Data Profiling is the initial step in Data Quality Management. It involves analyzing data sources to understand their structure, content, and interrelationships. By profiling data, organizations can identify anomalies, inconsistencies, and patterns that may indicate data quality issues.

Data Lineage traces the flow of data from its origin to its final destination. Understanding data lineage helps organizations track how data is transformed and used across various systems, ensuring its accuracy and reliability.

Data Governance provides the framework for Data Quality Management by establishing policies, standards, and procedures for data handling. It ensures that data management practices align with organizational objectives and regulatory requirements.

Data Standardization involves converting data into a common format to ensure consistency across different systems and sources. Standardized data is easier to manage, integrate, and analyze, which enhances overall data quality.

Data Stewardship assigns responsibility for managing and overseeing data assets to specific individuals or teams. Data stewards play a crucial role in ensuring data quality by monitoring data usage, enforcing data governance policies, and addressing data quality issues.

Data Enrichment enhances existing data by adding additional information from external sources. This process improves data quality by providing more context and depth, making the data more valuable for analysis and decision-making.

Data Integration combines data from different sources into a unified view. Effective integration is essential for maintaining data quality, as it ensures that data from disparate systems is accurate, consistent, and up-to-date.

Master Data Management (MDM) focuses on creating a single, authoritative source of truth for critical business data. By consolidating and harmonizing data from various sources, MDM enhances data quality and supports better decision-making.

Data Validation involves checking data for accuracy and completeness before it is processed or stored. Validation rules and procedures help identify and correct errors, ensuring that only high-quality data is used in business operations.

Data Cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in data. It is a crucial step in Data Quality Management, as clean data is essential for reliable analysis and decision-making.