Data migration is the strategy adopted by firms to facilitate the transfer of enormous data from one location to another Projects that comply with data migration, need a strategy to retain the quality control, an effective backup system, and testing methods that ensure successful data migration. The strategy aligns with larger business projects involving the expansion of storage capacities, transition to a new system for a successful and complete data migration. Data is a substantial part of the business activities relevant to the products and services. Several errors and problems arise if the data is inaccurate, corrupt, and invalid. A combination of precise and accurate customer information and a meticulous accounting system enables the firm to gain a competitive advantage in the marketplace through effective data migration. Updating the data-driven information facilitates proper upgradation that is feasible through data migration. Data migration takes place in three phases: data cleansing, data mapping, and data validation. In the data cleansing phase, the data is cleaned and prepared for migration. In the data mapping phase, the source and target systems are mapped to ensure that the data is migrated accurately. Finally, in the data validation phase, the migrated data is validated to ensure that it meets the desired quality standards. The process of data loading is used in the extraction and loading techniques of database. The process of loading data into the destination Is performed through the last phase known as the "extract, transform, and load" (ETL)
Data mapping is the procedure that ensures leverages the business value of the firms by grouping the data as per the requirement. It involves the process of creating a guide or a map that helps organizations to understand the location of their sensitive data and its flow through the system, accessibility etc. Data mapping combines the fields from many datasets into a template or a central database. Joining distinct sets of data with similar points makes them accurate and functional at the final location. The mapping of the framework of the data from one dataset to another facilitates interoperability between data in various formats. Progressive growth and expansion of the business are driving organizations to adopt a method of data-driven decision making that necessitates the effective storage and management of data, thereby reducing the tedious and time-consuming tasks involved in the process. Keeping in mind the privacy of the data, data mapping enables one to precisely link the sensitive information related to an individual or a data source, thereby providing a holistic view of the individual data. Data mapping is necessary for the transport, intake, processing, and management of data and for merging various sets of data into a single one. Merging databases into a single database facilitates the retrieval of data and provides a comprehensive overview of the data assets of the organization. This enables easy analysis and consumption of the data. The quality and utility of the data are enhanced as the mapping of the framework involves aligning the source with the destination. Mapping the database is useful to perform a variety of data integration and transformation tasks aligned to the organizational needs and the capabilities of the tools being used. The production team must ensure the sensitivity and confidentiality of the information provided by the stakeholders before beginning the data mapping process. The procedure may involve quick data quality checks to mitigate the risk of any error or leakage.
