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What Comes After Cloud Computing?

Technology is a constantly changing, from the latest mobile devices to the emergence of big data in automotive, education, and digital marketing. If you blink, you could miss the next big thing.

At Perficient, we often talk about technology in the present. After all, upgrading to the cloud, adopting digital transformation, and being customer obsessed are strategies that can’t wait. However, from time to time, it’s also worth taking a look at the future, in the case of Silicon Valley investor Peter Levine at venture capital firm Andreesen Horowitz.

In a recent video, Levine discusses how cloud computing will soon make way for edge computing, where processing and analysis is expected to occur at the device level. His logic, based on the evolution of technology where devices such as drones, autonomous cars, and robots will require fast processing that sending data up to the cloud and back to get an answer will simply be too slow. Though cloud will remain important for a litany of use cases, it’s role will begin to change where it will be processing data for machine learning purposes, acting as an adjunct to more immediate data processing needs.

The Patterns of Technology

How data moves is a trend that has changed throughout the years, from a centralized fashion during the mainframe era to a distributed manner during the rise of the Internet. In the last twelve years, there has been a return to centralized philosophies with the cloud, and according to Levine, this will change yet again. In this instance of digital transformation, everything including the self-driving car becomes its own data store.

The process makes sense too. If a car needs to a make decision, it needs the information instantly and no amount of latency is going to be acceptable save for a tragic car accident or unbearable street traffic. Furthermore, the addition of data that is shared instantaneously as well as over time also provides learning moments for everything from self-driving cars to more manufacturing-based products. If data is shared, each machine can begin learning from one another in this virtuous cycle of data creation, processing and recirculation.

What Does the Future Look Like?

Some vendors including Amazon Web Services (AWS) have committed to moving forward, developing a solution called Greengrass which is providing a set of compute services directly on IoT devices when public cloud resources aren’t available for whatever reason. As this market comes into full view, we expect that other vendors will jump on board as well, offering their own compute solutions or support systems that make edge computing possible.

On a greater level, it’s going to have a profound impact on computing as we know it. As Levine discusses later on in his video, such a trend will send shockwaves through the industry and require new ways of programming, securing and storing data, and will change how we think about machine learning. That however, doesn’t mean that client-server networks, on-premises storage, or mainframes are going away – there will always be a need somewhere.

Finally, as leaders in the market, this also has implications for us. As we continue to serve our customers and understand their customer-obsessed digital transformation initiatives, we’ll also look at how edge computing will influence customer interaction and buyer interests. Look for more blogs exploring the future and email our sales specialists at for additional guidance.

In today’s innovative enterprise, IT exists in an ecosystem. In contrast to the past where many implemented solutions existed in individual silos, today’s implementations work together to influence positive business outcomes. This post is part of a series focused on guiding enterprises through an overwhelming process to compete, scale, and innovate in a fast-moving world. Follow more of the series here.

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Albert Qian

Albert Qian is a Marketing Manager at Perficient for our IBM PCS, DevOps, and Enterprise Solutions Partners focused on cloud computing technologies.

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