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Data management is the method by which companies collect, store and protect their data to ensure it is reliable and usable. It also includes the tools and processes that assist in achieving these goals.

The information that runs the majority of businesses comes from multiple sources, is stored in many different systems and places and is typically delivered in various formats. This means it isn’t easy for data analysts and engineers to find the appropriate data to carry out their tasks. This leads to disparate data silos, as well as inconsistent data sets, and other issues with the quality of data that limit the utility and accuracy of BI and Analytics applications.

Data management processes improve visibility, reliability, and security. It also helps teams understand their customers and deliver the correct content at the right moment. It is crucial to establish specific data goals for the business and then create best practices that can expand with the company.

For instance, a successful process should be able to handle both unstructured and structured information in addition to real-time, batch and sensor/IoT-based workloads. It should also provide out-of-the accelerators and business Lifecycle of IMLS Software rules as well as self-service tools for roles that assist analyze, prepare and clean data. It must also be scalable and work with the workflow of every department. In addition, it should be able to accommodate different taxonomies and allow for the integration of machine learning. It should also be simple to use, with integrated collaborative solutions and governance councils.