Data management is how companies collect, store, and secure their data to ensure it is reliable and usable. It also includes the techniques and tools that support these goals.
The data used to run the majority of companies is gathered from various sources, stored in multiple systems, and subsequently delivered in various formats. It is often difficult for engineers and data analysts to locate the data they require for their job. This leads to unreliable data silos and inconsistent data sets, and other data quality issues that can limit the usefulness and accuracy of BI and Analytics applications.
Data management can increase visibility, reliability and security while allowing teams to better comprehend their customers and provide the appropriate content at the right time. It’s crucial to begin with clear goals for business data and then develop a set of best practices that can grow as the company grows.
For instance, a reputable process should accommodate 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 that are based on roles to help analyze, prepare and clean data. It must also be scalable and work with the workflow of every department. Furthermore, it should be able to accommodate a variety of taxonomies and allow for the integration of machine learning. It should also be easy to use, and include integrated solutions for collaboration and governance councils.
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