Data management transformation is a method that optimizes raw data for both business and analytical purposes. This can involve removing redundant columns, changing types of data, and changing the name of an attribute or identifying it. It could also involve aggregation, deduplication and joining. This is usually performed by analytics engineers, who write SQL or Python codes to programmatically load and transform data. The resulting data is stored in an analytic database.

To harness big data effectively, an enterprise-wide effort is required to standardize and organize it. This can be a difficult task since data is generated from a variety of sources and then distributed across various systems, applications as well as data formats and storage locations. The management of this data in a streamlined method is made difficult by inconsistencies in metadata and missing or duplicated attributes.

A successful data transformation strategy will yield numerous advantages, such as improving the quality of data and making it easier to query. It can also make data management through reducing redundant data, and improving the discoverability of information.

The most crucial step to achieving a successful data management change is to clearly define the goals and objectives of your business. Defining these will help guide your initiatives, get the approval of senior executives and enable you to focus on the process of innovation. This will allow you to utilize existing frameworks, such as Master Data Management, to simplify processes and decrease data fragmentation. This method will allow you to realize tangible benefits in terms of risk, cost and revenue by allowing more informed choices to be made using the data available.