You're facing a complex data migration project. How do you ensure data quality remains a top priority?

To start with any data migration project, it is obvious to assess the data first. You have to analyze the following thing. , 1. Verify the data sources 2. the quality of raw data 3. Find out the data impurities. 4. Assess the Target data format 5. Based on target data format identify the benchmark of the data quality. These are all some data assessment steps

One thing I've found helpful is trying to visualize the data quality for the entire organization. How? By creating dashboards and alerts, and making visible the next steps and the things to change, prioritize, and sacrifice to accomplish the migration. That is a valuable lesson that I learned from my boss at that time. Internally, it is critical to define the MVDQ (Minimum Viable Data Quality) to go to production. My formula for MVDQ = no nulls + no duplicates + all necessary fields and tables + perform the migration quickly and whenever needed. We need to redo the migration many times; that is something that is going to happen. Data quality is about iteration 100% of the time.

Even before developing an inventory of data quality on the legacy system you want to migrate, you should understand which data is present in the new data model, and also if the new system has validations in place to ensure that new data is going to meet the data quality standards. Once you have this information you can proceed with the assessment of the data quality you are going to migrate prioritizing key fields and tables and establishing a plan to fix any issues you identify during the migration process.

The detailed plan starts with the prioritized list from the previous step. It would be best if you made sure that the new system is going to validate new data as it is entered into it, otherwise, all the fixes you perform during the migration are going to become obsolete very quickly. Regarding issues at current data, you should also prioritize them to design the fixing plan, keeping in mind that you want to ensure not only accuracy and consistency but also completeness.

There are tools, and there are solutions. The difference between them is that tools help you build the process, while solutions are the tool and the process already built. For example, you can use Microsoft Azure tools to build the database (SQL Server) and the ETL process (Data Factory pipelines) to populate the database while fixing identified data quality issues, or you can choose a solution like Bintelligenz that has everything already built so you don't need to invest time and resources in developing the whole process from scratch.

To ensure the completeness principle in your data, you should never delete inaccurate records, only duplicated ones. For the first ones, you should develop a process to clean them. Remember that you can always spot an inconsistency in the data, but you can't spot missing records in your database.

In my experience, some points can help you are: > Migrate transactional dataset with functional purpose: the point is to test it on new environment. > Migrate segments of the dataset are grouped based on time or/and geograph aspect. > Have end-to-end and functional testing cases that are equivalent both systems to detect possible problem on data migration. > Use alpha release on development environment, and beta release on QA environment to execute and test processes. With these points, data quality metrics and data mapping & convertion ;you can check the data quality in proportional and control way to evaluate if it can pass to production environment.

The data quality quest is a never-ending game. If you want to play it safe, you should have automated dashboards to constantly monitor the health of your data and fix any new problem you detect, in the source system first. Once this step is done you can fix the data to get back to a healthy state.