What do you do if your data quality is compromised as a data architect?

2 Analyze the root causes and impacts

The next step is to analyze the root causes and impacts of the data quality issues. You need to understand why the data quality issues occurred, where they originated, how they propagated, and what they affected. You can use techniques such as root cause analysis, data lineage analysis, data impact analysis, or data cleansing analysis to trace and evaluate the data quality issues. You should also communicate and collaborate with the data stakeholders, such as data owners, data users, data producers, or data consumers, to gather feedback and insights on the data quality issues.

3 Prioritize and plan the solutions

The third step is to prioritize and plan the solutions for the data quality issues. You need to consider the urgency, severity, and feasibility of the data quality issues, and decide which ones to address first, and which ones to defer or ignore. You can use methods such as cost-benefit analysis, risk analysis, or value analysis to prioritize and justify the data quality issues. You should also design and document the solutions for the data quality issues, such as data correction, data enrichment, data transformation, data integration, or data governance.

4 Implement and test the solutions

The fourth step is to implement and test the solutions for the data quality issues. You need to execute the solutions according to the plan, and verify that they resolved the data quality issues effectively and efficiently. You can use tools and processes such as data migration, data loading, data cleansing, data testing, or data verification to implement and test the solutions. You should also monitor and evaluate the performance and outcomes of the solutions, such as data quality improvement, data quality assurance, or data quality reporting.

5 Learn and improve from the experience

The fifth step is to learn and improve from the experience of dealing with the data quality issues. You need to review and reflect on the lessons learned, best practices, and challenges faced during the data quality issue resolution process. You can use frameworks and methods such as after-action review, post-mortem analysis, or continuous improvement cycle to learn and improve from the experience. You should also update and enhance the data quality rules, standards, policies, and procedures based on the feedback and findings from the experience.

6 Prevent or mitigate future data quality issues

The sixth and final step is to prevent or mitigate future data quality issues. You need to anticipate and avoid potential data quality issues, or reduce their likelihood and impact. You can use strategies and techniques such as data quality assessment, data quality control, data quality management, data quality assurance, or data quality governance to prevent or mitigate future data quality issues. You should also foster a data quality culture and awareness among the data stakeholders, and promote data quality best practices and education.