Connect your data to Telmai and detect patterns and values that stand out. These outliers could be related to data quality issues such as unexpected string values in a numeric field or could be outliers in business metrics such as transaction totals that fall outside the expected distribution of your data.
Telmai uses time series analysis to learn about the trend of values in your data over time and predict what can be reasonably expected. With Telmai’s observations, you are able to identify data points that drift from expected thresholds. You can decide how much variability is standard and set rules and expectations to get notified when outliers and anomalies occur.
Telmai uses the historical analysis of your data to create a baseline for normal behavior. With that baseline established, Telmai automates your anomaly detection. When your data crosses a certain threshold or falls outside historical patterns, alerts and notifications help you uncover changes in your data and key KPIs.