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Data management is the process by which companies collect, store and secure their data to ensure it is reliable and usable. It also covers the processes and technologies that support these goals.
The data used to run the majority of companies is gathered from a variety of sources, and stored in a variety of systems, and delivered in various formats. As a result, it can be difficult for engineers and data analysts to locate the right data to carry out their tasks. This can lead to disparate data silos, as well as inconsistent data sets, as well as other issues with data quality that can limit the usefulness and accuracy of BI and Analytics applications.
Data management can improve transparency and security, as well as enabling teams to better understand their customers and deliver the right content at the right time. It’s essential to begin with clear goals for business data and then create a set of best practices that can be developed as the company expands.
For instance, a reputable process should support both unstructured and structured information in addition to real-time, batch, and sensor/IoT workloads–while offering out-of-the-box accelerators and business rules as well as self-service tools that are based on roles to help analyze, prepare and cleanse data. It should also be scalable enough to fit the workflow of any department. It must also be flexible enough to allow machine learning integration and accommodate different taxonomies. It should also be simple to use, with integrated solutions for collaboration and governance councils.