Data management is a strategy to how businesses manage, store, and secure their data so it remains reliable and actionable. It also encompasses the technologies and processes that aid in achieving these goals.

The data that drives most companies comes from diverse sources, and is stored in many different locations and systems, and is often delivered in a variety of formats. This means it is often difficult for data analysts and engineers to find the right information to carry out their tasks. This leads to incompatible data silos, inconsistent data sets and other issues with data quality that may limit the usefulness of BI and analytics applications and lead to faulty findings.

A data management system can improve visibility, reliability and security while allowing teams to better comprehend their customers and provide relevant content at the right time. It’s crucial to set clear data goals for the company, and then establish best practices that can grow with the company.

A good process, for example, should support both structured and unstructured data and also sensors and batch workloads, and provide pre-defined business rules and accelerators, as well as role-based tools that help analyze and prepare data. It should also be scalable and 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. In addition it should be able to be accessed via built-in collaborative solutions and governance councils for consistency.

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