Dealing With Bad Data During Cloud Migration

      Posted by Sydney Boman on Sep 27, 2018 10:30:00 AM

      dealing-wtih-bad-data-during-cloud-migration

      The use of the cloud is rapidly becoming the gold standard for data storage and management by businesses. There has been an exponential increase in the number of companies that have migrated their data to the cloud over the past few years. At present, at least 96 percent of all organizations use the cloud in one form or another.

      As part of the data migration strategy, it is necessary to assess the quality of the data being migrated. Data of low quality or that is flawed is known as bad data and can have an adverse impact on a business. These adverse consequences may negate any benefit derived from data migration to the cloud. Therefore the cleaning up of any bad data is an important aspect of a cloud migration strategy.

      Causes of bad data during cloud migration

      While there are many factors that could result in bad data, there are two principal causes:

      • Poor data management: For data to retain its integrity, it needs to be managed on a regular and frequent basis. This entails updating the data as needed, removing data that is no longer valid, ensuring that the data is accurate, removing duplicate data, and ensuring that data is error free. Failure to properly manage data frequently can result in the migration of bad or faulty data.

      • Structural differences between applications: Data migration from the legacy to the cloud applications may cause bad data. This is because the legacy and cloud applications are structurally different; as such the data from the legacy application is incompatible with the cloud applications. This is especially the case if the lift and shift method is used to migrate the data without any form of cloud optimization to ensure its compatibility with cloud applications.

      Types of bad data

      Some of the types of bad data that can pose issues after a cloud migration include:

      • Inert data: This is dormant data that has not been updated or modified for a long period of time. As a result, the data has become obsolete and is no longer of any use.

      • Non-factual data: This is data containing inaccurate information such as word misspellings, missing or wrong customer details, among others. The inaccuracy of the data makes it difficult if not impossible to use for business or analytic purposes.

      • Non-compliant data: In some industries such as healthcare, the utilization and storage of data is guided by government regulations. In these industries, data can be considered to be bad if it is not handled in accordance with the specifications detailed in the government regulations.

      Consequences of bad data during cloud migration

      Some of the negative consequences of bad data include:

      • Faulty analytics: Businesses use analytics for several reasons such as to monitor trends and make predictions. With bad data, any research or analytics done will provide incorrect results. If not recognized and corrected, this may lead businesses to make faulty decisions.

      • Lost revenue: Bad data may result in lost revenue for businesses as a result of faulty decisions made or due to the expenses incurred in correcting and validating the bad data.

      • Loss of customer trust: Businesses may lose the trust of their customers as a result of the poor management of their data. This can result in the loss of their customer base and ultimately, decreased profits.

      How to clean bad data during cloud migration

      Several options are available to clean up bad data during cloud migration and ensure that maximal benefits are realized from the transition:

      • Correct data en route to the cloud: With this option, the bad data is corrected once it is picked up from the legacy application before it is moved over to the cloud. This approach, however, adds a layer of complexity to the cloud migration process.

      • Correct data prior to the cloud: This is an easier method of correcting the bad data. However, it lengthens the cloud migration process, because the data cannot be migrated until all the bad data has been corrected.

      • Correct data post-migration: This is the most popular and effective option. Even though the cloud migration process is lengthened, the businesses have access to the applications sooner and can utilize them while the data is being cleaned up.

      At NewCloud Networks, we have the expertise needed to migrate your data to the cloud. For more information about our services, contact us.

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      Topics: Cloud Computing, Backup, Cloud Service Providers, Cloud Storage