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Data management is an approach to the way companies gather, store and protect their data so it remains efficient and actionable. It also includes the technologies and processes that assist in achieving these goals.

The data utilized to run a lot of businesses is gathered from many different sources, stored in multiple systems, and presented in various formats. As a result, it isn’t easy for data analysts and engineers to find the appropriate data for their work. This creates incompatible data silos, inconsistent data sets and other data quality issues which can hinder the use of BI and analytics applications and lead to faulty findings.

A data management system improves transparency, reliability, and security. It also helps teams comprehend their customers and provide the correct content at the right time. It’s essential to establish specific data goals for the business, and then develop the best practices to expand with the company.

For instance, a great process should support both structured and unstructured data–in addition to batch, real-time and sensor/IoT-based workloads. It should also provide out-of-the accelerators and business rules along with 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. In addition, it must be able to accommodate different taxonomies as well as allow for the integration of machine learning. It should also be easy to use, and include integrated solutions for collaboration and governance councils.