Data management is a strategy to the way companies gather, store and protect their data to ensure it is useful and actionable. It also encompasses the technologies and processes that aid in achieving these goals.
Data that is utilized to run a lot of businesses is gathered from a variety of sources, and stored in a variety of systems, and subsequently delivered in various formats. It can be difficult for engineers and analysts to locate the data they require to carry out their work. This can lead to incompatible data silos, data sets that are inconsistent and other issues with the quality of data that could limit the utility of BI and analytics software and result in inaccurate conclusions.
Data management can increase visibility and security, as well as enabling teams to better understand their customers and provide the appropriate content at the right time. It is crucial to establish precise data goals for the business, and then devise best practices that expand with the company.
For instance, a reputable process should accommodate both unstructured and structured information in addition to real-time, batch and sensor/IoT applications. All of this is while providing out-of-the- accelerators and business rules along with self-service tools based on roles that allow you to analyze, prepare and cleanse data. It should be scalable to adapt to the needs of any department’s workflow. It should also be able to allow integration of machine learning and accommodate different taxonomies. Lastly it should be available with built-in collaborative solutions and governance councils to ensure consistency.
https://taeglichedata.de/pflege-von-datenprozessen-nach-sitzungssaal
