Three common data migration pitfalls to avoid in 2022
To reap the benefits of modernization, organizations are gradually moving from legacy application systems to new technologies by adopting cloud-based storage. However, unless the data is transferred to a contemporary and relevant database stored on-premises or in the cloud, their modernization journeys would be incomplete.
Infrastructure and operations professionals need to design agile infrastructure and programs that meet rapidly growing business needs as the business embraces digital commerce. With the volume and variety of data increasing at a rapid rate, effective data migration is critical to digital transformation.
According to a new analysis published by Reports and data, the global data migration industry is expected to reach USD 30.73 billion by 2028. Data is a critical component of an organization’s success. It has a significant impact on key areas of the conceptualization and planning stages, as well as process optimization.
Here are some data migration hurdles to avoid in 2022 and beyond.
Undervaluing data analysis
Information may be concealed in encrypted areas due to limitations in the computer system, such as the lack of particular fields to hold all parts of the data or users’ ignorance of the function of existing fields. Therefore, throughout the migration, partial, erroneous and outdated data will be transferred, which will often be found later in the day, even after the project is finished. Therefore, companies may not be able to discover and update this data due to lack of time or resources. Companies can find these hidden errors by performing a thorough data analysis as soon as possible, usually when designing and preparing for data migration.
Data cleaning and coding
Transferring complete databases seems like a straightforward procedure. However, the data in these systems can come in a variety of forms and come from a variety of sources. Since data comes from a variety of sources, it needs to be cleansed, normalized, or converted so that organizations can examine it with data from multiple sources.
Read also : Four Best Practices Companies Can Adopt for Successful Data Migrations
Companies may need to modify their data model under these circumstances to accommodate the mix of structured and unstructured data, as well as any discrepancies that may arise simply by migrating data from one database system to another. other.
Lack of knowledge of legacy data
The success of any data migration project will always depend on a thorough understanding of the data companies already have. Moving to a new system is often driven by a lack of understanding of the data from previous systems, and over time people have devised workarounds to get the data they need. As a result, “Shadow IT” is seeping into businesses, with spreadsheets being used to collect and augment data that is not documented or managed on a central server.
Many of the first employees establishing a legacy system may already have left without properly documenting how, what, and why data is retained. How can companies analyze the data now that these employees are gone? Without this knowledge, there is a good chance that the effort required will be overestimated, leading to unforeseen costs later.