3. Implement Data Quality Tools
To achieve good data quality, you need to implement data quality tools that can help you automate the data quality process. These tools can help you with data profiling, data cleansing, data enrichment, and data governance. Some of the popular data quality tools include Talend Data Quality, Informatica Data Quality, and IBM InfoSphere Information Server.
4. Establish Data Quality Metrics
To measure the effectiveness of your data quality efforts, you need to establish data quality metrics. These metrics should align with your data quality requirements and measure the accuracy, completeness, and consistency of your data. Some of the common data quality metrics include data accuracy, data completeness, data consistency, and data timeliness.
