How This Workflow Works
This workflow detects and groups similar names within a dataset, even when there are small spelling differences. It analyzes the data, applies fuzzy matching algorithms to identify potential duplicates, and presents the results in a clear, actionable format for review and reporting.
Key Features:
- Detect and groups similar names to reduce duplicate records
- Apply fuzzy matching logic on selectable columns for flexible detection of similar names
- Provide automated reporting and visualization of matched results
- Support a wide range of data types and business scenarios
Step-by-step:
1. Analyze and Validate Data:
The workflow begins by examining the dataset for completeness and consistency. It checks for missing values, validates data types, and ensures that numeric, string, and date fields meet basic quality standards. This step helps prevent errors in later analysis.
2. Apply Fuzzy Matching Logic:
The core of the workflow uses hierarchical clustering to compare name fields and calculate similarity scores. It clusters entries 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.
3. Review and Refine Matches:
After grouping similar entries, the workflow allows users to review the matched clusters. If needed, users can adjust thresholds or parameters in the clustering nodes to fine-tune the sensitivity of the matching process, ensuring that the results align with business needs.
4. Visualize and Share Insights:
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.