How many of you remember the cumbersome GPS navigation systems used in cars in the early 2000s?
If you drove in the 2000s you may remember these gigantic, unintuitive, and often inaccurate satellite navigation systems. These systems were so unreliable that drivers often had to turn to fellow motorists and pedestrians for directions to reach their destination.
Younger generations, however, have no idea what a satnav is. This is understandable given the fact that almost nobody uses satnavs these days. Modern navigation solutions like Google Maps and Apple Maps—two leading names in the market—come in pocket-sized devices.
Even in this market, which is practically a duopoly, one solution stands head and shoulders above the rest in the field—Google Maps.
Google’s solution can provide information about everything from commute details and public transport schedules to information on hotspots on the map. All of this has combined to make Google Maps the most dominant player in the navigation market.
Statistics reveal that Google Maps accounts for two-thirds of the entire market. Google’s market domination is due to the fact that its offering is by far the most reliable, most accessible, and the most accurate.
At times, people even wonder at the accuracy of Google Maps at detecting traffic congestions ahead. Google Maps’ accuracy and reliability can be attributed to many technologies, chief among them is data analytics.
In this blog post, we will explore how data analytics has helped Google dominate the navigation market.
You might have seen Google-branded vehicles loaded with cameras and other equipment roam the streets of your city.
Google uses these vehicles to create virtual maps of real places, which it then uses to power its navigation service. The success of Google Maps, however, does not solely rely on accurate mapping of the existing roads but also on providing accurate traffic information.
The search giant collects data from satellite imagery, cameras at traffic signals, GPS on mobile phones and many other real-time data sources. The collected data is fed into real-time data analysing tools that turn it into accurate information for their users. According to the company, this information is accurate to about a 20-meter radius.
For example, let’s assume someone needs to travel to the Sydney Opera House from the Sydney Harbour. Google collects location information from the traveller to ascertain their current location, and then identifies the shortest route possible with algorithms. Google Maps can even predict the traveller’s ETA as they travel across the city by collecting GPS location data constantly.
Location information from devices along the route and traffic cameras are used to create a real-time virtual representation of the traffic situation along the route to make the journey easier for the traveller.
With this approach, Google Maps can provide a timeline of a user’s travel history, which is useful in specific applications.
Google Maps users not only use the app to get travel details but also to get information about locations and hotspots on the map. Many people even use this information to plan their trips.
Regardless of whether it is a small shop, a large mall or a tourist destination, users can count on Google Maps to have information about that specific place.
That said, have you ever wondered how the search giant can provide such information inside a navigation app?
The answer is user-generated information and data analytics!
Google relies on users to obtain information about hotspots in their city and surrounding areas. The company then uses data analytics tools to aggregate the user-generated information about a particular location and offers this information on the map to all users.
Google Maps is arguably the most important navigational tool we use right now with its accuracy and reliability.
This accuracy and reliability, however, are results of constant data collection and data analytics. Conventional and real-time analytics are what drive Google Maps.