Data management refers to how businesses manage, store, and secure their data so it remains useful and actionable. It also includes the tools and processes that support these goals.
The data that powers most firms comes from various sources, is stored in many different systems and places, and is often delivered in different formats. This means it isn’t easy for data analysts and engineers to find the appropriate data to complete their tasks. This results in incompatible data silos and inconsistent data sets, as well as other data quality issues that can limit the usefulness and accuracy of BI and Analytics applications.
A process for managing data improves visibility, reliability, as well as security. It also allows teams to better comprehend the needs of customers and provide correct content at the right time. It is essential to establish precise data goals for the business and then establish best practices that will develop with the business.
For instance, a reputable process should support both unstructured and structured information in addition to real-time, batch and sensor/IoT applications. All of this is while providing out-of-the- business rules and accelerators plus role-based self-service tools that help analyze, prepare and clean data. It should be scalable to fit any department’s workflow. Additionally, it should be able to handle various taxonomies and allow for the integration of machine learning. Lastly it should be able to be accessed through built-in collaborative solutions as well as governance councils to ensure coherence.
https://taeglichedata.de/verwalten-von-datenprozessen-mit-data-center-management-anwendungen
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