To realize the benefits of modernization, organizations are gradually moving from legacy application systems to new technologies by embracing 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 are being expected to design agile infrastructure and programs that enable speedy and growing corporate needs as the company embraces digital business. With the volume and variety of data growing at a rapid pace, effective data migration is critical to digital transformation.
According to a new analysis published by Reports and Data, the global data migration industry is anticipated 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 conceptualizing and planning stages, as well as on process optimization.
Here are some data migration stumbling blocks to avoid in 2022 and beyond.
Undervaluing data analysis
Information can be concealed in cryptic areas due to computer system limits, such as the lack of particular fields to retain all parts of the data or users’ ignorance of the function of the existing fields. As a result, throughout the migration, partial, erroneous, and outdated data will be transferred, which will often be found later in the day, even after the project is completed. Therefore, businesses may be unable to discover and update this data due to a lack of time or resources. Enterprises can find these hidden fallacies by doing thorough data analysis as soon as feasible, usually when devising and preparing data migration.
Cleaning and coding of data
Transferring complete databases appears to be a straightforward procedure. However, the data in those systems could be in a variety of forms and come from a variety of sources. Since the data originates from a variety of sources, it must be cleansed, standardized, or converted so that organizations can examine it alongside data from multiple sources.
Businesses may need to change their data model in these circumstances to accommodate for the blend of structured and unstructured data, as well as any discrepancies that may arise merely by migrating data from one database system to another.
Absence of legacy data knowledge
The accomplishment of any data migration project will always hinge on a thorough interpretation of the data that firms already have. Switching to a new system is frequently motivated by a lack of comprehension of the data in previous systems, and as time has passed, people have devised workarounds to obtain the data they require. As a result, ‘Shadow IT’ infiltrates companies, with spreadsheets used to collect and augment data that isn’t documented or managed in a central server.
Many of the initial employees who establish a legacy system may have already left without properly documenting how, what, and why data is preserved. How can companies possibly analyze the data now that these employees have left? Without this knowledge, there’s a good chance that the effort required would be overestimated, resulting in unforeseen costs down the road.