Data management is how companies collect, store, and secure their data to ensure that it remains reliable and usable. It also encompasses the technologies and processes that aid in achieving these goals.
The data used to manage most businesses is gathered from a variety of sources, stored in multiple systems, and delivered in various formats. Therefore, it is often difficult for data analysts and engineers to locate the right data for their work. This leads to incompatible data silos in which data sets are inconsistent, as well as other issues with the quality of data which can hinder the use of BI and analytics software and result in inaccurate conclusions.
Data management processes improve visibility, reliability and security. It also allows teams to better understand customers and deliver the most relevant content at the appropriate time. It is essential to begin with clear objectives for data management and then come up with a list of best practices that can develop as the business expands.
For instance, a successful process should be able to handle both unstructured and structured 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 flexible enough to accommodate the workflow of any department. It should also be able to allow machine learning integration and to accommodate various taxonomies. It should also be easy to use, with integrated solutions for collaboration and governance councils.
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