Data management is the method by which businesses store, manage and protect their data to ensure it remains reliable and usable. It also includes the methods and technologies that help achieve these goals.
Data that is used to run most companies is gathered from a variety of sources, stored in multiple systems, and delivered in various formats. Therefore, it can be a challenge for engineers and data analysts to locate the correct data to carry out their tasks. This leads to disparate data silos, as well as inconsistent data sets, as well as other issues with data quality that could limit the use and accuracy of BI and Analytics applications.
A data management maintaining data processes the information lifecycle process improves visibility, reliability, and security. It can also help teams better comprehend the needs of customers and provide appropriate content at the right time. It’s important to start with clear business data goals and then formulate a set of best practices that will develop as the business grows.
For example, a good process should be able to handle both unstructured and structured data, in addition to real-time, batch and sensor/IoT workloads–while offering out-of-the-box accelerators and business rules, as well as self-service tools based on roles that allow you to analyze, prepare and cleanse data. It should be scalable enough to fit any department’s workflow. Furthermore, it should be able to accommodate various taxonomies and allow for the integration of machine learning. In addition it should be able to be accessed through built-in collaborative solutions as well as governance councils to ensure uniformity.