Anomaly detection is a quick way to begin making sense of data and identifying outliers without having a historical data set or well understood use-case.

Anomaly Detection Example

Anomaly detection is a quick way to begin making sense of data and identifying outliers without having a historical data set or well understood use-case. Anomaly detection leads to actionable insights such as in quality assessment, predictive maintenance, predictive sales, and much more.

When is anomaly detection important?

Little Historical Data

When starting with a new system with very little data, it is difficult to know when the system is running under “normal” operations. Anomaly detection is a great place to start when there are so many unknowns.

Solid Historical Data With Labeled Anomalies

Anomaly detection is also useful when the system is well understood. While machine learning models can be helpful, sometimes they are unnecessary to solve the problem.

Anomaly detection compared to…

Rule Engines

Rule engines rely on expertise of subject matter experts, but sometimes that expertise isn’t available. Anomaly detectors, on the other hand, can work alone or in tandem with rule engines to bring actionable insights. Anomaly detectors can find complicated patterns that humans can’t identify, and rules can even be built on top of anomaly detectors to alert a business to act. These rules can be determined by subject matter experts and defined by company priorities.

Machine Learning

Traditional machine learning methods require a well-defined problem to solve and relevant historical data. When starting out with little data or undefined use cases, anomaly detection can still make use of the newly collected data. It’s possible machine learning isn’t even needed in order to gain actionable insights.

Business Impact

Anomaly detection can have a strong business impact across a range of industries without requiring a large amount of data. These industries include transportation, manufacturing, oil & gas, water & power, energy, and retail. The following are examples in which anomaly detection can help your business:

  • Save costs by fixing equipment before a catastrophic failure occurs.
  • Increase revenue by identifying when a quick service restaurant will be busier than expected.
  • Fix labor shortages by identifying anomalies more experienced employees would have detected such as with labeled industrial pump data.
  • Increase customer satisfaction by preparing for their needs in advance based on anomaly data.

Conclusion & Next Steps

Anomaly detection has a variety of promising applications in industrial IoT including quality assessment, predictive maintenance, predictive sales, and customer assessment. I hope that these examples have given you some ideas of how anomaly detection can be applied to your own data. Find out how Bsquare Data Science Team can help harness the full potential of your data to optimize your ROI. For additional insights, check out our data science webinar on Dec. 12.

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