What's your approach to data quality management?

1 Define data quality criteria

The first step in data quality management is to define what data quality means for your specific context and goals. Data quality criteria are the standards and expectations that you set for your data, based on its purpose, source, and format. For example, some common data quality criteria are completeness, consistency, timeliness, accuracy, and relevance. You can use these criteria to measure and evaluate the quality of your data, and identify any gaps or issues that need to be addressed.

4 Monitor and control data quality

The final step is to monitor and control data quality, using various metrics and indicators. Data quality monitoring and control is the process of measuring and reporting the data quality performance, and ensuring that it meets the requirements and expectations. Some of the metrics and indicators you can use for data quality monitoring and control are data quality scorecards, dashboards, reports, and alerts. Data quality scorecards are tools that summarize and compare the data quality results against the criteria and targets. Data quality dashboards are visual displays that show the current status and trends of the data quality indicators. Data quality reports are documents that provide detailed information and analysis of the data quality issues and actions taken. Data quality alerts are notifications that inform the stakeholders of any data quality problems or changes.

Data quality management is a continuous and iterative process, that requires constant attention and improvement. By following these steps, you can ensure that your data analysis process is based on high-quality data, and that you can deliver reliable and valuable insights to your clients and stakeholders.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?