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module-05-data-quality-mapping

Evaluates source data quality and maps fields to Senzing Generic Specification.

Updated Jul 13, 2026
Search Tags:#data quality#data mapping#entity resolution#data profiling#json transformation#senzing
One-click install
npx skills add https://github.com/docktermj/senzing-bootcamp-claude-plugin --skill module-05-data-quality-mapping
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: module-05-data-quality-mapping
Source: https://github.com/docktermj/senzing-bootcamp-claude-plugin/tree/main/plugins/senzing-bootcamp/skills/module-05-data-quality-mapping
Command: npx skills add https://github.com/docktermj/senzing-bootcamp-claude-plugin --skill module-05-data-quality-mapping

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill resolves the uncertainty of whether raw data is ready for entity resolution by providing a structured, automated path to assess quality and map source fields to the Senzing Entity Specification.

Core Features & Use Cases

  • Quality Assessment: Automatically calculates completeness, format consistency, and duplicate rates for data sources.
  • Schema Mapping: Guides the user through mapping source fields to Senzing attributes using the official Entity Specification.
  • Use Case: A data analyst needs to ingest a new CRM export but is unsure if the field names match Senzing requirements; this skill profiles the data, identifies mapping gaps, and validates the transformation before loading.

Quick Start

Start the module-05-data-quality-mapping skill to begin the quality assessment and mapping workflow for your data sources.

Frequently Asked Questions about module-05-data-quality-mapping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I map raw CRM data fields to the Senzing Generic Entity Specification?▼

Schema mapping for entity resolution requires transforming source fields to match the Senzing Generic Entity Specification. This skill automates that by evaluating data source quality and validating the mapping against authoritative entity schemas before generating load-ready JSON.

How do I assess data quality before loading records into an entity resolution system?▼

Assessing data quality before loading records into an entity resolution system involves calculating completeness, format consistency, and duplicate rates. This skill automatically profiles data sources to evaluate these metrics and identifies mapping gaps before transformation.

What is the process for transforming raw data into load-ready JSON for Senzing?▼

Transforming raw data into load-ready JSON for Senzing requires iterative profiling and schema mapping. This skill evaluates source fields against the Senzing Entity Specification, validates structural compliance, and outputs correctly formatted JSON records for ingestion.

Can I validate my data mapping against the Senzing Entity Specification before loading?▼

You can validate data mapping against the Senzing Entity Specification before loading. This skill ensures compliance with Senzing structural requirements by validating transformed JSON records against authoritative entity schemas to prevent ingestion errors.

What data quality metrics should I check before running entity resolution?▼

Before running entity resolution, you should check data completeness, format consistency, and duplicate rates. This skill automatically calculates these data quality metrics during profiling to ensure source records are ready for Senzing schema mapping and JSON transformation.

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