Smart Monitoring: Predictive Maintenance and Anomaly Detection Explained
January 15th, 2021Predictive maintenance reduces downtime, operational costs, and unscheduled maintenance while improving service quality. For Fednot (Royal Federation of Belgian Notaries), we built a smart monitoring dashboard with Azure and Power BI — not just as a proof of concept, but also to introduce their infrastructure team to different levels of Azure AI services.
Converting unstructured data to structured data
Log files are text-heavy and unstructured. The first step was extracting features — structured attributes close to the true factors for prediction. This requires deep understanding of both the data and the business problem. We created a transitional dashboard for feature exploration, letting Fednot select different feature combinations and trace back to original log entries — no coding required.
Azure AI Services across skill levels
The data pipeline ran on Azure Databricks (code-heavy, for data experts). Anomaly detection used the Anomaly Detector API from Cognitive Services (low-code, a few lines of code). Visualisation used Power BI dashboards (no-code, for decision-makers). The Anomaly Detector takes any time-series dataset, automatically fits a model, and returns expected values, boundaries, and abnormalities — useful for both monitoring and predictive scaling.
Results
Even with limited effort, Fednot gained significant additional value from their monitoring data. The project is a first step towards a data-driven infrastructure environment: reducing unexpected downtime, enabling incident pattern discovery, and improving application quality and customer experience. — Fisher Kuan & Wouter Baetens
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