Dataset
enrichment

Extracted and assigned data form the basis for further processing but are often still incomplete.

For correct processing in ERP and business systems, additional information is required. This includes, for example, account assignments, classifications, or process-specific mappings.

Only when this information is added, does a reliable data foundation emerge that can be processed further without manual intervention.

Data enrichment describes the targeted extension of existing information with additional, contextually relevant content.

Data is not only transferred, but expanded. This is based on:

• master data and reference data
• defined rules and mapping logic
• contextual information from the document and the process

The goal is to complete datasets so they can be processed directly and correctly within systems.

Especially in invoice processing, the following information is added:

• accounts and booking logic
• cost centers or cost objects
• classifications and categories
• references to purchase orders or contracts

This information does not originate directly from the document, but is derived from the interaction between existing data and system information.

Data enrichment does not happen in isolation, but through the interaction of multiple factors.

Extracted and assigned data is combined with existing reference information. Based on this, missing content is added and existing information is placed into a clear context.

Typically, this follows a logic where document information is linked with system data. For example, account assignments or mappings can be derived even if they are not explicitly included in the document.

The process is designed to be reproducible, ensuring that similar documents are processed consistently.

bluDELTA integrates data enrichment as a core part of the mapping process. Extracted and assigned data is combined with multiple sources of information:

• master data and reference data from existing systemsStammdaten und Referenzdaten aus bestehenden Systemen
• process-specific rules and mapping logic
• contextual information from the document and processing step

bluDELTA does not process this information in isolation, but in combination. This allows complex relationships to be considered and datasets to be completed in a targeted way.

Different variants, deviations or incomplete information are processed in a way that ensures a consistent and traceable data foundation.

Only fully enriched datasets enable stable and automated processes.

When all relevant information is available and clearly assigned, downstream systems can operate without additional checks. Manual intervention is reduced and processes become reproducible.

Data enrichment is therefore a key prerequisite for a high level of automation in invoice processing.

Data enrichment is part of the mapping module within bluDELTA:

• Class & Split: Documents are identified and structured
• Extract: Content is extracted and prepared
• Mapping: Data is assigned, enriched and made system-ready

bluDELTA ensures that these steps work together to create a complete and consistent data foundation.