Data storage and management in the cloud are quickly becoming the gold standard for businesses. Over the past few years, the number of companies migrating their data to the cloud has skyrocketed. Cloud computing is used in one form or another by at least 96 percent of all organizations.
As part of the data migration strategy, the quality of the data needs to be assessed. Bad or flawed data can negatively affect a business. This is known as bad data. Any benefits derived from data migration to the cloud may be negated by these adverse effects. A cloud migration strategy must therefore include the removal of bad data.
During Cloud Migration, Bad Data is a Common Occurrence
Bad data can be caused by many factors. However, there are two principal causes:
- Poor Data Management: To maintain data integrity, management needs to be performed regularly and frequently. As part of this process, data is updated as needed, data that is no longer valid is removed, data is accurate, duplicate data is removed, and data is error-free. The migration of bad or faulty data can be caused by improper management of data.
- Differences in Applications’ Structures: Data migration from legacy applications to cloud applications may result in bad data. Due to the structural differences between legacy and cloud applications, legacy data cannot be used with cloud applications. In particular, this is true if the lift and shift method is used to migrate data without optimizing the data for cloud compatibility.
Types of Bad Data During Cloud Migration
The following types of bad data can cause issues after a cloud migration:
- Inert Data: Inert data means that it has not been updated or modified for an extended period of time. As a result, the data has become obsolete and is no longer useful.
- Non-Factual Data: Among other things, non-factual data contains inaccurate information such as spelling mistakes and wrong customer information. Using the data for business or analytic purposes is difficult if not impossible due to its inaccuracy.
- Non-Compliant Data: Government regulations guide how data is used and stored in some industries, such as healthcare. When data is not handled according to the specifications detailed in government regulations, it can be regarded as bad.
Problems Caused by Bad Data During Cloud Migration
Bad data has the following negative consequences:
- Faulty Analytics: Businesses use analytics for a variety of purposes, including monitoring trends and predicting the future. If the data is faulty, the results of any research will be incorrect. Businesses may make faulty decisions if this is not recognized and corrected.
- Lost Revenue: Businesses may lose revenue due to faulty decisions made or because of expenses incurred in resolving and validating bad data as a result of bad data.
- Customer Trust Loss: Businesses may lose their customers’ trust due to poor data management. Customers and profits can be affected if a company loses its customer base.
Clean Up Bad Data When Migrating to the Cloud
In order to get the most out of cloud migration, it is important to clean up bad data:
- Correct data on the way to the cloud: With this option, the bad data is corrected once it has been picked up from the legacy application before it is transferred to the cloud. However, this approach adds a layer of complexity to cloud migration.
- It is easier to correct data before uploading it to the cloud. It lengthens the migration process, though, since all bad data must be corrected before the data can be migrated.
- The most popular and effective option is to correct data after migration. Even though the cloud migration process is longer, businesses can access the applications earlier and utilize them while the data is being cleaned up.
Data migration to the cloud is what we do are specialized at BITS Secure IT Infrastructure. Please contact us for more information.
