SAP Fuzzy Name Matching

This workflow uses the DVW Analytics SAP extension for KNIME to extract vendor master data directly from SAP, apply a series of validation and fuzzy matching techniques to identify potential duplicates with similar names, and generate a report highlighting these findings. The process is designed to support data quality initiatives by reducing duplicate SAP records and improving the reliability of master data.

• Detect and groups similar vendor names in SAP using fuzzy matching algorithms
• Highlight potential duplicate SAP master data records for review and remediation
• Generate a structured report to support audit and data quality processes
• Enable flexible analysis and visualization of duplicate patterns

The workflow begins by extracting vendor records from SAP using the DVW Analytics SAP extension for KNIME and performing a series of validation checks. It reviews numeric, string, date, and missing values to ensure the dataset is suitable for further analysis. This step helps identify and address common SAP master data quality issues before proceeding.

The core of the workflow uses hierarchical clustering to compare name fields and calculate similarity scores. It clusters records that are likely to refer to the same entity, even if their names are not exact matches. This process helps uncover duplicates that would be missed by simple exact matching.

After clustering, the workflow aggregates duplicate groups and prepares a summary of findings. It ensures that each group contains all relevant records and that unique entries are retained. This step provides a clear view of potential duplicates and their relationships.

The workflow generates reports and visualizations, such as summary tables and bar charts, to present the findings. Users can export results or share them with stakeholders, supporting data-driven decisions and ongoing data quality improvements.