Today, we would like to invite a Data Engineer from SCB TechX, Khun Nat Jessada Weeradetkumpon,
to recommend 6 checklists that should be considered before improving Data Quality in order to solve it efficiently. If anyone is interested, please read these 1-minute tips here.
- Return on Investment: There are many aspects to improve Data Quality, but covering all aspects may require a lot of resources. So, we must look at the ROI to see if it’s worth improving that aspect!
- Levels of Quality: The resources are always limited, so it’s important to carefully study the requirements that businesses need in order to scope the problem and solve it efficiently.
- Data Quality Trends: Improving data requires solving and monitoring. If the Data Quality trend has improved, we’re probably on the right track.
- Data Issue Management Metrics: For easier improvement, the specific details should be identified.
– The aspect of error (Accuracy, Completeness, Consistency…)
– The Status that affects the business (Resolved, Outstanding, Escalated…)
– The problem levels.
– The duration used.
- Conformance to Service Levels: Data Quality should not affect the business or users like data must be available at the specified time, etc.
- Data Quality Plan Rollout: Once the above checklist has been completed, the roadmap for improving Data Quality must be clearly defined.
From the 6 checklists above, please enjoy applying them and sharing some advice or use cases if this content is fruitful for you. Lastly, SCB TechX is ready to provide any organization with professional advice, technology solutions, and comprehensive Data Platform services through TechX Data Platform.