It’s becoming a hybrid cloud world, as more enterprises move to deploy workloads across multiple public cloud providers as well as private clouds.
In order to take full advantage of a hybrid computing model, enterprises must also adopt a data model that enables users to work with the data wherever it makes sense to do so, whether in a public or private cloud, in an on-prem data center, or even across a multi-organization data exchange, and do so with minimal friction.
The first step in any hybrid cloud strategy is to make sure that the data itself is portable and fully managed. Indeed, the ultimate goal is to provide a uniform data repository for your analytics and AI software platform that is available to all public and private cloud implementations, while still evolving towards a truly “agnostic” platform that can transparently support any workloads on any cloud platform.
Another key consideration for a distributed, hybrid environment: as more enterprises move to AI-enabled systems, and build out models based on their data sets, it becomes much more critical that the quality of the data is confirmed. Models used for machine learning and artificial intelligence algorithms based on corrupted data create bad models, and unreliable insights.
To maximize the value of enterprise data, and directly drive quality insights provided by that data, companies need a data platform providing clean, up to date, fully managed and secure data. That also requires an ability to see who has “touched” the data, and not just by individuals but also by AI models and workload processes.
Organizations often begin the transition to cloud by adopting cloud-based modern application strategies like containers and microservices, use them on-prem first, and then move them to the public cloud. This methodology enables safe experimentation and can confirm which workloads can safely and effectively move to the public cloud and which are best to stay on-prem.
This practice may not be a one-way street, as workloads in the cloud may make sense to move back to on-prem deployment, or move to another public cloud provider as desired. This requires a way to create cloud instances that are not locked-down by using proprietary APIs and system components that so often prevent moving both apps and data from one cloud to another. Lock-in is a real problem, even with those vendors that claim fully open cloud environments. The point: you can’t manage data properly in a locked-in environment.
The use of an enterprise hybrid-enabled data platform increases the ability to move data to the required workloads running on various processors on-prem or in the cloud. Further, it assures the quality of data by managing a single instance and not having diverse data sets disbursed across multiple platforms.
Finally, in regulated industries and/or data restricted geographic compliance situations, maximizing governance of the data is critical to preventing regulatory fines and data breaches. Ultimately, we expect that top data platforms will enable a broader ability to write once and run on any cloud environment, thus allowing a true mix-and-match workload processing capability. Enterprises must set up true hybrid-enabled data cloud capability if they are to derive the maximum benefit from a hybrid cloud environment.