https://taeglichedata.de/information-lifecycle-management-establishing-data-processes
Data management is a method to the way businesses collect, store and secure their data, ensuring that it remains effective and reliable. It also includes the processes and technologies that support these goals.
The data that powers most businesses comes from multiple sources, is stored in numerous locations and systems and is often presented in different formats. As a result, it can be a challenge for engineers and data analysts to locate the right data to complete their tasks. This results in unreliable data silos and inconsistent data sets, in addition to other data quality problems that could limit the use and accuracy of BI and Analytics applications.
A data management process improves transparency, reliability, and security. It also allows teams to better understand customers and deliver the appropriate content at the right moment. It is crucial to establish clear data goals for the company, and then create best practices that can develop with the business.
For instance, a great process should be able to handle both structured and unstructured data–in addition to real-time, batch and sensor/IoT-based workloads. It should also provide out-of-the accelerators and business rules, as well as self-service tools based on roles that allow you to analyze, prepare and cleanse data. It should be scalable to meet the requirements of any department’s workflow. It should also be able to allow integration of machine learning and allow for different taxonomies. It should also be easy to use, with integrated solutions for collaboration and governance councils.
