For the last couple of weeks, I’ve been discussing the topic of pervasive intelligence—embedding analytics and other intelligence capabilities into the organization enterprise-wide, at its most elemental level. The drive to make intelligence pervasive will change how you interact with customers and how you operate. It will also change how you deal with data. However, the most fundamental change you’ll undergo in your efforts to embed intelligence in your organizational DNA will be in how you deploy the technologies that underpin that intelligence.

Since the dawn of the computer age—roughly 80 years ago—we’ve looked at machines as tools to help us make computations, to help us perform mundane, repetitive tasks faster and more accurately. Even with the advent of analytics, much of the effort in IT departments today is still focused on making routine operations more efficient. The definition of “routine” may have changed, but the focus on it hasn’t.

However, this paradigm of viewing analytics technologies as tools won’t work with the concept of pervasive intelligence. Instead, if you really want to embed intelligence into your organization at the cellular level, you’ll change your view of analytics technologies. Rather than viewing technology as a tool, it’s essential to view it as actual intelligence—not the vehicle for intelligence, but intelligence itself.

I know. I’ve heard, and used, the adage myself. Computers only do what you tell them to do. That was yesterday. Today, they learn. And if you want pervasive intelligence, you’ll understand that, and you’ll invest in technologies that are intelligent, and you’ll set your intelligence operations up in an environment that enables you to use that intelligence when, where, and how you need it.
The paradigm of viewing analytics technologies as tools won’t work with the concept of pervasive intelligence.

Intelligent Intelligence

The intelligence you choose to embed is critical. Analytics is table stakes now. Just about any mid-size-and-above company has analytics capabilities. Edge companies are investing in new technology—AI and machine learning—that enables them to go beyond traditional predictive analytics and use the intelligence inherent in the technology to truly understand their environment—to learn about it—and make accurate predictions about their future.

But you know this. What you may not know is that even these technologies aren’t enough, if you don’t change the how you view their use. It’s not enough to feed a machine-learning algorithm and let it make predictions about outcomes. Instead, treat the algorithm like a child you’re raising.

Feed it the right kind of information—the kind that will help it learn while avoiding biases that may result in faulty assumptions or decisions. Continually feed it new information, from highly-curated, diverse sources, so that its knowledge base broadens, as well as deepens. Feed it accurate information so that decisions it makes aren’t skewed by incorrect data.

And like a child, if you feed your intelligence algorithms with the right information, they’ll learn and grow, and that intelligence will become innate throughout the organization—it will become pervasive.

Intelligent Environments

In addition to choosing the right technology and feeding it good information, it’s also essential to put that technology in the right environment. Unlike a child, however, what’s right for you is what counts in this case. The technology can function anywhere. There are just some environments in which it functions better for you.

That right environment is an intelligent cloud. I’d estimate that 70 percent of all businesses have at least some applications in the cloud, and many are moving most of their critical workloads to the cloud—especially managed clouds. From a security, storage, and flexibility perspective it simply makes sense to have your data (in all its voluminous glory) managed and ready for access when and where you need it, so that you can focus on value-added activities rather than data management.

However, even managed clouds aren’t enough to make analytics truly pervasive. Why? Because if you have your data workloads in the cloud, but you’re still managing your analytics capabilities, there’s a disconnect that will slow you down and make you less agile in responding to problems.

With an intelligent cloud—a cloud with analytics capabilities embedded in it—you can get the analytics capabilities you need, when you need them, and as you need them, without having to worry about availability, updates, pricing, footprint—or the myriad other problems inherent with managing your own infrastructure. Spin up for new intelligence projects; spin down when they’re over.

The concept is simple: an intelligent cloud makes pervasive intelligence possible because it’s built on that premise—analytics wherever, whenever, however you need it.

Intelligent Organization

As I’ve said, analytics isn’t enough anymore. Everyone has analytics capabilities, so if that’s all you have, you’re stuck in the pack, not ahead of it. Intelligence requires an enterprise effort to make analytics part of the culture, part of the fabric, part of the ethos of the organization. The goal is to acquire intelligence capabilities that can mimic human learning and feed those tools with the right information, and to utilize those tools in the right environment—one that wraps the tools up in a cloud platform to enable agility and responsiveness.

When you have those two things, you’ll be well on your way to making intelligence pervasive.
Anu Jain
Anu Jain, Vice President, Americas, is at the forefront of the analytics, machine learning, and workflow orchestration revolution. Anu is a leader in Teradata’s transformation from a perpetual license model to a service organization that will drive innovation in open source, business solutions adoption, analytics, and workflow. He has deep technology and domain-specific thought-leadership and expertise in ad tech, media, front-office effectiveness, digital media and analytics-powered industry solutions. His expertise in technology-driven business transformation includes big data, cognitive analytics, predictive analytics, data mining, data warehousing, and business intelligence. Before coming to Teradata, Anu worked for IBM and Deloitte Consulting.

Anu also frequently blogs on his personal site:
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