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Data management refers to how businesses collect, store and protect their data to ensure it is efficient and actionable. It also encompasses processes and technology that support these goals.

The data that is used to manage most businesses is gathered from multiple sources, stored in multiple systems, and subsequently delivered in various formats. It is often difficult for engineers and data analysts to locate the data they require for their work. This results in disparate data silos, as well as inconsistent data sets, as well as other data quality problems that could limit the use and accuracy of BI and Analytics applications.

Data management processes improve visibility, reliability and security. It helps teams better comprehend the needs of customers and provide most relevant content at the appropriate moment. It’s crucial to begin with clear goals for business data and then formulate a set of best practices that can be developed as the company grows.

For instance, a great process should accommodate both structured and unstructured data–in addition to batch, real-time and sensor/IoT-based workloads. It should also provide out-of-the business rules and accelerators plus self-service tools that are based on roles to help analyze, prepare and cleanse data. It must also be scalable to work with the workflow of every department. Additionally, it should be able to handle different taxonomies and allow for the integration of machine learning. Furthermore it should be accessible with built-in collaborative solutions and governance councils to ensure the consistency.