Application of large data analysis and machine-to-machine in three fields

Large data is developed around customer behavior. Machine-to-machine technology, also known as the "Internet of Things", has a variety of successful histories. Over the past year, we've seen where machine-to-machine and big data start to combine to provide some impressive benefits for many industries.

Machine-to-machine devices, typically small wireless sensor devices, may be the ultimate tool for capturing user behavior information, whether they are user locations, energy consumption at home and office, or how much effort on the brakes on the way home. As the following machine-to-machine and large data examples show, the combination of large data and machine-to-machine is likely to improve the broader and more innovative applications of these two technologies.

Wireless carriers

Wireless carriers are a natural market for machine-to-machine and large data solutions because operators are a data-intensive business. This is mainly because operators have to track phone and data usage. Machine-to-machine and large data also have the prospect of value-added services applied to target markets such as health care. Position intelligence services will drive operators to adopt machine-to-machine and large data solutions.

One example of machine-to-machine and large data monetization options for wireless carriers is the start-up Enterprise Spacecurve launched a location-based service for wireless carriers last fall. Wireless carriers can use Spacecurve technology to improve marketing and service quality based on the location information obtained and analyzed.

Better energy Management

The current economic situation makes energy management an indispensable application of consumers and enterprises. Machine-to-machine introduces wireless remote sensors for energy management, and large data provides data storage and analysis for energy management. This will help build a system of reporting and analysis that could not be implemented a few years ago. This combination will look at peak energy usage and provide operational information on how energy costs can be cut.

The Machine-to-machine and large data will be a popular trend in the smart grid level, as the Smart Grid library recently published in the article entitled "Smart Grid trends to watch: Information and communication technology innovation and new entrants." This article deals with power grid applications such as revenue security and voltage protection.

From the smart grid to another level, large data and machine-to-machine can also help commercial and industrial buildings save electricity. IMS Research recently published a report on building intelligence trends, which delves into the perfect trend of cloud and large data as a building analysis. IMS research points out that with the popularization of wireless machine-to-machine sensors and large data analysis, large commercial buildings, especially U.S. Federal office buildings, will benefit from cost savings.

As Machine-to-machine and large data analysis technologies move from Smart grid applications to large buildings and eventually into consumer homes as intelligent thermostat controllers, these technologies will become future energy-saving tools.

Auto Insurance

In a recent study of Machine-to-machine's potential, Will Kelly said, how the auto insurance industry uses machine-to-machine and big data to monitor the cars that its customers drive. Although fleet management is a positive application of machine-to-machine technology to better determine the route of business vehicles to better serve customers and save petrol, auto insurers, which monitor the good driving of users, rarely use machine-to-machine devices to transmit data wirelessly back to the insurance company for subsequent reports.

An article entitled "Machine-to-machine and the direction of large data Development", published by the October 2012 Information Weekly, provides examples of the progressive Ping Insurance Company and its snapshot equipment. The device is plugged into the car's diagnostic connector to monitor 30-day driving habits. The device provides detailed information to the progressive insurance company, including how heavy the driver is on the brakes and how frequently the car travels between 12 o'clock in the morning and 4 o'clock in the afternoon.

The combination of machine-to-machine and large data can help auto insurers make informed decisions about their customers based on their driving habits. However, there is no way to say that machine-to-machine and large data technologies may violate user privacy in the auto insurance industry. The results of such data and the value of court subpoenas are also a matter of concern if the equipment is converted from a voluntary installation to a prescribed installation.

Conclusion

As Machine-to-machine technology matures and becomes more and more widely used, it will certainly become another technology component of the big Data revolution we are currently experiencing. Each of the examples in this article shows how machine-to-machine and large data are applied.