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Data management is an approach to how businesses manage, store, and secure their data, ensuring that it remains useful and actionable. It also includes the tools and processes that assist in achieving these goals.

The data that is used to manage most businesses is gathered from many different sources, compiled in various systems, and then delivered in different formats. It can be difficult for engineers and data analysts to find the information they require for their job. This can lead to incompatible data silos in which data sets are inconsistent, as well as other issues with data quality that can limit the effectiveness of BI and analytics software and result in inaccurate conclusions.

Data management processes improve visibility, reliability and security. It can also help teams better comprehend their customers and provide the appropriate content at the right moment. It’s crucial to set clear goals for data management for the company, and then create best practices that can grow with the company.

For instance, a reputable process should be able to accommodate both unstructured and structured data in addition to real-time, batch, and sensor/IoT tasks. In addition, it should provide out of the box business rules and accelerators plus self-service tools that are based on roles to help analyze, prepare and cleanse data. It should also be scalable and fit the workflow of any department. It should also be able to allow integration of machine learning and support different taxonomies. Additionally it should be accessible via built-in collaborative solutions and governance councils for the consistency.