Advance your analytics with anomaly detection

Unveil your business potential and mitigate risk proactively

Anomalies are unexpected changes or deviations from the expected pattern in a dataset. Visual analytics play a role in anomaly detection by tapping into humans' inherent abilities for spotting patterns and aiding in the rapid identification of abnormal behavior. It achieves this by isolating costly business issues before they happen and uncovering risks or opportunities in advance, in a scalable way, often spanning large processes across many complex data sources.

Why use Spotfire for anomaly detection?

Spotfire helps you detect, label, and visualize anomalies, which is crucial for spotting costly unexpected conditions, mitigating major fraud incidents, or providing early intervention for asset malfunctions.

Process optimization

Optimized processes result in better controls and improved product quality. With Spotfire, you can minimize risk by making it easy to rapidly detect emerging issues and take corrective actions. Scrap, rework, and quality issues can be significantly reduced with advanced quality-control analytics, root-cause analysis, and validated reports. And by increasing supply chain visibility, bottlenecks can easily be identified and resolved.

The benefits of process optimization include:

Increased uptime and reduced downtime
Minimized surplus and defects for better yields
Reduced costs due to better product quality
Fewer deviations and less non-conformance

Cost optimization

Costs can easily get out of control without proper management. With Spotfire, you can visualize exactly where spend is happening and how it is improving your business. Predict and manage future costs based on historical data—a better understanding of overspend and underspend areas is a click away. With a collaborative tool, you can eliminate hidden spending across departments.

The benefits of cost optimization include:

Maximized ROI
Increased financial transparency
Better cross-team budget collaboration
More accurate spending and planning

Asset optimization

Knowing exactly what resources you have and how they are working is crucial to business management. Optimized warehouses and supply chains help maintain business liquidity. With Spotfire advanced analytics, you can visualize which assets need maintenance, where inventory is lacking, or where there is an increase in traffic.

The benefits of asset optimization include:

Increased customer satisfaction
Less wasted inventory
Better forecasted demand
More insight into customer behavior

Anomaly detection benefits multiple industries

Fight financial crime

Financial transactions worth billions of dollars execute every minute. Identifying suspicious activity in real time provides leading financial companies a competitive edge by controlling costs and reducing false-positive instances to reduce losses.

Learn from ASX

Monitor equipment sensors

Track smart vehicles, machines, and IoT-connected device sensors on the edge. Monitor outputs to predict and prevent disruptions with a high cost of downtime. Employ unsupervised learning algorithms like autoencoders to sense—and alert you to—anomalous issues like manufacturing defects, equipment failures, and product issues.

Learn from ASPI

Uncover insurance claims fraud

Identify fraudulent claims in real time and prevent payments to bad actors. Build supervised, unsupervised, and semi-supervised models to reduce the likelihood of insurance fraud for each claim submitted.

Learn from AA Ireland

Anomaly detection techniques

Visual discovery

Run anomaly detection using visual discovery. Monitor dashboards to find unexpected behavior.

Supervised learning

Label a set of data points as normal or abnormal, and then use this labeled data to build machine learning models that can predict anomalies on unlabeled new data.

Unsupervised learning

Use unlabeled data to build unsupervised machine learning models that predict new data. Because the model is tailored to fit normal data, anomalous data points stand out.

Time series analysis

Detect anomalies through time series analytics by building models that capture trends, seasonality, and levels in time series data. When new data diverges too much from the model, either an anomaly or a model failure is indicated.

Autoencoders and machine learning

Use the latest machine learning techniques and autoencoders to detect and respond to anomalies in real time. Learn how to build a neural network in TensorFlow to predict anomalies from transaction and sensor data feeds on the AWS Marketplace.

Clustering

Classify each data point into one of many predefined clusters, then create a separate cluster for all data points that do not look similar to normal data.