Data discovery is the first step, involving the identification of data sources and analyzing their quality, relevance, and structure for the transformation process.

Data profiling involves examining datasets to uncover patterns, anomalies, and relationships, ensuring data quality for the transformation process.

This crucial step corrects inconsistencies, removes duplicate entries, and addresses missing or erroneous data, ensuring a clean dataset.

Data transformation converts raw data into meaningful formats through sorting, filtering, aggregating, and mapping to meet business needs.

Data validation ensures that transformed data meets predefined quality standards, confirming its accuracy and reliability for further use.

This step integrates data from different sources into a unified system, enabling comprehensive analysis and improved decision-making capabilities.

Data governance establishes policies and practices to ensure secure, compliant, and consistent data usage across the organization.

Monitoring transformed data ensures its quality remains consistent over time, adapting to evolving business requirements and data trends.