To compete in the marketplace, enterprises are increasingly employing performance-intensive tools and applications like machine learning, artificial intelligence, data-driven insights and decision-support analytics, technical computing, big data, modeling and simulation, cryptocurrency and other blockchain applications, automation and high-performance computing to differentiate their products and services.
In doing so, they may be unintentionally backing into performance-intensive computing because these technologies are computationally and/or data intensive. Without thinking through the compute performance you need as measured against your most demanding workloads – now and at least two years from now – you’re setting yourself up for failure or unnecessary expense. When it comes to performance-intensive computing: plan, don’t dabble.
There are questions you should ask before jumping in, too. In the cloud or on-premises? There are pluses and minuses to each. Is your data highly distributed? If so, you’ll need network services that won’t become a bottleneck. There’s a long list of environmental and technology needs that are required to make performance-intensive computing pay off. Among them is making it possible to scale. And, of course, planning and building out your environment in advance of your need is vastly preferable to stumbling into it.
The requirement that sometimes gets short shrift is organizational. Ultimately, this is about revealing data with which your company can make strategic decisions. There’s no longer anything mundane about enterprise technology and especially the data it manages. It has become so important that virtually every department in your company affects and is affected by it. If you double down on computational performance, the C-suite needs to be fully represented in how you use that power, not just the approval process. Leaving top leadership, marketing, finance, tax, design, manufacturing, HR or IT out of the picture would be a mistake. And those are just sample company building blocks. You also need measurable, meaningful metrics that will help your people determine the ROI of your efforts. Even so, it’s people who make the leap of faith that turns data into ideas.
Finally, if you don’t already have the expertise on staff to learn the ins and outs of this endeavor, hire or contract or enter into a consulting arrangement with smart people who clearly have the chops to do this right. You don’t want to be the company with a rocket ship that no one can fly.
So, don’t back into performance-intensive computing. But don’t back out of it either. Being able to take full advantage of your data at scale can play an important role in ensuring the viability of your company going forward.
Related Content:
- Gain Business Insights Faster by Building the Right Infrastructure for Performance-Intensive Computing
- Choosing the Right AI Infrastructure for Your Needs
- Are Your App Workloads Running in Parallel?