Data management is the method by which companies store, collect, and secure their data to ensure that it remains safe and useful. It also includes the processes and technology that support these goals.
The data that runs most companies comes from a variety of sources, and is stored in a variety of locations and systems and is typically delivered in different formats. It is often difficult for engineers and data analysts to locate the data they require to carry out their work. This leads to incompatible data silos, inconsistent data sets and other issues with data quality which can hinder the use of BI and analytics software and lead to inaccurate findings.
A data management system improves visibility, reliability, and security. It helps teams better understand customers and deliver the proper content at the right time. It is essential to begin with clear business data goals and then create a set of best practices that can be developed as the company expands.
A good process, for example it should be able to handle both structured data and unstructured, as well as real-time, batch, and sensor/IoT workloads, as well as pre-defined business rules and accelerators, plus tools based on roles that aid in the analysis and prepare data. It must also be scalable to fit the workflow of any department. It must also be flexible enough to allow integration of machine learning and to accommodate various taxonomies. Additionally it should be able to be accessed with built-in collaborative tools and governance councils for the consistency.