Data management is the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. The goal of data management is to help people, organizations, and connected things optimise the use of data within the bounds of policy and regulation so that they can make decisions and take actions that maximize the benefit to the organization. A robust data management strategy is becoming more important than ever as organizations increasingly rely on intangible assets to create value.
Today’s organizations need automate data management that provides an efficient way to manage data across a diverse but unified data tier. Data management systems are built on data management platforms and can include databases, data lakes and warehouses, big data management systems, data analytics, and more.
All these components work together as a “data utility” to deliver the data management capabilities an organization needs for its apps, and the analytics and algorithms that use the data originated by those apps. Although current tools help database administrators (DBAs) automate many of the traditional management tasks, manual intervention is still often required because of the size and complexity of most database deployments. Whenever manual intervention is required, the chance for errors increases. Reducing the need for manual data management is a key objective of a new data management technology, the autonomous database.