Businesses are now able to collect data, analyze and monetize data in greater quantities than ever before. This gives them an edge. To tap into this treasure trove of information businesses must follow proven best practices for managing data. This includes the collection, storage and administration of data throughout an company. Additionally, many data-driven applications require a high level of performance and scale in order to provide the information needed to be successful.
For instance, advanced analytics (like machine learning and generative AI) and IoT and Industrial IoT scenarios need vast amounts of data for proper operation, while big data environments must be able to handle very huge amounts of structured and unstructured data in real time. Without a strong foundation, these applications can fail to perform at their optimal levels or produce inconsistent and inaccurate results.
Data management is a variety of distinct disciplines that work together to automatize processes to improve communication and speed up the transfer of data. Teams typically include data architects, database administrators (DBAs), ETL developers as well as data analysts and engineers and data modelers. Some larger companies also employ master data management (MDM) experts to create an all-encompassing source of information for business entities like customers, products and suppliers.
Effective data management requires creating a culture that encourages the use of data to make decisions and giving employees the education and resources they need to be confident about making decisions based on data. Effective governance programs, such as clear requirements for data quality and compliance are another crucial element of an effective data management strategy.
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