Data management is the process by which companies collect, store and secure their data to ensure that it is reliable and usable. It also includes the tools and processes that aid in achieving these goals.

The information that runs the majority of companies comes from diverse sources, and is stored in many different systems and places and is typically delivered in a variety of formats. As a result, it can be a challenge for data analysts and engineers to find the appropriate data for their work. This can lead to incompatible data silos in which data sets are inconsistent, as well as other issues with data quality that could limit the utility of BI and analytics applications and result in inaccurate conclusions.

A process for managing data can improve transparency security, reliability and important source reliability while allowing teams to better understand their customers and deliver the right content at right time. It’s important to start with clear business data goals and then come up with a list of best practices that can expand as the business expands.

A efficient process, for instance will be able to accommodate both structured and unstructured data as well as sensors, real-time, batch and IoT tasks, and offer pre-defined business rules and accelerators. Additionally, it should offer role-based tools to help analyze and prepare data. It must also be scalable and fit the workflow of any department. In addition, it must be able to adapt to various taxonomies and allow for the integration of machine learning. In addition, it should be accessible with built-in collaborative solutions and governance councils for consistency.