Data management is an approach to the way companies manage, store, and secure their data to ensure it is useful and actionable. It also includes the technologies and processes that aid in achieving these goals.
Data that is used to manage most businesses is gathered from a variety of sources, storing it in various systems, and subsequently delivered in different formats. This means it is often difficult for data analysts and engineers to find the right information to carry out their tasks. This creates incompatible data silos in which data sets are inconsistent, as well as other data quality issues which can hinder the use of BI and analytics applications and lead to faulty findings.
A data management process improves visibility, reliability, as well as security. It also helps teams comprehend the needs of customers and provide correct content at the right time. It is essential to establish clear goals for data management for the company, and then establish best practices that evolve with the company.
A effective process, for example one that supports both structured data and unstructured as well as sensors, real-time, batch and IoT workloads, as well as pre-defined business rules and accelerators, as well as tools based on roles that aid in the analysis and prepare data. It should be scalable to accommodate the workflow of any department. It must also be flexible enough to allow integration of machine learning and to accommodate various taxonomies. In addition, it should be accessible with built-in collaborative solutions and governance councils to ensure uniformity.
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