Master data management is a very important tool, which ensures that multiple versions of the same master data are not used within a system as this might lead to inconsistency. Master data includes data, such as customers, products, employees, location, and records, which are keys for a business. The data chosen as master data by an organization depends upon the key elements of the respective organization. Master data can be further classified into sub entities. Consistency of the master data is very crucial for an offshore product development organization. Consistency mainly signifies that the same master data should be used by all the operations of an organizational system. Master data as well as all the copies of it used by an organization should be accurate and consistent.

Master data management is very important because master data is shared across many operations and applications in an organizational system. An error in the master data might result in errors in all the operations and applications using the master data. For example, an error in the price of a product in the product master will lead to an error in price of all the batches of the particular product. Master data management is also very important for maintaining data quality and integrity. Offshore product development companies nowadays use a single set of master data in order to avoid data inconsistency. Maintenance of a single set of master data is a very complex and challenging task and requires thorough analysis and advanced tools. For example, the same customer can have many different phone numbers. The tool used should be able to recognize that the different phone numbers belong to the same person and should be able to integrate them. The tool should perform all the tasks without losing the data quality and integrity.

Regular updating of the master data and the system are very important in order to ensure data reliability. Some of the major processes used by master management systems include normalization, data transformation, and data storage. Data normalization is a technique, which mainly ensures that the data stored in the relational database is not redundant or duplicate. It also ensures that some relationship can be established between the data stored in the database. The transformation process includes conversion of the source data to destination data by extraction, transformation, and loading.

Data extraction as the name suggests is the process of taking out data from various data sources for further processing. Transformation is a process of data conversion in which the source data is converted into the format of the destination data. The transformation process involves a series of operations and conditions for converting the source data obtained from the extraction process to the destination data. The destination data is loaded into the database or the data warehouse in the loading phase. Data loading takes time because the data has to be loaded without loss of its quality, integrity and consistency. The transformation process is very crucial for master data management. Master data management is used as a business intelligence tool nowadays for maintaining data across all mobile application development organizations.