Data Anomalies – Automated Detection¶
AI-Driven Module for Data Quality and Observability – digna Data Anomalies
Purpose¶
The Data Anomalies module identifies irregularities in your datasets automatically — no rule writing required.
It continuously monitors the quality of data delivery, learning what “normal” looks like and detecting deviations in real time.
By using AI-based detection, digna recognises silent data errors such as missing, duplicated, or corrupted records that can distort reports, ML models, and dashboards.
Technical Overview¶
Metrics analyzed¶
digna continuously profiles the following aspects of your data:
- Record volume – total number of rows, daily or batch-based
- Missing values – detection of null or empty fields
- Distributions and histograms – monitoring shape changes in data
- Value ranges – automatic identification of out-of-range or extreme values
- Uniqueness – checks for duplicate keys or repeated entries
Intelligent anomaly detection¶
- Uses historical learning to dynamically define expected boundaries
- Detects deviations in volume, value distributions, or logical relationships
- Employs AI to adapt thresholds automatically based on time-of-day or seasonal patterns
- Differentiates between statistical fluctuations and true anomalies
- Produces detailed metrics and confidence scores per dataset and column
Detection Scenarios¶
Below are examples of real-world problems automatically caught by the Data Anomalies module:
| Scenario | Description |
|---|---|
| Volume drops or spikes | Missing half of daily transactions, duplicated batch loads, or sudden data surges |
| Missing or null values | Data extractions completed but critical columns left empty |
| Distribution drifts | Average purchase amount or transaction count per region changes unexpectedly |
| Column swaps | Columns like first_name and last_name accidentally switched during ETL |
| Unexpected categorical values | e.g., “Zurich” appears under Austrian city list |
| Sudden uniqueness loss | Previously unique IDs start duplicating due to upstream join errors |
Architecture and Execution¶
- In-database execution: All anomaly detection logic is executed inside the database engine (Teradata, Snowflake, Databricks, PostgreSQL, etc.)
- No data movement: digna reads only metrics, never transfers raw data externally
- Incremental updates: Only new data segments are analyzed each run for efficiency
- Configurable inspection frequency: Hourly, daily, or triggered by upstream processes
- Result storage: Metrics and anomaly flags are written back to digna’s observability schema for visualization and alerting
Benefits¶
| Area | Benefit |
|---|---|
| Automation | Eliminates hundreds of manual SQL or rule definitions |
| Precision | Detects issues that static thresholds often miss |
| Scalability | Monitors millions of records per table efficiently |
| Integration | Works seamlessly with digna Data Analytics for trend analysis |
| Compliance | Ensures continuous control over the quality and observability of data |
| Transparency | Provides confidence scores, timestamps, and reason codes for every anomaly |
How digna Learns “Normal”¶
- Profiling phase: digna collects metrics from historical datasets.
- Learning phase: AI models identify recurring patterns (seasonal, weekly, daily).
- Monitoring phase: Future datasets are compared against dynamically learned thresholds.
- Alerting phase: Deviations beyond statistical confidence boundaries are raised as anomalies.
All models are explainable, deterministic, and optimized for enterprise data volumes.
Example Use Cases¶
- Monitoring data quality in banking transaction systems
- Detecting load failures in ETL or data warehouse jobs
- Identifying abnormal customer activity in telecommunication records
- Observing clinical data consistency in healthcare analytics pipelines
- Preventing broken dashboards in BI and reporting environments
Frequently Asked Questions¶
Does Data Anomalies require predefined rules?
No — the module learns from data behavior automatically.
Can I still define specific thresholds if needed?
Yes. digna allows combining AI-based and rule-based detection (via Data Validation).
How are false positives minimized?
The module uses adaptive learning and statistical confidence scoring to ignore normal seasonal variations.
Where does computation happen?
All processing runs within your database — digna never extracts raw data.
Is it suitable for sensitive or regulated data?
Yes. digna runs fully on-premises or in private cloud and adheres to European compliance standards.