How the Internet of Things Changes Big Data Analytics

How the Internet of Things Changes Big Data Analytics

How the Internet of Things Changes Big Data Analytics
Yes, there’s plenty of hype surrounding the Internet of Things (IoT). But this is one time the hype underestimates what comes next. IDC says there will be 28 billion sensors in use by 2020, with $1.7 trillion in economic value. The scale, breadth, and business value will exceed anything seen in the past. Ray Kurzweil’s Singularity is here. IoT will be an order of magnitude bigger than big data in scale and value.

Imagine a few billion sensors sending messages 20 times a second or even once a minute. The scale of the data is astonishing. Even Facebook addicts can’t talk that much. For many, IoT data volume will be in the petabyte range.

Fortunately, the cost of disk storage continues its free fall. Every person on the planet will be touched by sensors in 2025, or sooner. Even in the Outback, the Sahara, and especially grandma. Get ready for digital rivers of data driving new growth use cases in every industry.

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Sensors and things operate at the edge (Figure 1). “Things” are any item we can attach a sensor to – including you. The edge is where we find Operational Technology (OT). It includes manufacturing plants, cars, electrical grids, and train tracks. The OT engineers and operators have been using sensor data for decades. But now Information Technology (IT) is now pitching in to help out. Gateways are routers and servers that connect the OT to IT systems.

A majority of the ROI comes from analyzing sensor data. Note that analytics are spread throughout IoT systems like chocolate baked into a cake. IoT analytics are collectively called the Analytics of Things (AoT).

Where the Wild Things Are

Data now comes from devices with attached sensors. Some things are stationary (wind turbines), others are mobile (cars). While 70 percent of sensors are inside the intranet, 30 percent are “in the wild.” In many implementations, sensor data will be massively dispersed around the planet.  That’s vastly different from getting ERP or CRM data extracts.

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Consider Monsanto’s precision agriculture trying to collect data from farmers around the world. Monsanto must build on regional clouds. But even the cloud doesn’t touch most farms. Then imagine negotiating for data with farmers in Mexico, Nigeria, China, and Brazil.

Mobile sensor data arrives from thousands of airplanes, cars, patients, tools, and inventory pallets. These sensors disappear when in a tunnel or 10 kilometers up in the sky. Hence, sensors can be disconnected from the network. This means that data is sometimes lost, and also that developers must plan for “data catch-up mode” when the device is back online.

Data gathering is different than in the past.

Get over it, but have a plan.

Data integration changes enormously with IoT. Considering digital rivers of data from the edge, how can we manage the size of these real-time streams?

First, let’s be clear: never, ever lose data. My CTO tattooed that on my brain. But digital rivers can burn out network budgets. There are a few solutions:

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One solution is lossless compression done at the edge to keep costs down. Most people learned about lossy algorithms from music downloads. MP3 files sound tinny and flat because resolution is thrown away to save disk space. Do you want high-resolution data driving analytics with poor resolution? Imagine discarding all helicopter sensor data below safety thresholds. But the raw data shows 10 critical sensors running a smidgen below safety thresholds at the same instant.

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