The ONLY Data Cleaning Framework You Need (Data Director Explains)

About this Video

• Video Title: The ONLY Data Cleaning Framework You Need (Data Director Explains)
• Channel: Christine Jiang
• Speakers: Christine Jiang
• Duration: 00:25:07

Introduction

This video presents a five-step data cleaning framework ("CLEAN") used by the speaker, a former data director and hiring manager, to prepare data for analysis. The framework is explained using a real-world, albeit fictional, dataset from a company called GameZone. The speaker aims to show aspiring data analysts how to clean data effectively for job applications and real-world scenarios.

Key Takeaways

• The CLEAN Framework: A five-step process for data cleaning: Conceptualize, Locate solvable issues, Evaluate unsolvable issues, Augment, Note and Document.
• Data Cleaning Philosophy: The goal isn't perfect data, but data "good enough" for analysis, sharing, and iteration. Data cleaning is iterative, likened to peeling an onion in layers.
• Identifying Solvable Issues: Focus on inconsistent data formats, spellings, categorizations, duplicates, and sometimes null values (depending on context). These are problems that can be solved independently without additional business context.
• Evaluating Unsolvable Issues: Address missing data, outliers, and business logic violations by documenting their magnitude and considering if stakeholder or technical team input is needed. Imputation is discouraged unless there's a reliable alternative data source or clear business logic.
• Augmenting Data: Enhance the dataset's flexibility by adding categorical variables, calculated metrics, or different time grains. This allows more robust analysis.
• Note and Document: Maintain a detailed issues log tracking problems, resolutions, magnitude, and decisions made. This is vital for transparency and demonstrates thoroughness to potential employers.