Exploring the value of fleet data
The commercial use of vehicle data is driven by two primary purposes: to generate income and reduce costs. In this article, we highlight some of the ways in which fleet data can be utilised to do both.
Opportunities for generating income
Even though OEMs have been collecting data from connected vehicles for a few years, they’ve been reluctant to share it because of commercial sensitivities, or perhaps they just didn’t understand the value of it (according to a paper by KPMG). However, data marketplaces have formed and aggregators have helped establish sectors for data and thus various value streams are emerging, along with clarity around data security and protection. It’s now time to put data to work.
The different types of vehicle data
In a paper: Automotive Data Sharing, KPMG separates vehicle data into three different types: non-brand differentiated data, brand differentiated data and personal data. Within these three types there are five subcategories: data for improved traffic safety, data for cross brand services, data for brand-specific services, data for component analysis and product improvement and personal data. Let’s explore them in more detail.
Type 1: Non-branded differentiated data
This data comes from vehicles and sensors in the transport infrastructure that can provide social benefits, such as improved traffic management and safety. These defined data sets cross OEM brands and consist (usually) of anonymised vehicle data for potential third-party commercial services such as in-vehicle information (temperature, average speed, traffic flow, road sign recognition and on-street parking).
Alongside public and local authorities, this data is useful for commercial third-parties, such as app developers and aftermarket providers. Enablement of it requires agreement between vehicle manufacturer, customers and third-party participants.
Type 2: Branded differentiated data
This data comes from sensors and components within vehicles, and so is OEM-specific and IP relevant. It can help determine road and environmental conditions, software algorithms and a host of engine data (gearbox operation, actuator data, fuel pump performance, battery status, break pad condition and so on).
Aside from vehicle manufacturers, this data is useful for dealers, subsidiaries, suppliers, partners and particularly fleet managers wanting to cut costs and streamline operations. Enabling this data requires agreement between individual customers and third-party participants.
Type 3: Personal data
This is data that comes from the driver or customer (and in many cases they will be one and the same). It requires identification of the individual user and vehicle. This dataset is useful for vehicle location services, movement profiling, speed detection, navigation, infotainment, driving style and behaviour, in-car settings and so on.
This data can be used by only authorised parties who have the individual customer’s permission and those authorised parties must comply with the latest data protection regulations.
Opportunities to generate income from vehicle data
Carpooling, peer-to-peer car sharing, on-demand mobility and subscription services can all be elements of MaaS, which many fleet owners and operators are looking to exploit.
Insurance is another area where technology and data are enabling new, pay-as-you-drive and other services as new revenue generation opportunities for industry disruptors. Using black box technology, they’re able to provide proactive notifications, crash detection, alarms, accident management, on long, medium and short-term policies that suit the MaaS market.
In the near future we will see data from driving insights enabling targeted advertising and product promotions, route planning and optimisation, vehicle usage monitoring and scoring.
Opportunities for reducing costs
The opportunities for reducing fleet costs are primarily demand-driven services, such as: predictive maintenance, early recall detection, theft recovery, over-the-air software updates and vehicle condition monitoring. Type 1 and 2 data are required here.
Fleet management (or fleet-management-as-a-service), engineering lifecycle management, data-based research and development will all help optimise the fleet and thus save costs.
Energy and environment: driving style monitoring/training, incentives and optimisation, recharging monitoring and planning will save money and deliver substantial CSR (Corporate Social Responsibility) value. All three types of data will be required here as drivers will have to be identified and thus their permission will be required.
The potential to reduce fleet cost and generate income using fleet data is compelling but doing so requires strategy, planning and collaboration with the right data partners.
*Image of futuristic transport and mobility data landscape, courtesy of Shutterstock.