Data management is how companies collect, store and secure their data to ensure it remains reliable and usable. It also encompasses methods and technologies that help achieve these goals.
The data that is used to manage most businesses is gathered from many different sources, storing it in various systems, and delivered in different formats. It can be difficult for engineers and analysts to locate the data they require for their work. This can lead to incompatible data silos, data sets that are inconsistent and other issues with data quality that may limit the usefulness of BI and analytics software and lead to incorrect conclusions.
A data management system improves transparency, reliability, and security. It helps teams better comprehend the needs of customers and provide appropriate content at the right moment. It’s essential to begin with clear business data goals and then formulate a set of best practices that can grow as the company expands.
A good process, like, should support both structured data and unstructured and also sensors and batch workloads, and provide pre-defined business rules and accelerators. It should also include tools based on roles that aid in the analysis and prepare data. It should be scalable to accommodate the workflow of any department. Furthermore, it should be able to adapt to a variety of taxonomies and allow for the integration of machine learning. It should also be simple to use, with integrated collaborative solutions and governance councils.
https://taeglichedata.de/maintaining-data-processes-throughout-the-information-lifecycle
