This is the Electe Review. Today, why most anomaly detection fails the moment an alert fires, and why an unusual number on a Monday morning dashboard is a question, not a verdict. An anomaly alert only earns its place when someone can name the decision it supports. When sales jump, refunds shift, or a payment breaks a customer's pattern, a chart cannot tell you whether you are looking at campaign success, fraud, stock trouble, or duplicated records from a broken import. Treating every statistical departure as an answer sends teams chasing noise while real problems slip through. The industry sells complex algorithms, yet most small and mid-sized businesses lack the labeled incident history that supervised models demand. Historical benchmarks expose the trap of false confidence. In the KDD Cup 1999 data set of roughly 4.9 million connection records, about 78% were redundant, and traffic distributions shifted sharply between training and testing. Worse, the data pipeline often fails before the detector even runs. Research on data quality shows that 61.4% of significant issues went undetected for an average of 6.3 days, and teams without mature data operations often need two to three months of implementation support. Effective monitoring separates three shapes of unusual behavior: individual point anomalies, context-dependent spikes like heavy traffic during a planned sale, and collective patterns such as repeated refunds around a single account. It also accounts for concept drift using calendar-aware baselines, event annotations, and human feedback rather than blindly retraining on unreviewed alerts. Matching the method to the decision matters more than sophistication. Transparent thresholding, clustering across customer segments, isolation forests, forecasting detectors, and autoencoders each carry trade-offs. As NIST guidance notes, cutting false positives generally raises false negatives. What matters is review workload, missed event cost, and time to resolution. Whether teams rely on spreadsheet rules, custom models, or an AI-powered analytics platform like Electy, where an autonomous agent continuously monitors data and pre processes sources, automation never replaces human accountability. Start with a single decision linked metric, verify data freshness and schema stability, and assign a clear owner before expanding coverage. That's the review.