The digital era has enabled businesses to capture, analyze and monetize more data than ever before, giving companies the potential for a powerful competitive advantage. To access this wealth of data, companies must adhere to proven best practices for data management. This process involves the collection, storage, and governance of data within 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 example, advanced analytics (like machine learning and generative AI) and IoT and Industrial IoT scenarios need vast quantities of data in order to function properly, and big data environments have to be able to handle huge amounts of structured and unstructured data in real time. Without a strong foundation, these applications may not achieve their full potential or generate inaccurate and inconsistent results.
Data management encompasses a range of disciplines that are used in conjunction to automate processes improve communication and speed up delivery of data. Teams usually comprise data architects, ETL developers, database administrators (DBAs) and engineers, data analysts and data modelers. Some larger organizations employ master data management experts to provide a single point of reference for business entities like vendors, customers, and their products.
Effective data management also involves creating an environment that encourages data-driven decision-making and providing training and resources that help employees feel comfortable with making informed, data-driven decisions. Strong governance programs, including clear data quality and compliance requirements are another crucial element of the successful strategy of managing data.