Data management is the method by which businesses collect, store, and secure their data to ensure it remains secure and usable. It also encompasses the technology and processes that help achieve these goals.

The data utilized to run a lot of businesses is find gathered from many different sources, and stored in a variety of systems, and presented in different formats. Therefore, it can be a challenge for data analysts and engineers to locate the right data for their work. This leads to incompatible data silos as well as inconsistent data sets and other data quality issues that can limit the effectiveness of BI and analytics applications and result in inaccurate conclusions.

A data management process improves visibility, reliability, and security. It also helps teams understand their customers and deliver the proper content at the right moment. It’s essential to begin with clear business goals and then come up with a list of best practices that can be developed as the company expands.

A good process, like one that supports both unstructured and structured data and also batch, real-time, sensor/IoT workloads, and provide pre-defined business rules and accelerators, plus role-based tools that help analyze and prepare data. It must also be scalable to fit the workflow of any department. In addition, it must be able to handle different taxonomies as well as allow for the integration of machine learning. Lastly it should be accessible through built-in collaborative solutions as well as governance councils to ensure consistency.