There are many applications that will be capable of generating Petabytes or Exabytes of data, but I would like to share three applications that I believe will be a big contributor to the Machine to Machine data era:
1.Smart Cars: Cars already generate vast amounts of data that can be leveraged and used by different organizations to optimize the driving experience, optimize pricing strategies and drive revenue for manufactures and stakeholders. The connected car of the future will be able to talk to other cars on the road as well as traffic lights and even roads and determine for example the required distance and speed as well as recognize objects and take corresponding actions. In addition, telematics can be used to monitor the health of the car as well as the driving behavior of the driver. The driver’s performance can be monitored via a black box / app and this information can be returned to the driver as well as the insurance company.
2. Smart Homes: Smart homes can be said to be one of the biggest potential contributors for M2M data growth in the coming years. The City of Songdois a great example of how smart city could look like. In the smart homes of the future, smart meters will monitor the energy consumption of each individual device in real-time and if necessary suggest better timing to use the product. Intelligent windows will determine the amount of sunshine and the temperature and adjust the opacity if necessary. In addition, maintenance sensors can detect when something needs to be repaired and warn you via you smartphone. The possibilities are endless.
3. Robotics: Robots will use a vast amount of sensors to understand their environment and to be able to move around in it. Using intelligent self-learning algorithms the robots can be made self-learning. There are many tasks and jobs that can be done a lot better and more efficiently by robots from autopilots in airplanes, or even better the self-flying drones, to healthcare robots that help doctors do a better job. All the data created by the robotics can be used to improve the robots in real-time.
M2M data will become abundant in the logistics industry. From asset tracking, fleet management to transport inventory optimization, M2M data can provide logistic companies with a lot of insights previously unthinkable. Sensors on containers that are capable of monitoring location, temperature, humidity, exceptional movements or shocks, alerts based on pre-defined conditions, etc., saving time and money before, during and after transportation.
There are countless other examples of M2M applications in different industries. Many of the use cases show companies taking already advantage of the vast possibilities of Machine to Machine data. From airplane engines that generate approximately 2,5 billion Terabyte (which equals 2.5 Zettabyte) of data each year to John Deere equipment that uses sensor data to monitor machine optimization, control the growing fleet of farming machines and help farmers make better decisions.
In the coming decade, 40% of all data will come from sensor data and it will unlock a $ 1 trillion global market in 2020 (currently it is a $ 121 billion global market). In the next decade we will reach 1 trillion sensors that will drive the amount of available data far into the brontobytes. Organizations that will be able to harvest all that data correctly will deliver better products that are better suited to the needs of the consumers resulting in better results and outperforming their peers.
Mark van Rijmenam is the founder of BigData-Startups.com, the number one big data knowledge platform. Mark is a strategist who advises organizations on how to develop their big data strategies. As such, he is a well sought after speaker on this topic. He is aware of the latest trends in the world. Next to blogging on BigData-Startups, he also blogs on SmartDataCollective.com, which is a platform with the world's best thinkers on big data. As such, he is a well sought after speaker on this topic. He is co-founder of 'Data Donderdag' a bi-monthly (networking) event in The Netherlands on big data to help organizations better understand big data. His book Think Bigger is a great essential resource for big data strategy.