Data management refers to how businesses collect, store and protect their data so it remains useful and actionable. It also encompasses the technology and processes that support these goals.
Data that is used to run most companies is gathered from various sources, storing it in various systems, and presented in different formats. It is often difficult for engineers and data analysts to find the information they require for their job. This can lead to incompatible data silos, data sets that are inconsistent and other issues with data quality that may limit the usefulness of BI and analytics applications and lead to inaccurate findings.
A data management system improves visibility, reliability, as well as security. It also allows teams to better comprehend their customers and provide the proper content at the right time. It’s essential to establish precise data goals for the business and then establish best practices that will expand with the company.
A good process, like it should be able to handle both structured and unstructured and also batch, real-time, sensor/IoT tasks, and offer pre-defined business rules and accelerators. It should also include tools based on roles that aid in the analysis and prepare data. It should be scalable to fit any department’s workflow. It should also be able to allow machine learning integration and support different taxonomies. Additionally it should be able to be accessed via built-in collaborative solutions and governance councils for uniformity.