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How to take the Internet of Things over the finish line

Vaidy Krishnan, Tableau’s Asia Pacific Product Marketing Lead | Feb. 2, 2016
Addressing the challenges of extracting data from devices, machines and remote platforms, to those of interpreting it to drive productivity and peak performance, this byline focuses on the three keys to overcoming these hurdles and taking IoT over the finish line.

This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.

With the rise of low-cost sensors, ubiquitous connectivity and massive data volumes, the "Internet of Things" promises to change the world. We've all heard predictions about the billions of dollars and billion of things that will make up this mega-trend by 2050 ... but that doesn't tell the full story. Unlocking the true potential of IoT will require overcoming data challenges more so than problems surrounding the "things" themselves.

These data challenges are best described as a "last mile" problem, from the challenges of extracting data from devices, machines and remote platforms to those of interpreting it to drive productivity and peak performance. Whether we're talking about a connected home, a piece of wearable technology or an industry-scale solution, there's often a disconnect between collecting new data and actually exposing the information mined in a way that can be deeply understood and explored.

Here are three keys to overcoming these hurdles and taking IoT over the finish line:

1. Interactivity

Smartphones aren't just instrumental in the Internet of Things but actually offer a compelling analogy for one of its hurdles. Think back to when Steve Jobs first introduced the iPhone to the world. He contrasted the revolutionary new "giant screen" against the standard buttons on phones. His argument for the innovation was that every app needed its own screen and user interface. As he puts it, "buttons and the controls can't change. They can't change for each application, and they can't change down the road if you think of another great idea you want to add to this product."

A similar conundrum applies to analytics. Every question we ask of data needs its own chart and its own visual perspective -- and this is especially true when it comes to the exploding amounts of sensor data that form the foundation of IoT. Unfortunately, most IoT applications ship with "one-size-fits-all" views, perhaps better referred to as "dead-end dashboards." They answer a pre-determined set of questions, deemed worthy of answering by a small clan of "experts" - whether that means the health experts behind FitBit or the engineers behind GE's Predix platform.

To realise the full potential of the Internet of Things, tools need to be far more flexible, letting users sculpt and mould data in different ways depending on a user or organisation's needs. Interactivity, drillability and sharing are crucial to making IoT data useful without requiring a huge data project. Ideally, users will be able to have casual and in-depth conversations with their data and with other data explores so they can uncover all sorts of permutations and sometimes even reveal patterns they didn't know existed.

 

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