{"id":1488,"date":"2017-02-22T07:41:03","date_gmt":"2017-02-22T07:41:03","guid":{"rendered":"https:\/\/www.kovair.com\/blog\/?p=1488"},"modified":"2025-09-03T11:05:39","modified_gmt":"2025-09-03T11:05:39","slug":"data-quality-the-fundamental-of-any-data-migration-project","status":"publish","type":"post","link":"https:\/\/www.kovair.com\/blogs\/data-quality-the-fundamental-of-any-data-migration-project\/","title":{"rendered":"Data Quality: The Fundamental of Any Data Migration Project"},"content":{"rendered":"

Why data migration?<\/h2>\n

Data migration is necessary when an organization decides to use a new application keeping the business story and associated details as is. It is an exercise of transferring data from one system to another without affecting data consistency and business values of an application.<\/p>\n

Data migration is a crucial operation within an enterprise and failure can be catastrophic. Reports say that inefficient data migration process<\/strong><\/a> can limit an organization\u2019s ability to move forward with new technologies. Many companies, not using a proper migration tool<\/strong><\/a>, end up with significant expenditures with little or no value gained from their effort.<\/p>\n

\"data<\/p>\n

Scenario 1<\/strong>: Say Team A<\/strong> works on tool X<\/strong> as their collaboration platform for automated software delivery and the team B<\/strong> works on tool Y<\/strong> for some other purpose. They want to transfer data from one tool to another, yet continue with their own tools after migration is complete. Since they work on two separate tools, they need to be in sync to increase efficiency and reduce delivery time. Therefore, both the tools need to have easy access to the work items that the teams will use.\u00a0 This requires bidirectional synchronization of artifacts between the two systems, even during the post-migration stage.<\/p>\n

Scenario 2<\/strong>: Say Team A<\/strong> migrates data from tool X<\/strong> to tool Y<\/strong> which is managed by Team B<\/strong>. In a typical case for large enterprises, migration cannot take place overnight and both the tools need to run in parallel during transition period. Once the migration process is complete, tool X<\/strong> is discarded.<\/p>\n

In both the data migration scenarios, maintaining data quality is a difficult task.<\/p>\n

Challenges in data migration<\/strong><\/h2>\n

There are several challenges in data migration projects<\/a>. Some of them are as follows:<\/p>\n

Importance of data quality: <\/strong>As with most data integration efforts, maintaining data quality is one of the biggest challenges in data migration. Data quality issues are amplified when migration happens from a legacy system (with poor data quality) to a newer application that has a set of rich features and strict data model. This necessitates a lot of planning before the migration process commences. Poor quality of data imposes a significant cost to post migration maintenance; with issues ranging from poor business intelligence to delay or disruption in business processes.<\/p>\n

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What is data quality?<\/h2>\n

There is no theoretical definition for data quality, but they are considered as \u2018good\u2019 or \u2018bad\u2019. Quality data fulfills the underlying need of extracting information out of data in use. So, data quality is measured in the context in which it is used.<\/p>\n

Issues with poor quality of data \u00a0<\/strong><\/h2>\n