Billions of Nodes and Nerds: Making Us One With Everything
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As if Big Data wasn’t big enough, the tech world has picked a new, even bigger buzzword to fuel wild fantasies. This time about connecting everything with everyone and everything else. The newest hype is called the “Internet of Things.”
Experts hatch plans to instrument the planet, Google recently sunk billions into buying Nest and Dropcam, and the lines between media companies, hardware manufacturers and software houses are becoming blurred beyond recognition as the cloud fills with more and more data in all kinds of formats. (See a sampling of expectations.)
Perhaps the IoT will bring the great convergence we have been promised for so long — the day when every device and sensor is sending data to anything or any person who is listening. A day when data floats freely outside of silos or walled gardens, and any collective insight is just an API away.
Hold that thought for a moment and ask yourself who can listen in the full sense of the word. Who will have the tools to gather and parse all that raw information — from ambient measurements coming from wearables and the status updates of thousands of small drones to heavy construction equipment sending notifications to the home office. Some algorithm has to be able to sift through this deluge of flat files or bulging relational databases. Some service has to be powerful and fast enough to pick out the vital and the crucial signals, connect the ones that matter — and present it all in a way that allows other software or humans to make sense of what’s happening and arrive at decisions.
Therein lies the biggest challenge the IoT poses: start making sense of data. No matter what startups and big IT vendors claim, we’re far from solving it. I would argue we have barely begun to grasp the new dimension of data mining and analytics defined by billions of nodes in an almost infinite network.
Market researcher IDC predicts the worldwide market for IoT will grow almost fourfold, to more than $7 trillion by 2020. Experts are quick to point out that the Internet of Things is lacking when describing this transformation, suggesting we call it the “Internet of Everything” instead since it comprises machines, purely digital entities and humans. I’ll leave the proper definition to the theoreticians, since there is so much work to be done on the ground.
This much I know: The IoT will only mature into something tangible and useful that provides real value to consumers, citizens and companies if we think through and start designing the right services to manage and analyze the data that pulses through this emerging network of networks: smoke detectors, dog collars, mobiles, production lines, water pipes. Having all these bits available will certainly shake up industries that think they’ve already undergone serious upheaval, from finance and manufacturing to entertainment, publishing, even education.
And that’s for a simple reason: IoT data does not fit into the pivot tables and joins of traditional data management tools. Relational databases won’t cut it when hundreds of millions of sensors have something to say. Business intelligence was yesterday. What we need to come up with is IoT intelligence that goes way beyond business. A new beast with an innate, voracious appetite for data, whether it’s structured and unstructured.
We need systems that can ingest, analyze and support decision-making in almost real-time, or we will get buried by the batch. When a crane reports trouble in a mega-construction project, the builder needs to know now, not tomorrow, in order to adjust the timeline, the budget, the scheduling of men and machines. And the data and decisions made today will in turn influence what bid the company submits next quarter. In short, the IoT demands a kind of virtual brain that sees, hears, think, reacts.
That’s where platforms as a service (PaaS) comes in, particularly for analytical purposes, since they can combine the best of cloud and mobile computing. When the whole world is your data warehouse, only the cloud lets you pull it all together — including social sources — and try to make sense of it, fast.
Beyond systems we also need to prepare ourselves as humans. It’s not only about training or hiring data scientists, the IoT will affect every single person in their work and private lives. Coding is becoming a new lingua franca that ought to captivate school kids from an early age. The more children play with new programming languages and tinkerbot platforms like Arduino and Raspberry Pi to familiarize themselves with robotics and sensors, the better. We’re all invited to become part of a planetary board game, and not just as spectators.
Let’s assume you get all the data you want and have them presented in a dashboard that hides most complexity to merely highlight the salient features or emerging issues. That is the point at which the real work for humans begins. People do not collaborate on a dashboard after it’s done, they need to make it their own and pitch in while it’s being created. It is often their own data flowing into it, so why not shape its make-up, refine its look and have multiple views of the same reality as it’s happening, similar to having dozens of cameras track a single sports match?
There is one area where we can already see that happening, the Quantified Self movement. If you happen to wear a tracker or soon an iWatch, you’ll be the source and endpoint of the IoT experience: feeding data into the system, perturbing and querying it, blending and mashing up the bits that emanate from your body and your daily life.
Whether and how quickly we become familiar and comfortable with this phenomenon is not just a question for the companies that build the hardware and software. Wearables are laying the groundwork to bring IoT thinking into every company. It’s just another way the consumerization of IT shapes the business world. First, it was iPhones and tablets that snuck in, then apps and web services. Next on the agenda is the realization that technology, a million new nodes and nerds at a time, can truly make us one with everything.
Rachel Delacour is co-founder and CEO of BIME Analytics.
