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The digital age has enabled businesses to capture and analyze more data than ever before, giving companies the chance to gain a significant competitive advantage. To tap into this wealth of information companies must adhere to established best practices for data management. This process includes the collection, storage, and governance of data across the organization. Many data-driven applications require high-performance and scale in order to provide the necessary insights to be successful.

For instance advanced analytics (like machine learning and generative AI) and IoT and Industrial IoT scenarios need vast quantities of data in order for proper operation, while big data environments must be able to handle large volumes of unstructured and structured data in real-time. Without a strong foundation, the applications could fail to perform at their optimal levels or produce inconsistent and inaccurate results.

Data management involves a variety of disciplines that work together to automatize processes, improve communication and speed up delivery of data. Teams usually comprise data architects, ETL developers, database administrators (DBAs), data analysts, engineers, and data modelers. Some larger companies employ master data management experts to provide a single point of reference for all business entities such as customers, products and vendors.

Effective data management involves creating a culture of data-driven decisions and providing the necessary training and resources to ensure that employees feel comfortable with making informed, data-driven choices. Strong governance programs, which contain clear data quality and regulatory requirements, are also an essential component of any successful data management strategy.

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