digna Data Anomalies
digna Data Anomalies
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.
It learns what's "normal" in your data, and alerts you the moment something deviates. From revenue spikes to missing records, column swaps to wrong delivered values - digna flags anomalies automatically so you can act before issues escalate.
It learns what's "normal" in your data, and alerts you the moment something deviates. From revenue spikes to missing records, column swaps to wrong delivered values - digna flags anomalies automatically so you can act before issues escalate.
It learns what's "normal" in your data, and alerts you the moment something deviates. From revenue spikes to missing records, column swaps to wrong delivered values - digna flags anomalies automatically so you can act before issues escalate.
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:
NUMERICAL
NUMERICAL
CATEGORICAL
CATEGORICAL
UNSPECIFIED
UNSPECIFIED
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 |
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.
Static Datasets
Dynamic Datasets
Hybrid Datasets
Static Datasets
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence
Using Artificial Intelligence, digna learns the natural patterns in your data and alerts you whenever something looks implausible — whether it’s a sudden spike, a missing value, or a distribution that no longer matches expectations.
By combining AI-driven anomaly detection with flexible metric definitions, digna ensures that both data quality issues and business anomalies are flagged early — without the need to predefine thresholds or rules.
Using Artificial Intelligence, digna learns the natural patterns in your data and alerts you whenever something looks implausible — whether it’s a sudden spike, a missing value, or a distribution that no longer matches expectations.
By combining AI-driven anomaly detection with flexible metric definitions, digna ensures that both data quality issues and business anomalies are flagged early — without the need to predefine thresholds or rules.
Use Case: Bank Customer Monitoring
Numerical Data
Categorical Data
Unspecified Data
Numerical Data
Categorical Data
Unspecified Data
Numerical Data
Categorical Data
Unspecified Data
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
Key Benefits of
Key Benefits of
digna Data Anomalies
digna Data Anomalies
Runs entirely in-database
No thresholds, no rules —
anomaly detection on autopilot
Use one engine for both data quality monitoring
and business/operational KPIs
Scales across 100s of tables with zero maintenance
Metrics are reusable — feed into validation, analytics, or AI
Runs entirely in-database
No thresholds, no rules —
anomaly detection on autopilot
Use one engine for both data quality monitoring
and business/operational KPIs
Scales across 100s of tables with zero maintenance
Metrics are reusable — feed into validation, analytics, or AI
Runs entirely in-database
No thresholds, no rules —
anomaly detection on autopilot
Use one engine for both data quality monitoring
and business/operational KPIs
Scales across 100s of tables with zero maintenance
Metrics are reusable — feed into validation, analytics, or AI
Let your data tell you what’s off — before it breaks something.
A Vienna-based team of AI, data, and software experts backed by academic rigor and enterprise experience.
Let your data tell you what’s off — before it breaks something.
A Vienna-based team of AI, data, and software experts backed by academic rigor and enterprise experience.
Let your data tell you what’s off — before it breaks something.
A Vienna-based team of AI, data, and software experts backed by academic rigor and enterprise experience.
FAQs
How does AI-based data anomaly detection work in data observability?
How does AI-based data anomaly detection work in data observability?
How does AI-based data anomaly detection work in data observability?
How does AI improve data quality monitoring?
How does AI improve data quality monitoring?
How does AI improve data quality monitoring?
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