IoT analytics guide: Understanding Internet of Things data

Editor's note

The Internet of Things (IoT) sounds like a consumer fantasy come true -- who wouldn't want to be able to turn off the lights at home from two towns away, or leave it to their refrigerator to make sure they know when milk, butter and other staples need to be replenished? But there's more to the IoT than lifestyle enhancement. It also includes a corporate side, enabling organizations to collect and analyze data from sensors on manufacturing equipment, pipelines, weather stations, smart meters, delivery trucks and other types of machinery.

IoT analytics applications can help companies understand the Internet of Things data at their disposal, with an eye toward reducing maintenance costs, avoiding equipment failures and improving business operations. In addition, retailers, restaurant chains and makers of consumer goods can use data from smartphones, wearable technologies and in-home devices to do targeted marketing and promotions -- the business side of the IoT's futuristic world of connected consumer gear.

Prior to the emergence of the IoT, taking a step back to analyze all of the information provided by the assortment of devices it can encompass was exceedingly difficult, if not outright impossible. As David Smith, a principal program manager at Microsoft, noted in September 2014, "Getting access to data is something data scientists strive for. But all these devices are independent, and there's no way for anybody to aggregate that data together." IoT technology offers automated mechanisms for pulling machine data into data warehouses or Hadoop clusters and other big data platforms for analysis.

Building and running the kinds of big data analytics applications typically required with IoT data isn't a simple task, though. If your organization is looking to make sense of the data it collects from the Internet of Things, check out the expert insights, user stories and other resources in this IoT analytics Essential Guide.