In this interview, John Roese, president and chief technology officer at Dell EMC, shares his outlook on the state of data and the technologies that make sense of it.
What was the path that led to your role as CTO at Dell EMC?
JR: It’s a long one! The punchline is: I’m a serial CTO, but I’m a CTO that has crossed many different industries. I’ve been in telecom, enterprise networking, compute, security, cellular, unified communications, real-time communications, storage, and everything else.
It was 2012 when I got a call from EMC. I wasn’t actually looking for a job, but I had a feeling that EMC was going to end up right in the middle of something very disruptive. I didn’t know exactly what it was going to be, but it seemed like with VMware, Pivotal, and EMC, we were going to be in the middle of the action. For me, that’s the most exciting thing about the industry. Then when we announced we were going to combine with Dell, it seemed even more like being in the center of the technology universe. I led the overarching technology strategy to integrate Dell and EMC [and build Dell Technologies], then became the CTO for Dell EMC.
Funny enough, when heading to college, I flipped a coin between being a lawyer and an electrical engineer. I sometimes say I lost and picked the engineering path, but of course I don’t really think that.
What are you most passionate about as CTO at Dell EMC?
JR: As a technologist, probably the most important thing in your life is when you see the ideas and technologies that you saw first or that you created actually go into practice and change the human condition in a positive way.
There are a lot of technologies I’m passionate about—AI, Edge, 5G, take your pick. What’s most exciting, though, is the platform we have as Dell Technologies. CTOs are all about predicting the future, but also helping to make it happen. It’s very hard to make it happen if you don’t have a platform.
But if you’re Dell Technologies, a roughly $90 billion company with global reach and trusted customer relationships, what we say matters, our platform matters, and our point of view matters. In this time of massive transformation, we have a huge opportunity to put our fingerprints on the industry and help move it—and the world—forward.
What keeps you up at night?
JR: There are so many things changing so rapidly in the industry that if we don’t navigate any one of them properly, it could create a disadvantage for us. So, we need to be agile. We need to move fast to be ahead of the curve. We have a huge capability to do that, but we also have a lot going on. What keeps me up at night is making sure that we know not just what to do, but what not to do.
“What keeps me up at night is making sure that we know not just what to do, but what not to do…I have the responsibility of making sure we pick the right battles and focus our energy to best position ourselves in the industry.”
Some technologies are buzz words and will remain buzz words for a long time. Quantum computing is a great example. We should be aware of it, but if we took all of our engineering capability and tried to build a quantum computer, we’d go out of business. I have the responsibility of making sure we pick the right battles and focus our energy to best position ourselves in the industry. If we get it wrong, that’s where problems start to happen.
You’ve been outspoken about the “data era,” but data and big data have been around for decades. How is this era different?
JR: For the first time in history, we not only have an abundance of data, but we have also invented new, more economic ways to store and process that data. Then most importantly, the software and algorithms have now reached a point, primarily through artificial intelligence (AI) and machine learning (ML), that we can mine this data and turn it into something more interesting.
There’s a hierarchy in data. There’s data, which is raw. Information is when you organize the data into structures. Knowledge is when you gain insights from the information. And wisdom is when that knowledge becomes useful to predict the future and understand the past. We are entering an era when the compute infrastructure, amount of data, and the algorithms are all coalescing so we can get to knowledge and wisdom at scale, across almost any industry. I think that justifies saying that we’re entering a new era—the data era.
Contributing to the abundance of data are Internet of Things (IoT) devices, which are creating vast amounts of data at the edge. What is the edge, and why is it important?
JR: First, it’s important to recognize the edge isn’t a standalone environment. There are four layers that make up the multi-cloud architecture. There are the devices, which connect to the edge, which ultimately connects to private data centers, where you have full control and optimization. Then there are public clouds, which give you aggregation at an industry level.
Edge is the newest layer, and it offers a place where you can push some of your processing and analytics capabilities out to the physical location of the people and devices you interact with in real time. There are two advantages of doing this.
What are those advantages?
JR: First, because the compute and analytics are close to where the users are, the speed-of-light issue that we have when we move data over distance is no longer an issue. You can operate and make decisions in real time. This is incredibly important for things like autonomous vehicles and their ability to react to their environment. But there are several use cases–in factories, healthcare, gaming–where the goal is, first and foremost, to do things quickly and not have the latency of crossing the internet involved in the real-time service.
The other advantage to the edge is—as much as we think the internet has infinite bandwidth—it takes a lot of time, energy, and money to move huge quantities of data back and forth across the internet. The edge gives you the ability to run the analytics locally so that you don’t have an urgency to move all the data into the cloud or into a private data center. You might eventually move the data, but you don’t have to move it in a priority manner that costs a lot of money.
There’s a lot of hype around AI, both positive and negative. Where do you see AI having the greatest impact?
JR: I’m very bullish on machine intelligence. I believe we can’t progress without sharing the burden of thinking tasks with machines, and AI is a set of tools that allows us to do that.
“I’m very bullish on machine intelligence…As we look at the world today, there are so many things that, quite frankly, are incredibly inefficient and would improve if machines were to take over more of that responsibility.”
Largely today, we don’t use machines to do thinking tasks. We store data. We process data. But the actual empirical decision almost always happens at a human level. As we look at the world today, there are so many things that, quite frankly, are incredibly inefficient and would improve if machines were to take over more of that responsibility.
Where do you see these opportunities for machines to step in?
JR: The first is the user experience. Some simple examples are voice assistants and digital assistants that already make customer care more productive and our homes more effective. Those experiences are not possible with an army of human beings behind them. You need machine intelligence.
The second is business process improvement. Every business process we have today—financial, industrial, manufacturing, healthcare—involves thinking tasks, and most of them do not have enough human beings with the expertise to do those thinking tasks. Applying machine intelligence to take on just some of the thinking, such as with radiology, achieves a more effective operational outcome, without running out of humans to do the work.
Then the third is the creation of entirely new industries. The autonomous vehicle industry is the best example. Candidly, if you want cars to drive themselves, you can’t do it by adding more people.
All of these outcomes have different degrees of complexity, but none of them are possible without shifting most of the task—maybe all of the task—to a machine environment that operates with speed and efficiency. The upside is enormous.
How do you address concerns about job disruption?
JR: All technology, all industrialization changes disrupt jobs, full stop. AI will disrupt jobs. There are jobs that will go away, and there will be jobs created. But there’s also a third category, and that’s improvement of the human condition. What happens when machine intelligence achieves its outcome, like making cars autonomous? Or making healthcare more intuitive, or making customer service easier? What happens to the businesses that use them? It’s very likely those businesses will grow because some impediment to their growth suddenly disappears, which allows them to reach more customers and provide a better service.
It’s much more complex than just a one-dimensional consideration of jobs. New technology will always equal some job disruption—hopefully more job creation—plus a change in the human condition that is a net positive and makes our existence happier and better.
What is the biggest misconception leaders have about their data?
JR: Many leaders are wrestling with the idea that you have to understand your data—what to collect and what to keep—before you can develop a data strategy. That’s not true; flip it around. You have to pivot to the idea that it’s because you don’t understand all of your data that you should gather it and use tools like AI and ML to figure out what it’s telling you.
If you don’t pivot, two things will happen. One, your data strategy will probably never happen because you’ll never really understand all your data. Or two, you’ll throw away a bunch of data that could’ve been really valuable in getting better insight.
“In the data era, you want to take the data, get insight from it, and make it actionable.”
The other misconception people have is that the data era equals big data. Big data allowed us to use some new but rudimentary tools to mine lots of data to try to see patterns, then present it to a human being to do something with it. In the data era, you want to take the data, get insight from it, and make it actionable. To do that, you have to move much faster than human beings can operate. That means moving beyond big data and developing an AI strategy, so that you can get insights quicker than human beings, and then apply the outputs right back into your systems to change their behavior.
Where does Dell Technologies fit into the data era?
JR: We have hundreds of AI projects going across Dell Technologies, so we’re not just talking about it—we’re doing it, and we know what our customers are facing. We’ve used it in marketing, services, and engineering, and we’re applying AI to our core products to make them faster.
On our client devices, we apply machine intelligence to better manage power consumption. You want your battery life to last a long time? It’s not just about bigger batteries; it’s about smarter systems. And the best way to do that is with machine intelligence.
Another example is PowerMax, our flagship, high-end storage product. Its 10 million-plus IOPS performance wasn’t achieved just by brute force compute, memory, and IO, but by adding sophisticated machine learning technology. Then on the backend, predictive maintenance and predictive failure analysis have dramatically changed the economics of delivering service, while also improving uptime for customers of all sizes.
This is just a small sample of the pervasive activity inside of Dell Technologies to incorporate machine intelligence so we can create smarter products and solutions, and a smarter, more efficient and effective company.
Is there an innovation you are most excited about?
JR: I don’t have favorites, so I can’t pick just one innovation. But we are using these technologies across our product portfolio because the fact is the future winning product won’t be the product that’s just biggest, fastest, and cheapest. It will be the product that’s smartest.