Data Quality Thresholds that Adapt to Your Data

Telmai employs advanced machine learning techniques to determine tailored data quality thresholds for each data metric being monitored within your dataset.
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Data quality thresholds refer to the acceptable limits or boundaries for a particular metric, ensuring that the values are accurate, reliable, and within an expected range. By setting these thresholds, it becomes easier to identify anomalies, inconsistencies, or errors in the data.
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Take, for instance, a time-aware data metric such as row count. Telmai will establish an expected range of row updates at a given time interval. This range is derived from our ML model that considers factors such as seasonality to accurately predict fluctuations and trends in the data.