Data management is the process by which companies collect, store and protect their data to ensure that it remains safe and useful. It also includes the processes and technology that support these goals.

The information that runs the majority of companies comes from a variety of sources, and is stored in many different systems and places and is typically delivered in different formats. As a result, it is often difficult for data analysts and engineers to locate the correct data for their work. This leads to disparate data silos, as well as inconsistent data sets, as well as other issues with data quality that can limit the usefulness and accuracy of BI and Analytics applications.

A data management system improves visibility, reliability, and security. It helps teams better understand their customers and deliver the appropriate content at the right moment. It is crucial to establish clear goals for data management for the business, and then create best practices that can develop with the business.

For instance, a reputable process should be able to accommodate both unstructured and structured data, in addition to real-time, batch, and sensor/IoT applications. All of this is while providing out-of-the- business rules and accelerators plus role-based self-service tools that help analyze, prepare and cleanse data. It should also be scalable and be able to adapt to the workflow of any department. In addition, it must be flexible enough to accommodate different taxonomies as well as allow for the integration of machine learning. It should also be easy to use, with integrated collaboration solutions and governance councils.

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