Data Quality Framework Mind Map

Why This Resource Matters

Data analysis errors rarely originate from formula syntax; they stem from bad underlying data (whitespace anomalies, broken foreign keys, mixed types, truncation). The Data Quality Framework Mind Map decomposes data hygiene into actionable verification checks across the data preparation lifecycle.

Source Visual Artifact

Source Summary

The visual diagram outlines the core dimensions and validation stages of data quality:

• Completeness: Identifying missing values, differentiating operational blanks from data corruption.
• Uniqueness: Detecting accidental duplicate primary keys vs recurring dimension attributes.
• Validity & Conformance: Enforcing domain constraints (e.g. phone numbers, email regex, date formatting, rating scales 1-5).
• Accuracy & Integrity: Verifying reference integrity across fact and dimension tables.
• Consistency: Ensuring casing, naming conventions, and terminology match across ERP/CRM systems.

My Understanding

In our Call Center Performance Analysis audit, we applied this exact framework to 5,000 records. We proved that the 946 null values in Speed of answer, AvgTalkDuration, and Satisfaction rating are not "missing data defects"—they are 100% correlated with Answered (Y/N) == 'N'. Understanding this prevented naive imputation errors.

Key Takeaways

1. Never Impute Blanks Blindly: Always verify why a field is blank before replacing it with 0 or the column mean.
2. Standardize Early: Cleanse strings with TRIM and PROPER or Power Query text transformations prior to modeling.

Concepts Supported

• Six Dimensions of Data Quality
• Data Cleaning
• ETL Process

Related Course Lessons

• 01 Data Quality Dimensions and Audit
• 01 Data Types and Formatting

Practice Opportunities

• Ex05 Data Cleaning and Transformation

Project Connection

• 06 Projects/Call Center Performance Analysis/Data Quality Assessment

Original Source

Open Gemini Notebook Source Asset file: assets/data-quality-mind-map.png