Connected car use cases should focus on the prosaic - and on good data

Connected car use cases should focus on the prosaic - and on good data

Tom Weiss, Tue 02 April 2019

First, it was telephones, then televisions, and now connected cars too will have an Internet connection so they can both send and receive data; an estimated 20% of cars, or 250 million in total, will have such an Internet connection in 2020. With Internet-of-things sensors also becoming widespread, we don't see any let-up in the connected car hype in 2019.

While the holy grail of connected car manufacturers is to create a driverless experience, we don't expect that to become a mainstream reality until the next decade, but connected cars are already a reality. The main focus of connected cars is creating a great customer experience, unsurprisingly given manufacturers are the ones designing and building these cars, and focussing on the customer is the best way for them to sell this new product of connectivity. Given that the average American spends 10% of their waking life in a car, a great customer experience will become a powerful selling point.

Innovation and the connected car customer experience

OEMs are implementing a range of data-powered services in their connected cars, which we can broadly divide into V2V, where vehicles connect with each other, and V2I, where they connect to Internet-of-things infrastructures, such as sensors in traffic lights, street lamps, road markings and gas pumps.

Some of these innovations are merely about taking services we enjoy outside the car, into the car - e.g., allowing Spotify to stream directly into the car speakers. Others take this further, such as voice recognition technology that drivers can use to flick between tracks without taking their eyes off the road, but are still cut from the same cloth and are not really "new" ideas.

We think the most attractive innovations are those drivers can use to get from A to B swiftly and with minimal hassle. We are all used to watching ads where a driver takes their new car for a spin along empty city roads, projecting a dream that sadly doesn't fit in with the reality of congested roads. Indeed the average American spends 38 hours a year not moving in a traffic jam, which is not merely frustrating but also uses unnecessary gas and pumps out vast amounts of pollutants. If drivers could get where they want to get without being impeded by other vehicles with which they share the road, this would make a real difference.

Other innovations to lessen the impact of traffic jams include a smart GPS, which can indicate the best route from A to B to take right now, based on the latest traffic congestion reports, which in itself will reduce traffic delays by siphoning the connected cars away from the jam. Smart traffic lights for when a car is approaching a red light and, if the other way is clear, can switch to green, are another innovation which works best when the car and the traffic light can communicate with each other. Warning a car that a tire needs changing urgently and thus avoiding a blow-out which will likely cause a crash and delays, as well as potentially saving lives, is a more critical example of connected car innovation keeping the traffic flowing and thus improving the customer experience. Gas pumps that can automatically charge the customer by using the wifi connection and insurance companies offering reduced rates based on the big data they have about the acquired customer driving patterns are other innovations as is the concept of a software update as an alternative to taking the car into the garage to be upgraded or just serviced.

The key is in the data

Not only will all the connected cars offerings require huge amounts of data processing, but much of this will be necessary for real-time to yield the full benefits. This is one of many challenges facing data engineering and science teams when they set out to start managing connected car datasets (and is the reason that edge computing - processing connected car data on the edge of the network - the vehicle - and sending what is most pertinent to the cloud (e.g. when there has been an alert).

Many of our blog posts are about the media sector, but a lot of the issues and topics we discuss also apply to the car sector. As we have explored elsewhere, data scientists typically spend at least 75% of their working lives cleansing and validating data and integrating disparate data sets. So any data science team working on a connected car dataset needs to address the data quality they have and will have as new data enters the system, which it will do every time someone drives one of the connected cars, i.e., continuously. They should make decisions about how often this data needs to be sent in real-time and the business case for real-time. They need to work with the data governance team to understand which data sets are sensitive, which should be pseudonymized, and how it should be stored so that the business can make the most significant use of it.

Then there's the ultimate universal truth. In our experience, there is nothing more important than getting the data quality right in the first place. All the connected car processes will seize up with poor quality data but, once the data quality is right, the data science team can then focus on other tasks which will add business value to the connected car by creating a great customer experience.

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Dativa is a global consulting firm providing data consulting and engineering services to companies that want to build and implement strategies to put data to work. We work with primary data generators, businesses harvesting their own internal data, data-centric service providers, data brokers, agencies, media buyers and media sellers.

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