Today’s data environments are becoming more flexible and portable than their more rigid, siloed predecessors, thanks to emerging approaches. Strategies that are the building blocks of portability, adaptability, and rapid delivery—containers, microservices, DevOps, and DataOps—are transforming the way data is managed.
Welcome to the era of the flexible data environment. But how can data managers and professionals prepare for the roles they will play in this new world?
Fresh approaches to data agility are essential for keeping up with the escalating requirements of forward-looking initiatives such as AI and machine learning. “Training AI models depends on the use of large and high-quality datasets, but in enterprises, this data may be spread across different clouds, application silos, data centers in different countries, and subsidiaries—making it difficult to combine and analyze,” said Seth Dobrin, chief AI officer for IBM. “Data in different locations also may be subject to different regulatory and privacy requirements. That means bringing data together into a single repository for training is often not possible or practical.”