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

The data that is used to manage most businesses is gathered from a variety of sources, and stored in a variety of systems, and presented in different formats. It is often difficult for engineers and data analysts to find the information they require for their work. This can lead to discordant data silos and incompatible data sets, and other data quality issues that could limit the use and accuracy of BI and Analytics applications.

Data management processes improve visibility, reliability and security. It also allows teams to better understand their customers and deliver the proper content at the right moment. It’s crucial to set specific data goals for the company and then develop best practices that develop with the business.

A efficient process, for instance, should support both structured data and unstructured and also sensors and batch workloads, and provide pre-defined business rules and accelerators, as well as tools based on roles that aid in the analysis and prepare data. It must also be scalable and be able to adapt to the workflow of any department. In addition, it should be able to adapt to different taxonomies as well as allow for the integration of machine learning. Furthermore it should be available via built-in collaborative solutions and governance councils for the consistency.


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