To be successful, data quality must be continuously ____________ and the _________ fed back into the data governance process.

Continuous measurement of data quality is essential for ensuring that it remains high over time. This involves regularly evaluating the accuracy, completeness, consistency, uniqueness, and timeliness of the data. By measuring these aspects, organizations can identify areas where the data may be falling short and take appropriate actions to address any deficiencies.

Additionally, the results of this measurement should be fed back into the data governance process. Feedback loops are crucial because they enable data governance teams to adjust policies, practices, and standards based on real-time insights into data quality metrics. This integration of measurement results into data governance fosters accountability and supports ongoing improvements in data management practices.

While cleansing data is important, it does not suffice alone; the effectiveness of cleansing efforts must be periodically measured to ensure that they are having the desired impact. Monitoring reports can provide insights into data quality, but without the direct link to governance and actionable results, the process would not be as robust. Analyzing data is certainly valuable, but effective governance stems from a continuous cycle of measurement and feedback, reinforcing the importance of the first choice in maintaining high data quality in accordance with best practices highlighted in the DAMA-DMBOK.