More and more companies decide to move their application to Cloud, but they should keep in mind that while data is migrating it is also a way to improve data quality itself. Any system, including Cloud, is as good as data within it. Remember about this if you want to improve data quality.
The decision to start the process of “migrating data” to Cloud gives one an opportunity to make sure that existing data is of the best quality as well as install mechanism ensuring that future data, which will be entered into the system, is accurate, current and complete at the same time. This will be useful and essential especially in case of “migrating data” process from internal database to a Database-as-a-service product such as SQL Azure and Amazon RDS, or to a database which will be running on top of Cloud service, including MySQL or SQL Server on Amazon, Microsoft Azure, Rackspace, etc.
While data is migrating from its initial source database to its Cloud database, you should make use of this time to improve data quality by:
• ensuring all physical addresses are valid, accurate and complete
• ensuring all email addresses are valid, working and not changed
• ensuring all telephone numbers are valid, accurate and current as well
• ensuring all data fields are consistent in content as well as individual data elements are non-ambiguous
• filling in all missing data or updating data where possible
• eliminating duplicate contact and customers records as well
• incorporating other business-related rules which can be essential for your company
You should also remember about one more rule, which is quite easy, companies, which want to improve data quality, decide to put real-time data quality and data enhancement mechanism at all points of data collections, including “data migrating” process. By this way companies make sure that data will not lose its validity over time. Otherwise, this might lead to the cleansing process.
An enormous part of the success of any Cloud initiative is related to cleansing data during migration, using real-time data quality tools as well as establishing a data management with both metrics and goals. Be sure to use all these opportunities to improve data quality.