According to a number of statistical reports, the amount of digital data we produce is expected to double every year. That means the amount of digital data in the world will exceed 44 zetabytes by the end of 2020 – nearly 5,200 GB for every woman, man and child on earth. With this excess of transient data in the world, it’s no wonder organizations and individuals are developing creative new ways to mine this information and leverage it for interesting big data analysis applications.
TomTom, for instance, is a recent market example of data management and analysis done right. The company thinks it can leverage its GPS navigation system to bring self-driving cars to fruition. The company receives approximately 5.5 billion GPS measurements across all of its devices on any given day. It uses this data, along with crowd sourced traffic and road condition information from its MapShare program and trip information from its SatNAv feature, to provide an actual and up-to-date picture of the local traffic. TomTom captures this vast amount of information to improve the driving experience of its customers – it’s only logical to try to expand this data to grant drivers their ultimate dream: a completely autonomous vehicle.
TomTom is now expanding its big data net to build the maps self-driving cars need in order to work. By adding cameras, radars, and light detection and ranging units to a fleet of cars, the company is able to collect and process data that would allow a car to determine its position on the road down to the inch. While this is sufficient in closed environments with no variables, what about ever-changing road conditions? How will self-driving cars account for construction, changes in speed limits, etc.? TomTom has an answer for that as well: sensors that can compare what’s on the map to actual road conditions and the ability to report discrepancies for verification. This “living map” can then be updated and distributed to all TomTom units in every self-diving car on the road.
The number of data points TomTom needs to collect, process, and analyze to make the autonomous car a reality is astronomical, but it’s exciting to see how real this seemingly unattainable dream is thanks to big data. See it in action on TomTom’s YouTube page.
If you’d like to read about another interesting big data use cases, check out our previous post on iNaturalist and its open source mapping functionality. iNaturalist is using big data to create a virtual living record of life on earth – a true example of the endless potential of big data.