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As more businesses push forward with digital transformation projects, cloud computing has stood out as a powerful tool capable of fueling the analytics that drive new technologies like artificial intelligence (AI) and machine learning (ML)—two capabilities that are quickly becoming a must-have in nearly every organization. But getting to that point presents some unique challenges. Even with the benefits of cloud, there’s a reason that over 70% of Fortune 500 companies’ data is stored on the mainframe. That data is critical to their success and the mainframe delivers incredibly effective security and reliability to manage it.
Wholesale moves off of the mainframe are fraught with risk and ever-increasing costs, but the analytical capabilities of the cloud are full of opportunity and potential for innovation, fueled by AI and ML algorithms. So, how do organizations walk the tightrope and capture the best of the cloud without sacrificing the longstanding dependability of their mainframe systems? The answer is mixed—or, more accurately, hybrid. For any business looking to effectively modernize, a hybrid approach to cloud and mainframe infrastructure can help tap into the best of both worlds.
A hybrid approach means no data is left behind
Whether it’s a small business or a massive corporation using it, mainframes are home to huge amounts of data as well as layers of technology and platforms that have accumulated over years or even decades. With that reality in mind, attempting a wholesale move to the cloud could come at the expense of some data that is dispersed in various siloes across an organization. With so many moving pieces and complex webs of technology living on an organization’s tech stack, a hybrid cloud strategy can help optimize storage, taking things better suited for cloud into that environment and improving visibility.
Get the most out of analytics
Every business wants to get the most out of their own internal data. And there’s no better way to tap into that data than with cloud computing. On the other hand, the mainframe is full of data that could yield game-changing, innovative insights and give organizations a meaningful competitive edge. But moving mission-critical data out of the security of the mainframe is a risky step to take. A hybrid cloud strategy means that data that is best stored in the cloud can be fed into those analytical tools and algorithms while highly sensitive data that is better fit for the mainframe can stay right where it is.
Modernize without breaking the bank
One of the most important considerations for businesses weighing a move to the cloud comes down to costs. Cloud computing can reduce costs, but the path to migrate itself brings increased costs both in the migration itself, but also in the effort and time it takes to lift and shift decades-old technology stacks. At the same time, keeping everything in the mainframe can cause workloads to skyrocket, complicate maintenance and raise operating costs just as much. Hybrid cloud helps get data into an environment where it can be fully utilized. For instance, long-term, archivable data that exists on the mainframe can be moved to lower-cost cloud storage where it can be more effectively managed. With hybrid cloud strategies in place, businesses can get the most out of their data while reducing maintenance costs and avoiding expensive wholesale migration projects.
Modernization isn’t an either-or situation
The choice between the cloud and mainframe doesn’t need to be a choice at all. The further digital transformation projects move along, the more flexible the technology fueling them needs to become. A hybrid cloud approach presents businesses—both those that have traditionally depended on cloud or the mainframe—with a powerful means to reduce costs, optimize workloads and tap into the latest analytical tools and capabilities like AI and ML.
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