digna Data Anomalies
Catch what dashboards miss - automatically
digna Data Anomalies detects unexpected changes in your data quality and business/operational KPIs without any manual thresholds or rules.
How digna Data Anomalies Works
The module calculates and monitors key metrics like Sum, Min, and value counts across three types of data in every column:
Name of Column | Type of Data | Value Example | digna Column Type |
|---|---|---|---|
Customer Name | Text | John Smith | Unspecified Data |
Type of Customer | Text | Retail / Business | Categorical Data |
Account Number | Number | AT4097012346234 | Unspecified Data |
Account Balance | Number | 167.234,01 / 12.333,89 | Numerical Data |
Overdraft Limit | Number | 20.000 / 0 | Numerical Data |
Metrics can be scoped to the entire table or a filtered subset, which we call a “Dataset”. In such a case, digna calculates metrics for every dataset independently.
Dynamic Datasets
Hybrid Datasets
Use Case: Bank Customer Monitoring
You're in Control
Not every metric is useful for every column. digna lets you:
✦ Disable metrics per column, table, or project
✦ Focus only on what matters
✦ Keep your profiling clean, fast, and tailored
FAQs
What is data observability and why does it matter?
How does AI-based data anomaly detection work in data observability?
How does AI improve data quality monitoring?
How can digna help data scientists reuse calculated observability metrics?