Data management is the way companies store, collect and secure their data to ensure it is reliable and usable. It also encompasses the technology and processes that support these goals.
The data that runs most businesses comes from multiple sources, and is stored in many different locations and systems, and is often delivered in a variety of formats. It can be difficult for engineers and data analysts to locate the data they require to carry out their work. This results in unreliable data silos and inconsistent data sets, as well as other data quality problems that could limit the use and accuracy of BI and Analytics applications.
A data management process improves visibility, reliability, and security. It also allows teams to better understand customers and deliver the proper content at the right time. It is essential to begin with clear objectives for data management and then develop a set of best practices that can be developed as the company expands.
A efficient process, for instance, should support both unstructured and structured data, as well as sensors and batch workloads, and provide pre-defined business rules and accelerators, plus role-based tools to help analyze and prepare data. It should be flexible enough to meet the requirements of any department’s workflow. It should also be flexible enough to allow integration of machine learning and to accommodate various taxonomies. It should also be easy to use, with integrated https://taeglichedata.de/generated-post collaborative solutions and governance councils.
