Pentaho Data Quality
Data Quality
We are the largest Pentaho implementation and training partner in the EMEA region.
Data quality: the decisive factor for your business success and AI initiatives
In today’s data-driven world, data quality is absolutely critical to the success of any organization. Bad data is a serious business risk that jeopardizes data-driven projects and decisions. As your Pentaho partner, we help you eliminate this risk and make your data fit for the future.
Pentaho Data Quality: Your solution for trustworthy data and successful AI initiatives
With Pentaho Data Quality, you can ensure that your data is trustworthy and compliant – and thus strengthen your data governance in the long term. At the same time, you optimize your workflows and accelerate AI readiness by relying on accurate and AI-ready data right from the start. This gives you full transparency and control over your data and creates the basis for successful data-driven projects.
8 advantages of Pentaho Data Quality
Fast data profiling
Evaluate data structure, completeness and accuracy immediately
Intelligent anomaly detection
AI/ML-supported identification of outliers with prioritization.
Comprehensive quality standards
Over 250 predefined and customizable rules for governance & compliance
Real-time monitoring (observability)
Proactive alerts across entire data pipelines
Complete data lineage (data lineage)
Data tracking from source to destination incl. transformations in open lineage format
Intuitive interface
Central dashboard for a complete overview of data quality by domain and asset with clear scores
Self-service data pipelines
User-defined pipelines for data copying/moving, including protective measures such as pseudonymization
Seamless integration with Pentaho Data Catalog
Publication of quality scores and origin in the central data catalog
Fast data profiling
Evaluate data structure, completeness and accuracy immediately
Intelligent anomaly detection
AI/ML-supported identification of outliers with prioritization.
Comprehensive quality standards
Over 250 predefined and customizable rules for governance & compliance
Real-time monitoring (observability)
Proactive alerts across entire data pipelines
Complete data lineage (data lineage)
Data tracking from source to destination incl. transformations in open lineage format
Intuitive interface
Central dashboard for a complete overview of data quality by domain and asset with clear scores
Self-service data pipelines
User-defined pipelines for data copying/moving, including protective measures such as pseudonymization
Seamless integration with Pentaho Data Catalog
Publication of quality scores and origin in the central data catalog
Our professional services for your Pentaho Data Quality project
Strategic data quality consulting
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Definition of quality standards and threshold values
We avoid "over-cleaning" and tailor the guidelines to your business objectives. -
Development of a customized data quality strategy
All dimensions such as accuracy, completeness and consistency are taken into account end-to-end.
Implementation of Pentaho Data Quality
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Establishment of a central data quality platform
We rectify missing, inconsistent or inaccurate data before it has an impact on systems and decisions. -
Configuration of data quality rules
Use of over 250 predefined quality rules for governance and compliance. -
AI/ML-supported anomaly detection and observability
Establishment of intelligent systems for the automatic detection of outliers using AI/ML and real-time monitoring with proactive alerts. -
Seamless integration with Data Catalog
Ensuring complete data lineage in Open Lineage Format, publication of quality scores and origin in the Pentaho Data Catalog.
Integrate data sources, metadata and lineage in DataHub
- Development of a central data model (e.g. custom schemas, entity modeling).
- Automation of metadata capture and lineage updates with ETL pipelines.
- Optimization of the display of data flows and lineage
Training and enablement for high user acceptance
- For administrators: Workshops on operation, maintenance, scaling
- For users: enabling the effective use of data quality tools and principles
Continuous optimization and expansion
- Adaptation of rules and workflows to changing business requirements for permanently high data quality.
- Identification of use cases (e.g. data lineage, impact analysis, governance).
- Carrying out a metadata audit and recommending standards for improvement.
- Recommendation of an optimal DataHub architecture.