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June 29, 2009

Common Rules for Data Migration

Filed under: Data Migration, ETL — Tags: , — Olga Belokurskaya @ 1:21 am

Data migration is an inevitable evil for any enterprise. As business requirements change and demand for new applications, or there is a need of removing duplications and inefficiencies, business-critical data migration can’t be avoided. Data migration initiatives, unfortunately, still have a high rate of failures due to the complexity of the process. According to CRN Australia’s article I’ve found lately, there are four common data migration rulles which could help to drive the initiative to success.

The first rule says that data migration is primarily a business issue. Technical side is important, but only as a means to fulfill the process. IT people don’t always have the power or the necessary knowledge to deliver what is required by the business. It is business who is to make the decision on:

  • What is the purpose of migrating data?
  • What data should be migrated?
  • When should the data be migrated?

The second rule claims that business goals define the solution and approach selected for data migration. This rule supports the first one. It is important to bear in mind that the best technical solution is not always the best business solution. IT’s responsibility is to make sure the chosen migration technology is able to support possible changes in priority and direction without restarting every time. IT also should provide for the issues that may occur during the migration.

The third rule is about setting the level of acceptance of data quality level. Many data migrations have been scuppered by overestimating or not understanding the quality of the source data. While enhancing data quality is a worthy goal, it is really important not to go off on a data perfection crusade. It is a trap that drives many, many projects to inflating both the cost and the time to deliver the project. To avoid this trap, data owners and users need to determine the level of quality they require at the start of the project, in order that the technologists have an appropriate goal.

The fourth rule is about measuring data quality in order to determine the level of quality business users require. Those measures should make sense to business users and not just to technologists. It makes possible to rightly measure deliverables, perform gap analyses, and monitor and improve ongoing data quality. It also ensures that the efforts are concentrated on where business users see value and can quantify the benefits.

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