Data management is how companies store, collect and protect their data to ensure that it is reliable and usable. It also includes the technologies and processes that help achieve these goals.

The information that runs the majority of companies comes from a variety of sources, and is stored maintaining data processes the information lifecycle in various systems and places and is often presented in various formats. This means it can be a challenge for data analysts and engineers to find the right information for their work. This results in disparate data silos, as well as inconsistent data sets, in addition to other issues with the quality of data that can limit the usefulness and accuracy of BI and Analytics applications.

A data management system can improve visibility, reliability and security while allowing teams to better comprehend their customers and provide the right content at right time. It’s essential to begin with clear business goals and then come up with a list of best practices that will be developed as the company grows.

A good process, like it should be able to handle both structured and unstructured data in addition to sensors, real-time, batch and IoT tasks, and offer pre-defined business rules and accelerators. It should also include tools that can be used to analyze and prepare data. It should also be scalable enough to work with the workflow of every department. In addition, it should be flexible enough to accommodate various taxonomies and allow for the integration of machine learning. Furthermore it should be available with built-in collaborative solutions and governance councils to ensure coherence.