, such as security breaches, system failures, fraudulent activities, or rare events. The detection of anomalies is applicable across various domains, including finance, cybersecurity, healthcare, manufacturing, and more.
There are different approaches to anomaly detection, including:
Statistical Models
Statistical models assess whether a data point falls outside the expected range based on statistical measures like mean, median, standard deviation, or other distribution properties.
Machine Learning Models
Machine learning algorithms, such as clustering, classification, or regression models, can be trained to recognize patterns in normal data and identify anomalies.
Unsupervised Learning
Unsupervised learning methods, like clustering or autoencoders, don't require labeled data. They aim to identify patterns in the data without prior knowledge of what constitutes normal or anomalous behavior.
Supervised Learning
In supervised learning, models are trained on labeled datasets, distinguishing between normal and anomalous instances. This trained model can then be used to identify anomalies in new, unseen data.
Density Based Analysis
Density-based methods, like Local Outlier Factor (LOF), evaluate the density of data points in a neighborhood. Anomalies are often located in regions with lower data density.
Applications Of Anomaly Detection
There are multiple applications of anomaly detection.
Cybersecurity
Identifying unusual patterns or activities that may indicate a security breach.
Finance
Detecting fraudulent transactions or unusual patterns in financial data.
Healthcare
Identifying abnormal medical conditions or outliers in patient data.
Manufacturing
Detecting defects or anomalies in production processes.
Anomaly detection is a crucial component in various industries where identifying and addressing abnormal patterns quickly can lead to improved efficiency, reduced risks, and enhanced overall system performance.
Technical Foundations
Unfortunately, traditional tools and approaches to data and analytics do not scale to deliver solutions like this.
There are too many delays in the process, and the systems often used are not performant enough to process high volumes of data with low latency. In addition, traditional business intelligence tools are not rich and flexible enough to meet the business demands.
This technology stack needs to be re-invented for the cloud, with tools and architectural patterns that are built for real-time advanced use cases and predictive analytics:
Introducing Ensemble
We are Ensemble, and we help enterprise organisations build and run sophisticated data, analytics and AI systems that drive growth, increase efficiency, enhance their customer experience and reduce risks.
We have a particular focus on ClickHouse, the fastest open-source database in the market, which we believe is the fastest best data platform for systems like this.
Want to learn more? Visit our home page or download our free report that describes the process for implementing advanced analytics in your business.
