Data management refers to the way companies manage, store, and secure their data to ensure that it remains effective and reliable. It also encompasses the technologies and processes that support these goals.

The information utilized to run a lot of businesses is gathered from many different sources, and stored in a variety of systems, and presented in different formats. It can be difficult for engineers and analysts to locate the data they require to carry out their work. This can lead to discordant data silos and incompatible data sets, and other data quality issues which can hinder the effectiveness and accuracy of BI and Analytics applications.

A process for managing data improves transparency, reliability, and security. It also allows teams to better comprehend their customers and provide the appropriate content at the right moment. It’s crucial to begin with clear business goals and then formulate a set of best practices that will grow as the company expands.

A efficient process, for instance will be able to accommodate both structured data and unstructured as well as sensors and batch tasks, and offer pre-defined business rules and accelerators. Additionally, it should offer tools that can be used to analyze and prepare data. It should be scalable to accommodate the workflow of any department. It should also be flexible enough to allow integration of machine learning and accommodate different taxonomies. Lastly it should be accessible via built-in collaborative solutions and governance councils for coherence.