A robust and effective framework for data management is required to harness the power of data and enable a more innovative, agile and efficient business. To succeed, companies need an excellent foundation of reliable and consistent information across all their business processes products, services, and front-line activities.
Many leading financial institutions invest in programs that transform data architectures. They are combining siloed systems and implementing new infrastructures that create, store and instantly update huge amounts of unstructured and structured data from a variety of sources. They are also putting in place a centralized team to oversee the data transformation.
This is the process of the transformation of raw data into a form that is beneficial to users and compatible with the software for which it is used. Examples include converting non numeric features into numerical numbers and aggregating sales data weekly into monthly sums or combining daily information on website traffic to calculate trends in aggregate.
The successful completion of the transformation to manage data will result in better clarity, consistency, and reliability of information. It will also allow for the reuse of data for various purposes and increase access, searchability, and speed of data access.
Data transformation is an essential element of modern data governance. It’s essential to ensure the quality of data used by businesses, products and services. It is also crucial for the growth of intelligent businesses since systems such as machine learning and advanced analysis require huge amounts of high-quality information. Furthermore, companies require the right data to spot opportunities, optimize operations and cut costs.