Data Cleaning Readiness Framework

Overview

This project provides a structured framework for validating, cleaning, and preparing data for analytics environments. It ensures datasets meet professional standards for:

• Data quality
• Referential integrity
• Analytical readiness

This framework was developed to support analytics workflows involving:

• Retail pricing and elasticity modeling
• Sports analytics datasets
• Dimensional modeling and warehouse design
• SQL-based analytics pipelines

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Objectives

Ensure data is:

• Structurally valid
• Deduplicated and consistent
• Referentially intact
• Free of orphan records
• Safe from PII exposure
• Fully ready for analytical use

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📋 Data Readiness Checklist (Excel Tool)

The framework includes a structured Excel implementation:

➡️ Download: Data_Readiness_Checklist.xlsx

This checklist enables:

• Structured validation tracking
• Workday-based due date automation
• Task grouping and prioritization
• Audit-ready documentation

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🛠 How to Use the Checklist

The Data_Readiness_Checklist.xlsx file is designed to guide structured validation before data enters analytics environments.

Step 1: Define Project Start Date

• Enter the project start date at the top of the sheet.
• Due dates will automatically calculate using workday logic (excluding weekends and holidays).

Step 2: Review Validation Categories

Tasks are grouped into structured validation layers:

• Intake & Context Validation
• Data Ownership & Governance
• File & Structural Validation
• Table Structure
• Data Types & Formatting
• Grain & Uniqueness
• Missing, Null, and Invalid Data
• Consistency & Referential Integrity
• Outliers & Distribution Review
• Time Series & Historical Integrity
• Documentation & Transparency
• Readiness Gate (Go / No-Go)
• Ongoing / Recurring Data Feeds

Each section represents a required control before data is considered analytics-ready.

Step 3: Complete Task Tracking Columns

For each validation task:

• Update Status: Check box once task is complete
• Priority: Automatically assigned based on Due Date (<=7 "High", <=14 "Medium", >14 "Low")
• Monitor auto-calculated Due Date (Based off the Lookup tab under 'Cumulative Days' column
• Start Date, Complete Date, and Comments are editable fields

Note: This format is a suggested template. How you use it may vary.

Step 4: Validate Against Source Data

Use SQL, Excel, or BI tools to perform:

• Duplicate checks
• Null analysis
• Orphan detection
• Business rule validation
• PII screening

Document findings directly in the checklist for audit traceability.

Step 5: Confirm Readiness

Data should not move into warehouse or analytics layers until:

• All high-priority items are complete
• Referential integrity checks pass
• No critical data quality issues remain
• Governance checks are validated

The completed checklist serves as documentation of data readiness.