Normal Workflow and Key Strategies for Data Cleaning Toward Real-World Data: Viewpoint.

Area of Science:

• Data Science
• Medical Informatics
• Research Methodology

Background:

• Rapid scientific advancement generates vast amounts of real-world data, leading to a
• data disaster
• particularly in medical research.

Purpose of the Study:

• To propose a data cleaning framework for real-world research.
• To provide guidance on efficient and ethical data cleaning processes.
• To address the common issues of duplicate, missing, and outlier data.

Main Methods:

• Development of a data cleaning framework tailored for real-world research.
• Identification and focus on the three most prevalent types of dirty data: duplicate, missing, and outlier data.
• Establishment of a standard workflow for data cleaning procedures.

Main Results:

• A comprehensive data cleaning framework designed for real-world research settings.
• A structured workflow to guide data cleaning processes.
• Practical suggestions for overcoming common challenges encountered during data cleaning.

Conclusions:

• Effective data cleaning is crucial for reliable data analysis and management in medical research.
• The proposed framework and workflow offer valuable guidance for improving data quality.
• Addressing duplicate, missing, and outlier data is essential for robust real-world evidence generation.