Data Quality & Observability
Catch data errors before they land in the management report.
Flawed or incomplete data undermines trust in every analytics and AI project. Automated quality checks and observability tools continuously monitor completeness, freshness and plausibility across your data pipelines, catching deviations before they affect reports or models.
- Fewer flawed reports reaching management meetings.
- Earlier detection of problems in source systems.
- Greater trust in data-driven decisions.
undetected data errors
faster error detection
to live monitoring
effort spent troubleshooting
Calculated from the previous time between occurrence and detection of data errors.
- 01
Risk analysis
We identify critical data pipelines with high error potential.
- 02
Rule definition
We define check rules for completeness and plausibility.
- 03
Monitoring implementation
We set up automated checks and alerting.
- 04
Escalation process
We define who gets notified for which type of error.
- 05
Continuous improvement
We continuously refine rules based on real incidents.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Pipeline metadata
Run and execution logs of data pipelines.
Reference values
Historical values for detecting outliers.
Schema information
Structure and type definitions of data sources.
Business rules
Business plausibility thresholds from domain teams.
System logs
Error and event logs of connected systems.
Usage statistics
Access and usage data of the affected datasets.
Data engineering team
Early alerts instead of complaints from business teams.
Controlling
Reliable numbers without manual plausibility checks.
IT operations
Clear ownership when data quality issues arise.
Executive management
Lower risk of poor decisions caused by bad data.
Fewer flawed reports reaching management meetings.
Earlier detection of problems in source systems.
Greater trust in data-driven decisions.
Less time spent on after-the-fact troubleshooting.
A more stable foundation for AI and automation projects.
With a solid data foundation this use case gets faster, cheaper and far more stable.
A central platform enables quality checks in one place.
Existing metadata makes automated rule definitions easier.
Existing monitoring can be extended with quality checks.
Standardized pipelines simplify broad rollout.
Lakehouse architecture
Quality checks run directly along the bronze-silver-gold layers.
Data governance & permissions
Quality metrics feed into governance reporting.
MLOps & model operations
Clean training data sustainably improves model quality.
Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.