Data migration is an essential element of data management. It involves the transfer of data among different platforms and applications, such as moving data from an organization’s infrastructure and applications to cloud-based storage and applications. Data storage migration can also mean moving from paper to digital documents – a process handled efficiently by data conversion service providers – and switching to new server or software platform. The goal of data migration is to optimize or transform business performance.
Companies accumulate vast amounts of data about their customers, employees, products, digital and financial assets, etc. As systems get updated, usable and critical business information will need to be moved out of the old system to a new technologically advanced storage system or location.
Successful data migration can improve business agility, reduce data storage costs, and boost collaboration across your organization through increased visibility. However, research shows that data migration is not a cakewalk and many projects fail. To succeed, companies need to have a thorough understanding of what data migration involves and an effective plan in place to carry out the project by reducing the risks involved. Importantly, proper preparation of data and systems is a prerequisite for successful data migration.
Preparing for Data Migration
Preparing data for migration involves various steps:
- Analysis of the landscape to understand how the data within source systems is structured
- Validating the information obtaining through the analysis to ensure that it is fit for the intended purpose. This is known as data assurance.
- Data profiling to define what data to move and to identify data that is no longer needed. Data that will not be needed should be archived or retired.
- Data quality definition to determine whether or not the data is of the correct quality and format.
- Back up of all the data that is intended to be moved
Finally, data migration is carried out – the valid data is extracted from its current location and moved it to its new destination. When migrating data, make sure to pay attention to security settings or permissions. If not, the data would be susceptible to misuse or corruption.
Data Cleansing – A Vital Step in Data Migration
Data cleansing is an important step in the data migration project. The process involves addressing dirty data or fixing or removing inappropriate, corrupt, incorrectly formatted, duplicate, or incomplete data within a dataset.
- For successful data migration, the quality issues in the source data systems should be first identified.
- A data cleansing strategy should then be implemented to achieve the required data quality and standards for the migration to the new application or environment.
- Data cleansing is carried out using a hybrid approach of manual and automated techniques. Usually, manual cleansing is done at the source while automated cleansing takes place before the migration or in the initial phase.
- Data cleansing involves:
- Removing unwanted observations in a dataset such as duplicates and irrelevant data. Duplicate observations are those that are repeated in the dataset, while irrelevant observations are don’t actually fit the new environment.
- Addressing Missing Values: Missing values in fields are identified and filled in to create a complete data set and avoid gaps in information.
- Correcting Inconsistent and Incomplete Data: When data has not been entered in the system correctly it can impact the quality of other data.
Data cleansing will also address inconsistent formats and obsolete data. Data verification is carried out to check that the data is available, accessible, complete and in the correct format. When this step is completed, the data will ready for transfer.
The data cleaning process is a time-intensive task that is best handled by vendors of data cleansing services. Experienced service providers have the tools, technology and manpower to execute help businesses prepare their data for a hassle-free migration process.