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Data management is the method by which businesses collect, store, and secure their data to ensure it remains reliable and usable. It also encompasses the technologies and processes that support these goals.
The data that drives most businesses comes from multiple sources, is stored in a variety of systems and places, and is often delivered in different formats. This means it isn’t easy for data analysts and engineers to locate the right data to carry out their tasks. This results in discordant data silos and incompatible data sets, and other data quality issues that could limit the use and accuracy of BI and Analytics applications.
Data management can increase visibility and security, as well as allowing teams to better know their customers better and provide the right content at right time. It is essential to establish specific data goals for the business and then devise the best practices to develop with the business.
For instance, a reputable process should support both unstructured and structured data in addition to real-time, batch and sensor/IoT tasks. In addition, it should provide out of the box accelerators and business rules, as well as self-service tools for roles that assist analyze, prepare and clean data. It should be scalable enough to accommodate the workflow of any 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, with integrated solutions for collaboration and governance councils.
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