How to Detect Anomalies in Your Business Data

What Counts as an Anomaly

An anomaly is any data point that does not fit the expected pattern. In business data, anomalies fall into several categories:

• Point anomalies: Individual records that are unusually large, small, or different. An order 50 times the average size, a customer with a negative balance, or a transaction from an impossible location.
• Contextual anomalies: Values that are normal in one context but strange in another. Revenue of $50,000 on a Tuesday is fine for a large retailer but anomalous for a small consulting firm. Summer sales spikes are normal for ice cream shops but anomalous for a tax preparation service.
• Collective anomalies: Groups of records that together form an unusual pattern even though each individual record looks normal. Ten small transactions from different accounts all occurring within the same minute might indicate coordinated activity.

How to Run AI Anomaly Detection

Common Business Anomalies AI Catches

Financial Anomalies

Unusually large or small transactions, payments to unfamiliar vendors, duplicate invoices, transactions at unusual times, and spending patterns that break established norms. AI can cross-reference financial records against historical patterns to catch both obvious and subtle irregularities.

Customer Behavior Anomalies

Sudden changes in purchasing patterns, customers who go from highly active to silent, account access from unusual locations, subscription cancellations that spike on a specific day, and user activity patterns that deviate from their own history. These often indicate either a problem (impending churn) or an opportunity (a customer ramping up usage).

Operational Anomalies

Server response times that suddenly increase, delivery rates that drop unexpectedly, inventory levels that do not match expected consumption, support ticket volume spikes, and any operational metric that moves outside its normal range. Catching these early prevents small problems from becoming large ones.

Data Quality Anomalies

Duplicate records, missing values in fields that should always be populated, impossible dates, negative quantities, inconsistent formatting, and records that reference nonexistent related records. These are not business problems but data problems, and fixing them improves the accuracy of all your other analysis.

AI Anomaly Detection vs Machine Learning Anomaly Detection

AI analysis (what this article covers) detects anomalies in your data right now, in a single conversation. You upload data, ask questions, and get flagged items. This is ideal for ad hoc investigation and periodic data reviews.

For continuous, automated anomaly detection that runs on new data as it arrives, machine learning anomaly detection trains a persistent model that learns your normal patterns and automatically flags deviations. The platform's Data Aggregator supports both approaches: AI analysis for exploration and ML models for ongoing monitoring.

Setting Up Ongoing Monitoring

After using AI analysis to establish what normal looks like in your data, consider setting up automated monitoring. Use workflow automation to run anomaly detection checks on a schedule and send alerts when something unusual appears. Combine with ML-based unusual activity detection for continuous automated monitoring that does not require manual analysis sessions.