Why Use Anomaly Detection for Streaming Data?

The ability to effectively process and analyze streaming data in real time has become increasingly important in today's data-driven business environment. Anomaly detection is one of the critical capabilities businesses need to maximize the value of streaming data; it allows them to quickly identify patterns, trends, and irregularities that could have a significant impact on their operations and decision-making processes.

What is streaming data?

Streaming data is a continuous flow of data generated in real time. It is frequently voluminous, rapidly changing, and produced on a massive scale. Streaming data is generated by a wide range of modern hardware and software systems, including fintech applications, fraud detection systems, social media platforms, event-driven software, IoT devices, and sensors.

Streaming data processing can be usefully contrasted with batch processing, in which data is collected, stored in a static repository, and analyzed later, often long after the event that generated it. That approach is useful in many situations, but the ability to harness and analyze streaming data in real time enables businesses to react to events while they are timely and relevant, allowing system behavior to be modified in response to current conditions.

What is anomaly detection?

In the context of streaming data, an anomaly is a data instance or a pattern different from the norm. Anomalies can indicate unusual user behavior, errors, faults, frauds, attacks, or potential business opportunities. Anomaly detection for streaming data, which is sometimes called outlier detection, is the process of analyzing streaming data in real time to identify anomalies as they occur.

You might be wondering what counts as an anomaly. That, of course, depends on the specifics of the app and the data, but examples of Steamdal’s anomaly detection capabilities include:

Why use anomaly detection for your streaming data?

Real-time anomaly detection empowers businesses to discover and act on anomalies while the circumstances that produced them are still pertinent. For example, anomaly detection is often used in predictive maintenance systems. If an analysis of streaming sensor data indicates that a critical piece of industrial machinery requires maintenance, you want to know as soon as possible.

A retrospective data analysis may reveal the causes of the hardware's failure, but an effective anomaly detection system allows you to act now to minimize imminent disruption rather than just figuring out how to prevent similar events from occurring in the future.

Similarly, if streaming data generated by bank or stock trading software matches a pattern that indicates potential fraud, you want detection systems in place so you can act before money is lost.

Compliance with privacy regulations is another common application of anomaly detection. Personally identifiable information (PII) processing and storage can pose a significant risk. Companies want to know that the data flowing into and between their application's components does not contain PII that exposes them to compliance risks.

An effective anomaly detection solution like Streamdal’s is able to identify PII and obfuscate or strip it from the data stream, optionally alerting on detected PII so the root cause of a potential compliance failure can be investigated and addressed.

Benefits like these are why businesses typically want to process streaming data in real-time so that they can analyze and respond to incoming information almost instantly. The more quickly they can identify useful information, the more valuable the data is.

The challenges of real-time anomaly detection for streaming data

Despite the numerous advantages of real-time anomaly detection, many businesses struggle to implement effective anomaly detection systems for their streaming data. Often, these challenges stem from the need to build systems that analyze and respond to data over short time spans.

Streaming data may arrive in large quantities and at high speeds, necessitating efficient and scalable infrastructure and algorithms capable of handling massive amounts of data while maintaining accuracy in real time.

Additional challenges arise as a result of the nature of streaming data generated by evolving software systems. As the software changes, so do the format and content of its streaming data, including features that may indicate anomalies, making it difficult to identify and act on anomalous events in a reliable manner.

Nonetheless, the scalability and adaptability of modern anomaly detection solutions for streaming data enable these systems to be implemented by businesses of all sizes and across multiple industries without having to build their own in-house solution. By leveraging state-of-the-art anomaly detection, organizations can effectively address the challenges associated with high-volume and rapidly changing streaming data, ensuring accurate and reliable identification of anomalies in real-time.