Data management is a strategy to how businesses collect, store and secure their data to ensure it is efficient and actionable. It also includes the methods and technologies that help achieve these goals.
The data that runs most companies comes from diverse sources, is stored in numerous systems and places, and is often delivered in a variety of formats. Therefore, it isn’t easy for engineers and data analysts to locate the correct data to perform their job. This results in data silos that are not compatible, data sets that are inconsistent and other issues with data quality which can hinder the use of BI and analytics applications and lead to incorrect conclusions.
A process for managing data improves transparency, reliability, and security. It helps teams better comprehend their customers and provide the appropriate content at the right moment. It’s important to start with clear goals for business data and then come up with a list of best practices that will be developed as the company expands.
A good process, like, should support both structured and unstructured in addition to sensors, real-time, batch and IoT workloads, as well as 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 enough to adapt to the needs of any department’s workflow. In addition, it should be able to adapt to different taxonomies as well as allow for the integration of machine learning. It should also be easy to use, and include integrated solutions for collaboration and governance councils.
https://taeglichedata.de/pflege-von-datenprozessen-nach-sitzungssaal
