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Data management is how companies collect, store and secure their data to ensure that it remains safe and useful. It also encompasses processes and technology that support these goals.

The data that powers most firms comes from various sources, and is stored in numerous locations and systems and is usually delivered in various formats. Therefore, it isn’t easy for data analysts and engineers to find the right information to perform their job. This leads to unreliable data silos and inconsistent data sets, in addition to other issues with the quality of data that could limit the use and accuracy of BI and Analytics applications.

A data management system can improve visibility and security as well as reliability, enabling teams to better comprehend their customers and provide relevant content at the right time. It’s important to start with clear objectives for data management and then come up with a list of best practices that can expand as the business grows.

A effective process, for example will be able to accommodate both structured and unstructured in addition to batch, real-time, sensor/IoT tasks, and offer pre-defined business rules and accelerators. It should also include tools based on roles that aid in the analysis and prepare data. It must also be scalable to fit the workflow of any department. In addition, it must be able to handle a variety of taxonomies and allow for the integration of machine learning. Additionally, it should be accessible via built-in collaborative solutions and governance councils for uniformity.