Data Services – Data Quality Management – DS30 — Lesson 2: Data Cleanse, Parsing, Standardization, Dictionaries, Reference Data, and Custom Transform Rules

Learning Objectives

• Explain the SAP structures, data, settings, roles, and dependencies governing data cleansing and standardization.
• Perform and validate parsing, dictionaries, and reference-data controls using reviewable system evidence.
• Evaluate the condition where duplicate records remain after cleansing because standardization, match criteria, survivorship, and review thresholds are configured for different business definitions and select proportionate corrective action.

Detailed Topics

• Data Cleanse applied to the course scenario, expected SAP behavior, dependencies, and evidence.
• Parsing applied to the course scenario, expected SAP behavior, dependencies, and evidence.
• Standardization applied to the course scenario, expected SAP behavior, dependencies, and evidence.
• Dictionaries applied to the course scenario, expected SAP behavior, dependencies, and evidence.
• Reference Data applied to the course scenario, expected SAP behavior, dependencies, and evidence.
• and Custom Transform Rules applied to the course scenario, expected SAP behavior, dependencies, and evidence.

Instructor Demonstration

The instructor traces a data-quality team improving customer and supplier records before migration and downstream analytics. The walkthrough connects navigation, configuration or process logic, system messages, and evidence captured in the DS30 profiling, cleansing, matching, and quality-score workbook. It also tests the exception where duplicate records remain after cleansing because standardization, match criteria, survivorship, and review thresholds are configured for different business definitions.

Hands-On Exercise

Complete a controlled SAP exercise covering Data Cleanse, Parsing, Standardization, Dictionaries, Reference Data, and Custom Transform Rules and preserve the verified result in the DS30 profiling, cleansing, matching, and quality-score workbook.

1. Confirm scope, prerequisites, master data, roles, dependencies, and acceptance criteria.
2. Execute the workflow while recording settings, transactions, outputs, and system messages.
3. Test normal behavior, one boundary condition, and the stated exception.
4. Document corrections, evidence, ownership, retest criteria, and handover actions.

Scenario Analysis

• Identify which object, rule, setting, or process step most strongly influences data cleansing and standardization.
• Compare two defensible responses and explain business, technical, control, and support trade-offs.
• Determine how the DS30 profiling, cleansing, matching, and quality-score workbook should be approved, retained, retested, and updated.

Troubleshooting Guidance

Start where expected SAP behavior first differs from the observed result. Check master data, configuration sequence, authorization, integration, status, and transaction evidence separately. Treat “duplicate records remain after cleansing because standardization, match criteria, survivorship, and review thresholds are configured for different business definitions” as a hypothesis until system settings, logs, documents, and outcomes confirm the cause.

Validation Checklist

• Required SAP structures, master data, roles, settings, and dependencies are present.
• The DS30 profiling, cleansing, matching, and quality-score workbook connects each conclusion to objective, reviewable evidence.
• Normal processing, the boundary condition, integration behavior, and exception were tested.
• Corrections, ownership, retest criteria, documentation, and operational handover are complete.

Lesson Outcome

Learners can apply Data Cleanse, Parsing, Standardization, Dictionaries, Reference Data, and Custom Transform Rules, explain the resulting SAP behavior, and defend the outcome through the DS30 profiling, cleansing, matching, and quality-score workbook.