Data management is how businesses collect, store and secure their data to ensure it is reliable and usable. It also encompasses the technologies and processes that assist in achieving these goals.

The data that powers most firms comes from various sources, is stored in various locations and systems and is typically delivered in various formats. It is often difficult for engineers and analysts to locate the data they need for their work. This leads to discordant data silos and incompatible data sets, in addition to other issues with the quality of data that can limit Data management the usefulness and accuracy of BI and Analytics applications.

A process for managing data will improve the visibility and security as well as reliability, allowing teams to better know their customers better and provide the appropriate content at the appropriate time. It’s crucial to set precise data goals for the business and then develop best practices that can expand with the company.

For instance, a reputable process should be able to handle both unstructured and structured data, in addition to real-time, batch and sensor/IoT tasks. In addition, it should provide out of the box business rules and accelerators plus self-service tools for roles that assist analyze, prepare and clean data. It should also be scalable and fit the workflow of any department. Furthermore, it should be flexible enough to accommodate different taxonomies and allow for the integration of machine learning. It should also be simple to use, and include integrated collaborative solutions and governance councils.