Defining key performance indicators (KPIs) to monitor and measure data quality. Implementing data quality tools to automate data cleansing, validation, and enrichment processes. Conducting training sessions and workshops to raise awareness about the importance of data quality.

• Data Cleansing: Identifying and rectifying errors, inconsistencies, and inaccuracies in data.
• Data Standardization: Ensuring data follows standardized formats and conventions.
• Data Enrichment: Enhancing data with additional information from external or internal sources.
• Data Validation: Checking data against predefined rules and criteria to ensure correctness.