The digital age has allowed businesses to capture, analyze and monetize more data than ever before, giving organizations the potential for a powerful competitive advantage. To gain access to this goldmine, companies must adhere to best practices in data management. This involves the collection of data and its storage, as well as the governance throughout an company. Additionally, many data-driven applications require an extremely high level of performance and capacity to provide the necessary insights to succeed.
For instance, advanced analytics like machine learning and generative AI, and IoT and Industrial IoT situations require vast amounts of data to function effectively. Big data environments need to be able handle very large amounts of unstructured and structured data in real time. Without a strong foundation, these applications can fail to perform at their best or produce inconsistent and inaccurate results.
Data management encompasses a range of disciplines that are used in conjunction to automate processes improve communication and speed up the transfer of data. Teams usually comprise data architects, ETL developers, database administrators (DBAs) as well as data analysts, engineers, and data modelers. Some larger organizations also employ master data management (MDM) professionals to create one source of reference for business entities, such as suppliers, customers, and clients.
Effective data management requires creating a culture that encourages data-driven decisions and providing employees with the training and resources they require to feel confident when making data-driven decisions. Strong governance programs, including clear requirements for data quality and compliance, are another critical component of a successful data management strategy.