Data management is an approach to the way businesses collect, store and protect their data to ensure that it remains effective and reliable. It also encompasses methods and technologies that help achieve these goals.

The data that runs most companies comes from diverse sources, and is stored in many different systems and places and is usually delivered in different formats. It can be difficult for engineers and data analysts to locate the data they require for their job. This results in unreliable data silos and inconsistent data sets, in addition to other issues with data quality that can limit the usefulness and accuracy of BI and Analytics applications.

A process for managing data will improve the visibility and security as well as reliability, enabling teams to better comprehend their customers and provide relevant content at the appropriate time. It’s essential to begin with clear objectives for data management and then formulate a set of best practices that can grow as the company expands.

A good process, for example it should be able to handle both structured and unstructured, as well as sensors, real-time, batch and IoT workloads, and provide pre-defined business rules and accelerators. Additionally, it should offer role-based tools that help analyze and prepare data. It must also be scalable to be able to adapt to the workflow of any department. In addition, it must be able to adapt to different taxonomies and allow for the integration of machine learning. Additionally it should be accessible with built-in collaborative solutions and governance councils to ensure consistency.

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