Left Shift Approach: Strengthening Data Migration with Data Quality

Data Quality is required to refine and enhance the quality of the data, which would act as the foundation of any data project, whether it would be a data migration project or an AI project.

High-quality data should be given high priority in any data-related project. In this blog, we are discussing the role of Data Quality in the Data Migration project.

Normally, we consider data quality in any data migration project on a small scale or a big scale, but its timing is the most important. If we perform the Data Quality during the early phase of the project,

when we are discussing with Business, then the efforts blossom in the later phase of the project.

To implement the Data Quality in the Data Migration requires project planning not only from the side of Data Quality but also from the Data Migration side to imbibe Data Quality from the Business Readiness phase.

The following are the main phases of Data Quality:

1.Data Quality Kick-Off

2.Data Quality Requirements

3.Data Quality Build

4.Data Cleansing (Iterative from Kick-Off to Prod Load in Data Migration)

5.Data Quality Monitoring

6.Data Quality Governance

What are the typical activities, benefits, dependencies, and risks/assumptions in each phase?

The following diagrammatic representation showcases all these in the picture below.

In this blog, we discussed how Data Quality fits into the Data Migration journey, the key phases involved, and the benefits and dependencies across each stage. Ensuring Data Quality from the early phases of Business Readiness sets a strong foundation for a successful migration.

I hope you found this post insightful and helpful in planning your Data Migration projects with a strong Data Quality focus.

I would love to hear your thoughts, experiences, or any questions you might have around Data Quality in Data Migration!
Feel free to leave a comment or connect with me for further discussion.

Thank you for reading!

​ Data Quality is required to refine and enhance the quality of the data, which would act as the foundation of any data project, whether it would be a data migration project or an AI project.High-quality data should be given high priority in any data-related project. In this blog, we are discussing the role of Data Quality in the Data Migration project.Normally, we consider data quality in any data migration project on a small scale or a big scale, but its timing is the most important. If we perform the Data Quality during the early phase of the project,when we are discussing with Business, then the efforts blossom in the later phase of the project.To implement the Data Quality in the Data Migration requires project planning not only from the side of Data Quality but also from the Data Migration side to imbibe Data Quality from the Business Readiness phase.The following are the main phases of Data Quality:1.Data Quality Kick-Off2.Data Quality Requirements3.Data Quality Build4.Data Cleansing (Iterative from Kick-Off to Prod Load in Data Migration)5.Data Quality Monitoring6.Data Quality GovernanceWhat are the typical activities, benefits, dependencies, and risks/assumptions in each phase?The following diagrammatic representation showcases all these in the picture below.In this blog, we discussed how Data Quality fits into the Data Migration journey, the key phases involved, and the benefits and dependencies across each stage. Ensuring Data Quality from the early phases of Business Readiness sets a strong foundation for a successful migration.I hope you found this post insightful and helpful in planning your Data Migration projects with a strong Data Quality focus.I would love to hear your thoughts, experiences, or any questions you might have around Data Quality in Data Migration!Feel free to leave a comment or connect with me for further discussion.Thank you for reading!   Read More Technology Blogs by Members articles 

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