Automakers Must Partner Around Big Data

A discussion on the need for auto manufacturers to come together and leverage Big Data.

Mobility Services Companies Constantly Exploit Big Data

The solution described in the previous section’s example is only made possible because of the analysis and exploitation of data. GM’s experience with OnStar, and success studies from mobility services companies such as Uber provide further proof that personalized and multimodal transportation solutions require the exploitation of big data. Mobility services companies in general, andridesharing companies in particular, are big data and machine intelligence companies from inception. There are three reasons for this claim:

  1. In the process of conducting business, mobility services companies generate, collect, analyze and exploit big data.
  2. As ridesharing and carsharing companies are working to reduce their operating costs and better control their pricing, they are collecting and utilizing big data about the vehicles in their fleets. Startups like Zendrive produce additional types of analytics that ridesharing and carsharing companies can utilize towards this goal.
  3. As ridesharing companies proceed to develop Electrified, Autonomous, Connected vehicles (here and here) in a further effort to control their costs (cost/mile can go from $1.6/mile today to as low as $0.31/mile with an Electrified, Autonomous car) they are starting to generate and collect new types of big data on which they can apply machine intelligence.

Big data car

In the course of conducting business on a global basis, ridesharing and carsharing companies capture, analyze and exploit:

  1. Passenger characteristics and preferences, including credit card data through which they can access a variety of financial and demographic information, as well as behaviors in different contexts, e.g., Passenger A prefers a black car for rides to the airport (travel-related), prefers multi-passenger rides to music concerts (entertainment-related), and prefers regular car rides to meetings (business-related).
  2. Driver characteristics and behaviors in different contexts, e.g., Driver B belongs to the top 1% of drivers in terms of service provided during trips of 30 miles or more, based on passenger reviews and ratings, and low accident reports.
  3. Vehicle data, e.g., vehicle breakdowns by make, model and year based on reported incidents.
  4. Geolocation data, including the starting point and destination of each trip, which they use in order to establish each ride’s price and thus remain competitive with other options each passenger has.
  5. Traffic data, including road conditions due to repairs and accidents, which they use in order to more accurately estimate the time of the ordered car’s arrival, offer routing instructions to drivers in order to shorten the ride, and provide a better overall experience to the passenger.
  6. Site condition data, e.g., airport construction projects, parking availability, conditions around event venues, e.g., stadiums, concert arenas in order to improve the passenger experience.

As they experiment with new services, such as on-demand shipping and delivery, ridesharing companies will collect additional consumer data, e.g., preferred grocery chains.


The implications of the multifaceted partnerships that automakers must establish around big data include:

  1. There will be no single owner of data. The development of a data-sharing culture will require an important adjustment by the automakers that today tend to regard that they own all vehicle-related and driver-related data they capture. They will also need to think about what happens to this data whencompanies participating in such partnerships fail. Internet companies had to make corresponding adjustments with regards to the data they capture.
  2. Monetization of the data will remain tricky in the short term. Consumers have shown the willingness to pay for some mobility services, e.g., ridesharing, parking, but not for every type of service. Automakers must exploit these partnerships to identify viable and scalable business models that enable them to monetize on the data they collect and own.
  3. No room for walled gardens. As the automotive industry opens up to accommodate more mobility services, walled gardens will fail in the same way they failed in the telco industry.
  4. Better user experience. In the process of accessing personalized transportation solutions and having superior user experiences, customers will need to interact with multiple companies.

As we are moving from a car ownership-centric to a car access-centric world where consumers increasingly demand personalized transportation solutions, automakers must augment their manufacturing and distribution expertise with broad big data management and exploitation expertise and develop a data-sharing culture. This can be achieved quickly and effectively through multifaceted and strategic partnerships between automakers and mobility services companies starting with those offering ridesharing and carsharing services. If executed correctly by both sides, such partnerships can provide great benefits to all parties involved, create new virtuous cycles and result in high-satisfaction consumer transportation experiences.

Bio: Evangelos Simoudis is a seasoned venture investor and senior advisor to global corporations. His investing career started 15 years ago at Apax Partners and continued with Trident Capital. Recently Evangelos co-founded Synapse Partners, a venture capital and corporate advisory firm, where he is a managing director and invests in early-stage companies developing big data applications for the enterprise.

Original. Reposted with permission.