The Importance of Data Management Transformation

Utilizing data to help drive a more agile, innovative and efficient business requires a robust and effective framework for managing data. To succeed, companies require a foundation of high-quality, reliable and consistent information across all their business processes, products, services and front-line operations.

Many of the top financial institutions invest in programs that reshape data architectures. They consolidate siloed system and build new infrastructures to collect, store and instantly update huge amounts of non-structured and structured data coming from multiple sources. They also have set up an centralized team to oversee the data transformation.

This is the process of transforming raw data into a format that is suitable for users and compatible with applications. Examples include converting features that are not numeric into numbers, aggregating weekly sales data into monthly sums and combining daily site traffic data to calculate trends for the entire web.

Achieving success in the data management transformation process will improve the accuracy, consistency, and clarity of data. It will also enable the reuse of data for various purposes, and improve the speed, accessibility, and searchability of data access.

Data transformation is a crucial element of modern data governance. It is essential to ensure the quality of data used by products, business processes and services. It is also crucial for the creation of intelligent businesses since systems such as machine learning and advanced analysis require huge amounts of high-quality information. Moreover, organizations need the appropriate data to find opportunities, optimize operations, and reduce costs.