Data management is a strategy to the way businesses collect, store and protect their data to ensure that it remains efficient and actionable. It also covers the techniques and tools that support these goals.
The data that powers most companies comes from diverse sources, and is stored in numerous locations and systems and is usually delivered in a variety of formats. This means it can be difficult for data analysts and engineers to locate the correct data to perform their job. This results in disparate data silos, as well as inconsistent data sets, in addition to other issues with the quality of data that can limit the usefulness and accuracy of BI and Analytics applications.
A data management system can increase visibility security, reliability and reliability while allowing teams to better know their customers better and provide the appropriate content at the appropriate time. It is crucial to establish precise data goals for the company, and then create best practices that can grow with the company.
A efficient process, for instance, should support both structured data and unstructured and also real-time, batch, and sensor/IoT workloads, while offering pre-defined business rules and accelerators, as well as role-based tools that help analyze and prepare data. It should also be scalable enough to work with the workflow of every department. It should also be flexible enough to allow integration of machine learning and to accommodate various taxonomies. It should also be easy to use, and include integrated collaboration solutions and governance councils.