Video: How to build a corporate culture that's ready to embrace big data
Software used to be the undisputed No. 1 concern for digital transformation[1]. Not anymore. While software remains key, its importance has been at least equalled, if not overshadowed, by data.
Software can not only support data-driven decision making, but also become data-driven itself[2], offering solutions in situations too complex to be dealt with using traditional procedural programming.
Read also: Business analytics: The essentials of data-driven decisions[3]
At the same time, the move of software development, and off-the-shelf applications, to the cloud creates an interplay with data. As more applications move to the cloud, the data they produce stay there, too. As data increasingly lives in the cloud, applications follow.
We have been following the rise of machine learning[4] and data-driven software development, or software 2.0[5], for a while now. We have talked about the strategic importance of machine learning for cloud providers[6]. But what about cloud users?
Database workloads on the move
Besides the economics of working with the cloud, one of the key concerns for organizations is lock-in. A good way to deal with both of those is multi-cloud and hybrid cloud strategies: Some data and applications stay on-premise in private clouds, while others move to a multitude of clouds.
Pivotal, the vendor behind Cloud Foundry[7], recently partnered with Microsoft and Forrester on research evaluating enterprise use of hybrid cloud[8], including challenges around multiple cloud platforms, and critical capabilities that CIOs and other businesses technology leaders expect from their platforms.
Read also: Welcome to the data-driven machine: Digital transformation[9]
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