Posted by Michael Morrison on January 12, 2015

Data analysis software and the Internet of Things (IoT) is like a match made in IT Heaven. While the latter technology provides organizations with an incredible amount of unique data that was unattainable 20 years ago, the former enables professionals to transform that data into helpful intelligence.

Sure, there's a lot IKEA can learn from a Web-connected recliner, but imagine the kind of insight a business such as General Motors or Ford could gain by scrutinizing data produced by a car equipped with smart devices.

GSMA, a global enterprise specializing in mobile connectivity, noted the Internet-connected vehicle industry is expected to explode over the next decade, citing two key points:

  • More than half of vehicles purchased in 2015 will be able to connect to the Web via embedded components or smartphones. 
  • In 2018, 31 percent of all cars shipped throughout the globe will possess integrated telematics.

With these projections in mind, here are three ways in which data analytics and IoT are changing the way automotive manufacturers are operating:

1. More informed research and development 
Production companies in every industry strive to improve their signature goods in whichever way they can. Suppose Ford, Toyota or some other business participating in the auto sector attached sensors to the suspensions, pistons, spark plugs and catalytic converters of its vehicles. This provides R&D teams with a wealth of information regarding how well current designs are filtering out harmful gases, minimizing chassis damage when cars traverse unpaved roads and whether engines are making optimal use of momentum. 

2. Stronger cybersecurity defenses 
Harvard Business Review contributors Michael Porter and James Heppelmann noted security is a major concern among enterprises integrating smart devices into their products. However, data collection tools can provide automotive companies with a bit of comfort. 

Suppose the Web-connected devices in a car are managed and secured through an on-board platform. Within this platform, an analytics engine can operate to find anomalies in the data being transmitted, alerting the owner and the manufacturer when and if an intrusion occurs. 

3. More applicable data 
It's important for those in the automotive industry to remember that aggregating all the information produced by their Web-connective vehicles may not be necessary. Instead, analysts should look at the data that best applies to certain endeavors. For example, if GM wants to improve its fuel injection mechanisms, it should only scrutinize data produced by sensors attached to the relevant components. 

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