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Data management is the process by which businesses store, manage and protect their data to ensure it remains safe and useful. It also includes the tools and processes that support these goals.

Data that is utilized to run a lot of businesses is gathered from various sources, stored in multiple systems, and delivered in various formats. It is often difficult for engineers and analysts to locate the data they require for their job. This creates incompatible data silos, inconsistent data sets and other data quality issues which can hinder the use of BI and analytics software and lead to faulty findings.

A process for managing data improves transparency, reliability, and security. It helps teams better understand customers and deliver the most relevant content at the appropriate time. It is essential to establish precise data goals for the business and then develop best practices that develop with the business.

For instance, a reputable process should be able to accommodate both unstructured and structured data, in addition to real-time, batch, and sensor/IoT applications. All of this is while providing out-of-the- accelerators and business rules, as well as self-service tools that are based on roles to help analyze, prepare and cleanse data. It must also be scalable and work with the workflow of every department. Additionally, it should be flexible enough to accommodate various taxonomies and allow for the integration of machine learning. It should also be simple to use, with integrated solutions for collaboration and governance councils.