Data management is the process by which businesses store, manage, and secure their data to ensure it remains safe and useful. It also encompasses processes and technology that support these goals.
Data that is used to run most companies is gathered from a variety of sources, and stored in a variety of systems, and delivered in different formats. As a result, it can be a challenge for engineers and data analysts to find the appropriate data to complete their tasks. This can lead to incompatible data silos as well as inconsistent data sets and other issues with the quality of data that may limit the usefulness of BI and analytics software and lead to inaccurate findings.
A data management system can increase visibility, reliability and security while enabling teams to better comprehend their customers and provide the right content at right time. It’s crucial to set specific data goals for the business, and then establish best practices that can expand with the company.
A efficient process, for instance will be able to accommodate both structured data and unstructured and also sensors, real-time, batch and 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 should be scalable enough to meet the requirements of any department’s workflow. Additionally, it should be able to adapt to different taxonomies as well as allow for the integration of machine learning. It should also be easy to use, and include integrated collaboration solutions and governance councils.
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