Validation and
data quality
Automatically extract invoice data from documents and prepare it in a structured way for further processing.
Data quality as a prerequisite in invoice processing
Invoices contain essential information for operational and financial processes. Amounts, line items, references, and supplier data must be captured correctly and transferred into systems.
Errors in this data directly impact downstream processes. Incomplete or incorrect information leads to follow-ups, delays, or incorrect postings. Data quality therefore determines the stability and efficiency of the entire process.
For this reason, data quality is not a downstream task – it is a fundamental prerequisite for reliable automation in invoice processing.
Extracted data is only useful, when it is correct.
What does data quality mean in document processing?
Data quality describes how reliably and completely information is available for further processing. In the context of invoices, this includes:
- Completeness: all relevant information is present
- Accuracy: values are correctly captured and reflect the document
- Consistency: data is logically coherent and aligned across all fields
Only when these criteria are met can data be processed without additional manual validation.
Why extraction alone is not enough
Extracting content from documents is a necessary step, but not sufficient to create reliable data. In practice, typical issues include:
- Values are recognized but not correctly assigned.
- Amounts do not match totals or line items.
- Information is missing or incomplete.
- References cannot be clearly matched.
- Data remains isolated and cannot be directly used.
Extracted content must therefore be validated, checked, and placed into a business context before it can be processed reliably in systems.
From document to usable information
How does validation work in practice?
Validation describes the systematic verification of extracted data in terms of completeness, plausibility, and logical consistency.
Typical validation steps include:
- checking whether amounts match totals and line items
- ensuring all required fields are present
- verifying references such as purchase orders or supplier data
Deviations are identified and can be reviewed or corrected through a human-in-the-loop approach. This ensures that only consistent and reliable data is passed on for further processing.
How does bluDELTA ensure data quality?
bluDELTA integrates validation as a core part of document processing. Within the Extract module, content is not only extracted but also structured and checked for basic plausibility. This combines AI-based methods with defined validation logic.
The resulting data is prepared for the next step, where it is matched and clearly assigned in a business context. This creates a consistent and reliable data foundation for further processing in ERP and business systems.
Adapting to changing requirements
In practice, documents vary significantly. Layouts, content, and structures differ depending on suppliers, formats, and processes. At the same time, requirements evolve over time.
In practice, documents vary significantly. Layouts, content, and structures differ depending on suppliers, formats, and processes. At the same time, requirements evolve over time.
Position within the overall process
Validation is part of a continuous processing chain within bluDELTA:
- Class & Split: Documents are identified, separated, and structured
- Extract: Content is extracted and validated
- Mapping: Data is assigned, enriched, and prepared for systems
bluDELTA operates upstream of downstream systems and ensures that they work with consistent and system-ready data.
